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Gamification has long been part of the shopping experience, but AI is making it more personal and effective than ever.

Retailers are exploring new methods to design and develop hyper-personalized games and contests within their loyalty programs, effectively using gamification to layer additional value on top of their existing loyalty propositions. With AI and predictive analytics, they can automate tailored challenges at scale, significantly enhancing their connection with loyalty program members and critical shopper segments.

Enhancing what consumers expect from gamification

Many retailers have already developed unique and creative ways to engage consumers through gamification. Asda, for example, has seen tremendous success engaging customers through its “Spin the Wheel” game inside its mobile app where the retailer has rewarded millions of customers with money and prizes as they unlock the chance to spin and win every time they meet the £5 minimum spend threshold.

One of Canada’s largest retailers, Loblaw, has also implemented gamification, but in a slightly different way and aimed at a different segment of shoppers. Loblaw’s PC Health proposition, which provides healthcare services to shoppers, introduced a gamified experience that engages with consumers using Fitbits or Apple watches, awarding users bonus points for hitting a step count goal, for example.

While both Asda and Loblaw deliver great gamified experiences for users, AI-powered personalization will take them further. By leveraging a shopper’s sales history, loyalty interactions, and additional data sources such as time, weather and preferences, AI-powered personalization tools can help transform gamified encounters into true 1-to-1 experiences.

This enhanced approach to gamification is exemplified by Tesco and Carrefour, both of which introduced personalized gamification through challenges inside their loyalty programs to great fanfare within the grocery industry. Both retailers are using AI to automate the creation and execution of fully personalized challenges for each loyalty member enrolled in the challenges offering. As each consumer completes a custom, designated task, they receive a custom reward. The AI automatically selects contests and rewards based on the individual’s purchase history and preferences.

These programs offer gamification with a purpose—moving beyond simply engaging customers to rewarding them individually for displaying desirable behaviors.

Three benefits of personalized gamification at retail

Gamification has long been recognized as a powerful marketing strategy. Nearly 70% of respondents in a 2023 Euromonitor International survey cited it as a top loyalty tactic, noting that adding challenging and competitive elements significantly boost customer engagement and retention. Gamification also fulfills consumers’ desires for immersive and goal-oriented experiences.

By integrating AI-powered personalization, retailers can elevate their gamification efforts and enjoy three key benefits:

1. Improved loyalty

In a global shopper survey by Eagle Eye, 60% of North American shoppers said they rely on loyalty programs to save money, and 66% of global consumers say they would or already do participate in games and contests within their favorite loyalty programs. When retailers combine these two desires, the result is a more loyal shopper. Personalized challenges within the rewards program can inspire those consumers while they shop and help them save money in a fun way. By leveraging the member’s loyalty data, these personalized gaming experiences make shoppers feel recognized and valued.

2. Boosted engagement and incremental spending

Eagle Eye’s global survey also revealed that 84% of shoppers believe personalized rewards help them save money. Loyalty members see saving on their favorite items as the primary benefit of a loyalty program, and personalized gamification directly caters to this need. By offering tailored rewards and challenges, retailers can increase member engagement and drive both the consolidation of spend into their business, as well as encourage incremental spending, as members are more likely to participate in programs that provide tangible savings on items they frequently purchase.

3. Unlock a new revenue stream

By deploying new, gamified tactics retailers gain the opportunity to open new revenue streams by collaborating with suppliers who want to reach their shoppers through these highly engaging and personalized marketing initiatives. Games and challenges can become new, high-value items on a retailers’ Retail Media inventory, with suppliers keen to fund these tactics to drive their own objectives in collaboration with their retail partners.

Revamping Gamification in retail

In the near term, as retailers further their use of AI across their businesses, personalized loyalty programs will continue to expand. AI, automation, and personalization will be touching every part of the retail journey, including loyalty accounts.

Retailers like Asda, Loblaw, Tesco and Carrefour are navigating new waters around personalization and loyalty, but companies will see clear benefits—more loyalty, more active shoppers, and more incremental spending when they can create true 1-to-1 engagement across all touchpoints. Executing personalized programs at scale necessitates the use of AI and predictive analytics to automate challenges and tailor rewards.

Traditionally, switching direct deposits has been a cumbersome, paper-based process that could take days or even weeks to complete. Customers often had to fill out multiple forms and coordinate with both their employers and financial institutions to update their banking information. This friction-filled process deterred many from making the switch, leading to a significant number of newly opened accounts remaining inactive.

Pinwheel Prime: Revolutionizing Deposit Switching

Pinwheel, a fintech company specializing in account activation solutions, recently introduced Pinwheel Prime, the industry’s first direct deposit switching solution that boasts 100% coverage of the US workforce. This innovative fintech feature allows customers to move their direct deposits within seconds, regardless of how they receive their income. Pinwheel Prime simplifies the entire process to just a few clicks within a bank’s app or via a tablet at a bank branch, eliminating the traditional hassles and delays associated with direct deposit changes.

The Partnership with Narmi

Narmi, a leading provider of digital banking solutions, has integrated Pinwheel’s technology into its own fintech platform, offering its customers the ability to leverage Pinwheel Prime. This partnership aims to provide a seamless digital experience for account activation and deposit switching, thereby accelerating customer onboarding and enhancing deposit enrollment rates.

Angela Gentry Yue, SVP of Operations at Narmi, emphasized the transformative potential of this partnership, highlighting how it bridges the gap between traditional banking systems and modern technology. By offering frictionless direct deposit switching, Narmi aims to help financial institutions boost customer activation rates and reduce churn.

Read More : Future-Proofing Your Fintech: Essential Strategies for Mitigating Risk

Benefits To Financial Institutions

Financial institutions leveraging Pinwheel Prime gain real-time insights into customers’ income, enabling them to build deeper relationships and increase customer lifetime value by at least 14%. Research indicates that 72% of consumers are more likely to make a bank their primary financial institution if it offers Pinwheel Prime during the account acquisition process.

Moreover, the partnership between fintechs Pinwheel and Narmi is expected to drive significant growth in direct deposit enrollment, with some institutions experiencing up to a 32% increase. This growth not only strengthens customer loyalty but also enhances the overall value of the customer relationship.

The Broader Impact

The collaboration between Pinwheel and Narmi is more than just a financial technological integration; it represents a shift towards a more customer-centric approach in banking. By reducing the friction from direct deposit switching, financial institutions can provide a superior digital experience, positioning themselves as forward-thinking and responsive to customer needs.

Transforming first-party data into useful insights is hard. But with more pressure than ever to hit revenue goals, APAC marketers need to find smarter ways to acquire new customers. They also need to simultaneously navigate the privacy revolution, which adds an extra layer of complexity. Consent rules, regulations and the decline of third-party signals make it harder for marketers to connect with and convert the audiences they want.

First-party data can help overcome these challenges. But first you need to know how to work with it and what to do with it. In this final installment of our three-part series, we now unpack the dos and don’ts of first-party data. From tapping into the value of every interaction you have with your customers to why you should never leave your customer data in silos, these are the key strategies and pitfalls to guide your data management journey.

First-Party Data Dos and Don’ts

Do: Collect first-party data across your brand’s myriad touchpoints

Every interaction you have with your customers is a chance to enhance your first-party data. Build a unified view that includes online and offline purchases, social media interactions and requests for customer care. This will help you build an identity across the entire lifecycle from anonymous to partially known to fully known.

Don’t: Leave your customer data in silos

Siloed data is not healthy data. It creates barriers to information sharing and collaboration across departments. Inconsistencies in data across silos can cause data quality to suffer. You simply will not have a holistic view of your customers.

Do: Explore emerging technologies that allow you to share data securely and in privacy-compliant ways with your partners

This will be essential to enrich your first-party data. Become familiar with concepts like data clean rooms (DCRs), which aggregate and anonymise personal data. They can provide marketers with non-PII data to enhance targeting and campaign measurement.

Don’t: Wait for a new industry standard to replace third-party cookies

There are numerous identifiers vying to be the solution in the post-cookie world – but in the end, it’s still data rental. Take the time (and investment) to focus on your owned first-party data for better targeting and ROAS.

Do: Find a data partner who can support your first-party data strategy

You don’t need to go at it alone as you navigate the post-cookie landscape and focus on your first-party data to effectively message customers. Find a partner who is willing to show you the ropes and help your organisation develop a data strategy that meets your business goals.

Don’t: Leave data quality and governance to chance

Ensuring the accuracy, completeness and consistency of first-party data is paramount to its effectiveness as a marketing asset. Implement robust data quality assurance processes to identify and rectify errors, duplicates and inconsistencies within your data sets. Establish clear data governance policies and procedures to govern data access, usage and sharing, ensuring compliance with relevant privacy regulations, such as The Privacy Act.

Marketing Technology News: MarTech Interview with Chris Koehler, CMO @ Twilio

Do: Invest in AI, data analytics and machine learning

Harness the power of data analytics, AI and machine learning algorithms to extract actionable insights from your first-party data. By leveraging advanced analytical techniques, such as predictive modelling and customer segmentation, marketers can uncover hidden patterns, trends and correlations within their data, enabling more targeted and personalised marketing initiatives. Invest in cutting-edge analytics tools and AI-powered platforms that empower marketers to derive actionable insights and drive data-driven decision-making across the organisation.

Don’t: Forget to experiment and iterate

Adopt an iterative approach to data-driven marketing, embracing experimentation and continuous improvement as core principles. Leverage A/B testing, multivariate testing and other experimental methodologies to evaluate the effectiveness of different marketing strategies and tactics. Analyse the results of experiments rigorously, identifying successful approaches and areas for optimisation. Iterate on your marketing campaigns based on data-driven insights, refining your tactics over time to maximise ROI and customer engagement.

Do: Foster a culture of data literacy and empowerment

Empower your marketing team with the knowledge, skills and tools they need to effectively leverage first-party data in their day-to-day operations. With the recent launch of our two new generative AI capabilities – Explore and Assist – Amperity is empowering marketers to get the most out of their customer data – fast.

Assist, for example, supports marketers, analysts and data operators with creating marketing workflows more quickly. The first product within Assist is Ai Assistant, which removes the barriers to creating SQL queries and fixing potential errors within those queries. It’s been a gamechanger for our customers.

“AI Assistant saves me 7-8 hours of work per week. Instead of crafting SQL from scratch or searching for SQL to reuse, I turn to AI Assistant. It has been such a life saver!” says Hope Vlacich, Manager, Audiences & Digital Marketing Analytics at Wyndham Hotels & Resorts.

Don’t: Neglect ongoing data maintenance and optimisation

Data management is not a one-time endeavour. It’s an ongoing process that requires constant attention and optimisation. Continuously monitor and evaluate the performance of your data management processes, identifying opportunities for refinement and enhancement. Regularly audit your data infrastructure and systems, ensuring scalability, reliability and performance. Invest in data quality tools and technologies that automate data cleansing, enrichment and validation tasks, streamlining your data management workflows and improving efficiency.

Unify your data, accelerate your business

As we conclude this series on first-party data, it’s evident that transforming raw data into actionable insights is no simple task. In today’s competitive landscape, where revenue goals loom large and privacy regulations tighten, APAC marketers face unprecedented challenges in acquiring and retaining customers.

Unifying and understanding first-party data is paramount for businesses seeking to thrive in today’s digital landscape. By leveraging these insights effectively, they can drive customer engagement, maximise ROI and achieve sustainable business growth. Uncover even more ways to maximise the potential of your first-party data by downloading our new guide here.

About Amperity

The global cybersecurity workforce grew by 12.6% between 2022 and 2023, a significant year-over-year climb for any industry, but the talent gap is still far from filled. According to the World Economic Forum, four million workers are needed worldwide in the cybersecurity industry, and with the ongoing growth potential of artificial intelligence, this shortage leaves companies vulnerable to the ever-evolving threat landscape.

The rapid digital transformation across industries has heightened the need for robust cybersecurity measures. Yet, the pool of qualified cybersecurity talent remains small, while the competition to attract and retain these professionals grows stronger.

So, how can organizations bridge this growing gap and strengthen their cybersecurity practices?  The answer lies not in replacing human talent, but in strategically leveraging artificial intelligence (AI) to empower existing cybersecurity teams. By viewing AI as a force multiplier, companies can unlock a range of benefits that make their security teams more efficient, proactive, and insightful, ultimately enhancing overall security posture.

Also Read: AI Integration Roadmap Planning for Cybersecurity Experts

The Talent Crunch

The cybersecurity talent shortage isn’t new in the industry. For years, the demand for skilled professionals has outpaced supply, an imbalance driven by factors such as the increasing sophistication of cyberattacks and the evolving regulatory landscape. Attracting and retaining top cybersecurity talent is a fierce competition for organizations.

The number of tasks associated with cybersecurity can be overwhelming for organizations. Security teams often battle with manual processes such as data analysis, log file review, drawn-out assessments, and other time-consuming tasks. These leave them with less time for strategic planning – which is where human expertise and judgment are irreplaceable. With these tasks offloaded to AI, human cybersecurity professionals are freed to focus on more strategic processes. By handling the ‘heavy lifting’ of data processing and preliminary analysis, AI empowers security professionals to apply their skills where they are most effective, enhancing the overall security framework.

AI not only benefits the organization by enabling a more proactive security process but also offers a more attractive work environment for security professionals. By taking away the heavy burden of tedious and time-consuming tasks, AI allows them to utilize their skills for higher-level problem-solving and decision-making, ultimately leading to higher job satisfaction and improved retention rates.

Also Read: Will AI Replace or Enhance Cybersecurity Engineering?

From Reactive to Proactive

The traditional approach to cybersecurity has largely been reactive. Security teams typically wait for an attack to occur before identifying and responding to it. This reactive approach leaves organizations vulnerable since even the most well-designed defenses can be breached. The creation and implementation of security tools, protocols, and policies are also usually set in stone and then not revisited. AI offers a solution for companies looking to be more proactive in their cybersecurity approaches.

AI-powered tools can analyze vast amounts of data and quickly comb through it to identify any changes or patterns that might indicate an attack in the making. This allows security teams to take a proactive stance, mitigating risks before they translate into full-blown breaches. By identifying these potential threats early on, security teams can take preventative measures ahead of time to ensure cyber safety. Under an AI-backed proactive approach, organizations can keep a constant eye on emerging threats and the potential solutions to solving them.

Making Smarter Decisions Faster

Every second an attack goes undetected allows attackers to steal data, disrupt operations, and cause significant damage. In the fast-paced world of cybersecurity, timely and informed decision-making is essential. AI provides real-time insights that enhance the efficiency and effectiveness of cybersecurity. By integrating AI into workflows, cybersecurity teams can automate responses to threats, streamline security operations, and ensure that their strategies are based on the most current data.

These insights are based on the continuous analysis of data collected from various sources, which allows security teams to make informed decisions quickly and efficiently during critical incidents

Also Read: Cybersecurity Solutions to Safeguard against Financial Services Cyber Threats

Building a Collaborative Future

The global cybersecurity talent gap poses a significant challenge, but AI offers a promising solution. By automating routine tasks, enhancing threat detection, and enabling smarter decision-making, AI can empower cybersecurity teams to do more with less.

As companies adopt AI-powered tools, they not only bolster their defense mechanisms but also create a more attractive and fulfilling work environment for cybersecurity professionals. In this way, AI is not just a tool for managing threats but a strategic asset in building a resilient cybersecurity workforce. While the technology is powerful, it’s important to emphasize that AI is not intended to replace human cybersecurity professionals. Although AI expertly  automates tasks, analyzes data, and identifies patterns, it lacks the critical thinking skills, judgment, and creativity that are essential for effective cybersecurity practices.

And, while AI continues to show promise, organizations must proceed with caution for their internal AI development initiatives which could be exposed to data poisoning, cyberattacks, AI model interference, and Intellectual Property (IP) leakage. Ongoing vigilance and proactive security measures are key to balancing the vast promise and real risks associated with AI.

1. Introduction

In the digital age, the intersection of technology and finance is reshaping the financial landscape, driving innovation, efficiency, and accessibility. This transformative era is evident in financial markets, where algorithmic trading, robo-advisors, blockchain technology, and digital banking are revolutionizing service delivery and consumption. The collaboration between IT and finance departments has become critical as companies pursue digital transformation in their financial operations.

A Workday survey highlights the importance of this partnership, revealing that only 28% of companies with disconnected CFOs and CIOs have successfully launched a digital finance strategy. This underscores the need for cohesive collaboration to harness the full potential of technological advancements in finance.

Rimini Street, a global provider of end-to-end enterprise software support and services, recently announced the findings of the Censuswide survey, “C-suite Imperatives: Evolving IT and Enterprise Investments.” This Rimini Street-sponsored research surveyed nearly 3,000 CFOs and CIOs worldwide, examining the relationship between these key business leaders and the drivers behind their technology investments and decisions. The analysis revealed that as IT costs and spending continue to rise, CFOs are increasing their influence over IT. Budgetary considerations and the demand for results require CIOs to deliver strong ROI from technology investments. CIOs who work closely with their CFO counterparts can drive profitable results by prioritizing projects that support the company’s financial and growth goals.

Also Read: Transforming IT Service Management Through AIOps

2. CFO-CIO Partnership

As organizations strive to align financial strategies with technological advancements, the synergy between CIO and CFO can drive significant business outcomes. Recent findings from a Censuswide survey sponsored by Rimini Street highlight the dynamics of this partnership and reveal essential insights into how CFOs and CIOs work together to navigate the complexities of IT investments and deliver substantial ROI. Below are the key findings from the survey, highlighting the impact of a strengthened CFO-CIO collaboration.

#1: Strengthening CFO/CIO Partnership

The survey reveals a significant strengthening of the relationship between CFOs and CIOs, with 86% of respondents noting improvements. This collaboration is crucial for aligning financial strategies with technological advancements.

The bond has grown stronger due to several factors: a focus on security, compliance, and risk; the urgent need for collaboration to make agile technology decisions; engagement from other leaders; and the necessity to cut IT costs smartly.

Takeaway: A robust partnership enables CFOs and CIOs to make informed decisions on critical areas such as emerging technologies, security, agility, ROI, and hybrid environments. Working closely together, they can ensure technology investments align with business objectives and drive tangible outcomes like revenue growth.

#2: Differing Perspectives on IT Investments

CFOs are increasingly involved in technology investments, prioritizing those that deliver tangible business outcomes and de-prioritizing low-value projects. Only 20% of CFOs are satisfied with the impact of tech investments, highlighting the need for improvement. CFOs are more involved in the details of technology investments, including timing, spending, and selection.

CIOs, on the other hand, focus on addressing business and technology pressures by leveraging emerging technologies, with about 50% of respondents investing heavily in this area. However, 31% of CIOs adopt a cautious approach, finding fit-for-purpose solutions preferable to upgrading to the newest platforms or completely replacing current systems.

Takeaway: CIOs should emphasize the ROI of each technology project, ensuring every tech dollar contributes to strategic goals. This results-driven focus is vital for securing executive buy-in and demonstrating the value of technology initiatives. CFOs should collaborate with CIOs to understand how technology investments address challenges and accelerate business opportunities, fostering better collaboration for successful investments.

#3: Need for Mutual Understanding and Communication

The survey indicates that 85% of CFOs believe their CIO counterparts need to be more business-savvy for better communication. Conversely, 86% of CIOs think their CFO counterparts need to be more technology-savvy.

CFOs rely on CIOs for expertise in managing technology-driven priorities like security and emerging technologies, while CIOs look to CFOs for budgeting support and executive advocacy.

Takeaway: CIOs and CFOs should familiarize themselves with each other’s business goals, terminologies, and industry trends. Communicating in terms and metrics that matter to the other ensures mutual understanding and better outcomes.

3. CIO Strategies for Managing Rising IT Costs

Leverage shared services

CIOs can indeed save money through IT shared services across different business lines in an organization. Economies of scale are developed as a result of this kind of strategy, offer potentials for the reduction of fixed cost by 15 percent to 20 percent within 18 to 36 months; some attain above 25 percent in savings.

Adopt a Cloud-First Policy

A cloud-first strategy delivers an extended range of infrastructure and software services, opening up greater agility, flexibility, scalability, and cost efficiency. Nevertheless, establishing robust governance of cloud spend management will be critical in ensuring cost savings are realized; otherwise, the costs of running infrastructure and applications could escalate and spiral out of control.

Consolidate Enterprise Data Centers

With the growth of business, traditional data center maintenance costs also increase. The IT department’s upgrading and consolidating data center environments with advanced switching architectures will definitely help to overcome it. It will help the customers to save 10% to 20% of the data center budget.

Optimize and Standardize Enterprise Applications

One of the biggest IT costs is managing an eclectic application portfolio. It is the capacity for standardization and rationalization of their application portfolios that helps CIOs reduce and then finally manage their costs better. Ultimately, this could result in a cost reduction of 15% to 25% against the application budget.

Bring about IT Financial Transparency

IT financial practices achieve transparency through a clear understanding of the costs of IT service delivery. Collaboration with chief financial officers and finance teams in mapping general ledger entries to technical and business service costs makes enterprise IT spend transparent. Thereby, such transparency creates opportunities for better cost optimization and value delivery with IT services.

Optimize Business Costs by Controlling IT Spending

This means that ninety-six percent of business spend occurs outside the IT function, creating opportunities for IT expense reduction. CIOs can work with peers in other functions to scrutinize, at a detailed level, processes, resources, and capacities in search of areas where IT can make a productive or efficient contribution.

Apply Robotic Process Automation

RPA and AI can be adopted for data analysis with the discovery of patterns. These technologies thus support intelligent business decisions and automation or improvement of inefficient processes.

Assess IT Asset Management Practices

Neglecting IT asset management practices leads to increased operational costs. When CIOs have already begun to pass on the ITAM responsibilities down to corporate governance, the main work provided by this shift is the change from tracking individual assets to something big that gives cost savings of 10% to 20%.

4. Challenges and Solutions in Technology Investments

Rapid Obsolescence: With rapid technological evolution, businesses must constantly invest in new systems and software to stay competitive. According to McKinsey, this cycle of rapid obsolescence requires companies to be agile, constantly evaluating and adapting their technology stacks to ensure they remain relevant and efficient. To overcome this challenge, companies must:

Continuously Learn and Adapt: Organizations should invest in continuous training and development programs to keep their workforce up-to-date with the latest technologies. This will create a culture of continuous learning that encourages innovation and adaptation.

Implement Flexible Technology Frameworks: Implementing flexible and scalable technology that can easily integrate new components or updates can help mitigate the risk of obsolescence. This could involve adopting cloud services that allow for modular updates and scalability.

High Initial Costs: Technology investment can be highly expensive, especially for startups and small businesses. Deloitte reports that the upfront cost of implementing advanced digital solutions, such as AI or blockchain, can deter businesses from adopting these technologies despite their potential long-term benefits. Solutions to high initial costs include:

Phased Implementation: To manage the high upfront costs, companies can phase the implementation of new technologies, starting with the most critical areas. This approach allows for breaking down the expenses over time and validating ROI step-by-step.

Seeking Financing and Grants: Businesses can look for external funding options such as l****, investor capital, and government grants designed to support technological innovation.

Integration Challenges: Integrating new technology with existing systems can be complex and resource-intensive. Poor integration can lead to inefficiencies, increased costs, and failure to achieve the desired ROI from technology investments.

Robust Planning and Expertise: Before implementation, thorough planning and consultation with IT experts can identify potential integration issues. Gartner emphasizes the importance of investing in in-house or outsourced expertise to ensure smooth integration.

Standardized Processes and Tools: Adopting standardized software and technologies across the organization can reduce compatibility issues and simplify the integration process.

Security Risks: As investments in technology grow, so do the associated security risks. Data breaches and cyber-attacks can have devastating financial and reputational consequences. The 2020 Cost of a Data Breach Report by IBM found that the average cost of a data breach is $3.86 million, underscoring the critical need for robust security measures. How security risks can be mitigated:

Investment in Security Infrastructure: Regularly updating cybersecurity measures and investing in advanced security infrastructure is crucial. The IBM report suggests implementing automated security solutions that can predict and mitigate potential breaches.

Regular Audits and Compliance Checks: Conducting regular security audits and maintaining strict compliance with data protection regulations can help prevent security lapses.

Skill Gaps: The rapid development of technology creates a gap between the skills available in the workforce and the skills needed to effectively utilize new technologies. The World Economic Forum has emphasized the importance of ongoing training and education to prepare employees for the digital economy.

Partnerships with Educational Institutions: Forming partnerships with universities and educational platforms can help access training resources and keep the workforce skilled in the latest technologies. The World Economic Forum supports such collaborations as a way to bridge the skill gap.

Hiring and Retaining Specialist Talent: Actively recruiting and retaining employees who specialize in emerging technologies can alleviate skill shortages.

Also Read: Three Strategies to Help You Solve Your IT Skills Gap

Regulatory Compliance: With the increasing amount of data collected and used by businesses, regulatory compliance has become a significant challenge. Compliance with regulations such as GDPR in Europe or CCPA in California requires substantial investment in technology and processes to ensure data protection and privacy. What solutions can be taken to overcome regulatory compliance challenges:

Dedicated Compliance Teams: Establishing a team specifically focused on compliance can ensure that all technology practices adhere to the latest regulations. These teams should stay informed about regulatory changes and adjust practices accordingly.

Technology-Assisted Compliance Tools: Utilizing compliance software that helps in maintaining records, managing data privacy, and generating compliance reports automatically can reduce the burden and risk of non-compliance.

“Organizations have a lot more tools and digital capabilities at their disposal than they did during the financial crisis of 2008-2009. A recession offers organizations the opportunity to experiment with disruptive innovations and to redefine the next decade. The CFO-CIO relationship is key in ensuring these innovations are given appropriate funding and the appropriate risk appetite is set in order to leverage digital against today’s economic headwinds and set up for a healthier future.” – says Sanil Solanki, Managing Vice President at Gartner.

5. Conclusion

Collaboration between CIOs and CFOs is essential for making sound financial and technological decisions that drive company growth. This partnership helps companies enhance their capabilities, reduce costs, and make data-driven decisions.

AI is a major disrupter, offering great benefits and free labor. However, small companies are being left behind while larger companies are embracing this new tech, writes Christine Telyan

In theory, AI represents a major opportunity for businesses of all sizes to unlock new opportunities and markets and to radically overhaul their operations. In some quarters, AI is being touted as a cure for all business ills. No matter what problem a business has, AI can solve it, enthusiasts say.

But while AI is certainly a disruptive force, we would be mistaken in thinking that its benefits are being distributed evenly. As it stands, AI is simply more useful for experienced teams than it is for solo entrepreneurs or, indeed, Mom-and-Pop type stores and businesses.

Also Read: Humanoid Robots And Their Potential Impact On the Future of Work

Small businesses, the vast majority of which have two employees or fewer, simply don’t have the time, knowledge or expertise to take full advantage of these new AI tools. Yes, theoretically, AI could save them vast amounts of time and enable all sorts of new activities. But many small business owners are so heavily focused on the day-to-day of their companies that learning how to effectively use new software, even a relatively straightforward one like ChatGPT, is more challenging and daunting than people think.

We surveyed our own customers and received over 800 responses. The message on AI was very clear. Only a quarter of small businesses regularly use AI-powered tools. So, despite all the commentary and discussion, 75 percent of small businesses have yet to embrace these new tools—even though many are low cost relative to the time saved. While other studies have shown that small businesses are widely adopting AI, such studies consider small businesses to be companies with up to 100 employees; the experience of such businesses is vastly different from the mom-and-pops we surveyed.

Indeed, for larger businesses, things look rather different. Surveys suggest that roughly 75 percent of medium and large businesses are using AI regularly. These results suggest that, rather than being a leveler and democratizing force, AI is creating a chasm between larger businesses and small ones. As one of my team suggested, AI could stand for ‘Absolute Inequality’, unless small businesses get some help.

Our survey revealed that nearly half (48%) of small business owners work more than 60 hours per week, with more than 73% working over 40 hours weekly. So small businesses are still working very long hours. So why aren’t they employing digital tools to make life easier? We all know tech can be used to schedule meetings and client calls, improve inventory management, track expenses, create targeted marketing and more. But why aren’t small businesses using it?

Well, of course some are, and the 25 percent in our survey who regularly use AI are saving up to five hours per week. But it seems that some need more encouragement, help and, probably, the support of platforms that do more of the heavy lifting for them.

So, what steps can small businesses and organizations take to better embrace AI? Here are some simple steps and questions that I would recommend business leaders to consider.

Identify low-hanging fruit

Let’s start where it’s easiest to find success. Looking for simple, repetitive tasks is a good place. Make a list of activities that you do regularly and which eat into your time. Even if you don’t know how to automate these yet, at least be aware of what the problems are. Also, add to the list things you’d like to do more regularly but don’t have the time to do properly. A list of problems might not sound like a good start but these are low-hanging fruit for you to pick later.

Look for help

Don’t suffer alone. Now that you know areas where you can potentially automate, it’s worth asking others how to do this. Engage with small business development centers (SBDCs), business networks and professionals that can help.

Use voice-powered tech

One of the big pluses of AI is that so much can now be done with voice. You can speak so much faster than you can type text, so get into the habit of using your voice to write and issue commands. A very simple but powerful application of this is to use tools like Google Gemini or Microsoft Copilot which integrate with Gmail or Outlook respectively to draft, send, and manage emails via voice commands. This can extend further into task management.

Find user-friendly platforms

There are many AI-powered platforms covering everything from accounting to calendar management to website creation. Look for what others in your network are using. But if it feels like too much, then look for a platform that comes with a service attached, too. Some banks and accounting firms are now using AI tools to help business owners gain better insight into their spending patterns. At UENI, our web design and copywriting experts leverage AI in our proprietary tools to deliver powerful SEO content for pennies you’d pay elsewhere. It’s a smart strategy to work with organizations that know how to get the most out of AI for small businesses.

Do it only if it adds value

Putting customers at the center of product development should be a given; after all, they will be using the product, so why wouldn’t they be front of mind? There is also a direct line between prioritizing customer feedback and bottom-line metrics. Companies who listen to their customers experience 1.6 times more growth.

Yet knowing that customer-centricity is a route to success and being customer-centric are two different matters.

The barriers to customer centricity: Data overload and team silos

What’s stopping customer-first development from becoming a reality? There are two fundamental reasons: an overwhelming volume of feedback to parse; and product functions that operate in silos.

Depending on the company and its industry, feedback might come from social media, support tickets, sales calls, customer interviews, face-to-face meetings, email, or chatbots.

The format might vary from structured data with strict rules and logic to informal, off-the-cuff remarks. The employees receiving the feedback might work in support, sales, or marketing; they might be employees with extensive training or contract workers brought in to help with a single project.

We know there is gold in all those insights. If a product team can capture, analyze, and action it, organizations can engage with their customers at a level most struggle to reach.

And that’s the other part of the equation that makes true customer-first development difficult: the silos between functions. Organizations could be gathering valuable insights already, but is it getting to the right people at the right time?

Many product organizations have invested significantly in user experience research to help understand which features could delight their customers and supercharge revenue growth. Yet, there’s a disconnect between the data collected and what product teams actually use when building their roadmap. The result is products that do what companies think customers want rather than what they truly need.

Marketing Technology News: MarTech Interview with Michael Hilts, SVP Products @ Stirista

AI is purpose-built for processing large volumes of data

When used appropriately, artificial intelligence could offer a solution to both of these issues.

Tackling the first part is pretty straightforward: AI lives for large volumes of data. Processing vast amounts of customer interviews, social media comments, support tickets, surveys, and emails becomes much easier using AI. Models can be trained to find and classify themes into feedback that help teams identify opportunities for improvement. For instance, in this way, what might appear to be isolated anecdotes  from customer interviews can be cross-referenced with support tickets to highlight issues impacting customers at scale. Or it could help by focusing on specific data ranges, such as after an update or fix, to help measure the impact of the change and see if it has had the desired effect. .

Used in this way, AI can partner with functions tasked with sorting through data and finding valuable insights. But it must be a partner, not a replacement. Unthinkingly relying on AI to tell you what customers want without interrogation or human involvement can have a negative impact.

For instance, models that aren’t monitored and prompted within closely defined guardrails run the risk of hallucinating, where the AI makes up the answers. If unchecked, this could distort insights and wrongly influence product development.

Keeping humans in the loop is vital to continually reviewing and quality-testing outputs. The AI provides signposts to key themes across your many channels of customer data to dive deeper into, whether you’re a researcher, designer, product manager, or leader.

Breaking down silos with AI

How can AI help solve the problem of functional silos? By giving everyone access to customer data, so they can pull out the insights they need without having to conduct their own research.

Embedding customer centricity throughout the organisation requires democratizing customer research. This involves giving people a way of finding valuable insights quickly in ways that fit their workflows. Not everyone needs or should be a UX researcher, but they should have access to the feedback that matters to their jobs.

For example, a CEO will not have time to sift through lengthy customer satisfaction surveys or hundreds of support tickets. However, a two-minute video of a customer struggling with a feature, perhaps captured as part of a live troubleshooting session, could be something they can consume and respond to.

It’s visual, it’s real, and they can literally see what the issue is. Previously, finding that clip might have been too much for an overworked and under-resourced product team; using AI to draw out the key themes, now they can home in on specific problems and find the feedback (and the format), that suits the team and their intended internal audience.

Silos remain when different functions can’t communicate. This is especially true at large organizations dealing with massive amounts of customer data. Different workflows, targets, and objectives all contribute to making it harder to communicate. Therefore, having AI that can rapidly uncover relevant information and help teams to  present feedback  to multiple audiences can help overcome those barriers. In doing so, this can bring everyone closer to the customer across the product development lifecycle.

The AI-powered customer-centricity opportunity

Once, not having enough customer feedback might have been an issue; now, it’s having too much and not knowing what to prioritize.

The Flavors of AI democracy

AI is not only for engineers and data scientists but also for managers, VPs and CXOs to leverage themselves and ensure usage of AI across their organization in bringing productivity and cost benefits. True democratization of AI is in enabling functions (HR, finance, operations, legal) across the organization to leverage its full potential in everyday work-life while making it simplified and amplified. It should also simplify the end customers routines and interactions with the company.

Read: Role of AI in Cybersecurity: Protecting Digital Assets From Cybercrime

A host of AI tools like ChatGPT, Midjourney, Gemini, Bedrock, Grammarly, Bing Search, etc are already used by masses. But there is more than one way to define the democratization of AI.

For London-based Stability AI, democratizing AI means opening up access to AI design and development to a larger community of users. This company open sourced its deep learning, text-to-image model to empower developers around the world to use, modify and innovate with the tool, subject to terms of use. The idea being that allowing developers closest to the end-users free access would produce the best outcomes, and evolve the model itself over time.

A more common way of democratizing AI development is allowing even non-technical users with little programming knowledge in an enterprise to build and modify models to suit their requirements with the help of low-code and no-code tools.

Less known but critical nonetheless is the democratization of AI governance. This is not about allowing anyone to use, monitor, and control AI as they please; rather it is about preventing the monopolization of AI by a few entities by allowing the interest and participation of a large number of stakeholders to influence decisions on how the technology should be used and by whom, within risk versus reward guardrails.

In the traditional environment, access to the data sandbox and the ability to utilize the data within it might be limited to a select group of technical specialists. Limiting access to data sandboxes hinders innovation in several ways in the AI era. A successful culture of experimentation is an ongoing process that requires continuous effort and commitment from everyone involved, not just the technical specialists. Democratizing access to data within sandboxes empowers business users with access to data in the sandbox. They can experiment with user-friendly AI tools and pre-trained models to automate tasks, identify patterns, and gain insights relevant to their roles. This enables them to leverage the power of AI without needing advanced technical expertise.

At times, democratization of AI development can clash with democratization of governance – for example, when developers are restricted from using data that is valuable for innovation but is protected by regulation. But eventually, all democratization efforts are directed at putting the power of AI within reach of every individual. About sharing the benefits of AI technologies throughout the organization. About making it possible for business users to create the use-cases best suited to their needs without depending on IT or undergoing extensive technical training.

In theory, at least. But execution is a little more complicated.

AI pays, but also punishes

Democratization leads to scale, and potentially, unprecedented benefits, but handled badly, it is a recipe for unmitigated disaster. Imagine a large community of developers around the world using an algorithm with biased training data to build other AI models; they will have magnified and perpetuated its discriminatory outcomes in next to no time. In democratized AI enterprises, marketers tap AI platforms and their customer data to identify leads, insurers study health information to price premiums, and relationship managers access customers’ financial statements to personalize investment portfolios. Without robust security measures – about access rights, permitted devices, consent – and sensitization to responsible use of AI and data, there is a real risk of the systems being breached by malicious actors, or information of a private or sensitive nature being leaked out of the organization. From financial and reputational loss to disruption of systems, to legal repercussions, uncontrolled, ungoverned, democratization can lead to very serious consequences.

Proceed with care

Which is why data and AI must be democratized the right way.

The first step is to understand how different users will engage with AI (use to query or to create content, use insights to make decisions, modify or develop models, etc.) Upskilling all users including non-technical business users ensures safe and effective use of AI. Before allowing employees free rein, employers should also provide guidance on responsible AI practices, such as how to avoid subjectiveness and bias while assigning data labels. Solutions like smart data fingerprinting uncovers more data with transparency and traceability, providing a foundation for experimentation for any AI or GenAI project. Easy access to diverse datasets opens doors for “responsible data sandboxes” for creative exploration to drive organizational growth. It also promotes data sharing and facilitates seamless data exchange between different systems including AI/ML applications. Organizations may want to consider using technology infrastructure, such as MLOps, to support AI training and deployment.

As more users work with and on AI, they expose the organization to greater cybersecurity risks; secure coding practices, rigorous testing, and responsible data handling will mitigate vulnerability to attack and prevent misuse of data.

Introducing fairness and accountability checks during development will lower the probability of biased or inaccurate algorithmic outcomes and inspire trust in the applications. Building transparent and explainable AI models is key. While allowing more people to participate in designing and building AI models, enterprises should appoint a qualified human in the loop for overseeing responsible development. But even a “fair” algorithm can produce unfair outcomes if trained on flawed data: think of a dataset of potential borrowers that excludes ethnic minorities; an AI loan approval system using this data may end up rejecting eligible borrowers from those communities.

While democratization of AI will help the majority of employees become more productive and creative, it could displace some workers; employers must reskill and upskill these people for redeployment in other roles.

Share the benefits

Democratizing AI is not just about giving access to the latest tools, but it is about empowering employees at all levels to make better data driven decisions using AI, innovate by collaborating with AI, reduce biases, and deliver tangible results with improved productivity and efficiency. It allows every individual in the organization, regardless of rank or function, a share in this revolutionary technology creates inclusion, improves morale, and elevates employee experience.

These benefits directly translate into tangible and intangible business value like increased sales, customer satisfaction, and brand reputation.

By democratizing AI, companies can bridge the gap between execution and value. Ultimately, businesses can create strategic advantage to the stakeholders and achieve long term sustainable success.

Employees in every role can use generative AI to create a variety of content – text, visual, audio, code – and to produce new and innovative ideas. For example, instead of deferring to brand custodians, marketing staff can use pre-trained gen AI tools to independently build “creatives” in conformance with brand guidelines, including color, font, and tone of voice. A product development team can input desired parameters into the tool and instruct it to create multiple design options, to save substantial costs and time.

Imagine living in a world where, in a matter of minutes, you could receive your favorite dish, order a quick gift for your loved ones, or get quick repairs done at your house. This is the magic of hyperlocal delivery, a company strategy that in 2024 will completely change the way we obtain products and delivery services.

Customers find hyperlocal delivery most appealing because their orders are fulfilled quickly. Whether it’s food, medication, or groceries, customers may get their orders quickly—often in less than an hour. Having quick access to products and services makes customers happier in general.

The hyperlocal delivery paradigm, which prioritizes location, speed, and convenience, is changing the face of service delivery. Its increasing appeal in 2024 demonstrates its effectiveness in satisfying the needs of modern customers while also fostering sustainable practices and local economies.

What Does The Term “Hyperlocal” Mean?

The term “hyperlocal” describes an emphasis on a very narrow geographic area, usually a town or neighborhood. Hyperlocal delivery makes use of technology to connect customers with nearby businesses for on-demand product and service delivery, leveraging on this priority. This niche sector is expanding rapidly due to consumer need for speed, convenience, and backing from nearby businesses.

A business approach known as “hyperlocal delivery” concentrates on offering products and services to clients or customers in a narrow and frequently defined geographic region. With the help of digital platforms, this approach makes use of neighborhood retailers and delivery personnel to quickly which is often in a matter of hours or minutes to fulfil requests.

Hyperlocal delivery is becoming more and more popular in 2024 because of its capacity to satisfy customers and meet their growing demands offering convenience with swiftness. This idea covers a range of applications, including hyperlocal news, weather predictions, and delivery, that allow the population in each area to provide highly relevant and localized information.

The Business Model for Hyperlocal Delivery

 A hyperlocal delivery model is a business strategy intended to address the urgent demands of residents. It entails quickly delivering goods or services from nearby retailers via digital channels, and meeting consumer demands for prescription drugs, alcoholic beverages, groceries, meals, and beverages.

Compared to typical e-commerce, this approach stands out for its capacity to guarantee same-day delivery, frequently in a matter of hours or even minutes. This USP (unique selling proposition) distinguishes hyperlocal delivery by putting convenience and quickness first.

Take ordering fresh groceries via a hyperlocal delivery service like Dunzo, Instamart, or Swiggy, for instance. The app notifies a nearby retailer and a delivery person when you place an order. After that, the agent comes to get your groceries and delivers them to you in a short amount of time—typically 15 to 20 minutes. This quick turnaround serves as an example of the hyperlocal delivery model’s effectiveness and responsiveness.

Small and medium-sized enterprises can reach a wider audience using hyperlocal delivery platforms without having to make large investments in their delivery infrastructure. Sales and client engagement increase because of this enhanced visibility are common. Local firms can outcompete larger e-commerce giants by providing quick delivery timeframes. Order fulfilment speed increases a company’s competitiveness and client retention.

A Hyperlocal Delivery Business Model’s Features

The features of the hyperlocal delivery business model include the following:

1. Geographic Focus:

The population inside a constrained area is the focus of the hyperlocal model. This emphasis enables the effective provision of on-demand goods and services that are specifically customized to meet the needs of the neighborhood.

2. Speed and Convenience:

This company model’s main USP is its capacity to deliver excellent services and goods at a remarkably rapid pace. Hyperlocal firms can match consumers’ expectations for immediacy by meeting orders considerably faster than typical e-commerce platforms by utilizing local merchants and delivery providers.

3. Local Economic Impact:

Hyperlocal firms support the local economy by focusing on a particular area. They foster a mutually beneficial partnership between local businesses and vendors, benefiting both parties. This narrow emphasis promotes economic growth and stability by facilitating the flow of money within the community.

4. Efficient Integration:

Hyperlocal models facilitate equitable market conditions by efficiently integrating consumers, marketplaces, and aggregators. Businesses gain from enhanced visibility and customer involvement, and consumers are guaranteed fast access to a range of goods and services from nearby sources thanks to this connection.

5. Operational Efficiency:

Businesses can optimize their operations and cut costs and delivery times by focusing on a limited geographic area through streamlined logistics. Higher profit margins and improved resource management may result from this efficiency.

6. Help for Local companies:

Using hyperlocal delivery systems to help local companies is something that many customers find appealing. In addition to helping their community, this support offers a more customized buying experience.

Hyperlocal Delivery’s Significance in Boosting Community Development and Resilience

The hyperlocal delivery model contributes in many ways to the development and welfare of people as well as communities. This concept encourages greater community relationships and resilience in addition to supporting economic activity by concentrating on the immediate needs of the local populace. Let us examine the main arguments for why adopting hyperlocal delivery is crucial.

1. Hyperlocal Delivery’s Function in Fighting COVID-19

The hyperlocal delivery model became increasingly important during the COVID-19 epidemic as local governments and communities looked for strategies to reconcile social alienation with continuing economic activity.

By minimizing the need for people to leave their homes, hyperlocal delivery helps lower the danger of virus transmission. Through the direct delivery of necessities like food, medications, and home items to consumers’ doorsteps, this approach efficiently promotes public health initiatives while preserving access to important products.

For example, a neighborhood delivery service can provide houses with fresh vegetables and prescription drugs, making sure that people follow social distance rules without sacrificing their necessities. By reducing in-person encounters in public places such as markets and stores, this direct-to-doorstep strategy contributes to slowing down the adoption curve.

2. Maintaining Regional Economy

Maintaining local economies becomes critical in the face of global economic uncertainty. The hyperlocal delivery model emphasizes localized production and consumption, which helps to build self-sufficient economic ecosystems. This strategy lessens the impact of fluctuations in the global market on local economies.

Hyperlocal delivery helps small businesses by giving them a way to connect with clients in their local area. For instance, a local bakery can provide neighbors with freshly baked goods and pastries by using a hyperlocal app. This guarantees local consumers have access to fresh items while also keeping the business operating.

3. Supporting Local Businesses

 Due to market dynamics that frequently favor huge firms, small and rural enterprises are at a disadvantage as a result of globalization. By giving local businesses priority, the hyperlocal delivery model provides a cure and makes it possible for them to compete more successfully.

Localization promotes a fairer economic climate that is conducive to the success of small enterprises. For example, a local craftsman can sell handmade goods directly to local clients by using a hyperlocal platform, saving money by avoiding the need for costly shop space or middlemen fees. Local businesses benefit from this direct link, which also gives consumers access to distinctive, locally produced goods.

4. Getting Rid of Middlemen

An important obstacle that small producers encounter, particularly in the agricultural sector, is the existence of middlemen who siphon off a sizeable portion of earnings. By putting producers and customers in direct contact, hyperlocal delivery methods can help with this problem.

For instance, a neighborhood farm cooperative can start a delivery business to provide residents with fresh fruits and vegetables. Through this direct connection, farmers are guaranteed a fair price for their crops, while customers gain access to fresher, more reasonably priced goods. Reducing middlemen promotes a more equitable distribution of revenue throughout the community and lessens needless price inflation.

5. Hyperlocal Models’ Effect on Contemporary Business

The environment of service delivery and customer expectations has undergone a substantial transformation with the rise of hyperlocal business models. Hyperlocal delivery systems prioritize location, speed, and convenience which sets the new standards for business efficiency and customer satisfaction.

Improving the Client Experience

Consumers appreciate time and convenience more than ever in the fast-paced world of today. Hyperlocal delivery services satisfy these desires by offering nearly instantaneous satisfaction. Hyperlocal services guarantee that customers receive what they need quickly, regardless of the situation—whether it’s an urgent need for medication or a last-minute dinner plan that necessitates a speedy shopping delivery. This promptness not only improves the client experience but also fosters a sense of loyalty and trust toward the service providers.

Endorsing Regional Companies

Supporting local companies is greatly aided by hyperlocal models. Through digital platforms, these models enable small and medium-sized firms (SMEs) to compete with larger e-commerce giants by establishing a connection between them and consumers. Local merchants don’t have to make significant investments in their own delivery infrastructure to reach a wider client base. This democratization of market access fosters economic variety within communities and aids in the survival of small firms.

Offers Environmental Benefits

The possibility for environmental sustainability offered by hyperlocal distribution is another noteworthy benefit. The strategy has the potential to mitigate the transportation-related carbon impact, as delivery is limited to specific geographic regions. Comparing shorter delivery routes to traditional delivery systems that cover greater distances, shorter routes result in reduced emissions and fuel use. Furthermore, a lot of hyperlocal firms are investigating environmentally friendly delivery options, such as bicycle couriers, to save the environment.

6. Extra Advantages and Things to Think About

Compared to long-distance transportation, hyperlocal delivery has a lower carbon footprint. Environmentally sustainable transportation is further promoted by the frequent usage of bicycles and electric vehicles for local deliveries.

Hyperlocal delivery builds stronger community ties by encouraging deeper relationships between local businesses and their patrons. Consumers are more inclined to support companies with whom they have a personal relationship because this fosters a sense of community and support amongst them.

Hyperlocal delivery is now more practical and efficient because of technological advancements including real-time tracking and sophisticated logistics management. These technologies improve client satisfaction by offering dependability and transparency. The hyperlocal delivery model is an effective means of fostering environmental sustainability, economic resilience, and community development. This strategy fosters a more just and self-sufficient economic environment by emphasizing the needs of the local populace and assisting small companies.

Hyperlocal delivery demonstrates its worth during emergencies, like the COVID-19 epidemic, by making sure necessary supplies reach people in need while abiding by public health regulations. Adopting hyperlocal delivery can help us move forward by creating more resilient and long-lasting local economies.

Challenges and Considerations to Think About In 2024

The hyperlocal delivery model has many advantages, but it also has certain drawbacks. It can be difficult to maintain delivery operations’ efficiency, manage inventories in real time, and guarantee product quality. To effectively tackle these problems, businesses must invest in strong digital platforms and logistics networks.

Hyperlocal services also need to be flexible enough to accommodate changing community requirements and preferences due to their reliance on local factors, which calls for a thorough comprehension of local market dynamics.

However, 2024 will see a further rise in the use of the hyperlocal delivery model due to advances in technology and changing consumer demands. Customers can now place and track orders more easily, while businesses can better manage deliveries thanks to the widespread use of mobile apps and real-time tracking systems. The growth of hyperlocal services has also been aided by the growing urban lifestyle demand for ease and immediacy.

Furthermore, the hyperlocal concept and the emphasis on sustainability and lowering carbon footprints mesh nicely. A more sustainable delivery ecology is facilitated by shorter delivery routes and environmentally friendly modes of transportation like electric or bicycle-powered vehicles.

Hyperlocal Delivery Business Models

Not every hyperlocal delivery company is made equal. Below is a summary of the most popular models:

1. Inventory-Based Model: The company uses a central warehouse or smaller fulfillment facilities within the delivery radius to pick and pack orders. Although this strategy gives you more control over the quality and range of products, it comes with a hefty upfront cost for storage and inventory.

Benefits:

Companies directly manage their inventories, therefore the quality and freshness of their items are guaranteed.

The products are consistently available since the corporation manages the stock.

The distribution procedure can be made more effective and streamlined with unified inventory management.

Drawbacks:

Keeping inventory demands a large financial commitment to administration, distribution, and storage.

Inventory mishandling can result in either too much inventory, which raises expenses or not enough inventory, which reduces revenues.

Customers may have fewer options because the company can only sell items that are stocked in its inventory.

2. Marketplace Model: The company serves as a conduit between clients and nearby retailers. Direct orders are placed with the stores, which use either independent couriers or the platform’s logistics network to manage fulfillment and delivery. This approach is less capital-intensive up front, but it is highly dependent on forming reliable alliances with nearby companies.

Benefits:

A greater selection of products from various vendors is available to customers.

There is no chance of overstocking or understocking because the company does not keep inventory.

Adding new vendors doesn’t require additional data capacity, so this approach can readily grow.

Drawbacks:

Since many vendors maintain the inventory, it can be difficult to guarantee consistent product quality.

The efficiency of the vendors affects both delivery times and dependability, and this can change.

Handling relationships with multiple vendors can be difficult and resource-intensive. This is known as complex vendor management.

3. Aggregation Model: To expand the range of goods and services available on its platform, the company collaborates with several neighborhood retailers and delivery providers. Customers will find this model convenient, but it requires a sophisticated back-end infrastructure to coordinate order fulfillment and delivery among several vendors.

Benefits:

Customers have access to a large selection of goods and services, just like in a marketplace.

Without having to worry about inventory, the company can swiftly modify its products in response to demand.

Reduced operating expenses because inventory doesn’t need to be kept up to date.

Drawbacks:

The overall quality of services might be impacted by disparities in standards between various providers.

Third-party providers have a major role in the platform’s dependability and repute.

It might be logistically difficult to manage and coordinate with several providers.

4. Hybrid Model: This model blends aspects from the previous models. For specialized goods, a company may collaborate with nearby retailers while keeping a core inventory of well-liked products. This provides an equilibrium between control and diversity.

Benefits:

The marketplace model’s diversity and scalability are combined with the inventory-based model’s control and dependability to create the best of both worlds.

Guarantees the availability of well-liked products while providing a large selection of extra products from suppliers.

Companies can continue to provide goods from outside vendors while modifying their inventory levels in response to changes in demand.

Drawbacks:

Keeping track of inventory and managing connections with vendors can be difficult and call for advanced technologies.

Keeping some inventory results in expenses, but not as much as with the pure inventory-based strategy.

It might be difficult to maintain quality control over both products supplied by vendors and inventory kept in-house.

The hyperlocal delivery model is transforming the way that companies and customers engage, placing a strong emphasis on quality, speed, and local engagement. The hyperlocal model is set to become an increasingly essential component of today’s retail environment as technology advances and customer tastes continue to favor ease and immediacy.

Read More: SalesTechStar Interview with Juan Jaysingh, CEO at Zingtree

Knowing How to Operate in a Hyperlocal Marketplace

To effectively satisfy urgent customer needs, a hyperlocal marketplace makes use of the infrastructure and components already present in local markets. We may divide hyperlocal e-commerce into two main categories to help us better comprehend this idea:

Hyperlocal Services: These comprise residential services, medical, laundry, logistics, and related services.

Hyperlocal Delivery: This refers to the delivery of goods like groceries, meals, electronics, and essentials for the house.

The hyperlocal delivery model can work in the following way to be successful if you are just starting with it:

1. Begin Small and Use Selected Products

Start with a small number of products that correspond to your initial investment capacity. You can test the local market and gain insight into consumer behavior by starting with a small selection and avoiding overspending. Beginning small lowers risk and yields insightful information that can direct future growth. Concentrate on 3–4 core products and provide attentive customer service.

2. Acknowledge Your Presence

Promote your service to your target audience concurrently with the introduction of your product. Make use of community events, local leaflets, and social media platforms as effective means of reaching the local population with your advertising. Examine the feedback received from prior local firms to successfully customize your marketing tactics. Creating a buzz in the community is essential to getting things started.

3. Establish Local Collaborations

As your company expands, determine what additional services and goods your clients are in need of. Establish alliances with delivery services and local retailers. These partnerships are advantageous to both parties and can be established by preliminary contracts. By taking this approach, you can ensure that you exceed consumer expectations while managing growth and minimizing risk.

4. Identify Your Commercial Connections

Focus on stabilizing your business as soon as earnings start to come in by establishing clear boundaries with your most important stakeholders, including customers, delivery drivers, and retail merchants. Long-term success in business requires the development of solid, long-lasting partnerships. Make a good first impression and cultivate confidence in the neighborhood.

5. Select a Model of Income

Select the best income model based on your early operations’ data and experience. The inventory-led paradigm, the aggregator model, and the hybrid model are available options. You will have enough knowledge at this point to make a choice that is in line with the dynamics of the market and your company’s objectives.

6. Launch a mobile application

Use a mobile app to streamline operations and make order administration, delivery tracking, and customer interaction easier. The intricacy of running a hyperlocal delivery service is reduced by technology. Delivery scheduling and upholding high service standards can be facilitated by the integration of logistics, Supplier Relationship Management (SRM), and Customer Relationship Management (CRM) in a well-designed app.

As a hyperlocal delivery company you should start small, raise awareness, establish strategic alliances, and make use of technology. You can establish a dependable and effective delivery business that satisfies the urgent demands of your community by getting to know your local market and gradually broadening your products. A hyperlocal delivery model can succeed by supporting local companies and giving customers convenience via thoughtful planning and execution.

Essential Elements Of A Hyperlocal Marketplace

The following are the important elements of a Hyperlocal Marketplace:

1. Committed staff with necessary tools/technologies

A committed staff combined with well-integrated technology is the foundation of a hyperlocal marketplace. This is how it operates:

Digital Platforms: To link customers with local suppliers and service providers, hyperlocal firms mostly rely on websites and mobile apps. These digital channels are the main means of communication that allow customers to place orders and request services quite easily.

Management of the Supply Chain: An essential part of the Hyperlocal Business Model is the Supply Chain. The vital conduit that connects customers with nearby retailers or service providers is the delivery agent. They guarantee the timely delivery of goods and services, upholding hyperlocal commerce’s characteristic immediacy.

Local Vendors and Stores: Local companies are essential to the hyperlocal economy. They can reach a larger customer base in their local area by taking part in this approach. These companies manage orders, list their goods and services, and work with delivery drivers via smartphone apps.

2. Physical retailers:

Since selling is a company’s primary duty, goods and services play a critical role in the success of a firm. These actual establishments supply the merchandise needed by these aggregators in hyperlocal commerce. It’s also imperative to collaborate with some of these retailers.

3. Logistics delivery:

You need a delivery system in place in order to transport goods. You have two options for handling that: working with a logistics company or hiring your own delivery personnel. These suppliers are experienced in this industry and will lessen your workload.

4. Mobile application:

Having a mobile application is essential once you’ve established partnerships. It is this smartphone application that will make you known to others. In addition, it is where clients will go to make purchases from you. Therefore, it is essential to ensure that the app functions properly to prevent users from abandoning it.

5. Customers:

You need consumers who are interested in you in order to sell your product. These are the ones who will actually inject money into the company. Your company won’t be able to survive long without them.

The Technologies Driving Hyperlocal  Marketplaces

Hyperlocal marketplaces depend on several essential technologies to function well:

1. Mobile Applications:

The hyperlocal marketplace’s foundation is made up of these applications. They make it easier for clients, sellers, and delivery agents to list goods and services, place orders, and communicate in real-time.

2. Geolocation Services:

This technology assists in pinpointing the precise location of suppliers and clients. By using this, delivery personnel may locate the fastest and most effective routes, cutting down on delivery times and improving the clientele’s experience in general.

3. Real-Time Order Tracking:

Allowing customers to follow their orders in real time increases trust in the hyperlocal business by increasing transparency. Real-time tracking helps delivery agents as well because it makes it easier for them to plan and organize their schedules.

4. Inventory Management Systems:

Effective inventory management is essential for small businesses. By monitoring their stock levels, these systems assist firms in meeting client demand without going overboard or running out of necessities.

5. Payment Gateways:

The availability of safe and practical payment methods is essential for any e-commerce site. Hyperlocal markets allow users to pay using a variety of methods, such as credit/debit cards, mobile wallets, and cash on delivery, by integrating different payment gateways.

Final Thoughts:

In order to establish a profitable hyperlocal delivery company in 2024, one must take a calculated risk and give top priority to client happiness, operational effectiveness, and market trend adaptation. The hyperlocal delivery business model, which prioritizes location, speed, and convenience, is completely changing how goods and services are delivered. By focusing on particular regions, these companies can deliver quick and effective services that satisfy customers’ urgent needs.

This concept encourages sustainable practices and boosts local economies in addition to improving customer happiness. The hyperlocal model is expected to develop further as technology advances, providing even more creative answers to satisfy the needs of contemporary customers.

The hyperlocal delivery model is an agile business strategy created to address customers’ urgent demands in a narrowly defined geographic area. It works by using digital platforms to link customers with nearby physical establishments, guaranteeing quick delivery of on-demand requests for goods and services including laundry, groceries, prescription drugs, and more. This strategy is different from regular e-commerce, which usually entails lengthier delivery times, in that it prioritizes both quality and speed.

Corporate training programs mean well. Large companies and enterprises will go to great lengths to construct training centers outfitted with the latest equipment. But if you ignore practical training options like remote or hybrid classes, you risk sacrificing employee productivity.

Insisting on in-person training upsets production schedules. It also takes a significant cost investment to build a training facility, fly employees there, and house them for several days. Modern learning tools offer a better solution without compromising what trainees will learn. 

For instance, interactive whiteboards and other digital devices enable companies to offer effective corporate training programs in a hybrid setting. More importantly, they keep production up while ensuring workers learn the skills they need to do their jobs effectively.

How Companies Still Mess Up Corporate Training

It makes sense that enterprises want a healthy return on investment (ROI) on the behemoth training centers they built. Despite the availability of less costly and more efficient training programs, human resources still have to fill the training classrooms with warm bodies. 

We all know the hidden costs of flying trainees in to attend a weeklong seminar at corporate headquarters. Even if travel and lodging costs are safely within budget, pulling employees out of their daily routines can cost more in the long run. 

Another issue with traditional corporate training programs is that attendees tend to forget information that doesn’t apply to them and their jobs directly. Why should your internal IT team learn how to better deal with customer calls? What’s more, you risk creating a gap between employees that receive training and those that don’t. This can affect teamwork and morale.

Fostering Hybrid Training Programs Within Your Business

The COVID-19 pandemic proved that modern communication technology is mature enough to handle business affairs remotely. This includes client meetings, project management huddles, and corporate training. Even after the pandemic, the convenience and cost-efficiency of online training made it hard for corporations to return fully to the traditional, in-person mode of learning. 

But what about their earlier investments in training centers filled with state-of-the-art equipment? Hybrid training solutions let corporate trainers enjoy the best of both worlds. 

Employees who already work at corporate headquarters can still attend training sessions without losing valuable work time due to travel. Meanwhile, employees who live in other parts of the world can simply log in to a training program and learn from the convenience of their laptops. Instead of going away for a few days, they only need to attend sessions for a few hours daily. 

Setting up a Hybrid Training System: What Do You Need? 

The best part of setting up a hybrid training system is that most corporations already have what they need. During the pandemic, companies spent billions to set up remote networks so employees could stay home to work. Even as the world returned to normal and workers returned to the office, remote systems remained a great alternative to in-person meetings. 

Hybrid learning gives in-person and remote trainees equal access to training. So, your equipment should offer the same opportunities. For instance, a standard office whiteboard won’t promote engagement for online members of the class. 

In-person trainees facing the interactive whiteboard have the same access as the virtual students. During interactive activities, in-person students can answer directly on the board, while remote participants can reply online. They can work together to solve problems, collaborate on discussions, and brainstorm solutions.

Preventing Digital Overwhelm for Better Retention

To use interactive whiteboards effectively, you must maintain, manage, and secure each device. Managing a corporate fleet of interactive whiteboards requires a device management platform that keeps system software, firmware, and applications updated to the latest versions. The right device manager also enables administrators to remotely perform needed repairs or apply patches without upending any previous training schedules.

In addition, the device manager can safeguard the use of interactive whiteboards by assigning different access levels to different user types. For instance, end-users can launch applications and open modules, but they can’t tweak system settings or erase or copy data files. IT administrators can look into system folders and perform repair and maintenance tasks. However, they won’t have access to training files and can’t run training software. Analysts and managers can extract user data to generate insights to improve training systems. 

Finally, security will always be a top concern. Whether in-person or remotely, some trainees might try to hack into the whiteboard. The right device management platform can fend off these attempts by remotely shutting down or freezing devices. Should an attendee try to steal or copy data, admins can wipe the device free of any information. 

Support Your Employees Through Hybrid Training

Online learning is here to stay, but maintaining in-person training systems is also a good idea. Companies that want to keep both training options can implement a hybrid learning system. This allows both online and in-person systems to thrive and coexist. 

There’s no need to choose online over in-person. A hybrid setup enables companies to train more people without incurring productivity downtimes or travel costs.

The affiliate marketing industry has rapidly evolved in recent years, as the industry looks to keep pace with the latest technologies and tactics transforming the sector. Affiliate marketers are always seeking new ways to stay ahead of the curve to captivate their audiences and foster long term engagement. In 2024, marketers are witnessing emerging technologies and shifting consumer behaviours reshape the affiliate marketing playbook.

From leveraging Artificial Intelligence (AI) to deliver more personalised experiences, to embracing immersive technologies such as Augmented and Virtual Reality (AR and VR), the strategies for building and retaining engagement are changing.

So, what are the latest trends and techniques that will shape affiliate marketing in 2024? And, how can affiliate marketers leverage emerging technologies to captivate audiences and retain engagement?

Here’s a guide on how to stay ahead and foster long-term engagement this year.

Get personal – The rise of AI-driven personalisation

One of the most significant trends shaping affiliate marketing this year is the increasing adoption of AI to deliver more personalised experiences. At Alpha Affiliates, we’re already seeing AI prove invaluable in areas such as banner creation, enhanced analytics and predicting user behaviour.

AI tools not only simplify the content creation process for affiliate marketers, but amplify the effectiveness of campaigns, ensuring they resonate with the target audience. By analysing past patterns and behaviours, AI can provide valuable insights, enabling affiliate marketers to anticipate the actions a user will take, and offer more tailored content and promotions as a result.

For instance, in the world of iGaming, where user engagement is everything, personalisation with the help of AI can be a gamechanger. Imagine being able to predict when a user is likely to become a VIP player, and tailoring your marketing efforts accordingly. This level of personalisation not only enhances the user experience, but fosters long-term loyalty and engagement, as a result.

Embracing AI tools will be crucial in producing more effective, creative and impactful affiliate marketing campaigns.

Marketing Technology News: MarTech Interview with Jonathan Joseph, Head of Solutions @ Ketch

Make the unbelievable, believable – Creating immersive experiences through AR and VR

Today’s consumers expect more. As the world becomes increasingly more tangible online, consumers seek more interactive, fun and immersive experiences. Enter Augmented Reality (AR) and Virtual Reality (VR), which create a sense of realness and blur the lines between the digital and physical world.

These technologies hold huge potential for captivating audiences and driving engagement. While AR and VR remains a new concept for many and is yet to disrupt the affiliate marketing space, as their use becomes more and more widespread among consumers, the need to integrate these technologies within affiliate marketing campaigns will become a critical consideration.

For affiliate marketers working across any sector, AR and VR can be very effective – but particularly in the world of iGaming. Imagine an VR experience that transforms users to a virtual casino, surrounded with lifelike graphics and social interactions. By offering these immersive experiences, affiliate marketers can create “pinch me” moments that leave a lasting impression and foster deeper connections with their audiences.

Know your audience – Diverse strategies for diverse audiences

In today’s crowded digital space, capturing and retaining audiences can be challenging and requires diversity in content and communication channels. Affiliate marketers in the iGaming sector must leverage a multi-channel approach, tailoring their messaging and content to resonate with different audience segments across various platforms.

Consider the most effective ways to connect with each audience, and how they will respond. This may involve creating engaging and shareable video content for social media platforms, crafting blog posts that resonate with industry enthusiasts or leveraging influencer marketing to tap into niche communities.

With a diverse and dynamic strategy, affiliates can reach audiences globally and provide value in a way that aligns with their audiences’ unique preferences, interests and tastes.

Catch this HRTech Talk by HRTech Series featuring Holly Grogan, Chief Experience Officer, Appspace where we discuss the nuances around HRTech adoption patterns and why tech brands need to align better on using HRtech to drive both, the customer and employee experience

Listen now!

About the HRTech Talk Series:

A chat series featuring HRTech founders, HRTech innovators, HRTech product specialists and other HRTech executives who have an extensive presence in the global HRTech Realm.

About Appspace:

What do the following have in common?

A service in the cloud data warehouse in the company went rogue and suddenly created 4,000 tables unexpectedly.

A test automation glitch in another company caused a sudden spike in usage for one of the test Snowflake warehouses, leading to queries running unexpectedly for hours.

An external vendor that the company was relying on, made a breaking change to the data that resulted in its own critical customer-facing table to get only 10% of the expected data.

Data operations – everyday is a brand new challenge

On the surface, these appear to be disconnected issues – three different issues, three different companies. However, left undetected, each of these issues has the power to single-handedly disrupt the customer’s data environment, and in a very bad way.

 The first one is an unexpected blow up in metadata space where 4,000 tables could have easily multiplied by 10x by the time the team realized it was happening. The second one is about leaking tens of thousands of dollars that would derail the company’s data budget had this leak gone undetected or waited until the next bill to find out. The third one is classically centered around data quality that would have directly put the company’s own reputation at stake in front of its own customers.

Common thread

The examples above are representative of the challenges data engineers face every day. These engineers are busy building new data pipelines, and at the same time dealing with completely unexpected issues that touch different parts of the stack. Worse yet, on a number of occasions, those challenges involve parts of the stack where engineers do not have any control (take case #3 above, for example) and must rely on external parties for smooth operations.

Challenge

The bigger challenge, however, is that engineers do not have the of time to define all the data and resource checks that could capture the dynamic and complex nature of their data stack. And on top of that they neither have the budget to buy different solutions for such problems, nor the bandwidth to build different solutions for each problem.

 To put into perspective, building such a solution in house for production is not a small task. It needs sustained long term commitment to the cause of making data operations super efficient that in turn requires deep expertise in building distributed systems, large scale AI models for the space, strong SQL knowledge and in-depth understanding of underlying cloud data warehouses architecture to know how to optimize and that skill set requirement is just the start. Then there is building the consumption layer and on call rotation to maintain such a system plus gathering requirements to continue to adapt to changing data and internal customer requirement.

In short they need a million dollar team!

Emerging trend in cloud data operations

As companies advance their cloud data warehouse adoption, we see a critical need for the following wrt data operations.

 1. Merging of FinOps and DataOps responsibilities

CxOs and data leaders are rightfully wary of their spiraling out of control cloud data warehouse spend. That’s not a surprise, as teams brought in the traditional capex mindset when they followed the company’s mandate to transition to cloud. Three to four years into their cloud adoption journey, and even sooner, companies are now discovering that the capex mindset and old data practices don’t work at all in the cloud.

 When was the last time a business asked its data teams to remove a data pipeline because they were no longer using it? It’s a rhetorical question. Of course the answer, without exception, is NEVER. There is no exit strategy for data and compute

Consumption-based pricing requires a strong data and compute exit strategy to have healthy data adoption. Otherwise, it’s a one-way road to increasing the spend.

 The reality is that FinOps can not optimize spend in isolation; they need to deeply understand the usage of the data to make meaningful business decisions. Likewise, data teams need to understand the risks to budget when they onboard new data sets, and to identify trends to make the necessary trade-offs between the cost of having data delivered to business vs. the current dollar impact of that data.

 The fact that every CDW has a unique pricing structure (for example, Snowflake compute spend depends not only on query latency but also on how these are distributed over time) and enterprises typically have more than one CDW, makes it very difficult for the teams to overall maximize ROI of their data investment across such an expansive data footprint.

Gartner called out the four critical features of data observability that extend all the way from monitoring and detection to resolution and better prevention.

 This holistic, end-to-end approach is the right way of looking at the space from the vantage point of data teams.

Today’s state-of-the-art data observability offerings fall remarkably short. Previously, we shared how instead of taking on the really hard problem of surfacing the “unexpected” data quality issues for data engineering teams automatically, current vendors in this space primarily focus on the easiest piece of the pie: leading with manual data quality checks. This fundamental limited approach has not worked well in practice.

Indeed, it was not a surprise when, during our discussions with data practitioners, they questioned the ROI of such data observability systems repeatedly, asking: How is the system really helping us reduce our own effort if we have to define and manage everything?

 There’s an imperative need to automatically connect the dots between quality, spend, performance and usage

Spend and data quality: yin and yang of healthy CDW adoption. For one of our Google BigQuery customers, a large public company, businesses were seeing bad data. Why? Because the load failed due to resource usage being above capacity. Why? There was a query running at the time, consuming a large fraction of its company-wide resources. Getting rid of the offending query: Priceless. Powering the 5-whys. If the focus was purely on alerting people of underlying data quality issues, the obvious conclusion would be to bring the problem to the CFO and ask for more capacity – aka, dollars. However, by empowering the data team to get to the 5 whys, the journey starts from data quality and gets down to query performance and resource utilization, and by connecting them with the right business team, the outcome was magical: the user removed the query that was the real root cause of the problem – saving them time and money.

Impact $0.5M worth of slot capacity.

This was all possible because the data engineering team got a holistic picture of the problem that spanned quality, usage and spend. Without the ability for the data teams to look at these together, companies waste dollars and time, overrun budgets and continue to suffer from organically growing bad practices.

We strongly believe that for healthy data adoption to exist, data observability out of the box augmented with recommendations and actions as outlined above “end goal” by Gartner, seamless 5-whys across different pillars of quality, spend, usage and performance is key. Only then are data teams empowered to deliver the right data at the right time and at the right cost to businesses.

 3. Leading with automation

Previously, we had talked about my co-founder Shashank’s experience at Meta, where he led the data quality and observability effort with an automation-first (1.5M + tables, 10K+ data practitioners) approach. With automation, coverage for data quality went from single digits (~7%) to monitoring the entire CDW for data quality.

Data teams simply don’t have the time to define and manage checks as the underlying data ecosystem continues to evolve and change. The key empowerment to these teams comes from a maniacal focus on automation. They need a system that automatically selects what, how and when to monitor on their behalf.

Only through such automation can teams get coverage for the entire CDW – be it for quality, performance, spend or usage. That, in turn, provides data engineering teams with the confidence that the system has their back, which is key to adoption of such a technology.

Be respectful of data teams’ time. They simply don’t have time to define and chase every issue. Understand and address the real problems that these teams are facing, stop the out-of-control spending, and set up your entire business for healthy data adoption.

Healthy data adoption falls within the purview of data engineering, but it’s not one problem. Instead of creating more silos and continuing to layer more observability above such silos:

Onboarding is a crucial part of any new hire’s employee experience. Not only does this process give employees’ their first impression of a company, but this initial training period lays the foundation for their personal professional growth.

However, our Industry’s State of the Employee Experience Report revealed employees today are less than impressed with onboarding and company training, going so far as labeling both as “outdated” or “overwhelming.” To understand these issues, we’ll explore why employees are dissatisfied with training, and offer advice on how employers can make learning programs more relevant to employee needs.

Dissatisfaction Starts During Onboarding

When employees first begin their job, their optimism to learn more about their role quickly fades as many employees say they experienced stale onboarding and dull training processes from day one, leading 1 in 3 new hires to regret their decision to join a company.

To make matters worse, disengaging onboarding processes — often described as “information overload” — have also made it harder for new hires to comprehend everything they’re expected to learn. In fact, nearly a quarter (24%) of all new hires had to retrain on all the information they were taught during onboarding, which frustratingly takes up more valuable time and resources.

This is a clear indication that something has gone terribly wrong in the onboarding process. New hires need to be able to understand their company’s values and mission from the start, so employers should explain the jargon and their culture, but take it easy with the paperwork. Getting new hires overwhelmed won’t get you anywhere, so give them a warm welcome and maintain the momentum.

Also, new hires have to be able to execute what’s expected of them. So, within at least the first 90 days, onboarding training needs to give a realistic idea of the job and set a comfortable pace for new members to become part of the team. By using videos, 1 to 1 talks, assigning them a coach, offering classroom lectures, written presentations and online learning — you can get them ready to make waves.

Today’s Training Isn’t Engaging, Motivating…It’s a Checkbox

Seemingly, employers are no longer creating training content that motivates and inspires their staff. Instead, it’s become a forced requirement used as a performance metric. Over 3 in 5 (63%) employees complete workplace training because it’s a mandate, and required for their performance reviews.

This approach to training is all wrong, and leads to disengaged, unmotivated and burned out employees who don’t feel supported in their professional growth.

To make training more engaging, workplaces must determine their audience and where training falls short. Then, they must eliminate defective or outdated training materials, such as by removing fluff with an LMS for onboarding.

Training is imperative to helping employees be innovative, tackle industry shifts and get up to speed with changing trends. It’s critical to make the experience easy and accessible so people can improve skills fast and in the flow of work. Most importantly, training gives teams a sense of belonging and collaboration right from the start, so by making training a part of employees’ career development, employers will count many wins.

The Need for Updated Training — Especially Technology Training

Nearly half (43%) of employees say their company’s training is “outdated,” especially because it lacks content on in-demand skills and emerging technologies. This is concerning given that today’s workforce must continue to find ways to work alongside emerging technologies, like generative AI.

Furthermore, 2 in 5 employees worry AI could replace their jobs (39%) but feel overwhelmed about the idea of having to learn how to use AI (40%). Up-skilling and continuous learning, especially in technology, can lessen the angst that employees have when it comes to new technology and skills needed for the modern day workplace. As AI tools are popping up by the dozen, the workforce needs to be future-proofed. The above highlights even more the necessity of having skills development and assessment tools in your arsenal.

AI is a powerful tool and can be used to increase productivity and reduce administrative or mundane tasks.

It’s up to managers to show employees that AI is not a technology they should fear. In fact, AI paves the way to time and productivity savings. At the same time, it actually enhances the employee experience. AI for business can benefit employees and help them understand customers better.

Most importantly, learning to use AI will lead to better insights and decisions for both business and training, as it can identify skills gaps and offer training tailored to the learner’s needs.

The Upside of Updated Training (A Connected, Engaged Culture)

In addition to building a workforce that can evolve alongside emerging technologies, continuous learning can also build a stronger company culture. Most (63%) employees agree that when done the right way, company training serves as a great chance to bond and collaborate with team members.

However, 1 in 5 employees say they “don’t have time” to participate in company training sessions, which prevents camaraderie, peer-to-peer engagement and mentorship from happening. Employers must build in time for employees to attend training sessions. In doing so, employers foster a culture of collaboration, engagement and support.

Overall, training is critical for improving employee productivity, morale, engagement and ultimately, retention. Plus, it helps you instill a competitive edge to your organization. To ensure corporate learning sets employees up for long-term success, employers should evaluate their current training programs to not only ensure learning content is updated, but also that it’s being provided in a format (and time) that all employees can access when and how they decide. By creating a program that is purpose-built for continued learning, employees will actively seek out these resources as they continue growing in their professional roles.

Increasing cloud costs is a major pain point for IT leaders in 2024. Four reasons are causing an increase in the cost of cloud workloads. These are:

Excessive storage (52%)

Lack of integration strategy (44%)

Overconsumption of network bandwidth (42%)

Inefficient Cloud architecture (40%)

FinOps leaders are finding it difficult to control the cost of cloud governance, management, and modernization despite having an analytics-powered operational framework and supporting cultural practices. According to the latest report published by Boomi, cloud costs are spiraling out of control despite extensive use of Cloud Cost Management and Optimization tools, also called CCMO tools. As per Forrester, modern FinOps teams are leaning toward proactive cloud optimization— this is exactly what Boomi’s new ‘Cloud Costs Are Out Of Control: Integration and Modernization Can Help Rein Them In’ report has found out.

Boomi’s new report on proactive cloud optimization strategies found why companies are fraught with uncontrolled and wasteful cloud spend in 2024. It also pointed out the role of Integration-platform-as-a-service (iPaaS) solutions in cloud cost remediation tactics. It was found that FinOps teams either introduced these tactics too late or without having a full picture of the environment.

So, we ask you: Are FinOps teams prepared to effectively utilize the current crop of Cloud Cost Management and Optimization or CCMO tools in their current environment?

Based on the latest trends, nearly three-fourths of global cloud customers (72%) have exceeded their budgets in the last fiscal year. By 2026, public cloud spending could exceed $1 trillion globally, which means Cloud management and FinOps teams must better utilize CCMO strategies to reduce spending. This is possible by adopting proactive CCMO strategies, even before the actual cloud development process begins at the earlier architecture level. Currently, only 65% of leaders are prioritizing CCMO tactics very early in their cloud development process.

Where do FinOps teams fail to plug their cloud spending?

Almost every organization knows they can optimize cloud costs at the early solution architecture stage.  Then, why do FinOps teams fail with CCMO strategies?

40% of cloud leaders surveyed for the report said they manage to contain costs at the solution architecture stage! To further complicate the situation, only 6% of decision-makers report their cloud cost remediation strategies are ever proactive. So, there is a clear lack of visibility, proactiveness, and effort to resolve the crisis. CCMO tools are only as effective as the users who utilize them at the early stages of the development process.

Ed Macosky, Chief Product & Technology Officer at Boomi believes these findings pointed to a lack of dependence on the iPaaS solutions in the cloud cost equation. In most cases, integration is completely ignored. Ed said, “When systems are disconnected and data is siloed, companies are only seeing part of their organizations’ cloud cost picture, and this lack of visibility impacts tracking and decision-making.”

Back to our question: Are CCMO tools and strategies effective?

Only 10% of leaders say CCMO tools allow them to fully maximize their cloud cost savings. Despite the help of CCMO tooling, leaders failed to track 40% of their total spending.

When used sensibly, CCMO tools could be a compelling choice for cloud users. They can track increased cloud usage and optimize costs to justify the ROI, making them great for cloud cost management and optimization.

Boomi found Cloud workloads would rise disproportionately compared to cloud costs, in the next two years. It is attributed to the growing cloud complexity that is expected to arrive. Most leaders stated data management and cost tracking as the biggest challenges in deriving an accurate picture of their company’s cloud cost. Egress charges, compute resources, and off-hours operations were other areas where leaders failed to use CCMO tooling effectively.

CCMO Challenges for Cloud Management Teams

FinOps teams, in all likelihood, haven’t heard about the importance of iPaaS solutions for enterprise cloud optimization and modernization practices. This leads to unnecessary incidents in governance and cloud sprawl. 

According to Boomi’s report on CCMO tooling and strategies, misconfiguration of CCMO tools, and their recommendations prevent organizations from looking further left in the cloud architecture and development process for cost optimization. In a CloudZero report, 49% of organizations agreed their cloud costs are higher than the optimum levels, and only 3 out of 10 organizations knew where their cloud spending are headed.

Boomi found that 56% of leaders said CCMO recommendations are only as good as the data they ingest. 44% of leaders feel CCMO tools are inconsistent with the company’s reporting requirements. On the contrary, 19% of users don’t trust the native CCMO tools. With poor integration and recommendations from CCMO tools, 41% of respondents said they were unable to build cost governance into the architecture; 40% of user groups acknowledged they failed to fix the root problem of wasteful cloud spending.

All these point to the ineffectiveness of CCMO tooling in helping FinOps teams during cloud cost remediation strategies.

“More than half (56%) believe their CCMO recommendations are only as good as the data their company can provide. Yet 40% are struggling to fix the root problem of their wasteful cloud spend. The problem is integration is being viewed as a separate entity, when it actually has significant potential to act as a control layer in the reckoning of cloud costs,” said Macosky.

Having an iPaaS solution for cloud architecture development and modernization can improve efficiencies and reduce spend. Boomi, a leading iPaaS solution provider can solve CCMO challenges by connecting apps, people, and data in a single platform. With GenAI DevOps workflows coming into the picture, the importance of the integration platforms in cloud cost optimization and governance become even more important.

While CCMO tools and strategies can help cut back on cloud bills and reduce sprawl, FinOps and IT leaders should collaborate to bring iPaaS platform into the scenario. Currently, Boomi provides an automated self-managed platform to deploy synchronized changes to integration processes. This allows users to adapt to a hybrid FinOps CCMO strategy that can address the full-stack cloud technology spending across organizations at multiple locations.

Therefore, you can operate, modernize, and eliminate cloud wastage on AWS, Microsoft Azure, and Google Cloud by gaining full control of CCMO tools that are integrated with a reliable iPaaS solution platform for better results.

Why do people engage with a marketing message or ad? A new consumer study from Epsilon shows it’s because they are “familiar with and like the brand”. This was the most selected option at 60%, whereas only 37% said their engagement was driven by a need for what was being advertised.

Marketers have long acknowledged the importance of brand marketing, given ‘brand’ is the context in which all purchase decisions are made. But at the same time, performance marketing – which focuses on driving more near-term outcomes – commands most digital budgets, as marketers strive to demonstrate their investments are driving a return.

In a world where marketing spend is under pressure, which of these two objectives should you prioritize?

Astute marketers have come to realize the flaw behind this question. Specifically, that brand marketing and performance marketing need to be treated as two separate and mutually exclusive objectives.

It’s time we stop talking about ‘brand marketing’ versus ‘performance marketing’ and just talk about ‘effective marketing’. With effective marketing being defined as driving sales in the moment, while at the same time building the brand relationships needed to drive sales longer term.

Think about it this way: Each time a marketer engages a consumer to drive near-term sales, they are also impacting the consumer’s perception of their brand. And it’s that perception that drives sales over time.

Maybe you’re thinking, “Cool, that’s all great in theory, but how do you actually do that?”. You can start by ensuring every message you deliver is actually relevant to each consumer who receives it at any given moment in time. This will go a long way towards ensuring you both achieve your objective (for example, reach an in-market consumer with an offer) and deliver a positive brand-building experience when you engage them. Because they like your brand, they choose your product over any other options – harking back to the study’s findings of why consumers engage with brands in the first place.

As we know, however, this is easier said than done. Particularly in digital channels, where our ability to recognize the consumers we want to engage is limited by the nature of the channels themselves.

‘People-based marketing’ and the underpinning technology referred to as ‘identity resolution’ were supposed to address this, but the research also shows that they clearly have not – and there are consequences:

91% of respondents say they see at least one irrelevant ad or marketing message every single day.

More than three-quarters (76%) say they view brands negatively when they include inaccurate information about them in their marketing message.

Marketing Technology News: MarTech Interview with Lina Tonk, Chief Marketing Officer @ Recurly

These stats haven’t really changed much since the advent of the term or the technology around people-based marketing. Given the identity graphs used for resolution were primarily based in the digital world, ‘people-based’ often equated to ‘audience-based’, with the people in those audiences generally being collections of digital identifiers. As the world of third-party cookies continues to crumble, so will the efficacy of the legacy technologies that were built on them.

So, will things just continue to get worse? Will 91% be closer to 100% when Google finally makes good on its cookie-killing initiative? Will we have to continue to think of branding and performance as disconnected initiatives, given that when we deliver our message, we often don’t even know who we are speaking to?

The good news here is that a new generation of technology, which leverages artificial intelligence working in conjunction with what many are referring to as ‘person-first’ identity, can not only address the immediate Google-initiated crisis, but also deliver the improved personalization that legacy technologies have failed to do.

Person-first is a term I’ve heard a lot of marketers start to use when it comes to personalization. It generally implies five elements:

It starts with first-party data – and it ends with first-party data. Marketers who embrace this concept have realized they can’t evolve approaches that were originally built on third-party data to fit a first-party data world.

When it comes to using this data to recognize consumers in channels, the foundational element is an understanding of a true person, that is rooted in offline, verified data.

The person-first understanding of the consumer is used to unify martech and adtech, ensuring communications are harmonized across paid and owned channels.

When it comes to engaging consumers, the decisioning that takes place in the tech stack, whether driven by AI or classical algorithms, starts with the person you encounter, as opposed to where you find them (i.e. it’s person-first, not channel-first, which is what guides much of marketing today).

The approach puts the ‘person’ the marketing is delivered to first. Both in terms of consumer consent and ensuring they receive relevant content.   

In the ultra-competitive financial sector, Financial Institutions (FIs) understand they need to innovate to stay relevant.  Given competing priorities, they often fall behind or struggle to keep up with the latest technology when trying to tackle innovation on their own. FIs that choose to partner with fintechs that match their organizational objectives to help cover the gaps in their products and services, on the other hand, thrive. Experts predict that the market for embedded financial services will expand by 40.4% annually over the next several years.

As FIs tighten lending criteria in 2024, embedded fintech partnerships can provide key services to help institutions sustain customer growth. Embedded fintechs enable FIs to offer the innovative tools customers need to build credit, access loans and achieve financial success.

Fintech Insights: Hyper-personalization in Banking: The Tech Journey to Serving a Segment of One

Fintech partnership perks

In many cases, FIs will delay initiating fintech partnerships because of uncertainty around the organizational value of the solution, but the advantages of these solutions far outweigh the risks. Here’s how the three keys to success for embedded fintechs can benefit FIs.

Trustworthy solutions are a linchpin for success, and FIs can prove their trustworthiness through transparent communication and clear data practices. By openly communicating solution features, user data processing and risk mitigation strategies, fintechs can foster trust and reduce uncertainty for potential partners. Traditional FIs approach adopting new technologies with caution because of regulatory and risk concerns, but reliable, secure fintechs can bridge this innovation gap by providing the modern, high-tech solutions that consumers crave.

Partnering with an agile fintech allows FIs to tap into a constant stream of cutting-edge technological advancements that help FIs stay ahead of the curve to offer competitive products and services faster, without the burden of internal R&D investments. Fintechs often possess deep expertise in specific areas like digital payments, credit scores or lending. Partnering with fintechs allows FIs to expand their service offerings and cater to diverse customer needs without internal staff expansion.

User-friendly and efficient solutions that streamline processes and personalize services lead to increased customer satisfaction and loyalty. A trustworthy fintech can also mitigate certain risks by sharing the responsibility for compliance and security, further reducing the burden on the FI.

Easy implementation translates to shorter onboarding times and quicker access to the fintech’s value proposition. FIs can start reaping the benefits of the partnership sooner and accelerate their ROI. Simple and adaptable solutions minimize the risk of complex integration issues or misalignment with existing systems, reducing the potential for costly project failures.

Minimal disruptions to existing workflows decrease staff training requirements and ensure smoother continuity throughout the implementation process, keeping business running as usual while the new solution takes root. When the learning curve for bank employees is minimized, the solution can be adopted more quickly and result in higher user satisfaction.

Institutional goal alignment puts everyone on the same page. Strategic alignment can speed up goal achievement. When both parties share clear objectives, the partnership becomes a focused collaboration, driving faster progress toward shared benchmarks. Aligned goals minimize friction and confusion, ensuring resources are channeled efficiently on the path to achieving joint objectives such as helping consumers improve their credit scores.

FIs can leverage the fintech’s expertise in specific areas to free up internal resources for core functions, leading to cost-effective operations. Aligned goals ensure the partnership prioritizes future-proof solutions that adapt to changing needs and can be scaled efficiently as the FI grows.