Data analytics and mass-customization in production: Delivering personalization at scale
Personalization is increasingly becoming an important part of the shopping experience. Customers who may have hesitated to provide their personal information in the past now appreciate customized product recommendations based on their engagement history with a brand. Brands feel the need to cater to this growing demand for personalization. If they can create value for customers on a personal level, they can then create meaningful connections and build a loyal customer base.
Customer-centricity has been driving business strategies for quite some time. Brands had been making an effort to deliver personalized experiences, even before the advent of innovative technologies that make customization easier. However, understanding consumer preferences and forecasting demand was primarily done through guesswork or by manually applying basic statistical analysis to historical data. This yielded little to no success. Consumer behavior simply changes too frequently for manual analysis to keep up.
Know exactly what your customer wants
With modern digital analytics solutions, brands don’t have to rely on conjecture any more. Instead, they can mine and act on actionable information about consumers, their preferences, and interactions across engagement channels. With these insights they can now coordinate sales, marketing, and operations around customer expectations. Data-driven brands tend to deliver a more consistent omni-channel experience. They are also able to offer customized products and services based on intimate knowledge of their customers.
Deliver the right product at the right time
Research suggests that production has now become more customization-oriented. Manufacturers are more concerned with agility and responsiveness than with efficiency and productivity. Analytics solutions are able to generate consumer profiles that help simplify customized production for manufacturers. These solutions enable them to design precisely configured individual products. The insights they deliver can assist manufacturers in product planning by predicting which features will sell more.
Retailers can also benefit from these insights by offering services where customers can create personalized products using popular features. This helps reduce inventory costs, as manufacturers create products tailored to customer requirements and deliver them without having to stock them.
Must-have analytics features
A good analytics solution will offer a unified view of business operations with advanced features like:
AI-driven customer insights to help segment customers based on RFM (recency, frequency, and monetary) analysis, maximize ROI, and predict which customers are likely to churn.
Market basket analysis to identify customer-buying patterns so brands can optimize marketing messages, give intelligent product recommendations, and optimize store layout.
Inventory optimization to help brands manage operations and minimize inventory-related costs.
Product performance measurement to keep track of product sales over time and formulate more effective strategies for top and bottom performers.
Sales and operational intelligence to stay up to date with critical metrics and track the performance of staff, sales, and marketing campaigns.
You can find all these features and more in AcuitySpark, Visionet’s advanced analytics solution. It delivers operational and customer intelligence to multichannel retailers so they can establish an elastic, consumer-centric value chain. To learn more about AcuitySpark and our full range of advanced analytics solutions, please contact Visionet Systems.
