BevyUp

BY MAURICIO CUEVAS

In my last several posts, I’ve discussed some key developments in online retail (e-tail) and how these are impacting the ability of website operators to increase time on site, conversation rates, average order value, etc. These include the emergence of social networks as a primary source for sharing buying recommendations, the inability for consumers to shop together online as they do in the brick-and-mortar stores and the lack of tools for capturing online shoppers’ buying sentiments.

This leads us to today’s a topic: Social Revenue Optimization or SRO. 

SRO is a concept that sums up how e-tailers can increase revenues, improve profit margins, and deepen user engagement byunlocking the deep reserves of business intelligence that exist on their sites. The ability to mine buyer sentiment is what will ultimately enable e-tailers to move past the current status quo and surgically optimize pricing, promotions, and products. The richness of this data, which is unavailable in brick-and-mortal retailing, can directly help boost revenue, average order value, and conversation rates when used effectively. 

So what makes up SRO analytics? Essentially, it is broken down into four categories that are supported by visualization techniques for easy identification of trends, opportunities, etc.:

Sentiment: Structured and unstructured data about how customers feel regarding products and pricing. This data enables e-tailers to use positive and negative predispositions in order to personalize product placement and pricing per customer. 

Revenue: By closely tracking changes in incremental conversion rates, revenue per visit and average order value that result from real-time adjustments made based on customer sentiment and social signals, e-tailers can reliably assess the success and failure of individual pricing and promotion strategies.

Margin: Every dollar invested in SRO generates on average approximately $3 in incremental revenues. Through the dynamic analysis of positive, negative, and neutral price changes, e-tailers can precisely pinpoint what worked and what didn’t in order to achieve optimal price elasticity.

Engagement: Allows e-tailers to link the ability of customers to collaborate with friends without leaving the website and increases in time-on-site and pages viewed.  

BY MAURICIO CUEVAS 

Last time I discussed the importance of “engagement” intelligence. That is the ability to gather sentiment data from e-commerce site visitors on products they have viewed, such as like, don’t like, too expensive, wrong color, not my style, etc. This information enables retailers to optimize pricing, promotion, and products. It plays a vital in growing revenues, and a big chuck of this data is now coming in on a new channel—mobile. 

Mobile web traffic is exploding. Today, more than 93 percent of U.S. consumers regularly use mobile devices for discovery, sharing, and purchasing. According to eMarketer, 24 percent of all retail purchases are made via mobile. Meanwhile, $24 billion in retail spending is expected to take place via tablets this year. 

Like in traditional e-commerce, mobile is being used for collaboration and sentiment sharing to make buying decisions. Its portability has spawned two new consumer practices: show rooming and UPC price checks. 

Armed with a mobile device, consumers can now visit a brick-and-mortar store (i.e., the show room) to “touch and feel” products and then order them online. In fact, UPC code scanner apps for mobile devices, like Price Check from Amazon, take the show rooming concept full circle. They enable shoppers to compare the price of a product in the retail store with online sellers. If they find a lower price online, they can buy it in real-time, while still in the shop. Ouch!

All this mobile activity is generating huge amounts of sentiment analytics from devices when they visit e-commerce sites. The ability to collect, analyze, correlate, and extrapolate intelligence from this data is going to separate the winners from the losers. The good news is, technology is coming to market that allows e-tailers to capture sentiment data, dissect it and act upon it. 

BY MAURICIO CUEVAS 

In my last post, I briefly touched on the fact that due to a lack of engagement, e-commerce sites do not produce very much intelligence on buyer intentions and preferences. Let’s examine the evolution of shopping data on retail websites. 

Initially, historic and behavioral data, gathered from click streams, was used as a proxy of intent. This is a crude instrument that allows e-commerce sites to see where shoppers navigate to and from. However, it does not provide any visibility into buying sentiments. For example, what does a buyer like or not like? 

Next came algorithmic, predictive data tools. A good example is Amazon (and most other big brands), which use behavior, past purchases, and demographic data to suggest products you might be interested in. While this was a significant improvement, one common drawback with this approach is that some product recommendations are based on gift purchases (buying for others) and from opening a link someone shared with the buyer/visitor.  Also, in many cases even after a purchase is made, some engines keep recommending the same product. 

More recently, social networks have emerged as a better source for demographics and insights into product sentiment through likes and pins. Nevertheless, the percentage of visits from social media sites that convert into e-commerce sales still remains at a relatively low 1 percent. 

All of these approaches have provided incremental, but limited visibility into shoppers’ buying sentiments. Collaborative shopping technology, however, has the potential to deliver the first major breakthrough in this area. Here’s why. Today, customer conversations are happening outside the “walls” of e-commerce sites in social networks, emails, or phone conversations. This prevents online retailers from accessing this valuable intelligence.

By integrating collaboration technology directly within an e-commerce site, retailers can capture “engagement” intelligence. Namely, buying sentiments (like, don't like, maybe, etc.) and preferences (too expensive, wrong color, not my style, etc.). This information can open up a whole new world of personalization, recommendations, buying incentives and cross-sell/upsell opportunities.

BY MAURICIO CUEVAS

The social media era has expanded the universe of platforms, where buyers can discuss and exchange information on purchasing products, goods and services. These include Facebook, Pinterest, Twitter and more. Each of which is producing a continuous stream of information on consumers buying sentiments and preferences. This is posing a major problem for online retailers. By capturing and holding captive valuable intelligence on the needs, likes and wants of shoppers who have visited their online stores, social networks are stealing online retailers’ lunch.

So what can retailers do to tip the scales and reclaim business intelligence about products on their websites that is rightfully theirs? The answer, as odd as it may seem, can be found in the brick-and- mortar retail world. The only reason that social media networks are capturing e-commerce business intelligence boils down to the simple fact that many buyers like to consult with others before making a buying decision. Think about the typical brick-and-mortar shopping experience when friends go to a store together. They try on clothes, test power tools, compare electronics, etc. All the while, they are asking their shopping companions for opinions, feedback and recommendations. All these interactions are kept within the store and not captured by a third party.

Shopping with friends has been a mainstay of the retail industry since stores were first introduced. Online shopping on the other hand has largely remained a solo experience. This is changing. That’s because the Internet is becoming a social platform. Witness the explosive growth and worldwide popularity of social networks like Facebook, Pinterest, LinkedIn and Twitter to name a few. Shoppers now expect a social experience when they visit online stores. For proof, we need look no further than the social media networks themselves. They are being used to try to recreate collaborative brick-and-mortar shopping online. The problem is they were not designed as shopping platforms.

With collaborative shopping, on-line retailers have the opportunity to create deeper brand engagement for consumers. They can increase conversion rates by enabling collaborative idea sharing and conversation on their websites to move shoppers from consideration to purchase. Building collaborative shopping into the fabric of an e-commerce site also provides retailers with exclusive business intelligence that can be analyzed to distinguish between influencers, advocates, and traditional shoppers.

What Shoppers Want

Although most of us know this intrinsically, our most influential resource for making buying decisions are friends and family. This is followed by social friends and followers, printed articles and celebrities (http://slidesha.re/12pc7aK). In a physical store asking for a buying validation or a recommendation from a friend or family member is easy and frictionless. Trying to recreate this process online has been cumbersome and disconnected.

Our research has found that online buyers consider the following five elements to be the most important for meeting their collaborative shopping needs:

1.     The ability to privately invite people they trust to “shop with them online”

2.     To be able to shop side-by-side as if they were together looking at the same products at the same time, but with the freedom to “roam the store” separately

3.     The ability to place products in a virtual shopping bag to narrow down buying options and make a purchase decision

4.     To be able to collaborate in real-time and also asynchronously in order to accommodate busy schedules, time zone differences, etc.

5.     The ability to easily access stylists or experts during an online shopping session to ask questions, get advice, etc.

  Virtually every major retail vertical market segment, travel, electronics, jewelry, apparel, etc., can benefit from collaboration. For example, when making travel arrangements involving friends and family, there are often multiple variables that need to be taken into account. In addition, airline and hotel availability and pricing tend to change in real-time. This forces buyers to resort to tools that are not designed for collaborative shopping, like email, Facebook and instant message services. This process is painful and inefficient, and ripe for innovation.

What Retailers Get

For retailers, incorporating collaborative shopping capabilities into their e-commerce sites provides key business advantages on the following fronts.

Brand engagement: Collaboration transforms on-line transactions into rewarding shopping experiences. With each collaborative session retailers not only augment their current user base but also promote their brands directly on their sites.

Time on site and page views: When shoppers engage in collaborative sessions they tend to view a greater number of products (than when they shop alone), exchange ideas, and engage in real conversations (just as they do in a physical store).  The entire visit quickly becomes an experience, rather than just a transaction.

Buying sentiments: Current tool sets allow e-commerce sites to measure click-streams (e.g. what you did), but this data does not reveal customer sentiments (i.e. why you took the action you did).  Collaboration technology has the potential to capture “engagement” business intelligence such as buying sentiments (like, don’t like, maybe, etc.) and preferences (too expensive, wrong color, too far, etc.). This information can then be used for targeting and personalization.

Opportunity cost: Without onsite collaboration, customer conversations are happening outside of the site’s “walls” and intelligence remains captive in social networks, emails or phone conversations. This prevents online retailers from offering buying incentives, and capitalizing on up-sell and cross sell opportunities.

Conclusion