8thBridge Hires Chief Scientist to Harness Social Data | 8thBridge | Social Commerce Platform

8thBridge Hires Chief Scientist to Harness Social Data

January 30, 2013 / By Jon Kubo

Early on 8thBridge recognized that the major problem with harnessing the power of Social Data was there wasn’t compelling Social Commerce functionality to engage users and collect this data. Hence the Graphite Platform was born. With Graphite, companies can integrate social sharing, rewards, curation, and discovery into their ecommerce and mobile shopping experiences and collect social data.

There has been a lot of talk about the value of Social Data; significantly more hype than results. 8thBridge has had countless discussions with clients and prospective clients on the power social data and we hear two recurring themes:

“We believe the real value of Social Commerce will be in using the resulting data to both market and personalize the shopping experience like never before possible.”

“We have no idea how to do this.”

Social Commerce is unique in that the data consists of both Social Network Data (networks of friends and personal profile information) and Interest Graph Data (networks of people which share the same interests connected by the products and collections they share). One of the impediments to usage of this information is that it is a big data problem (unstructured, high velocity, and high volume). However, we feel that the combination of Social Network Data and Interest Graph Data will power the next generation of shopping. In fact, based on early results from Graphite Analytics we believe the Brand Interest Graph (B.I.G.) data will provide the first proven benefit around this unstructured information.

Figure 1 shows a real example of Social Commerce Data from one of our clients. The red/pink dots (called nodes) represent people, the green dots represent products, and the lines (called edges) connecting the dots represent either friendships or following between people, or social expressions on products. As you can see from Figure 1, the data is meaningless without social analytics applied. Figure 2 shows a small section of this Interest Graph. The size of the node represents the popularity of that person or product and the graph is arranged in segments to show people with similar social groups and product interests. Social profile data is used to understand the type of people who influence the segment. Figure 2 shows the power of Social Analytics to identify segments, influencers, and key products.

Figure 1 – Unstructured Interest Graph

Figure 2 – Segments, Influencers, and Key Products

A social analytics engine can provide real time offers to these interest groups and feeds to Marketing systems for outbound offers. Influencer identification can be provided to CRM systems to enable outbound social influencer campaigns. There are numerous ways to monetize the information from a social analytics engine in marketing, personalization, and merchandising. The use of this data is truly the end goal of Social Commerce.

After creating the technology platform to process this data and several months of analysis, we decided to fully invest in developing an industry leading social analytics engine specifically for retail. To lead that effort we have hired Dr. Linda Whitaker as our Chief Scientist. As one of the leading practitioners of retail science in the country, Linda provides the research, innovation and advanced science for 8thBridge solutions. Linda is focusing most of her time on our Brand Interest Graph (B.I.G.) Data Analytics capabilities. Previous to 8thBridge, Linda was the Chief Product Officer at Quantum Retail Technology, a software company that delivers innovative merchandise optimization solutions to the retail industry. Linda co-founded Quantum in 2004, and is the originator of the science in Quantum’s software platform, Q. Prior to that, Linda was an integral part of the science development and product management at Retek Information Systems, working in the areas of replenishment, logistics, pricing, promotion and consumer behavior.

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