Social Analytics and the Customer
- by David Dorf
Many successful retailers put the customer at the center of everything they do, so its important that the customer is modeled correctly across all their systems. The path to omni-channel starts and ends with the customer so at ARTS, our next big project is focused on ensuring a consistent representation of customers across our transactional data model, datawarehouse model, and XML schemas. Further, we've started a new whitepaper that describes how Big Data and Social Media Analytics should be leveraged by retailers to add and additional level of customer insight.
Let's start by taking a closer look at the meaning of social analytics. Here's my definition:
Social Analytics, in the retail context, describes the analysis of data obtained from social media sources in an effort to better comprehend and interact with the community of consumers. This discipline seeks to understand what’s being said by the community about brands and products (“monitoring”), as well as understand the behaviors of those in the community (“profiling”). The results are used to enforce the brand image, improve product decisions, and better focus marketing, all of which lead to increased sales.
To help illustrate the facets of social analytics, I drew the diagram below which was originally published by Retail Touchpoints.
There are lots of tools on the market that allow retailers to monitor social media for brand and product mentions. These include analysis of sentiment, reach, share of voice, engagement, etc. When your brand is mentioned, good or bad, its an opportunity to engage with the customer and possibly lead to a sale. Because products are not always unique, its much more difficult to monitor product mentions, but detecting product trends early can help a retailer make better merchandising decisions, especially in fashion.
Once a retailer understands what's being said, the next step is learn more about who's saying it. That involves profiling customers beyond simple demographics to understand their motivations. Much can be learned from patterns, and even more when customers voluntarily share their data. Knowing that a customer is passionate about, for example, mountain biking allows the retailer to make relevant offers on helmets, ask for opinions on hydration, and help spread marketing messages.
Social analytics has many facets that benefit retailers, some of which are easy but many of which are hard. Its important for the CMO and CIO to work closely together to plan for these capabilities and monitor the maturity of tools on the market. This is an area that will separate winners from losers.
