The value of social data to businesses can’t be expressed by a single number, or tracked on a ticker like the price of a precious metal. It’s exceptionally contextual, and worth far more when filtered and processed than in raw form. But, when companies take the right social data and apply the right analyses to improve decision-making,  they’ve got social data equity.

“Data equity” is a term coined recently in The Economist as it explored the disruptive potential of big data, and the powerful advantages it can lend companies that use it over those that don’t. Social data analysis is now a critical part of the decision making mix for some companies, and as it fuels their success others will follow suit.

How can companies use the social data equity they’ve created?

Andrei Hagiu writes that social data can shorten product cycles and help companies “leverage input from consumers.” Echoing this second point, an excellent report from MGI argues that companies with social data equity can design products based on their actual—as opposed to intended—uses. For example, after a popular sink mat was reformulated for bacterial resistance, Rubbermaid analyzed a rise in negative reviews to determine that the reformulation made the product less stain-resistant and ultimately less desirable to consumers. They quickly reverted to the previous formulation and resolved the core issue.

Sentiment analysis can deliver product- and service-specific insights that can be used by businesses to evaluate and adjust, often in real time. Gaylord Hotels used sentiment analysis to determine that the initial 20 minutes of a guest’s stay had more effect on satisfaction and positive word of mouth than the rest of the visit. It then designed better ways of delivering the highest level of service during that crucially-important window.

By studying social and interest graphs, companies can uncover insights about things like behavioral influences, the flow of content, and even customer retention. From the MGI report:

Telecom companies have found that some information from social networks is useful in predicting customer churn. They discovered that customers who know others who have stopped using a certain telecom are more likely to do so themselves, so these likely-to-churn customers are then targeted for retention programs.

Social data can also enable powerful personalization and segmentation. Google’s inclusion of what your friends have shared in search results is one well-known example. Looking at an individual’s social presence and dynamically customizing an experience for them is, essentially, creating “segments of one,” as Jeffrey Hayzlett points out in Future of Marketing 2: The Personalization Revolution. Amazon once attributed 30% of its sales to its recommendation engine, which looked at user behavior when browsing products. Social data can be used to build a similar model for content, says Pete Krainik in the same report.

Does social data give us more accurate forecasting? It depends what you’re trying to predict.  Hedge funds are betting on findings that show a correlation between our collective anxiety as detected through sentiment analysis on Twitter and dips in the Dow Jones Industrial Average. A firm called WiseWindow has found “an immensely strong correlation (r2 of .94) between record sales and social media activity,” and has set up shop to help clients with demand forecasting and related research.

Better together

 

Some social data is largely useless—having 100,000 followers on Twitter, for instance, tells us nothing about a brand’s true reach or influence unless we filter out the 50,000 spam-bot followers and apply a handful of other analyses. Some social data provides only a weak, superficial view unless combined and correlated with data from other sources, on-site and off. Compare the following statements:

  1. My brand has 4,000 Facebook fans
  2. My brand has 1,500 Facebook fans that are existing customers, 2,000 that are prospects, 250 that are employees, and 250 that are job seekers
  3. The 1,500 Facebook fans that are existing customers are twice as likely to share our content with their networks as the 2,000 Facebook fans that are prospects

Another set:

  1. Traffic from social sites accounts for 10% of all visitors to my brand’s site
  2. Visitors from Twitter are twice as likely to purchase something than visitors from LinkedIn
  3. But visitors from LinkedIn have an average order value that is 300% higher than visitors from Twitter

As companies build social data equity, they’ll be able to answer more advanced questions, draw more powerful insights, conquer the competition and provide better value to more customers. What is your company doing to hone this new edge?

Note: Rubbermaid is a Bazaarvoice client.

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  • Ian Greenleigh

    Thanks, Michael. Our research has found similar results. Big potential for big social data!

  • Ian Greenleigh

    Thanks, Michael. Our research has found similar results. Big potential for big social data!

  • http://www.facebook.com/profile.php?id=7905343 Michael Griffin

    Great article Ian.  We’ve been analyzing the impact of social data on predicting which products to promote aggressively online and the results have been very promising.  We share some of those results here: http://www.adlucent.com/blog/category/ratings-and-reviews/.  We are finding that social data (e.g. ratings and reviews) correlates to the conversion rate of products.  Furthermore, we can use that data to help forecast future sales which helps bring a newfound level of accountability to social data.  Social data not only helps improve conversion rates, but it gives us actionable insight in how to improve business performance.