Predictive social analytics must clear data silo hurdles

A combination of social media listening and analytics may someday provide predictive insights about trends and consumers, but siloed data remains an issue.

Social media listening platforms allow enterprises to follow Web discussions on topics of interest, and there's growing interest in the idea that analytics could convert knowledge into actionable insights about the future.

Social media listening looks at what people are talking about on sites like Facebook, Twitter, LinkedIn, as well as in blog posts, reviews and comment sections on the Internet. It's a vast data set, but analytics offers the prospect of distilling relevant conversations into discernable signals that will help businesses proactively meet consumer needs.

Using analytics to anticipate the future isn't new, said Olivia Parr-Rud of the Olivia Group, which offers thought leadership and research on predictive analytics.

"Predictive analytics in a pure sense has been used for a long time," she said. Over the past 20 years, banks, credit card and insurance companies have used that type of information to evaluate if customers are a good credit risk.

The difference with predictive analytics for social media is the immediacy of the information. "It really shows what the person is interested in right now," Parr-Rud said. Businesses that have that information can make real-time offers, knowing that the offer is relevant to a customer's current interest and therefore more likely to be acted upon.

Still, using insights from today to predict the future is easier said than done.  Companies often struggle with whether the insights they have gathered are truly predictors of the future. They also struggle with how to translate the insights they have gathered into an action plan. And, of course, many companies have siloed departments and data that make aggregating data and creating a strategy in unison is an obstacle.

Social listening tools help -- but not enough yet

Despite social listening tools, most companies still struggle to anticipate what their most valued customers will do next, said Allison Smith, analyst at Forrester Research in Cambridge, Mass. Smith said it could be years before that changes, and in the meantime customer insight professionals are under pressure to show return on investment for the high cost of data mining.

The problem is finding the right sources of data and tying that information correctly to a customer, Smith said. Social listening platforms scour the media, searching for mentions, keywords, and sentiments. Of the three types of media -- owned (i.e., the business website or company owned Facebook page), paid (social ads or banners) and earned (reader reviews, customer blogs) -- earned media is hardest to find and track.

Once that information is found, businesses can exploit it when they know who said it and why. For example, it would be useful to know if that person who posted negatively about your product was a longtime customer or not. Connecting that sentiment through a company's customer relationship management (CRM) system would add context to those sentiments, but Smith said many CRMs are not integrated with social listening platforms.

"The piece that is missing is the data layer with the customer.[Is] this Allison Smith on Twitter the same Allison who is our customer?" Smith explained. Businesses often ask customers for their email addresses, but they perhaps should begin asking for Twitter handles and other social media identification.

"A lot of customers may struggle with [giving up some privacy] but the brand can then provide more targeted offers, and more relevant advertising," Smith said. If customers understand that they get something in return, they may be more likely to divulge information.

Businesses still making headway

Until organizations can drill down to specific customers, they continue to look for ways to identify their target markets and send directed messages to them.

Pulsar, a social intelligence platform, uses aggregated data from Facebook to help its clients understand what people from certain demographics are talking about.

The Food Standards Agency, a governmental agency in the U.K., used Pulsar when it wanted to get the word out about the dangers of DNP, fat-burning supplement popular among teenagers. Through social media listening the agency identified where those key terms were used in social media, which indicated where the user activity was. The Food Standards Agency then targeted those areas with educational messages and used influencers to spread the word in social media hot spots.

"Through the profiling of target audiences, we were able to get messages out to the right demographics, via their trusted influencers, and across the channels that they use," explained James Baker, social media manager for the agency.

As businesses look to use predictive social media analytics, Smith and Parr-Rud offered the following recommendations.

  • Know what you're looking for, and ask the right questions to get those answers. In her Forrester brief, "Predictive Social Analytics Is on the Horizon," Smith said that when an online ticket events company searched on the name of the artist Beyoncé, that name alone didn't correlate with buying concert tickets. Other sentiments, such as favorable comments about Beyoncé's concerts, are a more likely indicator.
  • Know how you'll use the data to relate to the customer. Do you have a plan to use this as part of your marketing or business goals? How will you make this information forward-thinking and not just backward-looking?
  • Determine from the beginning how to measure efforts. Many companies consider this midstream but should plan this from the start. This involves choosing key performance indicators that outline the results you hope to gain. But keep in mind that some results are not direct, i.e. increased website activity by a customer does not always translate to an immediate purchase.
  • Can you integrate data from social listening/intelligence with your CRM data so you get a customized view of your target customers?

Most organizations are still maturing in their use of predictive social analytics, Smith said. These companies may understand how to monitor and listen to data on social media. More advanced businesses are the ones using the analysis for use in business intelligence. The most mature businesses integrate social media predictive analytics with other sources of information, such as CRM to provide social and nonsocial feedback.

Although it may take some time to get to that last level of maturity, the companies that do will have a distinct advantage.

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