Social media listening hasn't cleared predictive hurdles … yet

Predictive insights could someday provide clear ROI for social media listening programs, but the analytic tools and business processes are not yet mature.

Can the power of analytics reliably transmute Web chatter into actionable predictive insights? Experts think the potential is there with social media listening and predictive analytics, but only if the technology and business processes continue to mature.

Chandler Wilson is familiar with the challenges. He's director of insight and analytics at Walmart, the Bentonville, Ark.-based national retailer, which uses Brandwatch social media listening and analytics as part of its corporate strategy team. A firm believer that analytics can provide valuable insight by drawing correlations that humans miss, Wilson would like to be able to use automated predictive analytics tools that could continuously comb relevant conversations on social media platforms and then pass along recommendations as situations arise -- but he added much of that process remains manual.

"From an information-to-action standpoint, I still have to look at Brandwatch and do a report … and that takes a long time," he said. "Reporting and dissemination are time-consuming."

Most enterprises deploy social media listening to keep abreast of pertinent Web discussions. Applied correctly, intelligence from social media monitoring can reduce response time during a crisis and help businesses get ahead of emerging issues.

Predictive analytics offers the potential to take that process further by creating a formula in which social conversation, contextual data, and the gauging of current trends to predict future events can help a company capitalize on a coming trend or avoid an imminent problem.

But tool immaturity remains a major obstacle, said Real Story Group Analyst Kashyap Kompella. Given the sheer volume of Web traffic and social conversation, predictive social analytics requires some degree of automation, which in turn requires developing algorithms that can reliably interpret sentiment and help draw correlations between key data points. Sentiment analysis tools are being piloted to help make those connections, but Kompella said that technology is accurate only 50% to 70% of the time.

"If you are going to make big changes based on sentiment, you would want the accuracy to be higher than it currently is," he said. "Most of the tools demo well, because they're using very clean data sets, but when you meet messy, real-world data -- which is complex, multilingual and with all sorts of new patterns that keep coming up -- that breaks down."

Silos need bridges

Kompella listed context as another key issue, in that social media profiles are typically not connected to consumer profiles that reside in customer relationship management systems, which makes it difficult to gauge key metrics, such as a consumer’s intent to buy.

"If a 14-year-old says he's going to buy a Ferrari, there's purchase intent, but it's probably 30 years down the road, versus an executive saying they're going to buy a Ferrari when they get a bonus," he said. "They both voiced intent, but from a marketing or actionable perspective, you really need to know something beyond what is being said in the conversation itself."

"The current challenge is that there's the social silo and there's the enterprise silo," he added. "The real ROI is when you're able to merge these things, it's a problem that has not effectively been solved yet."

It's not just an external issue. Provided that software vendors can get predictive algorithms up to speed and break down data silos, Gartner analyst Jenny Sussin said companies need to face the internal challenge of mapping-out organizational responses to the data they receive.

"A lot of it is process stuff when it comes to actionable insights," she said. "Even if you have a predictive algorithm, if you don't have anybody who can actually take action on the insight, it doesn't really mean anything."

Wilson expected Walmart will continue to invest in predictive analytics tools, saying they plan on applying data-driven insights to every department, from supply chain to product sales in the near future. But he anticipated that would likely require updating the company's decision architecture.

"We have started to move in the direction of being more aligned strategically," he said. We have to monitor our internal information flow as much as the external, because otherwise we can't leverage the information."

Moving toward predictive

Despite the obstacles, Kompella said businesses are increasingly investing in social analytics, with the immediate goal of showing clearer ROI for social media outlay. He cited the most recent CMO survey, where social media on average consumed 9.9% of marketing budgets -- with that number projected to hit 22% in the next five years. But analytics is only a small fraction of that budget: about 1% to 2% he said. So companies still have a hill to climb in terms of using analytics to govern business decisions.

"Most of the current analytics are geared toward vanity metrics, such as people engaged, rather than how many leads [contact information for customer prospects] you actually generated," he said. "The way you demonstrate ROI is through better use of analytics, and that requires maturity of the tools themselves, as well as the capacity of the companies to exploit these tools better."

UrbanBound is a Chicago-based company that produces software to help HR departments manage employee relocation. The company uses HubSpot technology to handle social media listening and analytics, primarily for customer interaction and marketing efforts, said UrbanBound VP of marketing Erin Wasson.

Wasson said the tools are used primarily to evaluate which posts are creating the most new business. Content marketing is UrbanBound's primary source of leads, with social  generating roughly 15% -- but she expected that to rise soon, when the company hires a social media specialist.

"I definitely see social media growing and we're going to invest in the opportunities there," she said.

In general, Kompella suggested it's a good idea focus on the added value when considering analytics. Analytics should flesh out a picture and do more than reinforce known insights. Predictive data should also challenge executives to see the business environment in new ways and make different, even unorthodox, decisions based on the data.

"Are these analytics really helping you validate what you already know," he said, "or are they telling you something you hadn't thought of?"

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