Predictive analytics is no crystal ball, but businesses are increasingly turning to it to anticipate trends and consumer behavior.
The concept of using predictive analytics to project probabilities is not new; financial institutions have long used a form of it when reviewing loans. But the rise of machine learning and big data are unlocking new tactical and strategic possibilities, where seemingly disparate data points can be correlated to form clear calls to action for savvy users.
That's the goal, but in many cases predictive analytic technology is still maturing and businesses processes will need to evolve as data-driven analysis takes a larger role in decision making.
A timely example is predictive analytics for social media. Enterprises wield unprecedented access to consumer sentiment through social media listening platforms, and there’s growing interest in using that information to get a jump on coming trends or opportunities. But even the largest companies still face technological and organizational challenges for making that scenario a reality.
When it comes to anticipating large-scale trends, a combination of social media listening and analytics can provide strategic insights, but key junctures of the process -- such as query and analysis -- remain largely manual. Listening platforms can pick up relevant conversations, identify influential voices and reveal which topics are starting to trend, but in most cases human intervention is required to form a corollary hypothesis between specific data points, and to distill the resultant data into an actionable report. Increased automation for queries and reporting could help, but those technologies are still emerging.
Identifying a strategic insight is one thing. Capitalizing on that intelligence requires establishing procedures to notify the stakeholders who can take timely action. In many cases, businesses will need to re-evaluate decision-making practices to take advantage of what predictive analytics has to offer.
On a smaller scale, predictive social analytics also have potential to provide insights on individual consumer behavior, but challenges persist there as well.
The sheer volume of Web chatter demands some degree of automation for social media listening. Tools such a sentiment analysis programs have the potential to fill that need and have shown promise, but the tools are not yet up
-to -snuff when exposed to raw Web discussions, which are rife with irony, emoticons and ever-evolving figures of speech.
Data silos and lack of context presents another hurdle for using social media discussions to predict consumer behavior. Accurate information is crucial for predictive efforts and, while companies increasingly stockpile extensive consumer information, disconnects between CRM databases and social profiles remain the norm. It's difficult to predict what a consumer wants without a firm handle on their true identity and what kind of demographic and consumer history they bring to the discussion. Bridging those silos can speed a variety of personalized and automated marketing efforts, but there's no standard answer for that issue yet.
In the big picture, potential is there for predictive social analytics to someday provide an automated source of tactical and strategic insights for business. But for now, human analysis remains a key cog in the process.
Predictive social has yet to clear key hurdles
Applying predictive analytics to business questions
Contrasting new and old analytics tools