The word analytics doesn’t exactly conjure warm, fuzzy feelings. It’s something you expect to hear from Mr. Spock, not Dr. Phil.
Yet new analytics are helping retailers get in touch with their customers’ feelings to create better products and better customer experiences. Generally, data analytics fall into one of three categories: the past (historical data), the present (transactional data), and the future (hypothetical data).
With the ability to now analyze social media through big data and contextually based solutions, retailers are adding a fourth category of analytics around sentimental data. For example, how do customers feel about a particular brand or product? Are they deeply loyal to the extent that change—even a change for the better—would be unwelcome?
Social media platforms such as Facebook and Twitter are gold mines of sentiment. There, consumers are free to compliment or complain about companies and their products in an unfiltered, open exchange. Love your lawnmower? Hate that frozen pizza? You’re free to vent or praise in social media—and so are your friends and followers.
Analyzing customer sentiment has its broadest application in predictive modeling. Companies such as Apple may seem to be natural futurists, but they prepare for that future using social media analytics to make sure that new products and features align with consumer interests and expectations.
This kind of modeling becomes extremely important as companies experiment with potentially disruptive products and services, because it helps them gauge the impact before they make the investment. And as we’ve seen, not all disruption is good, especially if consumers aren’t ready for it.
The field of sentiment analysis is being driven in part by the availability of new technology that provides more contextual intelligence around data. HP’s new Intelligent Data Operating Layer (or IDOL) solution, for example, is designed to augment traditional analytics (i.e., “What happened?”) by answering the question “What happens next?”
Although big data and contextual analytics shouldn’t be viewed in isolation from other business intelligence initiatives, keeping them on a separate IT platform does allow the lines of business to better control the size and speed of these analytics. For this reason, many CIOs prefer to run these applications on converged infrastructure that can be quickly launched and scaled up or down as demands change.
In the future, data collected from social media may even outweigh transactional data in terms of driving business decisions. We’ve already seen companies shift their marketing efforts to social media channels during the last holiday season, with many of the most sophisticated retailers (e.g., Walmart, Target) using social media as their primary platform for customer engagement.
IDOL and other next-generation analytics platforms (including big data) are designed to leverage rich, non-traditional data sources such as social media posts because, in the future, social media is where the customer conversation will be happening. ▪