By Dave Poulos, Chief Consultant, Granite Partners, LLC

Big Data – There, I said it, now the bots can find this article and show it to the millions of eyeballs watching the internet for articles on big data and Big Brother. We are all contributing daily to this giant cache of data, every move we make, from buying groceries, to pumping gas, to using a toll road, to making a phone call or sending a text, to posting on social media, we’re adding to the huge pool of information called big data. But here’s the problem – data isn’t knowledge, and knowledge isn’t wisdom. Just because we collect facts, aggregate them, sift them, analyze them, order and rank them, connect the dots between them – doesn’t mean we really KNOW those individuals who originated the data. We can only make educated guesses, informed by history, not intent.

It has been said that trying to harness that massive stream of data and use it to make decisions is like trying to drink from a fully-charged municipal fire hose – the power can literally blow your head off! The real trick to using data to make, or at least inform decisions, is to select the bits that get you closer to the truth of the motivation you’re trying to trigger. From a marketing standpoint, finding what to measure is at least as big a challenge as how tomeasure it, and how you use the answers to guide marketing outreach activity. Once you’ve made some determination as to what data you need, you can nearly always find a way to extract and aggregate it to use to your advantage. But how do you decide what you’re looking for?

One of the best sources of solid, reliable, workable data we’ve discovered in actual practice is primary research with a split pool of the target audience. We use both digital survey and long-form, In-Depth Interview (IDI) methodology to glean primary customer insight data from a split pool of logical likely customers and actual purchasers. The phone interviews are structured like a conversation, the questions asked in a seemingly logical order, although not always in the same order from call to call. Each call is recorded, and transcripts made of each. This is what assures the research staff that they have “covered all the bases” and that each call is consistent with the goal of the study. The responses are analyzed to glean insights as to satisfaction or awareness, or preference, or attitude toward, or dislike of a product, service or brand. There are many uses for this methodology, but the results are almost always enlightening and revealing. Unfortunately for the enterprise IT specialist, this is one methodology you can’t throw more hardware and software at to scale up or solve a problem – the only compatible hard drive available is in each person’s head, and the software is custom made and varies by individual’s emotional and intellectual make-up.

Once the IDI data has been analyzed, some consistent issues will invariably present themselves among the target audience – they will all or almost all mention one or two specific likes, dislikes, preferences, or peeves, about the product or service. Now the challenge is to see how wide and how deep the problem with these elements runs, and if it affects the buying decision to a significant degree. Survey research is now employed to drill down and discover the depth of the problem. The ins and outs of survey marketing are myriad, and best practices are easily found elsewhere. Suffice to say here that by overlaying the survey data upon the IDI inputs, a very accurate, true picture of the customer’s viewpoint can be created.

This data, when analyzed, can give you invaluable insights in to all sorts of different emotional triggers, life-stage triggers, off-label uses, alternative audiences, and a host of intelligence regarding the product with respect to the intended target audience. For companies wishing to be or become customer-centric in their approach, these insights are vital to the effort, as they form the platform from which you build an engaging customer experience. If you know what the audience wants, you can deliver it, preferably in a way that resonates with that audience, is economically feasible to produce and buy, at the time when it is most advantageous to both.

The best part of using primary insights to guide your creation of marketing outreach is that not only do you know you’re correct – the customer said so – but that it will hold up over time. Customer insights are not dependent on past activity, on a transaction that has been made at some point in the past. If you study how consumers make buying decisions, you’ll discover that there are a host of factors involved in making that decision, many of which are situational. In other words, if that same products were presented to the consumer under different circumstances, it may not be as preferable as it was when the transaction occurred. The time of day, the financial situation of the purchaser, the proximity to other destinations or products, the lighting in the aisle, the breath or cologne of the sales person, a huge number of variables you have no access to have to align in order to drive that transaction forward.

Transactional data will never really reveal those variables, and while some can be controlled for using predictive software algorithms, the technology is imperfect and incomplete – humans are regrettably inconsistent. When you’re betting tens of millions of dollars on a marketing campaign, it may not be a good idea to rely solely on past purchase history and algorithms, and ground your decisions in data that considers the present, the future, and the emotional triggers of the purchase, not just the variables you can’t control.

Removal of those variables, or at least controlling for them, can provide you with insights far beyond the current strategy or campaign plan, and lead you down roads to revenue you never even considered. An ongoing program of gathering and analyzing customer insights only strengthens and broadens the value of the insight data, and the richer that data, the more on-target you can be. Those additional streams of revenue from off-label usages, new markets, affinity and co-branded products, follow-on services and upsell strategies that actually work can add up to millions of dollars in the positive column for an enterprise-scale firm. The upfront investment in a customer insight program may appear steep at the outset, but when balanced against the potential upside and the lack of waste or loss due to misinterpretation or contaminated data, it looks like a bargain in the long run, one most enterprises would be smart to leverage.