Right now Big Data is REALLY big – HUGE even (don’t you hate that word). Not a day goes by without someone posting a blog or writing an article on the value of Big Data.  And why shouldn’t they? The reality is that all of our clients are sitting on huge amounts of information about their customers and prospects – yes, terabytes and terabytes as most are fond of saying. The value of this information is immeasurable. And when we can derive actionable insights from this data the return is immense. The data itself is free (although sometimes integration of multiple databases can be a challenge) therefore insights gleaned from this data can have a major  impact on our product development and customer experience initiatives without requiring significant financial resources (OK, I realize that the advanced analytics aren’t cheap, but they are relatively inexpensive in the grand scheme of things).

But is Big Data really big enough to drive truly actionable market insights by itself?  OK, I realize that this is somewhat self-serving since MSI is in the business of gathering incremental data, but think about the value of integrating Big Data with your ongoing marketing research initiatives. The blending of the two results in an integrated view of your customers and prospects that often can’t be achieved using one or the other.

One can integrate Big Data into your proprietary marketing research (let’s call it Little Data) in one of two ways.

  • Aggregate level integration – analyzing the Big Data and blending these insights with your ongoing initiatives, and
  • Individual level integration – integrating customer or prospect level Big Data into your survey findings.

The following sections review each of these in greater detail.

Aggregate Level Integration

This is often the simplest and easiest to accomplish but not necessarily the most informative. Very simply your analysis of an issue incorporates what you know directly from the customer/prospect databases with additional insights garnered from focused research efforts.

But this isn’t really a new method of analyzing data. Long before data got BIG we’ve been integrating in-house data with survey work. Before the internet we were using client supplied data to either enlighten or guide primary research. Think about things as simple as percentage of product returns, customer renewal rates, switching to another cable service, number of product support calls required for problem resolution. All of these have been historically used to drive primary research initiatives or help understand customer alignment with our brands.

And this continued with the evolution of the internet. Now we have the capability to integrate web analytics, search failures, page abandonment, online sales conversion and a whole host of other information as we provide marketing insights into specific problems.

Individual Level Integration

The same goes for integration at the individual level. We’ve been integrating Big data with Little data for a long time. Many of you can remember client supplied sample where we were able to link survey data to product ownership, length of ownership, support usage, numbers of purchases all supplied from the data warehouses.  In our early days this was often used to simply reduce the burden of our surveys (I mean if we knew exactly which products were purchased why did we burden the respondent with these questions – hmmm, something we probably should be doing even more of today).

But this too evolved. With the evolution of the web, ecommerce, etc., our information on our customers and prospects increased exponentially. And this information can also be integrated into our ongoing primary research efforts. For example, if we’re doing a site or micro site evaluation, there are a number of opportunities to integrate individual level data…

  • Feature/function usage
  • Frequency of visit (heavy versus light)
  • Frequency of purchase
  • Site abandonment
  • Demographics of the user
  • Etc.

If we want to know how a consumer feels about failed searches, inability to purchase, a specific feature/function of a site – why not ask the ones that experienced this. Even for a topic as broad as total site evaluation, why don’t we interview folks who:

  1. Have been to the site in the past 30-60 days,
  2. With very specific knowledge of their behavior while on the site.

With this combination of Big and Little Data, we have been able to develop engagement models where we know the effect of the online experience, the customer’s attitudes towards the experience and the ultimate impact on the desired outcome (whether it be engagement or purchase). These types of models also provide clues as to where we should be focusing our site improvement resources to maximize the desired outcome.

In summary, Big Data has been around for some time and is here to stay. The potential impact on our business has increased with the ever expanding complexity of the data we capture. However, the integration of Big Data with our traditional marketing research initiatives provides greater insights than either source on their own. Contact us today to see how MSI can help you take advantage of the insights Big Data can provide for your brand.