The Importance of Inserting ‘Small Data’ into the Big Data Discussion

 By Don Tennant

If the value that data analytics has brought to businesses can be measured in the extent to which it enables those businesses to retain their customers, it makes sense to drill down on exactly what that enabler is. Most observers would argue that the enabler is Big Data. But the real enabler just might be small data.

That was my key takeaway from a recent conversation with John Rode, senior director of demand generation at Preact, a provider of cloud-based data analytics services in San Francisco that’s focused on reducing customer churn. According to Rode, “small data” is typically CRM data, which he said is the starting point for almost every decision about customers, whether it’s targeting prospects, conversion, up-sell or retention. Rode explained the significance of that this way:

While this data is most definitely “small,” it tells a lot about the customer—how much they pay, for which product, how many employees they have, which industry they are in, their decision-making authority, and so on. Once you begin to analyze customer behavior [associated with] your product, you are essentially operating a dial that takes you from small data to Big Data, depending on the sophistication of your analysis. You can analyze the behavior of each individual separately … and apply algorithms that analyze how their behavior is trending, and thus determine whether they are a churn risk. While this is a lot of data, most folks would still characterize this as small data.

Rode noted that as you increase the size of the data set via more historical data and a larger population, you can apply more sophisticated algorithms to make more exact predictions about the likelihood of a specific outcome:

The limiter tends to be the availability of Big Data, so it’s more about matching the correct analysis method to the business problem and available information. Most companies begin with their CRM data, then begin to pull more behavioral data as they get more sophisticated, and accurate, in their analysis. A company with many users or that generates a high number of events in their application will quickly cross the often subjective line into Big Data.

Rode went on to suggest that a topic that may be worth exploring is the difference between a data-driven approach to business analysis that incorporates a fluid stream of live customer data, and many current approaches that are rules-based.

For instance, a typical precursor to the use of Big Data in customer success and marketing is the use of rules. Rules are entirely subjective; that is, you use your experience and human observations to determine how to segment customers and their behaviors in determining who is at risk of churn or ready for up-sell. But this also creates much complexity, as your team will need to create and maintain that structure. If you sell three different products, and have three different-sized customers with three different personas using your product in three different regions, you will find you have 81 segments to build rules for. That is a lot of overhead, and a lot of judgment calls. Looking at the pros and cons of each, or whether it's worth combining them, is a worthy discussion.

Turning the conversation to the company itself, I mentioned to Rode I had noticed that on the Preact website, the lists of leadership team members and board members include zero females. So I asked him whether this imbalance handicaps Preact in any way, and if so, what Preact is doing to address it. His response:

We’re proud of our ability to attract top talent in a very competitive market. The percentage of our work force that is female has increased from about 10 percent to about 20 percent, and is represented on the engineering, product, strategy and marketing teams. I expect this trend to continue.

Finally, I asked Rode what Preact’s approach is to recruiting and retaining the data analytics talent it needs, and what his assessment is of the availability of that talent. He said there’s no question that they’re operating in a competitive environment in the acquisition of talent:

Fortunately, one of our fundamental beliefs is that customer success begins with employee success. So, from continuing education to internal tech talks, and from employee peer bonus plans to offsite team building, we are working hard to create a fantastic work environment. Specifically for analytics talent, we are working closely with universities to help students plan a clear path into the business world. This involves mentorships, internships, and relationships with professors.