My fascination with segmentation started early in my marketing career when I realized the power of extracting, analyzing and applying data.  While the technologies have advanced, the need for marketers to understand who their ideal customers are remains constant.

Sophisticated relational segmentation techniques balance the principles of statistics with the realities of today’s marketing budgets and, in most cases, can predict the likely success of B2B marketing programs.

My goal in writing this “Smart Segmentation” blog series is to share information that can save you significant dollars, time and resources by providing tips to help you:

•  Avoid the missed potential that occurs with traditional database clean-ups.

•  Understand why a clean list doesn’t equate to a high-performing list.

•  Identify your most valuable segments and apply that knowledge predictably to generate higher return.

•  Achieve a higher number of more profitable sales in a timely manner at a lower cost.

Conversations about data today are blurred by the very popular focus on “big data.” Big data is all the rage. Nevertheless, despite the wealth of insight it contains, big data has the potential to complicate, instead of simplify, decision making. When big data becomes the primary focus, “little data” errors often get overlooked—and the results are extremely costly.

I have worked in data (big and little) for more than 30 years. I once worked with three Ph.D. statisticians, and all they did was big data analysis. We routinely analyzed data on 100,000,000 prospects before deciding to whom we’d mail 5,000, 10,000, 500,000, or even several million catalogs. Our multivariate data analytics capabilities between 1985 and 1989 were more sophisticated than most B2B companies today. The process, a form of Smart Segmentation, resulted in improvements from just single digit to multiple digit increases in response over control groups (not segmented).

So I am not naive about the potential for big data. My concern, however, is that companies are ignoring the existing, little data already in front of them.

According to a Harvard Business Review article:

The biggest reason that investments in big data fail to pay off is that most companies don’t do a good job with the information they already have… Until a company learns how to use data and analysis to support its operating decisions, it will not be in a position to benefit from big data.

HBR’s analysis is spot-on. Until a company learns how to use information they already have to improve results, they are not in the position to gain much from more sophisticated “Big Data” analysis because they will be faced with the problem of having too much data and no idea what to do with it or about it. As such, prior to performing Smart Segmentation we recommend driving all of the benefit out of the existing little data that is mostly unused.

Here are some examples of little data that can be mined for value:

1. According to SiriusDecisions: “Both sales operations and marketing operations recognize the importance of data management, but most survey respondents reported little success with collaborating on data. The biggest issue is data validation and duplication between sales and marketing databases; 63% of respondents indicated that they are unsatisfied with sales and marketing data alignment”. The problem with most big data clean-up exercises is the money is gone before any value is recognized. That is because cleaning up data is like painting the Golden Gate Bridge. Once you have completed painting the bridge, it is time to start over and paint it again. With data, once you have cleaned up everything in your database, you have run out of money and a lot of the data is already out-of-date again. In most cases, you can derive close to 100% of the value of database clean-up with about 20 to 30% of the investment. Specifically, you should divide your entire customer and prospective client  database into homogeneous groups or  “cubes.” (I call them cubes because the segmentation is not always limited to two dimensions.) Next, take a small segment of each cube and clean it up. Finally, test market to the cleaned-up segments using some intuition about which should be more heavily marketed to. You will find some segments are as much as nine times more productive than others (more on that later in the series).

2. Lists are hard and there is no such thing as the perfect list. It has always amazed me that the same executive who approves spending $25 on a “lumpy direct mail package” fights over 25 cents per name for a list. Pay attention to the little data details because bad list decisions lead to ugly databases. Lists should be tested. Smart testing includes list segmentation; which will begin to stratify the suspects so that more of them turn into prospects. No matter how smart your big data strategy, it will go nowhere with bad lists and data.

3. Apply laser-like focus on metrics as leads move from suspects to prospects to customers. On average, five percent of any given B2B market (for more complex products or solutions) is sales-qualified. Most companies start and stop there. In reality, however, another five percent of the same B2B target market is what we call “pipeline.” These have met the qualification criteria and shown interest, but have not yet committed to the next step. As many as four out of ten of these will be turned over as highly qualified sales opportunities in as few as one to two additional conversations. Another 25 to 30 percent of the same B2B target market is qualified from an environment perspective, but don’t have any interest or need at the current time and should be placed in a nurturing program.

Stay tuned to my next post on Smart Segmentation. We will talk about what causes CMOs to lose their marbles over little data and what to do about it.

This article is part 1 of a 3-part series on Smart Segmentation titled “Nailing Optimized Lead Development With Smart Segmentation” and was published originally at Dun & Bradstreet Connectors