Why Data Segmentation Is Not Enough

People Are More Complicated Than Their Data Segmentation Parameters

When evaluating customer wants and needs, there are many ways of looking at data from the sublime to ridiculous. Commonly accepted ways of looking at customer data  include generating a “gut feel” from experience, sampling at random,looking at exceptions, reviewing periodic totals, and SQL selection. SQL, the most common tool used by database marketers, stands for Structured Query Language, but what it boils down to is averages or data segmentation. It gives you general information about groups of people, often by age or income.

Let’s say, for instance, that you want to find people that fit a certain category, such as empty-nesters. To use data segmentation to find these people, you might search for people in the 50 – 60 year age range and within a certain income bracket. SQL would look at your data to find the people who fit both of these parameters.

The problem with SQL is that you’re potentially overlooking the right people. In this example, your data segment would be missing all of the empty nesters who are under 50 as well as those who fall outside your chosen income range. SQL is just a fancy way to put people into buckets. The problem is, not everyone you seek is in the obvious bucket.

People are more complicated than just their income or age, but when analyzing populations, you can become trapped by these segmentation tools and rules.

Where Your Customers Really Are

The truth is that you’re not really looking for empty-nesters within certain parameters (shown in brown on the chart) or even empty-nesters in general.  You are looking for people who will be receptive to what you have to offer (shown in yellow on the chart).

To find these potential customers, you need to stop relying on buckets and start looking at the bigger picture. And to do that, you need statistics. Statistics can help you find the best people that correspond to your potential customer base.

 

Predictive Analytics: Better than Buckets

The most effective form of selection would be to use the most information and use it optimally. With this light, predictive analytics can be seen as the clear winner for selecting target prospects or customers.

Predicative analytics looks at the bigger picture of marketing. It has the capability to consider all kinds of factors, rather than just one of two. It also looks at patterns in behavior, and makes predictions about future behaviors. This lets you more accurately pinpoint your potential customers, as well as stop marketing to people who are not interested in what you have to offer.

That sounds like a much more effective way to do business, right? The problem is that you can’t do it by yourself. Predictive analytics is a science, and it can take years to develop accurate predictive models. You don’t want to make the top 10 mistakes a novice with predictive analytics would make. That’s why turning to a reputable predictive analytics provider can save you time and money.

At Stics, we have spent year’s fine tuning our software and processes so our customers get extremely accurate predictions. We can take your data and give you insightful information about potential customers and the effectiveness of future marketing campaigns. With Stics predictive analytics solutions you can make your business more efficient and cost-effective.