Editor's Note: This is a primer. As such, it's longer and more detailed than a typical blog post.
In my 30+ years of data-driven marketing experience, I can make the following statement with the utmost confidence: Combining customer segments, consumer data and predictive modeling will significantly improve your marketing results.
Most companies use one.
Some use two.
Far fewer use all three.
When you combine all three…customer segments, consumer data, and predictive models…you will achieve significant gains in your marketing results.
In this primer, I will discuss all three and how you can combine them to optimize your results. I’ll conclude with specific recommendations to help you achieve more, regardless of your starting point.
Let's get started.
Definition: “Market segmentation is the subdividing of a market into homogeneous subsections of customers, where any subsection may conceivably be selected as a market target to be reached with a distinct marketing mix.” (Philip Kotler)
Note: The words “cluster,” “cohort,” “target group” are often used synonymously with “segment.” For the purposes of consistency and clarity, I will use “segment” throughout this blog post.
It is impossible and impractical to target the entire market. So, segmentation allows you to:
Clearly, segmentation is a vital part of every modern marketer’s toolbox. There are several ways to segment your market or customer base, including:
While segmentation holds great power for marketers, you often need to target your customers and prospects more granularly. This is where consumer data from compiled sources comes to the rescue.
Consumer data, once appended to your files, improves virtually every aspect of your research, product development and target marketing. This includes customer acquisition, cross-selling and retention.
By using consumer data from many different sources, marketers tap a wealth of powerful marketing insights. Compiled consumer data consists of the following types of information:
The sources for compiled data include consumer transactions, public sources, online surveys, telephone directories, auto registrations, warranty cards, sweepstakes and predictive models. Consumer compiled data can be classified into specialty groups or lists such as newly married, ethnic groups, or new parents. These consumer specialty lists help niche marketers who precisely target their prospects.
When combined with segmentation, consumer data helps you:
Segmentation and consumer data are vital to your marketing success. But, you can go farther and faster.
Predictive modeling gives you the unique ability to anticipate a specific prospect or customer need, behavior, or preference before it happens. This allows you to communicate with each person at the right time, with the right offer and using the right message.
Predictive modeling uses behavioral data, consumer data and modeling techniques to predict the future behavior of an individual or household.
For example, predictive modeling is often used to predict the likelihood that a customer will make a purchase. Many companies have a portfolio of predictive models that are optimized for:
It is not uncommon for our predictive models to use hundreds of data points to generate the most reliable models for scoring a prospect or customer file for marketing.
We have established the power and importance of segmentation, consumer data and predictive modeling. Now, let’s discuss how you can combine them for maximum impact.
While there are dozens of ways to leverage the combination, I will touch on just two:
Many companies develop a single predictive model and deploy it across their entire customer or prospect base, regardless of their segments. While this gives them better results vs. the absence of modeling, they are leaving a ton of lift on the table. Enter segment-specific modeling.
Segment-specific modeling refers to the process of developing different predictive models for (or within) each customer segment. The objective is to improve your marketing results by developing models that are more predictive and result in higher response and conversion rates.
While segment-specific modeling is far more sophisticated, the marketing improvements can be eye-popping. It’s not uncommon for this approach to yields dozens or hundreds of different models vs. a single predictive model. By using the right model for the right segment, predictive accuracy soars, and so does your campaign performance and marketing ROI.
Many companies deploy models to score each customer’s likelihood to buy a product or respond to an offer or campaign. But many companies fail to customize those offers. Enter segment/data-specific communications.
Segment/data-specific communications refer to the process of customizing your communications to each customer based on their segments and/or detailed data profiles.
Again, while segment/data-specific communications are far more sophisticated, the marketing improvements are dramatic. Customers respond far better to creative, copy and offers that resonate with their unique personal profiles. We all like it when a company caters to our unique desires and preferences. Your customers are no different.
I hope you found this primer helpful.
Depending on where you are today, here are some quick recommendations to help you achieve better results.
Contact us today to discuss how you can improve your results.
We will review your segmentation, data and/or modeling strategies and give your our best recommendations and ideas.