Marketers have access to tons of customer data.
Most marketers can pull customer data on past purchases and basic contact information. Many marketers can access demographic information and can tell you things like what percentage of their customers are married and have kids. And some marketers can describe to you their buyer personas and their segmentation techniques.
But the best marketers use their data to predict which customers and prospects are most likely to respond to their offers, make a purchase, and remain loyal to their brand.
Data can indeed predict the future. To understand how, let’s take a look at two common forms of marketing data analytics: descriptive and predictive analytics.
Descriptive analytics is, in essence, the creation of a customer profile, which involves creating categories and understanding what your typical customers “look” like using demographic overlays. Many times this includes appending some elements to a loyalty data file. For example, through a descriptive analytics process, you may discover that your typical customer is college-educated, earns $150-250,000 per year, is between the ages of 45 and 60, is married, and has children. You can then use this information to identify prospects that fit that description.
To see how AccuData’s customer profiling tool helped a nonprofit organization significantly grow its donor base, read this case study.
Predictive analytics uses more complicated mathematical equations, regression analyses, and modeling techniques, to predict outcomes in the future using customer data to try to identify who are the best prospects to reach in future campaigns.
A basic predictive analysis involves a comparison of two types of customers within your database, such as responders vs. non-responders or renewals vs. cancellations. A more complicated analysis might use a blend of hundreds of models to identify what truly makes your customers unique on many key activities. An even more complex analysis involves the creation of specific algorithms by our data scientists tailored for your specific needs.
To see how an AccuData partnership helped a national commercial bank exceed its year-over-year revenue goals by 12%, read this study.
So, through complex data analysis, data scientists can predict customer and prospect behavior by identifying the likelihood that someone will respond to your message or offer. In this example, we helped a nationally recognized automotive services provider increase customer acquisition rates by 26%.
AccuData’s fully managed, cost-effective data analytics approach will provide you with a deeper understanding of your most valuable customers and a clearer picture of your most desirable target audience. Our clients benefit from greater efficiencies in pulling data, higher response rates, and more stability campaign after campaign. And our “crawl, walk, run” approach to analytics makes it easy to get started, no matter where you are in the data analytics journey.
To learn how we can help your organization, book a free Discovery Call with me, Sean Kellum, Marketing Analytics Expert.