By: Gaby Perham Accudata
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 what percentages 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 first look at the difference between descriptive and predictive analytics.
Descriptive analytics is, in essence, the creation of a customer profile, which involves creating some 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.
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. 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 uses a blend of hundreds of models to identify what truly makes your customers unique on key activities. An even more complex analysis involves the creation of specific algorithms by our data scientists tailored for your specific needs.
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.