Data Modeling and Analytics

Validate intuition with data

You may think you know who to prospect, but what if you could build a profile based on your ideal customers – using age, income, marital status, even credit score – to find more just like them?

When you take a multivariant approach, leveraging demographic, sociographic, lifestyle, health and financial attributes as well as channel preference, you can figure out what makes your customers tick and how target prospects are most likely to respond.

We analyze your data, using both human interpretation and deep machine learning, to profile and segment it so that you can execute truly personalized marketing.

Isolating the attributes of your best customers allows you to apply customized segmentation. Predictive algorithms based on business needs allow you to score and prioritize your prospect records. Customer personas and clusters tell you how you reach and talk to your ideal audience.

Together, these modeling and analytics techniques ensure you are targeting the right prospects with the right message in the right channel at the right time.


Client Case Study: Super-Regional Midwestern Bank Client

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  • Mission: New account acquisition and new product cross-sell (sort of every bank’s problem to solve)
  • Method: Predictive modeling to analyze branch locations and identify growth opportunities with key predictors, such as proximity, customer penetration and branch-by-branch activity

Did it work?

 

 

“For us to have a 10% year-over-year household growth, you know our marketing is working on all cylinders, especially since the industry is flat at 1% growth.”

— Chief Marketing Officer, SourceLink client, super-regional bank


Client Case Study: West Coast Medicare Advantage Plan

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  • Mission: Generate qualified inquiries and applicants during annual enrollment period. Build awareness and educate prospects audience.
  • Method: Compare list built using response modeling vs. the client’s existing list. All other campaign elements (creative, cadence, schedule) were the same between tests.

Did it work?

 

 


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