Modeling, Profiling & Segmentation
What makes your ideal customers different than those around them? This question is what separates marketing from personalized marketing. Using data analytics methodologies allows you to identify the audience most likely to respond to your offer or service, or that looks the most like your current customers.
How does your audience behave or respond differently from one another? Chances are, your audience is going to share a set of demographic or behavioral characteristics, and you want to find more people that "look" like them. This is where developing things like customer personas and clusters can greatly benefit how you reach and talk to your ideal audience. You can essentially "load the dice" to give every campaign the best chance to succeed.
We employ various response and predictive algorithms based on every client's individual business needs. This data allows you to score your prospect database or mail file and select only the best records for final direct marketing. All models are rigorously validated after development and then analyzed after the end of the marketing campaign.
Client Case Study: Super-Regional Midwestern Bank Client
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?
Client Case Study: West Coast Medicare Advantage Plan
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.