Case Study

Client:

CarMax

 

Challenge:

The client needed to understand and improve its overall marketing engagement rate, which was low at just 14.32%. The goal was to identify which factors—such as offer type, sales channel, and customer demographics—had the most significant impact on a customer's likelihood to engage.

 

Solution:

We performed a detailed data analysis using RStudio with the

dplyr, readr, and ggplot libraries. We started by creating a baseline engagement rate. Then, we broke down the data by various attributes to pinpoint key drivers of engagement. This included analyzing:

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• Offer Type: To see which offers resonated most with the audience4.

• Sales Channel: To determine if the medium of the offer affected engagement rates.

• Audience Segments: To find high-engaging customer segments based on vehicle class, vehicle size, customer lifetime value, and policy age.

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Results:

The analysis provided clear, actionable insights that can be used to optimize future marketing campaigns.

• Offer Type: Offer 2 was the most successful, with an engagement rate of 23.37%, significantly higher than the baseline. This was followed by Offer 1 at 15.83%.

• Sales Channel: The Agent sales channel drove the highest engagement, with a rate of 19.15%. This suggests that personalized, one-on-one interaction is highly effective for this audience.

• Customer Segmentation: We identified a highly engaged customer segment: those with a low customer lifetime value and a long-standing policy (high policy age). This segment showed the highest engagement rate at 16.24%. Additionally, for customers who received an offer via an Agent, those with a "MedSize" vehicle had the highest engagement rate at 14.49%. The segment with a four-door car showed the highest engagement with Offer 2 at 11.48%.

 

Conclusion:

By analyzing the data, we were able to move beyond the overall low engagement rate and identify specific, high-performing combinations. Future marketing efforts should prioritize using Offer 2 and the Agent sales channel, and specifically target customers with low lifetime value and older policies. These data-backed insights can inform a more effective marketing strategy to boost engagement and improve campaign ROI.