Conversion Modeling Leads to a Seven Times Return on Investment

Customer Experience

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March 28, 2017

Last summer, Express Oil engaged North Highland’s advanced analytics team to develop a behavioral profile of its loyal “oil-only” customers likely to use mechanical services for the first time.  With 65% of loyal oil change customers never using mechanical services, there was a huge upside to accurately identify the “persuadables.”

A predictive model was developed from data mining both customer variables and demographic overlays enabling Express Oil to target those with highest mechanical propensity.  After installing the scoring mechanism into their data warehouse, a one-time 20% coupon on mechanical services was created for 86,000 highest scoring customers.  These customers were flagged for associates and presented the coupon on their next visit.

So how did it work?  The client hoped to achieve 1,200 new mechanical conversions by the end of 2016 from the targeted list and expected a 6-month break-even point.  Actual results were 1,955 new conversions beating the client goal by 63% with breakeven occurring at 2 months!

As a bonus, 320 of the 1,955 customers have used mechanical services again without an incentive.  Total net new mechanical revenue from the effort was a 7.5 to 1 ROI.  The client was so impressed with the pilot results that the program was rolled out across all 300 corporate and franchisee locations and is anticipating a repeat performance.

What’s new?  To gain deep insights into customer behaviors and implications for new customer acquisition, the team is in process of developing a customer behavioral segmentation.  By “letting the data speak,” we are finding segments like “Countrified Pragmatics,” “Aspirational Undecideds,” and “Mechanical Converts” with clearly different needs and behaviors when it comes to maintaining their vehicles.  Two clusters representing a total of 20% of customers but 53% of revenue have been discovered and the client is currently creating CX/CRM strategies to keep these customers happy and coming back.

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What’s next?  The team is proposing that prospect “clone models” be built just for these lucrative segments enabling Express Oil to target households living around stores that demographically resemble known “best” customers.  Once identified, a prospect database of these high-quality leads can be created and selected for direct mail, email and social outreach campaigns.  This approach ensures that at the point of acquisition, Express Oil is longer-term focused by finding prospects most likely to become profitable customers.

In summary, the analytics journey for Express Oil has been:

  1. “Prove it” – Tackle a key business problem (conversion) and show clear ROI
  2. “Go Deep” – Understand holistic customer behaviors and identify implications for CRM/CX
  3. “Go Long” – Extend customer findings to prospect universe and acquire high value “look-alikes”

We are excited to be on this journey with Express Oil and believe this is a repeatable model for many of our existing clients.

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