For several years, the SVP of Dealer Operations tried to find a way to compare franchises in the regional dealer network. The department had attempted several times to find a suitable method of comparing the franchises, but none of them withstood scrutiny. Perhaps the biggest problem they faced regarded the size disparities between the largest, regional dealers and several smaller, urban dealers.
This project is a prominent example of what is possible with a small amount of data. The value was not found in the size; it was found in the amount of time and effort the team and I were willing to spend fully understanding and exploiting the information we had available.
During the initial review, it became notable that the largest dealers were all focusing on a similar product mix. Further analysis of the product mix showed 3 primary differences in product mix. The assumption quickly became product mix was a driver of different business models that were occurring under the surface in these franchises, and they must be accounted for.
Three of the segments had business drivers that aligned well with their product mix and business models. For example, the largest dealers focused on selling and maintaining the largest equipment the retailer sold. While very competitive, the market was also very lucrative. Another segment was almost completely focused on selling. Remarkably, this second segment was not only focused on the same product mix, but regionally was all knotted closely together.
The fourth segment, however, was rather bizarre. It had the least descriptive business model, and the most confusing driver of success – the average tenure of its staff. The longer the tenure the more successful the dealer was. Similarly, though almost counterintuitively, the more money a dealer spent on training, the less successful the dealer was. After much analysis, a hypothesis was formed. The segment wanted to sell through sales, but the customers wanted to buy because of the maintenance staff. Nearly the entire segment, or ¼ of the entire dealer network, was operating from a sub-optimal business model.
Dealers were scored from 1-100 for their ability to perform in their markets. About ¼ of all the dealers scored a 100, while the remaining dealers scored between a 65 and a 95. Nearly as importantly, the individual drivers could be analyzed for potential efficiency gains. All dealers who received a score less than 100 were provided a list of actions to improve the efficiency. The most notable gaps were the two dealers who could cut about $500k a year in training costs and divert the funds to employee retention, improving two drivers with a single action.
The team and I were able to identify approximately $50MM in potential efficiency gains, annually.
During the final presentation of results, the SVP of Dealer Operations asked me if I had ever been to any of the dealer sites after I had explained the business model of the largest dealers. Coincidentally, I had asked on several occasions to go visit a dealer, but we were unable to make the scheduling work, but the question wasn’t for me. The question was meant to probe his senior leadership to take a greater look at the power of data. “I was able to glean all of this through the data,” I said.
The notable detail was this analysis didn’t need a lot of data. We had less than 150 rows of data and about 50 attributes. This project is a prominent example of what is possible with a small amount of data. The value was not found in the size; it was found in the amount of time and effort the team and I were willing to spend fully understanding and exploiting the information we had available.
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