Lead qualification (scoring) not only improves dealer satisfaction on the leads they receive, it improves close rates and ultimately the ROI on your lead management efforts. And it all comes down to the science of accurately predicting a leads’ sales potential by estimating close rates. This in turn enables the development of relevant response strategies, and can lead to significantly improved dealer performance and increased sales. Although the concept is not new in automotive marketing, it is as important as ever. With effective lead qualification efforts, dealers can focus their efforts to maximize sales by identifying and targeting unique customer segments with the right differential treatments, at the right time.

Today, most OEMs have either implemented a program or are currently considering testing lead qualification as a means of bringing more value to their dealers. In one of our previous articles, we showed the value automakers have seen from lead scoring programs, including:

• 28% overall improvement in close rates
• Natural shift in dealer behavior to follow-up with high priority leads, while not neglecting any other leads
• Positive reactions from dealers that higher scoring leads do indeed result in sales, and that lead scoring allows them to reap the full benefit of an OEM-sponsored lead program

Simply put, a lead qualification program is only as good as the data and predictive models it’s built on. By focusing on the collection of optimal lead data to fuel the predictive models at its core and the continual refresh of the models to optimize quality, OEMs can improve the success of their lead scoring programs.

How strong is your data foundation?

Manufacturers often struggle with the desire to ask customers for the least amount of information required in order to increase their likelihood of submitting a lead, yet ask for enough information to enable lead enrichment through data appends as well as accurate sales matching on the back-end. The bottom line is that the more data you have, the more accurate the models, and the more likely you will be to get an accurate sales match. However, many OEMs are only asking customers to provide name and email address through online lead forms to avoid the dreaded drop-off in lead submission rates. But consider this; based on our industry experience:

• OEMs who have tested the addition of more fields (like address and phone number) experienced no more than a 4% decline in submission rates
• As a result of requiring more fields (like address and phone number) we have seen close rates triple as a result of the increased accuracy

Having either the customer’s address or phone number makes it possible to append information such as demographic data, buying behaviors, and previous ownership history and those appends provide a significant improvement in model quality. This close rate improvement can be seen in the visual below, which compares two models built on the same data set. The first model uses only the data contained in the lead generation form itself, whereas the second uses additional appended demographic variables. The model using appended demographic information captures up to 10 percentage points more sales for certain deciles, which means a 20% higher model lift.

Improving accuracy with fresh models

Whether you’ve implemented a lead qualification initiative or are considering one, it’s important to remember that continually monitoring and improving your data foundation and qualification models are key for success. It’s imperative to regularly refresh the models in order to maintain accuracy and effectiveness. Incorporating new data elements as they become available is key to ensuring model quality is continuously improved, as is testing formerly predictive variables to make sure they remain predictive. For example, an optional field on a lead generation form might prove predictive if it is filled out but if that field becomes mandatory it may not. It’s also important to explore patterns in the data that lead to enhanced model quality. For example, the length of customer comments and mention of certain key words by the customer may enhance model quality. Every OEM will see differences, however, in what is most predictive based on OEM-specific data.

Again, a lead qualification program is only as good as the data and predictive models it’s built on. To build a strong data foundation, lead generation forms should collect enough data to allow for rich and insightful append data such as demographics, buying behaviors, and previous ownership history. Those additional data points boost model quality and help inform a strong lead treatment strategy. But it doesn’t stop there. By committing to a continual process of predictive model analysis and annual refresh, OEMS will be able to ensure that model quality stays ahead of the curve.

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