Differentiating with Algorithms: A Case Study
- Published: June 27, 2019
- Written by Jim Sinur
One can't go anywhere today without hearing about AI, but I would say that half of it is around projecting how AI will be affecting our future and the other half is about data focused approaches. While there is nothing wrong about these discussions and articles, most of us are more interested in the results of AI, which represents a family of approaches and algorithms in action to apply to business and everyday problems. Even though there are three major approaches to AI, this case study is all about the algorithms in action in the highly competitive field of vehicle sales.
Most dealerships rely on a variety of different internal tools, outdated systems and virtually no predictive technologies to mine their customer profiles leading to limited insights and frustrating sales initiatives. There are both algorithm and data issues implied in this challenge, but the predictive nature of this case study stood out.
There was a two-pronged approach employed to uncover hot prospects and close deals that were not intuitively obvious. First, a dashboard was installed to suggest specific talk tracks to personalize the potential buyer's situation when a prospect was being engaged. This is a sensitive approach that made the prospect feel cared for when discussions happened by design in an outbound manner or in response to incoming calls. For outbound prospecting customers were sorted according to their Behavior Prediction Score (BPS) which armed sales folks with the right marketing approach at the right time to elicit action. Dealer employees were aided and more prepared for calls or visits building confidence in the sales process.
The obvious result is a better relationship with clients, but on the whole, sales were up for several dealerships. The typical numbers are a 15% increase in retention sales, a 3% increase in service-to-sold related sales and a 3% increase in new customers, varying on brand and dealer size. The hidden benefits were around cost savings during these efforts to deliver these impressive increases. The ROI was 15 times better and the costs of campaigns were 20% of previous efforts.
While there was a dynamic interaction of algorithms and data, the prediction characteristics were the secret sauce here. As organizations compete on algorithms and AI, in the future we will see more algorithm- driven success. Data is important, but it will be the co-pilot of success.
This Case Study was implemented using automativeMasterMind.
This article reprinted by permission from Cognitive World.