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 with data-driven, algorithm-driven, and process-driven (click here to read more), this case study is all about the algorithms in action in the highly competitive
field of vehicle sales.
The Challenge:
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tools, outdated systems and virtually no predictive technologies to mine their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.
This Solution:
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 Result:
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.
Net; Net:
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 automotive Mastermind
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