This post is one in our “Market Advantage through Technology” series, where we look at a specific problem within an organization or industry, and show how technology is used to not only solve it, but provide solid competitive edge. These posts are designed to provide real-world examples of digital transformation, demonstrate the power of digital solutions, and get you thinking about creative ways to leverage them within your own company.
Recently I invested in Next Level Automotive, a company that restores and sells supercars across Europe. If you aren’t familiar with supercars, think The Fast and the Furious – performance cars with a lot of horsepower.
The entrepreneurs behind the company, Jan and Marco, initially focused on one model, the Toyota Supra, and with great success. When I came on board, they were interested in expanding into additional luxury brands.
The problem Jan and Marco immediately ran into was this: It was taking forever to search the entire European car market to find cars they wanted to purchase and restore. In fact, they devoted most of their daily time to this task, rather than to the work that actually created profit.
When they focused on only the Supra, the searches were much simpler (and they had established relationships with dealers that sometimes eliminated the need for searching at all).
But now that they were adding several makes and models to their inventory, tracking down the right cars to purchase for restoration was taking hours every day. And because they weren’t as familiar with the markets for these other brands, they also had to spend time researching reasonable before-and-after price ranges based on model year and condition.
What they found especially frustrating was that their requirements were simple: They wanted well-priced Audis, Porches, and BMWs with a lot of horsepower. But actually finding them was highly inefficient and full of guesswork.
As an investor with no background in cars, but a strong background in data science, I set about solving this problem so we could all make more money, faster.
It really is amazing what’s available today in data modeling and analysis. Just a few years ago, it would have taken weeks or months to create and deploy a model to find the exact cars Jan and Marco were after. Today, with a tool like Microsoft’s Azure Machine Learning Studio, it can be done in a matter of hours.
I started by creating a database that pulls in all available 300+ horsepower Porches, Audis, and BMWs for sale across Europe. This database auto-updates in real time and instantly provides a list of cars that match their criteria.
Just this solution by itself would have saved them hours each day. But so much more is possible with machine learning; why stop there?
Jan and Marco also wanted a way to determine whether the asking price was reasonable based on the brand, mileage, and horsepower of each car. And they further wanted to know what the potential sales value was after restoration.
Let’s take a look at a few of the visualizations the new model provides:
The first image shows the correlations between price and model year, and price and horsepower. The second shows the same information, restricted to Porches.
Of course, as you would expect, the lower the mileage and higher the horsepower, the higher the price; no major news there. But the model shows much more than that.
It shows Jan and Marco what they need to know to make the best business decisions: A reasonable price range they should pay for a specific car, as well as the price range at which they can expect to sell it once restored. No more guesswork, no more research, just instant information.
To make things even easier, I included an at-a-glance overview that gets straight to heart of it all:
Cars with a green dot are the best deals – the cars with the lowest asking price in comparison to post-restoration sales price.
This information allows Jan and Marco to instantly see the exact cars, out of the hundreds of thousands available, that will generate the highest possible profit.
Think about that for a minute.
Jan and Marco’s “before” process was to scour dealership websites; sort through cars they didn’t want; keep cumbersome spreadsheets on price, horsepower, and mileage; research post-restoration market value; and hope they were making the right purchasing decisions.
Their “after” process is to open up the database and see what’s got a green dot.
As this example shows, digital transformation doesn’t have to mean huge, disruptive change. In fact, in most cases, it’s best achieved by targeted, incremental solutions to key problems.
What is it that’s causing inefficiencies, creating guesswork, and stalling profit at your organization? Maybe you immediately know which problems you’d like to solve, or what information you’d like to discover. Maybe you’re starting to see the possibilities for how you can run a digital-forward organization, but aren’t sure where to begin.
If you’re curious about uncovering the exact digital solution that best serves your goals, please get in touch. We’d love to think along with you, and next time write about your success!