At Hanging Steel Productions, we create buyer decision support apps, applications that help buyers make better decisions.
Do buyer decision support apps support any buyers? Do they support people who want to buy a Lexus or a house? No. Buyer decision support apps support professional buyers: people who buy as part of their job, who make purchases for their business or for their government agency.
Do these apps support any kind of procurement? Do they support for example buyers of desks and chairs? Not really. Buying office furniture is easy. You sit in a chair and see how it feels on your back and on your butt. You sit in another chair and compare it to the first. You try out a desk and see whether you can work. Buyers of office furniture do not need an application to support their purchase decisions. It’s the purchase of complex products (and services) that need support. When a business considers whether to move their IT to the cloud—and to which cloud provider—that’s a decision that warrants support by an application. When a border security agency considers whether to use electronic surveillance of their land border—and which surveillance technologies to use—that’s a decision that warrants app support.
Do buyers really want decision support? Don’t they know what they want to buy? In truth, some buyers do not need decision support. Some buyers do know exactly what they want to buy. But many buyers want and need decision support. It is difficult to understand all the ramifications of a complex product (or service). If I procure sales training, will the proficiency of my sales staff improve? How much? How will this affect their performance, and how quickly will the change happen? How will the business processes change? Are there unanticipated consequences? Buyers attempt to understand in advance the many consequences of a purchase in advance. Buyer decision support apps help them achieve that understanding.
Let’s consider an example, a fictional example to be sure, but one that illustrates the issues. Today, driverless cars—cars that drive themselves—are becoming technologically feasible. But driverless cars are still available only in the labs, and only driven with a just-in-case human behind the wheel, for safety. A few years from now, driverless cars will start to be available on the market. They will be expensive at first, perhaps $300,000 each. At that price, they will serve two markets: as ultra-luxury vehicles for wealthy geeks, and as practical vehicles for urban car services.
Suppose you run a such a car service, in three cities: New York, Chicago, and Los Angeles. You have 200 Lincoln Town Cars, employ 550 drivers, and provide thousands of rides every day, mostly to businessmen and businesswomen, on company accounts. Every year you replace 50 cars in your fleet—Town Cars are great vehicles, but they won’t last more than 250,000 or 300,000 miles. And you are considering replacing a few of those vehicles with self-driving cars.
Of course you will save some money on drivers. But do self-driving cars make sense for your business? Is it worth the experiment of buying 5 such cars, spending $1.5 million?
As you ponder these questions, you realize that you don’t know how your clientele will react to being driven by software, instead of a person. Will some customers demand only human drivers? Will others prefer human drivers, but be willing to ride in driverless cars? Maybe some people even prefer driverless; the software is new but already has a better safety record than professional drivers, who make mistakes, tire, and drink.
In addition to the business professionals who decide to call your service instead of a taxi, you have a second customer, the companies who employ these callers. The companies actually pay your invoices every month, and choose to have their employees use your service rather than one of your competitors. Some of these companies may reject driverless cars, perhaps worrying about their liability in the case of an accident.
And what about morale? If you buy a few driverless vehicles, with the morale of your drivers suffer, as they foresee the end of their jobs? Will your best drivers quit?
So what to do? You recently learned about a web application that helps car services like yours evaluate driverless cars. You point your tablet at the right URL, and try it out.
The web application begins by asking for some basic information: how many cars you have, how many drivers, when they work, how many trips each week, average fare, and so on. The app could read all this from your business records, but you are cautious, not sure you want to share all your business data with this app. So you don’t connect the app to your database, and instead enter some averages. You spend a few minutes configuring the app to the high level details of your business.
The app presents several options for you: replace half of your fleet with driverless cars, replace 20%, replace 5%, and replace none, i.e. leave your fleet and drivers the way they are today. As you expect, the app shows you costs of each of these different options. But surprisingly, the app also shows you revenue, how having driverless cars would affect your sales. And even more surprisingly, the app shows morale, how driverless cars affects the behavior of your existing drivers.
But you are skeptical. You did not become successful by believing everything you are told. How does the app get these results? You discover that the logic behind the app is transparent, that you can examine how the results are reached. You can even change the assumptions. And so you do. You make the customers more like your customers, more traditional minded, and somewhat more reluctant to accept driverless cars. And you examine the results.
Then you make other changes to the assumptions. You make some of your customers’ employers reluctant to allow their employees to book driverless vehicles. You will have to ensure that some employers are never dispatched a driverless vehicle, at least until later, when their lawyers are satisfied. You make that change, and examine the results.
And then you make another change, and another. You experiment in the app, trying out different scenarios, and testing different assumptions.
Later, after several hours of playing with the buyer decision support app, you decide to try it in the real world. You order 5 driverless cars.