Creating sales documents and pitches that list out all the shiny new things that our data science application can do is very tempting. We worked hard on those features and everyone will appreciate them, right?
Well, not really. For one, it’s very likely your target audience doesn’t have the technical ability to understand the point of what you’re selling. After all, if they had your technical skills, they wouldn’t be thinking of hiring a data science, they’d just be doing it themselves.
The next problem is that you can’t trust that the customer realises how your solution helps them out of their present predicament. Moreover, it’s disrespectful to get them to do your job for you. Hence, you need to make sure your pitch joins the dots between what you intend to do for the customer and how it’s going to make their life easier.
In sales parlance this is known as ‘selling the benefits’ – that is, making it clear to the potential customer how buying your product will improve their lives, and has been encapsulated in the phrase ‘nobody wants to buy a bed – they want a good night’s sleep’.The rub is that in most data science scenarios the problem that corresponds to the potential benefit is a business problem – such as reduced inventory or decreased cost of sales – rather than a human problem, such as a getting a good night’s sleep.
Therefore, being able to complete the journey from feature to benefit requires some knowledge of your customer’s business (whereas everyone knows the benefits of a good night sleep – and the horrors of not getting one – far fewer under the fine points of mattress springing and bed construction) and the ability to explain the links. This last is crucial, as the benefits of your work are too important to allow your customer an opportunity to miss them.
What all this means in the end is that the approach of inspecting data sets in the hope of finding ‘insights’ will often fail, and may border on being dangerous. Instead you need to start with what your customer is trying to achieve, what problems they are facing before seeing which problems correspond with data that can be used to build tools that can overcome the problem.
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