The case study presented here - including root cause analysis and solution - was performed for a digital publisher. It offers a different perspective on what data scientists are capable of. The expert involved here is not a coder, certainly not a production guy, yet is able to leverage his business acumen and domain expertise to
Such a data scientist who can save billions to a company, is usually not hired, for the following reasons
This is why companies erroneously think that data scientists are unicorns, because they won't even interview such a professional when a position becomes vacant. Yet this guy does not consider himself a unicorn. And it's not the story of a smart data scientist, highly capable, misunderstood and poor, unable to find a job. It's the opposite: a data scientist (granted, not on any payroll, not considering himself a worker), who will compete aggressively against whoever (small or big) is in his path, and win time and over, including financially.
This story also illustrates that data science is not necessarily about big data, big statistical models, or big Python code. It might indeed involves none of them. Anyway, this "unicorn" shares his secret with you in this article. Bookmark it, it might become very handy one day! And you won't need to find that elusive unicorn: just follow his recipe below.
Unicorn Data Scientist Finds Root Causes of Sales Drop, Fixes it
Helping a digital publisher, this data scientist (let's call him the unicorn), among other things, monitors the company finances, using a dashboard - Freshbooks - that advertises itself as "bookkeeping in the cloud". And it's true that Freshbooks is fantastic, making many high level KPIs easily accessible. One set of metrics is in the "invoicing tab", where you can see all invoices submitted , sorted by date. Gaps between subsequent invoices range between 1 to a7 days. So by looking at the distribution of these gaps (no need to develop a stats model) it is obvious after a 30 second visual inspection, that something happened if the most recent invoice is two weeks old.
This was the starting point for this investigation; the discovery of an issue with invoicing. Note that the discovery and the choice of the data tracking platform (Freshbooks), was the job of the unicorn, in this company. In all other companies, it takes much more time to react, as data scientists look at sales, rarely at invoicing. In short, detection took place one month earlier than in most companies. Interestingly, the alarm was raised at a time when revenue was higher than ever before - but there's a 40 days lag between invoicing and revenue.
The unicorn then imaged scenarios, and the kind of data needed to rule out most of them, and detect the real culprits:
Since lack of inventory was the issue, growing traffic by adding a relevant channel, and offering new advertising products, was the proposed solution. Also, blocking some inventory in advance for potential large clients won't solve the invoicing issue, but can boost total revenue in the long term. Again, the unicorn came with the solutions.
Note that this investigation required collaborating with many teams: sales, inventory management, finance, marketing, competitive intelligence. As always, multiple factors were involved, with two dominating factors (inventory sold out, bookkeeper working with client less frequently because she has acquired more clients).
Note that this is a causal analysis, not just a search for correlations. Easier to perform quickly if your company has fewer than (say) 400 employees.