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When the answer isn’t in the data

Director of Content, Intercom

John Collins


Main illustration: Jillian Adel

Easy availability of data has fundamentally changed the game when it comes to validating business activities.

But if we’re all data and no gut we risk becoming mindless drones who only pursue demonstrably ROI-positive activities.

Data is ideal for answering questions like whether developing a new product feature was a profitable move, if a Facebook ad campaign is paying off, or which version of a subject line will get more message engagement. Long gone are the days of yore where you launched a product and had to wait until the end of the quarter to even find out if customers liked it.

So it’s easy to see why we’ve become reliant on, even addicted to, data. The right data significantly reduces the risks associated with decisions. The problem occurs when our addiction becomes so acute, we can’t act without data. Peter Drucker was extremely prescient when he said: “what gets measured, gets managed”. The tendency is to favor activities where we can easily show we’ve succeeded; and that tendency has got more pronounced in a world awash in data.

Of course, Drucker wasn’t saying if you can’t measure it, you can’t manage it i.e. don’t do anything without data. For really important decisions you’re unlikely to find the answer buried in Google Analytics. If it was that easy algorithms would be far more effective at running startups than entrepreneurs.

Here’s one way to think about it. Do you want to know if you are providing good customer service? You’ve probably got lots of data on call volumes, Net Promoter Scores, average time to resolution, all of which will help you better manage the day-to-day of your support operations. But if you want to know if support is one of your strengths or weaknesses simply searching Twitter for “<company name> + customer support” will provide a quality and quantity of data that rarely shows up in surveys.

In its 40 year history Apple has made several successful decisions which seem to fly in the face of data. For example, no one came to the Genius Bars at Apple Stores for the first couple of years, despite incentives such as free Evian water to get people in the door. But Apple stuck with the Genius Bar concept because it “knew that face-to-face support was the very best way to help customers” not because of any data that suggested it enhanced retail sales. The company was proven right and within three years it had to introduce a booking system to handle demand for the service.

So next time you’re faced with a tough decision don’t assume the answer can only be surfaced after several hours of poring over data. Maybe it’s time to look up from your spreadsheet and make a call on what you see.