By Michael Fertik
Analytics are now king. And they should be. (If you're not already convinced, read Competing on Analytics, one of the best HBR articles I've ever read). It's so much easier to collect and digest numbers on your business than it was even ten years ago.
No less than 5% of your payroll should go toward data analysis. Who is your customer? What is she buying? How often? After what event(s)? Which version of the product sells better? At which price point? Which version of the packaging is more appealing? Which salesperson is more effective with which customer cohort? What zip code is responding most to your ad? How quickly/reliably/effectively does your product accomplish its stated goal, or your vision for it? How satisfied are your customers?
Your analysts should be setting up systems to collect these data streams and then chugging through the numbers to help you drive the company.
However! Test a bad premise, and your analytics will be pretty useless. If your entire question is wrong (e.g. "Do customers prefer the 200-ounce burger to the 250-ounce burger?"), you'll get bad answers.
Marty Cagan, a thought leader on product development, wrote a piece distinguishing "product discovery" from "product optimization". He explains that product discovery — prototyping, user testing — is the right way to identify "significant new functionality" and product optimization — analytics-based A/B testing — is the correct way to "optimize the user experience and/or business results of an existing product." His implicit point is that analytics about the customer experience are a waste until you have some early feedback on the product or feature idea to know whether you are even in the zone of test-worthiness.
Marty is right. (Full disclosure: he advises my company, ReputationDefender.) However, there's more to it than his essay suggests. The jump from discovery to optimization requires good guesses. Without good guessers, the project is doomed. Good guessers know what is worth investigating in the first place. And they have strong instincts — usually coupled with a knack for scrappy, lightning-fast research — into where the best bets lie. They are great not just at product dev, but at hiring, market development, strategy, vendor selection, advertising, and market segmentation and definition.
Take our burger example. Let's imagine three versions of the story:
Version 1. A bad guesser may actually entertain the notion that a 12-pound burger will have market appeal. Fire him politely but immediately. The focus group he wants to set up will drain time and resources.
Version 2. If we modify the scenario and call it a repositioning exercise, in which a giant patty is tested as a "family burger," to be ordered and consumed like a family-sized pizza, it might be worth a second look. But it's still not an obviously great guess.
Version 3. But what if the idea was to make a twelve-ounce burger with small ridges at the edges, so that the condiments don't slide out quite as easily? That could be worth testing.
Read the rest here.
No comments:
Post a Comment