These may not be the first things you think of when it comes to managing your analytics projects, but they will help you keep your analytics customers happy, whether they're internal or external.
Analytics is complicated and, because it is designed to provide facts, is not often as welcome in a marketing discussion as other disciplines. Analytics sits between marketing and technology in a place where neither party feels altogether comfortable; and too often the analytics consultant finds herself taking arrows from both sides.
Here are some suggestions for the analyst in order to make sure your analytics customers remain happy, whether internal or external.
Do you know what you are talking about? Make sure your customer knows it. Often the more insecure a stakeholder is, the more skeptical they are of you. When you meet these kind of folks for the first time, have handy a brief “elevator pitch” that lists your qualifications and experience in a friendly way. Then, avoid jargon and instead, talk in a way that some folks call “storytelling” but which I call “narrative.” This requires you know the data and where it points. In this scenario, you are the scout-leader heading the group on a hike to the facts.
Don’t make yourself a burden by going over the top, but when you wonder “should I check in,” it often means your subconscious has already answered that question and is trying to get your attention. Making sure your stakeholders are nearly as well-informed is key to keeping them happy. Communicate more judiciously than you would with a colleague. Your customer is more averse to surprises, and that’s mainly because they often have their own reports to do, and when you surprise them, then they have to surprise their boss. And their boss really does not like surprises. Keep from surprising your stakeholder, even when you think the news is good.
Have you heard of the “small technical glitch” that “caused a big problem”? It happens a lot more than you’d expect. Almost always, this is because no one has set up a proper testing environment; and tested whether the program creates unexpected changes in data collection or reporting. A test environment is a great way of avoiding surprises (see above). In many cases, it can spell the difference between a good analytics program and loss of confidence.
People are storytellers. Data doesn’t tell a story, but it does provide you with reports so that you can tell a story. Perhaps one day there will be a truly engaging storytelling robot but today, it is still the job of the human being to look at seemingly unconnected threads of information, see patterns, understand nuance and relative importance, and to create a story out of raw numbers. You’ll likely need data from different sources to create a fully dimensional picture for yourself, which then you will use to create the narrative that comprises the insight needed to make changes based on data. Without the narrative, it’s just machines talking to other machines.
Technology is not business, it is a subset and a provider to business. So when a non-technical person says why not, the technologist is ill-advised to simply say “we can’t do that,” assuming the non-techie will accept that “the technology just cannot do that.” First, you may not be right. Very often, there is a solution out there, and maybe you need to find it. Second, many businesspeople see a technology lack as your lack, because without technology, you would not be there at all. It’s OK to say the technology cannot do it if the technology cannot do it, but you will need to communicate that as a business concept. For instance, “there is no data source” is not nearly as effective as “we need someone to give us access to the data sources, do you know who that might be?” All of your reasons for doing things (or not doing things) must serve a business purpose, or you need to supply a plausible business reason why you can’t do it.
With these five weird tricks you should be able to lose weight, get cheap car insurance, and even keep your analytics customers happy!
