Written by GoodData Author |
Over the years that I’ve been working in the BI space, I’ve answered certain questions time and again. “What are the advantages of using data analytics?” “What sort of returns will we see after spending time and resources building an analytic application product?”
Unfortunately, there’s rarely a clear-cut answer. There are simply too many variables at play to be able to reasonably predict just how much of an improvement advanced analytics will make for a given company’s business performance. Not only that, but those variables go way beyond the “standard” variables—like investment or vendor choice—that we consider when talking about analytics.
You can see what I mean by listening to a recent webinar we hosted with Aberdeen Group. At one point during the webinar, Mike Lock, SVP of Research, walked us through a chart that showed that companies who were considered “laggards” in their analytics efforts actually reported a 2% drop in customers after introducing analytics, while high achievers reported a 27% increase. For me, this is representative of a little-known variable: the degree to which a company goes “all in” with analytics.
Why go “all in” with analytics?
In my mind, going “all in” entails committing to doing analytics correctly from the get-go, instead of hesitantly wading into the analytics pool.
That means thinking through what business outcomes you want to drive, understanding your user personas and pain points in every department, and actually deploying analytics tools to everyone, not just a few power users.
This may sound daunting and you may be tempted to take an easier route, but there is a pretty serious payoff for going about it this way. Perhaps the most compelling reason—or most compelling three reasons—to go all in is that it improves efficiency and business performance, streamlines business processes, and helps users make better decisions at every level of the organization. A pretty big selling point for new customers and a great reason to stick with your company for existing ones.
What happens if you don’t?
Let’s say that you’re an ISV and you want to introduce insights and business intelligence to your offering. However, because it’s the first time your company has done so, you want to move cautiously: a single dashboard should do it. Enough to give your users a taste of the analytics experience and gauge their reaction...or so you think.
You’ll soon find that a single dashboard is not sufficient for all of your users. You likely built one that, in an effort to be useful to everyone, is useful to no one.
You’ve now potentially soured your customers on the idea of analytics altogether. “Oh, Company X gave us analytics, and we found we didn’t get much use out of it,” your customer will say in future engagements. “We’re just not the type of company that needs analytics.” In their minds, they’ve tried analytics and (because of you) it didn’t pan out.
A second, and much bigger, risk is that you really sold your customer on using analytics. They believed you when you said it would make a huge difference, and they were excited to reap the rewards you had spoken about. Then, when you half-heartedly slapped a single “test” dashboard into your application, your customer thought “Wow, this isn’t what I was promised, and I know there’s something better out there.” Because you told your customer about how much better their business could run with analytics, they’re bought-in and invested. But they also now know that your company can’t deliver, so they’ll go find another company who can.
If you explain to your customers how great using analytics will be, how revolutionary it is, how they’ll deliver exponentially more value, and then don’t deliver on that experience, you have a problem. To solve it, you need to go all in with your analytics effort. Much like the Karate Kid, you can walk on the left side or on the right side, but don’t walk down the middle.
If you only partially commit to your analytics effort, you’re just giving your customers a reason to churn away when they don’t get any value out of it.
Written by GoodData Author |
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