I’m guessing that last year, your team made a strategy plan focused on improving your data and analytics intelligence in 2016. By collecting the right data and using it to shape smarter tactics, your team was certain that they could turn their metrics into better sales performance and higher revenue. But now that the first quarter is drawing to a close, some of those resolutions might be slipping.
And that would be a shame, according to the State of Analytics report published by Salesforce. The report surveyed more than 2000 business leaders and executives on the evolving role and value of analytics and most importantly, the strategies high performers use to turn analytics into a revenue engine.
Let’s take a look at the findings – and revisit 3 of the reasons you really want to keep your 2016 data resolutions.
Analytics are a necessity, not an option.
90 percent of high performers call analytics “absolutely critical or very important” for shaping business strategy and boosting operational outcomes. A competitive advantage? More like the deciding advantage between organizations that succeed and organizations that struggle. In fact, high performers are 4.6 times more likely to say they’ve moved beyond using data to keep score and are now using it to drive business decisions.
Now compare that figure to this: underperformers are almost 5.7 times more likely to rely on their gut instinct. We all know business leaders who trust their experience and instinct over numbers. But when it comes to charting a map to growth and profit, it’s pretty clear that data is the path to revenue – not intuition.
Data is also proving key when it comes to staying agile and making real-time decisions. Again, the high performers aren’t going by their gut when it comes to in-the-moment moves; instead they’re 5.1 times more likely to pull real-time insights out of their analytic tools. They also prioritize the accessibility of analytics for employees, and are twice as likely to say half their workforce uses analytic tools. These leaders actively create a culture where information is shared from the C-suite to the front lines and their reward is an empowered workforce.
Too much data is left unanalyzed.
This finding’s a little disturbing. The report found 53 percent admit that too much data is left unanalyzed. If you’re thinking, “Well, that’s natural” or even “So what,” then think about this; more and more data is collecting all the time. The more that’s left unanalyzed now, the more will pile up this year and then next year – and that’s just at the current rate of collection.
Now consider that by 2020, the number of data sources analyzed by businesses is expected to rise 83 percent. Not only will the above businesses be facing a data tsunami, but their “insights” will be half guesswork, based on partial data rather than a complete outlook.
Analytics use cases have exploded.
Analytics have broken out of the data engineer’s office and spread into every aspect of the business. Expansion, improving efficiencies, understanding customers and refining marketing techniques: these are the priorities in using data today. Analytics are no longer viewed as an esoteric study but a language that needs to be spoken across departments.
Again we see that high performers are three times more likely to be heavy users of analytics in at least 10 or more disciplines. On average they analyze more than 17 different kinds of data. That’s almost double the number handled by underperformers.
How do they use those insights? The report provides these top use cases: driving operational efficiencies and facilitating growth come in at 37 percent; optimizing operational processes and improving existing products, services and features are tied at 35 percent. Identifying new revenue streams, new ideas, and monitoring customer loyalty rank at 33 percent. Ranking just a little lower are predicting customer behavior, improving employee collaboration and improving accuracy of decisions.
So take a look at your 2016 analytics goals again. Are you on track? Will you be a high performer or underperformer this year? Most importantly, how can you learn from other high performers to turn your analytics into revenue?