Enough analysis, already! 8 tips for avoiding data fatigue
Are you suffering from a data overload? Columnist Joshua Reynolds offers some do’s and don’ts for how to avoid data fatigue and focus on the analysis that truly matters.
[blockquote]“Not everything that counts can be counted, and not everything that can be counted counts.” — William Bruce Cameron, sociologist [/blockquote]
It may be heresy coming from a marketing analytics company like Quantifind (my employer), but today’s marketer simply has too much analysis. We’ve finally passed that tipping point where the human capacity to absorb and act on data has become over-saturated with a deluge of dashboards and visualizations.
Social media listening. Predictive analytics. Customer sentiment analysis. NPS scores. Brand studies. Real-time customer surveys. We’re past the point of diminishing returns.
Marketers are beginning to intentionally shy away from marketing analytics offerings for the simple reason that it’s all become too much to be useful. We’re like a kid who’s been to an amusement park, watched a 3-D movie, gobbled down some cotton candy and a cola, and now we’re crashing.
And the danger is that we’re shutting down before we come to the analysis that actually matters: What’s driving our growth? Why are customers buying what they’re buying? And how can we get them to buy more?
It’s time to filter out the noise
That means we’re entering a phase in marketing’s development when our focus should be on filters, not just visualizations. After all, what’s the use of a beautiful visualization if it depicts data that’s irrelevant to your business goals?
A strategy isn’t a strategy unless it tells you what not to do — which means filtering out the noise and focusing only on the minority of data points that contain actionable information.
In the case of marketing analytics, Quantifind has found that of all the data in the social media sphere that can be analyzed, only about 15 percent to 20 percent of it actually correlates to a business outcome brands care about. The trick is to find that 20 percent and look for intelligent correlations that teach us something about growth.
That’s the only way we can avoid this overwhelming epidemic of data fatigue and save our strength and concentration for the analysis that matters most.
Here are eight practical do’s and don’ts to help you avoid data fatigue:
- Don’t waste time with any data that isn’t somehow correlated to revenue. Demand that you spend your time only with analyses and visualizations that are quantitatively connected with a KPI you and your brand care about — sales, units shipped, customer churn and so on.
- Don’t forget to ask around internally for existing insights. Ignoring what you or your team members already know is one of the most common mistakes organizations make, and it results in redundant work streams.
- Don’t explore insights that aren’t ultimately actionable. Find out ahead of time what is and isn’t on the table in terms of changes in strategy or execution.
- Don’t expect the data to do all the work. It’s critical to keep a human in the loop at all times so they can exercise their human intuition and find the correlations that lead to new growth opportunities.
- Don’t work in departmental silos. Growth is everybody’s business. Curiosity is in everybody’s wheelhouse. Researchers and analysts need to work in close partnership with creative and brand strategists to discover — and apply — insights the right way.
- Do work with the KPIs and data sets that matter. Ask your colleagues and managers, “What KPIs most closely measure our overall success as a brand?” Find out what those KPIs are, and look for ways to correlate your external analysis against those internal data sets.
- Do learn the difference between intelligent filtering and correlation, as opposed to coincidence and co-visualization. Just because you have a picture of revenue performance against a depiction of buzz and sentiment, it does not mean the two have been intelligently correlated.
- Do demand tools that offer intuitive visualization and exploration. You shouldn’t have to have a degree in data science to put data to good use. Look for applications that offer a “choose your own adventure” approach and let you use your own curiosity and intuition as a guide.
Most data is junk, and if you don’t take out the trash, your insights will be garbage. To find the signals that matter, focus on correlations to KPIs instead of eye-catching visualizations, work collaboratively across your organization to unify data efforts, and don’t forget the importance of the human in the loop.