June 6, 2016

8 Tips for Avoiding Data Fatigue

It may sound crazy coming from a marketing analytics company like Quantifind, but today’s marketers simply have too many facts and figures at their disposal. We’ve finally moved beyond that tipping point where the human capacity to absorb and act on data has become over-saturated with a flood of dashboards.

This extremely human problem is one of data fatigue; marketers are suffering from collective stimulus overload.

Social media listening. Brand studies. Predictive analytics. Customer sentiment metrics. NPS scores. Real-time customer surveys. It’s become software sprawl.

Marketers are intentionally shying away from marketing analytics; it’s all become too overwhelming. We’re kids who have been to an amusement park, watched a 3D movie, gobbled down some cotton candy – and now we’re crashing.

The danger is that we’re shutting down before we come to the analysis that really matters: What’s driving our growth? Why are customers buying what they’re buying? And how can we get them to buy more?

Filter out the noise

We’re entering a phase in marketing’s development in which our focus should be on filters, not just visualizations.

Quantifind has found that of all the social media data 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.

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. Force yourself to spend time only with analyses and visualizations that are quantitatively connected with a KPI you and your brand care about — sales, units shipped, customer churn, etc.
  • Don’t forget to ask around internally for existing insights. Ignoring what 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. Scope 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 so we can exercise 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 offering 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 keeping humans in the loop.