Ten Common Analytic Mistakes
In This Chapter
Enhancing analytic accuracy
Increasing analysis efficiency
Collecting, analyzing, and making decisions from data is the heart of customer analytics. But whether you’re new to data analysis or have been doing it a while, ten common mistakes can affect the quality of your results. You should be on the lookout for them. They follow, and I include some ideas on how to avoid them as well.
Optimizing around the Wrong Metric
Metrics exist for just about anything in an organization and most probably are collected for a good reason. Be sure the metric you want to optimize will achieve not just your goals, but also your customers’ goals.
If airlines optimize around on-time departure instead of on-time arrival, an airplane that pulls away from the gate and sits on the tarmac is a metric success even though the customers feel the experience is disappointing as they arrive at their destination an hour late. If you optimize around the number of calls answered in one hour at a call center, you are placing quantity over quality. While customers generally want to get resolution quickly, are their issues being properly addressed?