Analytics can help companies unearth the kinds of business insights that at one time were little more than wishful thinking.
These tools can identify emerging trends in customer purchasing behavior, spot anomalies in regional business performance, and detect a variety of other market shifts. But whether companies are using the most suitable metrics or even the right mix of metrics to determine how their use of customer and market data is impacting business performance remains another question.
Following decades of total quality management initiatives, including business process re-engineering and Six Sigma exercises, companies are “awash” with a multitude of measures and key performance indicators, says Nigel Martin in a recent blog.
But as companies gather and act on a variety of data streams, including shifts in customer buying patterns by segments, they often struggle to apply the right metrics to the data they’re analyzing, the business outputs they’re trying to achieve or the problems they’re attempting to solve.
In some cases, it’s a matter of misplaced semantics. As Mike Kennedy points out in a blog about talent analytics, some people confuse HR analytics with HR metrics and what each attempts to measure as well as the business value that can be expected from them. Here’s a great example of the differences Kennedy cites in his blog:
- Talent metrics (HR): How many top sales reps left (the company) last quarter?
- Talent analytics (business): Why do my top performing employees keep leaving?
In some cases, companies are applying the wrong metrics to customer analytics or they’re simply not probing far enough to get at the real business impact that’s being achieved. “While customer analytics continues to drive acquisition and retention goals, firms continue to measure success of customer analytics using easy-to-track marketing metrics as opposed to deeper profitability or engagement measures,” says Forrester Research analyst Srividya Sridharan in a blog post about Forrester’s State of Customer Analytics 2012 report.
Another part of the problem is that many marketers and other decision makers tend to focus measurements on bigger strategic issues and overlook tactical opportunities for applying data and analytics.
For example, a sporting goods retailer might be focusing its data and analytics on big picture issues such as comparing the sales performance of stores in different locations or how sales of certain types of merchandise are faring. While these are important measurements that should be looked at, the retailer may be bypassing other opportunities for applying analytics.
For instance, which promotions are working most effectively for us and why? What would the impact be if we ran a promotion for soccer cleats for 14 days rather than 10 days? What’s the best time to launch a promotion on basketball sneakers? Which customers in our customer database should we be targeting?
Analytics can provide companies with incredible insights into their customers, as well as information about shifts in business conditions, etc. To obtain these insights, sometimes decision makers need to ask the right questions.