Throw Away Your Crystal Ball! Business Leaders Need Metrics that Predict Results

One of the key trends for 2017 in business was that business leaders were increasingly demanding to see an ROI on their marketing and marketing research. Increasingly, business leaders want to know that their investing will return to the bottom line. As Anne E. Beall, Ph.D, said, “We’ve increasingly seen our clients ask for market research that ties customer attitudes and behaviors to real financial performance of their company. I foresee this request becoming more of a requirement over time. Tools such as text analysis of customer reviews, actual store traffic, and in-the-moment research will be useful only if they actually predict financial outcomes. Our recent presidential election has shown that surveys don’t always synch with real world results and marketers can’t afford this luxury. They need market research that can truly predict their business results.”

The Infosurv Insider has written extensively about the Service Profit Chain, first proposed by James L. Heskett, Thomas O. Jones, Gary W. Loveman, W. Earl Sasser, Jr., and Leonard A. Schlesinger in the 1994 Harvard Business Review, and again in the August 2008 issue of the Harvard Business Review saying: “The new economics of service requires innovative measurement techniques. These techniques calibrate the impact of employee satisfaction, loyalty, and productivity on the value of products and services delivered so that managers can build customer satisfaction and loyalty and assess the corresponding impact on profitability and growth.”

Since business leaders are wanting to see an ROI on their research, the key to producing that might be the linkage between employees, customers, and profitability. If that is the case, there needs to be a regular system in place where Employees and Customers can be simultaneously measured and those results then tied to business results. Without connecting the dots, customer and employee measurements are only effective within their functional silos.

While it’s not easy, here are the steps to connecting these measurements:

  1. Defining the right question. What are the measurements you want to connect? Customer satisfaction or loyalty? Employee engagement or retention? Profit, sales, or production/employee? Or something else? All organizations probably have multiple metrics, but which ones should go into this analysis?
  2. Get the data. Think about the data you have available. What was it originally collected for? How old is it? What does it truly measure? All data has flaws and weaknesses. Make sure you thoroughly understand the data you are using before embarking on analysis. Additionally, it is likely that the data is stored or “owned” by different functions: marketing, HR, Sales, Finance. Gaining access to the data – and cooperation from the key players will be important to the success of this analysis.
  3. Start small. Data analysis is tricky and unless you have a data scientist on hand, you might want to get some help for this one. But whether you get help or go it alone, the first step in analysis is to understand how your data behaves. Don’t boil the ocean – that is a sure way to become overwhelmed, or to produce overly simplistic results. Perhaps you could take a look at sales employees and determine whether their engagement levels are connected to their sales results. Or whether customer satisfaction is connected to purchasing. In addition to learning about the information you have and how it can be used, you may provide key insights for these departments.
  4. See the bigger picture. One of the challenges you will have in connecting all of the data to your company-wide performance is what we call “confounding variables”. In other words, there may be variables that strongly influence the results that you cannot identify without additional analysis. Factors like age, gender, education may impact your results unless you control for them in analysis.
  5. Analyze the data. In terms of analysis, you will probably begin with correlation analysis , but you may need more advanced structural equation modelling (SEM) or regression analysis. And of course, you will want to do this analysis longitudinally, to make sure your results hold over time.
  6. What does it mean? Good research answers some questions, but always seems to lead to other questions. Is this the right data? Should we use other metrics, or measure more frequently? Don’t be discouraged if you don’t nail the analysis on the first try. Learn along the journey and strive for improvement.

Connecting the metrics involved in customer retention, employee engagement and profitability are the kinds of metrics that will demonstrate ROI for business leaders. While not an easy undertaking, without these demonstrable linkages, leaders will have a much more difficult time driving the business forward.

Leave a Reply

Your email address will not be published. Required fields are marked *