Tracking customer satisfaction is essential to business success, Rice University research finds
Houston voices
Back when people flew nonchalantly for business, an unabashed fan of Great Reputation Airline took a flight where almost everything went wrong. First there was a weather delay. Then there was a mechanical issue. The crew was surly, the pretzels stale. Finally, after landing, when she finally made it to baggage claim, her suitcase was MIA.
But instead of complaining on social media, Great Reputation's passenger wrote off the problems to a rare bad day for the airline – which showered her with drink coupons and later delivered her luggage to her hotel.
GRA's response exemplifies customer satisfaction principles outlined in a paper by Rice Business professor Vikas Mittal and former Rice Business doctoral student Carly Frennea. Summarizing the major research about customer satisfaction, the coauthors codified their findings into a checklist for managers.
While most people understand the general concept of customer satisfaction, in business it's a specific term summarizing a consumer's post-use evaluation of the extent to which a product or service met their expectations. Satisfied customers are more likely to buy again, buy more, recommend a business to others and cost less to serve in the future. A satisfied customer doesn't just cut customer-acquisition costs. She can also help a business attract the right customers through online recommendations.
But the most compelling reason to chase customer satisfaction, say Mittal and Frennea, comes from the University of Michigan's American Customer Satisfaction Index, which tracks customer satisfaction ratings of public companies. Decades of studies based on this data show that customer satisfaction and financial performance go hand-in-hand. While the strength of this association can vary, the link is indisputable. "Nowhere else in marketing has the impact of a customer-based metric on a firm's financial performance been so clearly and consistently established," Mittal and Frennea write.
To help make that satisfaction/revenue link a felicitous one, the researchers recommend the five kinds of data managers should collect.
- Overall customer satisfaction: A summary evaluation of an overall experience.
- Behavioral intentions: "Loyalty metrics" that measure the likelihood of buying again, recommending to others and intent to complain.
- Attribute-level perceptions: Evaluating specific product or service features. For a doctor, this may include time spent waiting in the office, quality of care and explanation of diagnosis. For an oilfield services company, this may include product quality, safety, ongoing service and support, billing and pricing.
- Contextual information: Comparisons to earlier experiences with a firm and against those with competitors.
- Customer background variables: Includes gender, age and use of competitors' products and services.
Once these data are collected, the researchers say, managers should use statistical analysis that includes all relevant variables (a method known as multiple regression). This allows companies to figure out which variables have the largest association with overall satisfaction, and which have none. For example, a multiple regression might show that the bad effect of dashing customer expectations is stronger than the good effect of exceeding those expectations. The analysis may also reveal that this effect is stronger for ongoing service and support, say, than for pricing and billing. Conclusion: The company should fix problems with ongoing service and support before tinkering with its pricing and billing strategy.
Companies should also share such customer satisfaction insights with employees and incentivize them to make customer satisfaction a top priority, the researchers write.
To achieve this, executives need to see customer satisfaction as a strategic tool, not just a "good-to-have" afterthought. For this:
- Treat customer satisfaction as a strategic investment and integrate it into the strategic planning process.
- Don't skimp on the science. Use the most advanced multiple regression models, and now machine-learning technologies, to distinguish the important from the unimportant, and prioritize the important.
- Using statistical science, link customer-loyalty patterns to actual behaviors such as repurchasing and repeat sales.
- Remember that your front-line employees are vital and motivate them by linking their performance to the right customer satisfaction metrics.
- Don't just maximize customer satisfaction. Balance decreasing and increasing returns on satisfaction initiatives. For this, don't rely on "voice-of-customer" based on casual interviews and discussions. Use rigorously designed customer studies that can be statistically linked to financial results.
- Share! Summarize satisfaction findings in understandable terms and train employees to act on them. Smart companies use this approach to derive their customer-value proposition and focus the company's strategy.
The formula, after all, is a simple one. If customers are a primary source of your company's cash flow, the first variable in your strategy needs to be making them happy.
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This story originally ran on Rice Business Wisdom. It's based on research from Vikas Mittal, the J. Hugh Liedtke Professor of Marketing at Jones Graduate School of Business at Rice University, and Carly Frennea, now an executive at Nike, who received her M.B.A. and Ph.D. at Jones Graduate School of Business.
