Why Good Surveys Still Matter in a Big Data World

Surveys still have a valuable role to play in a Big Data world, when done effectively.

By Fred Reichheld

min read


This article originally appeared on LinkedIn. Why bother customers with surveys now that businesses have Big Data analytics to guide their customer strategies? And if we don't need surveys, don’t loyalty metrics and programs such as the Net Promoter System® become far less relevant? In reality, it’s not so simple. The Net Promoter System is not based on surveys but rather on regular customer feedback in many forms. Big Data analytics, by capturing and integrating those many forms of feedback, can make the Net Promoter System more powerful. Big Data also makes it feasible to define the relevant groups of customers—promoters, passives and detractors—more accurately. Surveys still have a valuable role to play—that is, if they are fewer and shorter—as a proven way to find out why customers would recommend a company (or not). The feedback gathered through such surveys then launches productive conversations with the right company team members who can fix a problem mentioned by a customer or take a good experience from one area and spread it to other areas. Think of Big Data as the latest tool for implementing loyalty-based strategies, starting with methods in the 1980s that focused on customer retention among pioneering airlines and credit card companies. The explosion of Web-based social media in the 2010s intensified interest in the effects of customer advocacy, because it added a rich new vein of customer dialogues to learn from, and it put gripes about the experience on public display for all to see. Observe, predict and prescribe Now Big Data allows companies to augment surveys with ongoing analytics that can observe customer episodes, predict certain customer behaviors and perceptions, and prescribe how a company can engage with those behaviors to deliver more value to customers. Observational analytics serve as the foundation for creating a better customer experience. Many mobile telecom operators, for example, have used analytics to track the patterns of dropped calls, while software-as-a-service providers track their application performance and specific feature usage as basic indicators of the customer’s experience. This instrumentation can cover fine-grained usage patterns and expressed sentiment in human interactions. Predictive analytics go well beyond the recommendation engines used by firms such as Netflix. For example, when a customer goes through a series of negative interactions (such as long wait times, damaged items or service outages), analytics can predict the likelihood of that customer becoming a detractor—namely, reducing purchases, stopping visits to a website or even defecting to a competitor. Prescriptive analytics helps companies determine the most effective steps after one or more interactions. For example, one of my colleagues recently flew business class on Virgin America for the first time in almost a year. The plane was delayed on the tarmac for a half hour. As soon as the flight landed, he received an email from Virgin apologizing for the delay and offering a $50 voucher toward his next Virgin flight. Contrast that smart prescription with the way that many legacy airlines handle the problem. They know about delays—not to mention their old aircraft with uncomfortable seating and poor food—yet they persist in sending surveys after every flight without closing the loop by fixing these issues. With so much digital data now available through connections in devices, most companies should know in real time when problems occur. Despite the growing sophistication of Big Data and the resulting algorithms, loyalty leaders still complement their use of analytics with hands-on employee involvement. Only humans can improve the processes and policies that make up the customer’s experience. Only humans can tease out the nuances of, and empathetically respond to, a customer’s concerns. Big Data analytics, then, reinforce and extend some fundamental principles around earning customer advocacy. Get the basics of the experience right: Wine.com, a leading online wine retailer, has invested in recommendation algorithms and a convenient digital experience for selection and shipping, but it also encourages customers to chat with sommeliers. These experts help customers learn and explore a broader selection, which has increased customers’ loyalty and lifetime value to the firm. More data without action moves a company backward: The data will only be useful if you tie it into customer feedback loops that lead directly to improvements in the relevant experience. Think of software applications that ask millions of users to report crashes yet never close the loop with those customers or fix the stability of the application. Data itself does not create a customer-centered culture: Many companies have intensely instrumented their experience without truly incorporating the customer’s perspective. The enterprise software industry has an average Net Promoter Score® of negative 3 from clients and an even lower score among end users. The ethos of providing a great end-user experience has not permeated that industry despite the reams of data they collect. There is always more data pouring in through our five senses than our brains can handle. From that clutter, how can we harness Big Data to derive insights? Can we connect the next wave of analytical enhancements to our Net Promoter System feedback loops? How do we make analytics relevant to people on the front line, not just to headquarters? Companies that solve these challenges will be able to keep pushing out the leading edge of their customer advocacy programs.

This article originally appeared on LinkedIn.

Why bother customers with surveys now that businesses have Big Data analytics to guide their customer strategies? And if we don't need surveys, don’t loyalty metrics and programs such as the Net Promoter System® become far less relevant?

In reality, it’s not so simple. The Net Promoter System is not based on surveys but rather on regular customer feedback in many forms. Big Data analytics, by capturing and integrating those many forms of feedback, can make the Net Promoter System more powerful. Big Data also makes it feasible to define the relevant groups of customers—promoters, passives and detractors—more accurately.

Surveys still have a valuable role to play—that is, if they are fewer and shorter—as a proven way to find out why customers would recommend a company (or not). The feedback gathered through such surveys then launches productive conversations with the right company team members who can fix a problem mentioned by a customer or take a good experience from one area and spread it to other areas.

Think of Big Data as the latest tool for implementing loyalty-based strategies, starting with methods in the 1980s that focused on customer retention among pioneering airlines and credit card companies. The explosion of Web-based social media in the 2010s intensified interest in the effects of customer advocacy, because it added a rich new vein of customer dialogues to learn from, and it put gripes about the experience on public display for all to see.

Observe, predict and prescribe

Now Big Data allows companies to augment surveys with ongoing analytics that can observe customer episodes, predict certain customer behaviors and perceptions, and prescribe how a company can engage with those behaviors to deliver more value to customers.

Observational analytics serve as the foundation for creating a better customer experience. Many mobile telecom operators, for example, have used analytics to track the patterns of dropped calls, while software-as-a-service providers track their application performance and specific feature usage as basic indicators of the customer’s experience. This instrumentation can cover fine-grained usage patterns and expressed sentiment in human interactions.

Predictive analytics go well beyond the recommendation engines used by firms such as Netflix. For example, when a customer goes through a series of negative interactions (such as long wait times, damaged items or service outages), analytics can predict the likelihood of that customer becoming a detractor—namely, reducing purchases, stopping visits to a website or even defecting to a competitor.

Prescriptive analytics helps companies determine the most effective steps after one or more interactions. For example, one of my colleagues recently flew business class on Virgin America for the first time in almost a year. The plane was delayed on the tarmac for a half hour. As soon as the flight landed, he received an email from Virgin apologizing for the delay and offering a $50 voucher toward his next Virgin flight.

Contrast that smart prescription with the way that many legacy airlines handle the problem. They know about delays—not to mention their old aircraft with uncomfortable seating and poor food—yet they persist in sending surveys after every flight without closing the loop by fixing these issues. With so much digital data now available through connections in devices, most companies should know in real time when problems occur.

Despite the growing sophistication of Big Data and the resulting algorithms, loyalty leaders still complement their use of analytics with hands-on employee involvement. Only humans can improve the processes and policies that make up the customer’s experience. Only humans can tease out the nuances of, and empathetically respond to, a customer’s concerns. Big Data analytics, then, reinforce and extend some fundamental principles around earning customer advocacy.

Get the basics of the experience right: Wine.com, a leading online wine retailer, has invested in recommendation algorithms and a convenient digital experience for selection and shipping, but it also encourages customers to chat with sommeliers. These experts help customers learn and explore a broader selection, which has increased customers’ loyalty and lifetime value to the firm.
More data without action moves a company backward: The data will only be useful if you tie it into customer feedback loops that lead directly to improvements in the relevant experience. Think of software applications that ask millions of users to report crashes yet never close the loop with those customers or fix the stability of the application.
Data itself does not create a customer-centered culture: Many companies have intensely instrumented their experience without truly incorporating the customer’s perspective. The enterprise software industry has an average Net Promoter Score® of negative 3 from clients and an even lower score among end users. The ethos of providing a great end-user experience has not permeated that industry despite the reams of data they collect.

There is always more data pouring in through our five senses than our brains can handle. From that clutter, how can we harness Big Data to derive insights? Can we connect the next wave of analytical enhancements to our Net Promoter System feedback loops? How do we make analytics relevant to people on the front line, not just to headquarters? Companies that solve these challenges will be able to keep pushing out the leading edge of their customer advocacy programs.