Several questions came in on the ability of surveys to predict actual behavior, covered in the post Measuring the $$ Value of Customer Experience (see 2. Data with Surveys). My advice is this: if you are interested in taking action on survey results, make sure to survey specific visitors / people with known behavior if possible, then track subjects over time to see if there is a linkage between survey response and actual behavior. You should do this at least the first time out for any new type of survey you launch.
Why? Many times, you will find segments don’t behave as they say they will. In fact, I have seen quite a few cases where people do the opposite of what was implied from the survey. This happens particularly frequently with best customers – the specific people you most want to please with modifications to product or process. So this is important stuff.
You’ve Got Data!
Turns out there’s a new academic (meaning no ax to grind) research study out addressing this area, and it’s especially interesting because the topic of study is ability of customer feedback metrics to predict customer retention. You know, Net Promoter Score, Customer Effort Score and so forth, as well as standard customer satisfaction efforts like top-2-box.
The authors find the ability of any of one of these metrics to predict customer retention varies dramatically by industry. In other words, you might want to verify the approach / metric you are using by tying survey response to actual retention behavior over time.
Continue reading Do NPS / CES Feedback Metrics Predict Retention? Depends…
Marketing IS (Can Be?) an Experience
Early on I discovered something from the work of leaders in data-based marketing business models: they were always very concerned with post-campaign execution – not only from marketing, but also through product, distribution, and service. I thought this strange, until I realized they knew something I did not: when you have customer data, you can actually identify and fix negative customer value impacts caused by poor experience.
This means you can directly quantify the value of customer experience, budget for fixing it, and create a financial model that proves out the bottom line hard money profits (or losses) from paying attention to the business value as a result of customer experience.
And critically, this idea becomes much more important as you move from surface success metrics like conversion and sales down into deep success metrics like company profits. Frequently you see the profit / loss from “marketing” often has less to do with campaigns and more to do with the positive or negative experiences caused by campaigns.
You might think taking the time to provide special treatment to brand new customers would always encourage engagement and repeat purchase. You’d be wrong. Sometimes this works, sometimes this does not work, depending on the context of the customer. Does it surprise you to find out customers often do not want to be “delighted”?
Continue reading Measuring the $$ Value of Customer Experience