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Your customer survey is bad, and you should feel bad.

Your Customer Survey is Bad, and You Should Feel Bad


Rob Markey
is a loyalty rock star.

Not just because he’s the co-author of The Ultimate Question 2.0, one of my favorite business books of all time.

Not just because he heads up the Customer Strategy Practice at Bain and is one of the most important thinkers in the customer loyalty field.

All of these things are great, but what pushes Rob from pretty cool guy to loyalty rock star is the fact that he maintains a database of real customer surveys used by real companies. You can check it out by visiting http://www.robmarkey.com/survey-files/ (quick and painless registration required).

As you peruse through Rob’s collection of surveys, you will likely come to the same conclusion as I did – most customer surveys are awful.

Even companies that otherwise excel at customer experience have terrible surveys.

Five Guys asks customers for their receipt number, but then asks you for the time of your visit, order total, and 34 pages of other data that they should easily be able derive from information given on the first page.

It takes longer to complete the Starbucks survey than it does to drink a venti caramel macchiato.

Marriott, a company I love, asks over 80 questions, including an absurd matrix that asks you to rate on a scale of 1 to 10 whether the hotel was luxurious, sophisticated, or engaging (amongst a dozen more marketing words that mean nothing to the consumer).

And in the height of “do as I say, not as I do”, Forrester sent a 20 page survey to all attendees of a customer feedback management conference.

Overly long surveys tell your customers that you don’t value their time. This is especially true for surveys that ask questions to which you already know the answer. Don’t ask for my receipt number and then ask me whether or not I ordered breadsticks (looking at you, Pizza Hut).

The good news is that there is hope. And it starts with us, customer loyalty practioners.

We need to stress out about the length of our customer feedback surveys. It needs to keep us up at night. We should take it as a sign of professional failure if it takes longer to take our survey than it does to purchase, assemble, and use our products. Any time we are even considering adding another question to our survey, we should ask ourselves the following questions:

  • Do we really need to ask this?
  • Will we do anything with this information, or are we asking just to ask?
  • Can we answer this question ourselves with data we already have?
  • What can we remove to make room for this question?
  • OK, seriously – do we really need to ask this?

And it’s not enough to simply prevent new questions from being added. We need to critically evaluate existing questions to determine if they are truly needed.

I once read that you should turn all of your clothes hangers backwards, turning them the correct way when you’ve worn the garment at least once. At the end of a year, any item of clothing that is still on a backwards hanger should be donated to a worthy cause because you’ve demonstrated that you don’t wear it.

Likewise, you should turn a critical eye towards every question on your survey at least once a year (if not quarterly) and ask your team a simple question: “When was the last time someone used the data that this question collects?”. If no one can offer up a concrete instance in the past 12 months, you should seriously consider removing the question from your survey.

Customer surveys don’t have to suck. Companies like Rackspace, Logitech, Intuit and Domino’s all manage to collect meaningful, actionable customer feedback without torturing their customers with page after page of needless questions.

If you remain diligent about what you are asking and why, your reward will be increased response rate, more actionable data, and clearer line-of-site for employees.

And then you’ll be a loyalty rock star too.

Performing Driver Analysis in Microsoft Excel

Many Net Promoter programs start as small pilot programs without large investments in systems and infrastructure. This can put Net Promoter novices into a catch-22 situation – they can’t obtain the budget for more robust tools until they demonstrate results, but it is easier to demonstrate results with more robust tools.

The good news is that many of the most important analytical exercises needed for a successful Net Promoter pilot can be performed using software that you probably already own – Microsoft Excel.

 

What is Driver Analysis?

Driver Analysis is a powerful tool that can help you understand the factors that influence loyalty. Driver Analysis attempts to identify the attributes that are most correlated with loyalty (as measured by NPS), and illustrates areas where you are under (or over) delivering. This information can then be used to prioritize the investment of capital, time, and resources into areas that will yield the highest return in customer loyalty.

 

Example Scenario

In the scenario below, I will take you through a quick exercise in which we will create a Driver Analysis table for a fictional business.

You are the Vice President of Customer Experience for Acme Pizza Company. You ran a pilot Net Promoter survey that asked customers four questions: (1) Likelihood to Recommend [aka “the Net Promoter question”], (2) Satisfaction with Speed of Delivery, (3) Satisfaction with Quality of Pizza, and (4) Satisfaction with Customer Service. Each question had a response scale that ranged from 0 to 10. As a result of the survey, you see that Acme Pizza’s Net Promoter Score is 10%.

You have a limited budget to spend, and want to know which of the three areas (Speed, Quality, or Service) you should focus on improving. You have an excel spreadsheet with your survey responses that looks like this:



 

 

Step One: Calculating Satisfaction

To create a Driver Analysis table, you first need to calculate the average satisfaction for each attribute. This is relatively straightforward – for example, to calculate the average satisfaction for Speed (found in Column C), you would use the following formula:

=AVERAGE(C:C)

Once you repeated this step for all three attributes, the end result would look like this:

 

 

 

Step Two: Calculating Correlation

Unfortunately, many people stop at the previous step. Looking at the information generated, they would make the decision to direct their efforts towards improving the quality of the pizza – after all, that is the attribute for which customers are expressing the greatest amount of dissatisfaction.

In order to make a more informed decision, it’s not enough to know how satisfied our customers are with each attribute – we need to understand how strongly each attribute is correlated with loyalty. This is done by calculating the correlation between the Net Promoter question (likelihood to recommend), and each individual attribute.

Calculating correlation in Microsoft Excel is much easier than you might expect. The formula is simply:

=CORREL(array1,array2)

To create our Driver Analysis table, we’d first calculate the correlation between NPS (Column B) and Speed (Column C) like so:

=CORREL(B:B,C:C)

We would then repeat this step with the other two attributes – Quality (Column D) and Service (Column E) – using the following formulae:

=CORREL(B:B,D:D)
=CORREL(B:B,E:E)

Our finished product would look like this:

 

 

The result of this formula is called the correlation coefficient (or r value for short) with can range from -1.0 to +1.0. This rating can be interpreted using the following guide:

  • An r value close to 1 indicates that there is a strong relationship between the two variables
  • An r value close to 0 indicates that there is a weak relationship between the two variables
  • A positive r value means that as one variable increases in value, the other variable will increase in value. Likewise, as one variable decreases in value, the other variable decreases in value.
  • A negative r value means that as one variable increases in value, the other variable will decrease in value.

 

Step Three: Analyze the Data

We now have a table that tells us how satisfied our customers are with our speed, quality, and service, and how strongly each of those attributes are correlated with overall NPS.  Looking at our Driver Analysis for Acme Pizza, we can make the following observations:

  • Customers are very satisfied with our speed of delivery, which has very little correlation with loyalty
  • Customers are dissatisfied with the quality of our food, which has very little correlation with loyalty
  • Customers are mildly satisfied with our customer service, which is highly correlated with loyalty

Using Excel’s built-in graphing functionality, we might choose to display this information visually like so:

 

Now that we have completed the analysis, we are able to see that we should focus on improving Acme Pizza’s customer service. Of the three attributes, customer service represents the best opportunity for positively impacting Acme Pizza’s customer loyalty.

 

Driver Analysis is a powerful tool that can help ensure that you are focusing your time and energy on the items that will have the biggest impact on customer loyalty. By performing Driver Analysis using Microsoft Excel, you can now generate actionable data without making large investments into additional systems and tools.

 

7,960 Footsteps: A Measure of Loyalty

Customer experience professionals look at loyalty using a broad assortment of metrics. We measure Net Promoter Score, Retention Rate, WOM Mentions, Share of Wallet, Customer Lifetime Value, and countless other indicators that help us to better understand the level of loyalty our customers have towards our companies.

But what about counting footsteps?

I recently attended a training event held in San Francisco, California. The training was held at a Westin Hotel – not a bad hotel by any stretch of the imagination, but it wasn’t a Marriott. (I’ve previously written about the fact that I am a rabid promoter of the Marriott brand of hotels).

Rather than staying at the hotel where the event was held, I instead opted to stay at a Marriott located several blocks away, walking to and from the Westin multiple times each day. This decision cost me about 7,960 additional footsteps over the course of my trip.

As I walked to the event one morning, fighting my way through a sea of tourists as the clouds started to let down a light rain, it occurred to me that this was loyalty economics at their best – that I, as a Promoter of Marriott, had chosen to give them my business even when presented with an alternative that was significantly more convenient.

The business case for customer loyalty is simple: loyal customers will spend more money, buy additional products, refer more business, and share better feedback. When you go the extra step for your best customers, they’ll go the extra 7,960 steps for you.

Key elements for a succesful Net Promoter survey invitation

Elements of a Successful Net Promoter Survey Invitation

One of the keys to success for your Net Promoter Score program is generating a high response rate. Unlike many other customer feedback frameworks, Net Promoter is designed to emulate a census, with target response rates of 50% of more.

The are many factors that contribute to your overall response rate, one of which is the execution of your survey invitation. Many new NPS practitioners don’t know where to start when creating a survey invitation. The chart below lists some of the factors that I have used in my survey invitations to generate response rates of up to 68%.

Click on the image to enlarge

Key elements for a succesful Net Promoter survey invitation

Once your Net Promoter program evolves, you will no doubt develop your own best practices that work for your particular business and customer type. This chart is intended to give you a place to start.