3 Factors that Drive Customer Satisfaction Rating

When it comes to customer satisfaction rating, more of everything isn’t the answer. From regression analysis of years worth of customer satisfaction rating and from related works done by others, we find that customer satisfaction is driven by 3 basic factors (from stated rating studies):

  1. Buying experience: How easy it is to evaluate choices and complete the buying process? Customers treat buying experience as part of the product experience. While rational thinking dictates that these costs are incurred once and should be treated as sunk by the customers, research(Journal of Management Information Systems Winter 2007-08) shows that these costs remain sticky and customers treat buying experience as part of the product experience.
  2. Delivering what is promised: Does the product quality and its realized benefits match what was promised and most importantly what the customer expected it to be? This is not about delighting the customers are delivering more that what is promised. A customer who walks into WalMart has one set of expectation and the one who walks into Nordstrom has another. For the segment you are targeting, the product benefits must match your positioning and messaging.
  3. Experience when things go wrong:  In the case when things go wrong, customers need support, how easy it is to get support and how they are taken care of. No customer believes things will never go wrong but the type of support they receive and how the problems are handled are what customers treat as relevant to their overall satisfaction rating. For example, a Corolla customer does not expect the dealership to send a loaner car and tow-truck for services, but a Lexus customer does.

Go head test this out today. Run a very simple survey of 4 questions to your customers, (use 1-10 scale)

  1. Please rate your overall satisfaction rating with our products and services.
  2. Please rate how satisfied you are with your buying experience (how easy it is to find what you need, evaluate options and complete the buying process)
  3. Please rate how satisfied you are with our product quality (meeting your expectations, delivers what was promised)
  4. Please rate your support experience (ease of getting help, timeliness, how you were treated)

Run a regression using (1) as dependent variable and the rest as independent variable and you will find out how relevant the 3 factors are to your own situation.

Caution: Regression analysis still only finds correlation. There are numerous lurking variables that were not fully studied. But research from other data sets make it more likely that these variables have causation relation to customer satisfaction.

Predictive Power of Customer Metrics

[tweetmeme source="pricingright"]The usefulness of any customer metric depends on how actionable and how good a predictor of business success it is. Let us define here that business success refers to Sales growth and profitability.

  1. Of all the  metrics out there, is there one that serves as a good predictor of sales growth and profitability?
  2. Can there be really a single metric?
  3. What do you, as a small business owner, an entrepreneur or a decision maker for large enterprise need to know about the single metric trap?
  4. What other factors you should be aware of?

Read on.

Let us start with most common customer metrics, including but not limited to

  1. ACSI – Average Customer Satisfaction Index
  2. Top-2 Box (on a 5 point scale) Customer Satisfaction Score
  3. Number of recommendations – WoM, number of customers who actively recommend your product (service)
  4. Proportion of your customers complaining
  5. The Net Promoter Score

Supporters of some of the metrics claim theirs is the only metric any business need to track. In the data cited we will find a high positive correlation between these metrics and the two measures of business success. You do not need an advanced degree in statistics to question, “Does this correlation mean causation?”. But it does get a little tricky to sift through the data and flaws in analysis of the case for a single metric that predicts business success.

The biggest flaw that can occur in any argument that a single variable alone has predictive power is Omitted Variable Bias. Is there a lurking variable that was omitted in the model that drove both the metric and business success? This is not to say every argument that extends one predictor has Omitted Variable Bias but to raise the possibility that there may exist another variable that may explain the changes in your dependent variable.

Let me use an example to explain what it is before using it explain single metric trap.

This comes from Greg Mankiw. Suppose studies found a high correlation between test score of children and the number of bathrooms in their homes. Is this causation? Is this the single metric that determines success in tests? No. As Mankiw explains, the Omitted Variable here is the IQ of parents. It is possible that parents with high IQ earn high income and hence have large houses with more bathrooms. Their children may have high IQ because of the good genes passed on by their parents.

In the case of customer metrics, what could be the Omitted Variables? Some could be nature of products, your marketing strategy, channel strategy, nature of competition, etc. The question worth asking is,  Is the metric at hand with high correlation same as the number of bathrooms at homes? Let us take the third metric above, Number of Recommendations, as an example just for illustrative purposes. Is it possible that the nature of customers you are targeting have a high propensity to recommend? If you did not consider this possibility then you will incorrectly align all your resources and actions towards improving number of recommendations without any impact on business goals.

That would result in house full of bathrooms but still poor test scores.

I am not recommending that you give up on all metrics but  urge you to understand Omitted Variable Bias and consider the perils of tracking just one variable.

  1. What are all the different factors that are relevant to the business you are in and to your customers?
  2. How do these factors influence the single customer metric and your business success?
  3. After accounting for all these other variables, what percentage of changes in sales growth and profitability can be explained by the changes in that single customer metric you track?

In evidence based management any metric must be questioned for its predictive power and the methods by which the results are arrived at. Simplicity of a metric alone must not be the criteria.

Write to me, I will be happy to break this down more.

Footnote:

For a very readable and clear discussion of Omitted Variable Bias see also this post.

To Be A Substantial Business …

What does it mean to be a substantial business? Having the largest market share? How does a business get to be substantial? Manfred Hasseler, founder of Swoopo, a pennies auction site says,

“to be a substantial business you have to make as many people happy as possible.”

I disagree. First, I do not know what it means to be substantial and how long that lasts and second, should a business strive to make as many people happy as possible?  The key to any business is making choices, choosing the segment to serve and target them and make them “incredibly happy” and do so in a profitable way. Apple and Blackberry (RIM) do not have “substantial” market share in the mobile phone market and definitely they do not make as many people happy as possible, but those they chose to serve are incredibly happy and it shows in their lopsided profit share of 30% with a mere 3% market share.

Success is defined by the long term profit,  not by the size of market share and definitely not achieved by trying to make as many people happy as possible.