Some Thoughts about Average Satisfaction Scores

The Customer Institute was recently at a conference regarding customer satisfaction and loyalty. The topic of average satisfaction came up. As it turns out many of the companies represented at the conference use measurements of customer satisfaction and specifically they use measures of average satisfaction without considering the fact that customers exist in many states. This blog will examine one way to provide granularity to the customer base.

This is a partial discussion of the presentation made at the conference by the Customer Institute. The focus of the presentation was to provide an analogy between inventory and customers. The major point of the presentation was to show inventory has different values depending on its state and the same holds true for customers.

Inventory can be described as having the following characteristics:
1. Raw material
2. Work in process
3. Need to repair
4. Scrap
5. Finished goods.
The analogies between these states of inventory and customer states are described in the following paragraph.

The customer analogy to raw material inventory is a potential customer base. Similarly, the customer analogy to work in process inventory is a new customer in which value is being added with each encounter. The customer who has a problem with either the product or the service would be analogous to a inventory that is in need of repair. There are some customers that no longer offer a good fit between the customer demands in the product or service the company is providing. These customers have the same characteristic is scrap material. The fifth category, finished goods, is equivalent to the loyal customer.

The preceding paragraph gives an indication that old inventory and customers have multiple states. When a customer satisfaction measurement is taken is often an average of the satisfaction for customers in all the states combined. While in averages and indication of something it certainly is not a metric that provides management direction other than "feel-good" or "feel-bad."

The bottom line is that the customer satisfaction metric when used as an average for all customers measured, confounds the satisfaction metric for each state. It is only when the satisfaction level is measured for each customer state can management optimize them properly allocate resources.

I'll expand this discussion in future blogs to point out some of the ways in which customer metrics can be tuned to a different customer states as they relate to inventory.

Vendor Relationship Management

In the Saturday issue of the Wall Street Journal and the section "Review" there's a wonderful article about "The Customer as a God." The key point the author is making that with the advent of social media the customer is becoming stronger and stronger and, in fact, there may be a movement away from CRM which is Customer Relationship Management to VRM which is Vendor Relationship Management. The idea that companies will manage the customers may be evolving to the point where customers will be managing their vendors.

The time is quickly arriving when the availability of information on the Internet about companies will be so comprehensive that individual customers can quickly and accurately determine the best place to make purchases. The author of the article Mr. Searls is the author of "The Intention Economy: When Customers Take Charge" which was published by the Harvard business review press. The implications of this idea will ultimately have a profound effect on what we currently mean by customer satisfaction and customer loyalty. No longer will companies be the dominant force in the company/customer relationship. The customer will become the dominant force and will make decisions based on a greater set of data but also instantly available data.

It is not clear what the bottom line is for this perspective of changing the customer/company relationship. Perhaps the limiting factor for the time to make this transition will be the customers.

Referral Performance Score

Aite Group,a research firm has developed a new metric they have called "referral performance score. This metric tracks the percentage of customers of financial institutions recommend other customers and who may increse their own account balances and add new accounts.

The Aite Group suggests that this new metric, (the referral performance score, "RPS") is an improvement over the net promoter score (NPS). This new metric goes beyond intention and measures customer actions. They note that the new metric combines both growth and referral behavior. Since it is based on behavior Aite suggests that it provides a more accurate measure of customer loyalty. Their new metric were developed from the following industry statistics:
1. The percent of customers who referred new customers.
2. The percent of customers who grew thier relationship with the institution.

For example Aite surveyed 1115 consumers and found 5% referred their financial institution and grew their accounts in the year ending March, 2012. On the other hand 47% referred their credit unions compared to 32% who referred a large bank. They also found that 7.5% of credit union customers grew their assets.

The referral performance metric is computed by multiplying the percentage who refer the bank by the percentage who grew their assets. An example is the credit union with 47% referral and 7.5% grwoth in assets. This yields a referral performance metric of 47 times 7.5 which yields a score of 352.5. The range of values for the referral performance score is 0 to 10,000. I am not sure what 100% referrals means since a custoemr can provide more than one referral; but I think 100% of customers can provide growth iin thier accounts.

The score obviously needs some calibratio so that one can decide what a score of 352.5 means. Is it good or bad? It seems to count all financial institutions the same no matter the size. That may suggest another refinement.

The bottom line is this appears to be a more direct measure of customer loyalty than just intention. This metric has potential but until there is better understanding of the impact of institutional size on the metric and some understanding on what is a reasonable scale, the metric has limited value.