Monday, December 21, 2009

The Halo Effect

I was recently reading an article where the author noted, quite correctly I might add, that if your agents are rated highly and all your performance metrics are good, especially if they hold up when bench marked, then you know that it is the product or another area of business that is at fault. The other side of the coin is that sometimes a customer will rate the customer service agent low even though the agent performed adequately. When this happens the scores for all areas tend to have the same value. If the customer was extremely unhappy, the scores in all areas might be very low. On the other hand if all the scores are high the customer might have been extremely happy. When a survey response indicates a consistently high or low score, it is often noted as a halo effect.

I have often seen survey survey questionnaires that showed that the company scored top box in every area. While it is nice to imagine your company is perfect, it is probably not true. Similarly, I have also seen many surveys that showed the company scored bottom box in every area. Just as having all top box scores is unlikely, having all scores in the bottom box is equally unlikely. These responses indicate customers who are either very happy or very unhappy. The phrase halo effect was initially used to identify customers who were very happy and were inclined to report that everything was perfect. These customers saw a halo over everything the company did. It quickly expanded to include those customers who were very unhappy and were sending a message that that the company could do nothing correctly.

There are even variations on the halo effect. I saw a survey which had a scale of 1 to 10 and the customer had scored every question 9. The comment from the customer was that only God could receive a 10. Once again, there is a halo reflecting an overall perception rather than a thoughtful consideration of the products/services received.

The question is what to do with responses that indicate a possible halo. The first problem is which question triggered the halo. Without knowing which question instigated the halo, the choices are to either accept all the scores or none of the scores. If the comments suggest which question triggered the halo, that question score would be reasonable to include any analysis. Once again the problem is whether or not to include the other data or ignore them. If the sample is small, I suggest that the others be ignored since the variation caused by the small sample may distort the results and lead to erroneous conclusions.

The bottom line is that halos can be found in most survey data and if they are ignored the results and conclusions may be erroneous. Many people who offer themselves as survey experts know little about the many ways results can be distorted and what to do with them. When people talk about scrubbing the data, it is not a simple process. Remember the adage "garbage in gives you garbage out" is no longer valid. The real adage is "garbage in gives you gospel out" since no one ever questions the computer.

Tuesday, December 15, 2009

Some thoughts on Loyalty

Cornell University has School of Hotel Administration which is world renown. They do some wonderful research in the area of customer loyalty. (You might expect that hotel management would be interested in customer loyalty - nothing like a good repeat customer.) Michael McCall of Cornell made an excellent point recently when he noted that an effective loyalty program should decrease a customer's price sensitivity. He notes that many loyalty programs focus on discounts or other related concessions. These programs have the opposite of the desired effect since it directs the customer's attention toward pricing.

McCall suggests that loyalty program should focus on ways to give special treatment to top customers. Since most companies have a distinct group of customers who are responsible for much of the company's profits, they should be given elite status. These customers should be given different treatment than other customers. He suggests that loyalty programs should have the following elements:
1. Avoid provoking customers' price sensitivity
2. Manage program tiers carefully because it is easy to give rewards but painful to take them away.
3. Think carefully about customer values.
4. Reward customer engagement.
5. Separate true effects of a loyalty program from artifacts by choosing appropriate data.
6. Bridge the gap between academics and practice.

The goal of any loyalty program should be profitability to the company and repeated patronage from the customer. As noted by McCall the key is to find customers who will provide the best future profits (not just revenue).

The bottom line is that loyalty programs should stay away from prices. I think the most important point made in this presentation is that companies must get away from talking prices with respect to their loyalty programs. Loyalty programs should focus on how the relationship with the customer is a win-win for the company and the customer. Not many loyalty programs can attest to doing this.

Friday, December 11, 2009

Soliciting Feedback

There is some very interesting research being performed at Brigham Young University regarding the impact of employees fishing or compliments. Some of the researchers are Sterling Bone, Katie Liljenquist, Bruce Money and Kristen De Tienne. Their two research issues are (i)can soliciting compliments influence customer loyalties and (ii) how does a company's acknowledgement of feedback influence customer perceptions and behaviour.

Their first experiment involved a hotel bellman and showed that customers who were solicited for feedback viewed their encounter more favorable than those who were not solicited. The conclusion was that asking for feedback appears to have a positive impact on customers.

The second experiment related to a portrait studio chain. Some customers were asked to share a compliment regarding product quality, customer treatment quality and their likelihood to recommend. Those customers who were asked to share a compliment rated significantly higher that those where were not asked. Once again, the impact of asking the customer for feedback had a more positive impact on the customer than those who were not asked for feedback.

The bottom line is that these experiments track what has been demonstrated in previous studies and noted in previous blogs; namely, customers need to feel that their feedback both positive and negative is valued and will be acted on by the company. By asking the customer is led to believe that the company cares.

A while ago a company sent out cards soliciting feedback on their products and services to some customers. When they received the cards back, they discarded them without looking at them. However, the next time they measured customer satisfaction they found that those who received the cards scored higher satisfaction scores than those who did not. The message is clear!

Monday, December 7, 2009

Best-in-Class Statistics

The Aberdeen Group has published the results of a survey of approximately 150 enterprises during November and December, 2008. The survey examined how the enterprises implemented or evaluated performance optimization initiates. The survey respondents had the following characteristics:
1. Job function - 22% managers, 14% C-level Executive, 11% Directors and 53% others.
2. Industry - 13% IT consultants, 12% finance/banking/accounting, 9% software/hardware supplier, 7% health/medical and 59% others.
3. Geography - 61% North America, 16% Europe, 13% Asia/Pacific and 10% from the rest of the world.
4. Company size - 23% from large enterprises (annual revenue above US $1 billion), 39% from midsized enterprises (between $50 million and $1 billion) and 38% from small enterprises.

While there is a large percentage of the respondents who fall into the "other" category, the survey results appear reasonable.

The results for customer satisfaction showed that best-in-class increased 9% year over year compared with industry average of 3% and the laggards losing 2%.

The results for customer retention showed that best-in-class increased 15% year over year compared with the industry average of 5% and the laggards increasing 3%.

The results for employee turnover showed that best-in-class decreased by 2% compared with the industry average of 0% decrease and the laggards increased by 7%.

The primary areas of performance improvement were in the areas of understanding the link between back-office operations and customer service and the alignment of back-office operations and corporate operational goals.

The bottom line suggests that enterprises that are focusing on the value of back-office operations are achieving significant performance increase when compared to the industry averages. The improvement of customer satisfaction and customer loyalty certainly implies improved financial performance as has been shown in other studies. The reduction in turnover should lead to a direct saving in hiring and training expenses.

The idea of using the back-office to improve performance is something that appears to have significant improvement opportunities and should continue as IT is used effectively. Keeping IT in the back-office and the employees in front of the customers seems to be a winning strategy. We have seen over and over that the customers need to talk with people and that is one of the best ways to build a relationship. Computers generally do not build long-term relationships with customers.

Thursday, December 3, 2009


Writing about data has the same effect on me as eating dirt. All I can say is Yuck! Unfortunately the topic needs to be addressed from time to time. The market place today is overflowing with customer satisfaction and customer loyalty data. Millions of dollars (probably billions) are being spent to generate the data. I am sure there are computer databases overflowing with customer data. The problem I would like to discuss is that most companies have lots of data but little information. We have found very efficient ways to generate data and that is where many companies stop. They have computer data bases full of customer data and probably have books of data and charts on multiple desks and book shelves. The problem is not collecting data, the real problem is knowing what to do with it.

Several years ago I had a client who called me and asked me to come visit him. After I arrived at his office and we had a very cordial discussion about the state of his industry, he wondered if I had some time to help him. He had just spent several hundred thousand dollars to gather customer information. He had a box full of 3-ring binders, one for each quarter of the previous year. The books were filled with tabulations of data and some charts. His question to me was very direct and to the point. He asked me tell him what all this data said. In essence, he was overwhelmed with the data but had little idea what information was in the data.

I can assure you that the data was collected by a very reputable research firm and that the data was reliable. I was sure the data was collected using all the proper rules of statistical surveys. The problem was that the data was just that - data. There was no thought given as to how to extract actionable information from the data.

After pouring over the data for about 2 weeks there were several points that I could make; namely, that customer satisfaction was,in fact, increasing each quarter and that his small customers loved his operation and his large customers were ready to leave him. The sad news was that there was not sufficient planning prior to the administration of the survey that would allow him to determine the key indicators that would allow him to improve his operation (particularly with respect to his large customers).

Today, companies have multiple sources of data coming in with little or no idea how to integrate the data into a use able database. Customer intelligence is not restricted to customer satisfaction and loyalty surveys, it also includes sales and marketing data as well as feedback from customer service. The real value comes from developing metrics that assist managers and executives into seeing a more accurate picture of their business. Without that knowledge/vision, companies fall back into relying on experiential knowledge which may no longer be valid in the changing markets of today.

The first data commandment is to invest in analysis and operational metrics if you are going to collect data; otherwise, don't bother collecting the data in the first place.

Some recent polls indicate the following:
1. About 49% of companies believe they have adequate customer metrics,
2. About 46% do not believe they have adequate customer metrics,
3. About 2% do not think have adequate customer metrics is important, and
4. About 3% don't know and think it is not relevant.

The bottom line is that companies need to spend the time (and money) to not only integrate their information into a usable database, they need to allocate resources to building metrics that are meaningful and will assist the decision making processes. If a company wants to spend the time and money to create the data, they must also be willing to invest in converting the data into useful information. I would like to know the condition of the 5% who do not think adequate cusotmer information is important in the next few years.

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