Is Customer Loyalty A Predictor Of Profitability?

[tweetmeme source=”pricingright”] Much has been said and written about the need for customer loyalty. The need to focus and attain customer loyalty is intuitively clear to all marketers. Some of the key arguments for customer loyalty include

  1. Reduced Customer Acquisition costs – Since it costs $X to acquire new customers, any customer you hold on to saved you $X. For example, it takes mobile providers $350 to acquire new customers and there are similar metrics for most products.
  2. The Loyalty Effect: Longer a customer stays longer they keep paying you. There was a book by the same name that claimed up to 75% increase in lifetime value of a customer if they stayed longer.
  3. Cross-Sell & Up-Sell: Since you keep your customers and come to know more about them it creates additional revenue opportunities through cross-sell and up-sell opportunities.
  4. Price Tolerance: Loyal customers keep buying from you because they are delighted by your product and are less sensitive to prices.  Some even claim that loyal customers do not even bother to use coupons and promotions, thereby saving you money.
  5. Decreasing Cost to Serve: The more you understand your customer’s usage behavior and needs fewer the mistakes in servicing them and hence lower the cost to serve them.
  6. Bump From Word of Mouth: Loyal customers are also your best marketers, they are happy to write online reviews and promote your products to all their friends and web communities. This means they generate additional incremental revenue.

All these factors seem plausible and the “gut feel” says these must be true.  If even a subset of these six factors are a work, customer loyalty must be a very good predictor of sales growth and profitability.

We should be able to validate the following models

Sales Growth =   Constant  +   ß1 * (Customer Loyalty)

Profitability =  Constant  + ß2 * (Customer Loyalty)

(ß1 and ß2 are the weights of  customer loyalty )

In a study published in circa 2000 in the Total Quality Management journal, researchers studied precisely these two models for a large set of products and services. The result?

Loyalty is a poor predictor of both sales growth and profitability. Their R-square values are 6% for profitability and 2% for sales growth. (For services the number goes to 14.7% and 7.8% respectively). That means only a tiny fraction of the changes in sales growth and profitability are explained by changes in customer loyalty.

Loyalty has positive impact on sales growth but more strikingly, for products, the impact on profitability is negative, which means higher the loyalty lower the profitability. This means any attempt to “buy loyalty” with price cuts does bring you loyalty but at lower profitability.

The net is, what seems too obvious isn’t so. This is not to categorically dismiss need for loyalty but the positive effects of loyalty are clearly overrated. If their effects are so low then there is a high opportunity cost to improving them. You cannot put all the  wood behind the loyalty arrow!


Correlation means two variables are associated and the extent of association si expressed as correlation coefficient. It ranges from -1 (low,high)  to +1 (high,high). A value of 0 means no correlation.

Predictability, R-square, means one variable is a predictor of other. It is measured as a square of correlation coefficient. So two variables that have a correlation coefficient of 0.8 have a predictability of only 0.64. R-square is usually expressed in %, so 64% means 64% of changes in dependent variable are explained by changes in predictor variable. That said, correlation does not mean causation. There are other factors to consider including but not limited to statistical significance of weights of variables, omitted variable bias, etc

Implying Causation – Predictive Analytics Slippery Slope

Imagine, if you will, a child eating broccoli for the very first time. While eating broccoli, let us say the child sneezes a few times in succession and then proudly declares, “I think I am allergic to broccoli”. As a parent or simply as a grown-up it is not difficult for you to see the fallacy in child’s case. One does need an advanced degree in econometrics or statistics to  reply back, “eat your broccoli – correlation does not imply causation”.  Consider the following real cases:

  1. From The Times Economix Blog:
  2. There’s a very strong positive correlation between income and test scores. (For the math geeks out there, the R2 for each test average/income range chart is about 0.95.)

  3. From The WSJ opinion column:
  4. Study after study reveals that there are long-term career benefits to working as a teenager and that these benefits go well beyond the pay that these youths receive. A study by researchers at Stanford found that those who do not work as teenagers have lower long-term wages and employability even after 10 years.

  5. From WSJ half-page Ads targeting parents
  6. Students who read The Journal are 76% more likely to have a GPA of 36% or higher

  7. From a research paper on subscription to library resources by universities
  8. Working with Dr. Carol Tenopir of the University of Tennessee and consultant Judy Luther of Information Strategies, this single-case study demonstrates a $4.38 grant income for each $1.00 invested by the university in the library (ROI Value). The white paper External link University Investments in Information: What’s the Return? is posted on Library Connect. The results articulate the relationship between the value of research information and its impact on the funding of an institute.

  9. From a research paper from the London School of Economics
  10. In terms of percentage growth, a 7 point increase in word of mouth advocacy (net-promoter score)
    correlated with a 1% increase in growth (1 point increase = .147% more growth). The measurement was done through telephone survey in 2005 and the revenue growth numbers are for 2003-2004.

Can you spot the fallacies in these claims?  Are these seemingly erudite and well researched claims any different from the claims of a smart child that wants to avoid broccoli? Why do we want to see correlation when none exist or take correlation for causation? Why do we suspend our critical thinking when the results are presented by big brands, big universities and packed with tonnes of data and graphs?

Of all these cases I listed above, the last one is the winner. Suppose in the chronology of events,  event-2 follows event-1 in time. It is pardonable and a ubiquitous mistake when someone says event-1 might have caused event-2. This is the garden variety correlation causation confusion. But this example I quote says, “event-2 caused event-1”.

I do not know a word for this!

Correlation Causation Confusion

Here is a quote from today’s Journal’s opinion piece on minimum wage increase:

Study after study reveals that there are long-term career benefits to working as a teenager and that these benefits go well beyond the pay that these youths receive. A study by researchers at Stanford found that those who do not work as teenagers have lower long-term wages and employability even after 10 years.

What the study found is a correlation, but WSJ uses it to imply causation – not working as a teenager leads to getting lower long-term wages and employability. But isn’t is it possible that there is an underlying cause for both these observed characteristics (omitted variable bias)? Is it possible that the same reason that led to unemployment as a teenager is driving low-wages and employability in later years?

On the other hand, for those with high wages and employability is working as a teenager  just one tool? Would they have used any other means equally effectively to achieve what they want?

Correlation does not imply causation. The Stanford study was an observation, not a controlled experiment where they randomly selected teenagers, assigned them randomly to working and non-working groups and then years later look at their earning potential.

This is not the first time the Journal is pushing causation based on correlation. You can find more such causation confusion from WSJ here and here.

People Who Read WSJ Are 75% More Likely To …

Does reading The Wall Street Journal makes one more likely to get better jobs and bigger salaries?

The Saturday edition had a half page Ad for student subscription. You can find the claims made in that Ad here.


The problem with these claims is correlation does not imply causation. Regarding these claims:

  1. This is a survey, not a controlled experiment where they randomly assigned people to a control group and treatment group and followed them over years to see if there are statistically significant differences in their GPA, salary etc.
  2. There is omitted variable bias here. The same trait that made the students and others read the WSJ is possibly the driver behind their success. Self motivated and driven people are going to equip themselves with every possible tool and training to get ahead in life. If it is not WSJ they would have read other journals and newspapers to get ahead.  While the claim that “Journal helps the student get ahead with a robust set of career preparation resources”  is valid the following statement “Did you know students who read The Journal are 140% more likely to be starting a full-time job upon graduation?” is misleading because it implies causation.

Few  years back there was a TV commercial for WSJ that showed a man going up in career because of WSJ. The commercial starts with a man, walking in rain, stopping to pick a copy of WSJ from a news vendor to protect himself from the rain. He later runs into an executive of his company in the elevator, who upon seeing the newspaper in his hand offers him instant promotion.  It is one thing to use humor to imply causation, no one will take it seriously. It is however not factually correct when they use survey data and make a causation claim based on correlation.

Other reads: There was also an article on Fantasy Football that implies causation from correlation.

Analytics In Fantasy Football

Does the first week performance of your quarterback predict the performance for the rest of the season? This is a question posed and answered by Nando Di Fino in a column in The Wall Street Journal. I do not own a Fantasy Football league but I am excited to see analytics and econometrics being applied to player picks and trades. This is a very well written article and has some data analysis behind it. But I question the broad implications made by the article.

  1. Are the metrics used by CBSSports the right one to measure player   calibre and predict their performance?
  2. Is there omitted variable bias here? Could there be another  underlying factor that defines the overall season performance?
  3. I know Nando did not mean  causation but i think the   statement “Will a poor performance by a starting wide receiver
    foreshadow a season-long letdown? ” could be interpreted as such. If the  first week performance is indeed predictive of overall performance,  then the respective R-square values are very low (square of correlation coefficients) to fully explain the full season  performance.

This reminds me of a quote attributed Einstein, “Everything countable does not count and everything that counts is not countable”.