Chasing Loyalty at All Cost

[tweetmeme source=”pricingright”] When customers are not willing to buy branded products at premium prices, should the marketers  go after those customers with price cuts, discounts and promotions? Heinz, facing drop in sales and change in customer buying behavior, said it is doing exactly that:

Heinz warned analysts that it will likely have to lean heavily on promotions, discounts and advertising to stop the defection of consumers to cheaper, private-label brands. “This is a tough environment, there’s no doubt about it,” said Art Winkleblack, Heinz’s chief financial officer.

It is a surprise that Heinz that  reported increase in profits for the past four quarters is reverting to the conventional wisdom of maintaining loyalty. The expectation for any marketer holding on to customers through enticements is that:

  1. These will stay loyal when the price eventually improves . But, data shows that buying loyalty leads to profit destruction in the long run.
  2. Loyal customers are less price sensitive, based on a study which is a contradiction since marketers first have to buy loyalty with price cuts. There is no proof in the data that loyalty leads to price insensitivity.

In their 2002 book, Trading-Up, authors Silverstein and Fiske, talk about a behavioral shift in consumers – seeking New Luxury and willing to treat themselves to premium priced products. These are the customers who bought emotionally and justified rationally. Marketers chased these new breed of customers with price points that were high but attractive enough to this Trading-Up segment. (Note: If not for this segment, the prices would have been even higher. )When marketers got used to the volume and market share, they built capacity and made projections based on continued growth in the Trading-Up segment. But all that changed when the economy took a turn for the worse.

In the early days of the Great Recession, The Times profiled how customer’s buying behaviors started to change – from buying premium priced name brand food items to cheaper store brand items. A woman profiled in the story said she convinced her brand conscious husband he was getting A1 steak sauce by refilling empty A1 sauce bottles with store brand sauce.

Recession came as a shock, people losing jobs, seeing their friends and families lose their jobs and a fear for their own caused some to cut back on what used to be essentials. Economists call this the income effect. What the recession started was not just a blip but a behavior change that led  many customers to permanently trade down to cheaper private labels. Arguably these are the “Trading-Up” customers who sought New Luxury.

Instead of trying to hold on to these price sensitive customers  and buying their loyalty at all costs, marketers  must treat this as an opportunity to identify their truly brand loyal and price insensitive customers. When the price sensitive customers all take one step back, those who are left in the front row must be the brand loyal ones. Which means instead of discounting or dropping prices, marketers can actually  increase prices (See Starbucks case study).

What is your cost of loyalty?

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.


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

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

Should you listen to your most loyal customers?

Should you listen to your customers who take the time to write to you asking for specific products or features? The common sense answer seems yes, but should you always listen?

The WSJ has a story of loyal fans of British version of Cadbury’s refusing to accept the version sold in the US markets. The latter is manufactured by Hershey’s under its licensing agreement with Cadbury’s with a recipe tailored to the US market. Should Cadbury’s or Hershey’s listen to these most loyal fans and change their recipe? The article in WSJ describes these fans as someone who,

wants nothing to do with the stuff made in the U.S. “Oh, it’s so yuck,” she says. “You might as well eat a Hershey bar.”

With such loyal fans does it not make sense to a marketer to listen to them? The answer is not straightforward and need to be analyzed from at least three angles

  1. What is the customer saying and what do they really want?
  2. Is the customer insight actionable and profitable?
  3. What is the share of profit from these customers?

The first point is the most repeated story that if Ford had listened to its customers it would have developed faster buggies. In the book Game Changer, P&G’a retired CEO, Mr. A.G.Lafley calls this “customer driven and not customer led”. It is important you listen to your most loyal customers but everything you hear are not the customer insights – not to mention the biases in what we choose to hear and how we interpret it.

The second point is about relevance of customer insights to your business. In the Cadbury’s story the insight you may find is that the customers are really holding on to their memories of living abroad and are trying to relive by reminding themselves of the chocolate they ate there. This would be a valid information but it is insight only if you can act on it, the action required fits your business strategy, and you can generate profits from it through product innovations.

The third point is about  the share of profits you generate from these customers. What is this segment’s willingness to pay for such product variation? What are your costs to produce, distribute and get it to the target segment? It is not just how big this segment is or how much revenue you generate – the segment has to generate enough profit to justify new action.

Next time you see a flood of tweets, blog posts, and other social media expressions of customer likes and dislikes, ask the three questions before you rush to implement change.