Chipotle and Excellence in Pricing and Content Marketing

This is a story with two threads. One about Chipotle’s recent announcement that it will increase prices and the other about its hit Scarecrow video and associated iPad game. I will bring these two together to show you the excellence in pricing and how any content marketing activity must help serve this pricing purpose.

First the pricing news. In its last quarter earnings statement Chipotle said it will increase prices in 2014 .  This despite reporting spectacular improvement on all metrics

  • Revenue increased 16.7% to $2.37 billion

  • Comparable restaurant sales increased 4.3%

  • Restaurant level operating margin was 26.9%, a decrease of 110 basis points

  • Net income was $247.8 million, an increase of 14.4%

The decision to raise prices has nothing to do with how your current profits are growing. Their reason for this price increase?  You guessed it, costs.

 it may raise prices in the middle of 2014 to offset higher food costs. The price increase will be between 3% and 5%, CEO Steve Ells said

Nicely done, as we have seen before. And the stock market reaction? The bid up the stock, showing confidence in this better price realization.

I want to make sure you see that the food costs are used only as plausible reasons to help customers digest the price increase. Let us look at its current food costs and its share of revenue.

Their 2013 third quarter revenue was $827 M and food cost was $277 M. Purely for financial accounting purposes their percentage gross margin is 66.5%. As you can notice they are not just scarping by, trying to grab last morsel of cilantro rice from the bottom of Burrito Bol. That means they did not set the price based on food costs to begin with and hence to say they will increase prices because of food costs is just effective messaging.

That is one half of the pricing story.

Say costs increase twice as fast as inflation, by 4%. On average 3% to 5% price increase will recoup them if demand did not drop. How can they make sure the demand does not drop?

The other half is making sure customers continue to pay $8.20 for a burrito and feel good about it That requires investing in the brand and making customers feel great and hip about the brand.  That is served by their Scarecrow video.

Content marketing experts jumped on the incredible view metrics of this video to draw their own lessons about telling stories, talking about bigger picture etc. But think about the ROI on such content marketing effort. Imagine yourself going in front of CEO for an ask for running a content marketing campaign.   Social media experts may convince you to ignore ROI and focus on,  you got it, “the bigger picture”, “return on engagement”, “share of conversation”.

Here is some sane words on this from Eric Jacbson, CFO of  Amplifinity,

there is only one definition of marketing ROI. It is…

ROI = (Net Present Lifetime Value of Customer) / (Marketing Cost to Acquire Customer)

This brutal metric is the only way to know if a marketing initiative is working.

The second cold truth is that almost nothing works.

It is all about tying the bigger message and stories to price increases they can pass through without any associated demand drop. As you chomp down your spicy sofritas you feel good about being part of a bigger story and hardly even notice the price increase.

All those feel good stories are for naught if you cannot tie it back to ROI in the form of increased prices without drop in sales.

Who does your pricing and content marketing?

No Single Metric – Proof By Contradiction

There is significant interest among businesses – enterprises and startups alike – to look for a single metric, one magic number that will point to overall success, profitability etc. When a metric is pitched, if it is reasonably popular, seemingly rigorous yet very simple to understand and comes from a firm or person with a pedigree or position, the metric will find almost religious acceptance.

Can there really be one metric that predicts profitability? I used statistical biases to explain why there cannot be just one metric, now let me make another attempt with Proof by Contradiction.

Let us assume there is indeed a single metric, let us call it X. If by a logical sequence of arguments, I  show you that there  is in fact one more metric  it will be  a contradiction to the assumption, proving it is false.

If X is a predictor of profitability, then improving it is good for the business.  There are three cases:

  1. There are no  levers to move X
  2. There is exactly one lever that businesses can pull to move X
  3. There are more than one levers to pull

The first case is not possible, after all no one will pitch a metric that cannot be controlled and managed. Granted, this is not really part of proof by contradiction but I am simply eliminating this branch of the decision tree.

In the second case, if there is exactly one lever that businesses can pull to move the metric, then by extension that becomes the predictor of profitability and not X.  This is a contradiction.

In the third case, if there are more than one lever to pull, that means the whole set of those affect profitability and not X. This is again a contradiction of the assumption that there is a single metric.

What do you say?

A New Metric to Measure Word of Mouth Marketing

[tweetmeme source=”pricingright” single_only=”false”] Effect of  Word of Mouth (WoM) marketing is intuitively accepted by most and a few peer reviewed research in the journals of Marketing Science and Marketing Research found evidence supporting the predictability (somewhat weak however).  There are two key shortcomings I see with many of the popular Word of Mouth Marketing metrics:

  1. One that is most acknowledged in any marketing research study is the gap between customer’s stated attitude and their actual behavior. When the customer answers the question on paper (or screen) they are asked to answer about their expected behavior but the answer is still rooted in the attitude. For example, when you ask a customer about likelihood of purchase, paying for services or recommend the product/service to others the customer states their current attitude and not what they will do when presented with the exact situation in the real world.
  2. Two is trying to compute a net metric based on the stated attitude level. They result is data loss (because some responses are thrown away) and the need for extremely large number of samples to get statistically significant results. According to research published in Marketing Research, (Summer2007,Vol. 19 Issue 2) up to 26 times more samples are needed for net metrics.

I propose here a newer and actionable metric – iTRM  (iterative Total Recommendation Metric). This addresses both the shortcomings listed above. First, it gets closer to actual customer behavior when it comes to recommendations than any of the metrics. Second there is no data loss, all responses are counted.  Here is how the metric is calculated.

Customers are specifically asked about their actions, how many times they will recommend. The scale is -5 to 5. Negative values mean customers will be “de-recommending” your products. iTRM offers multiple advantages over existing metrics:

  1. The time horizon is specific and  limited, 1 month. The time limit however does not imply that the measurement must be repeated every month but if the metric is as a predictor of sales it requires monthly measurements.
  2. The customers do not have to make subjective judgments about rating scales. For example a 8 for one could be 6 for another in many ordinal scales measuring likelihoods. Here it is the exact number of actual recommendations.
  3. This is simple to administer and measure. It can be either a simple sum or can be continuously improved to be a weighted sum that is different for different industries and even for specific brands based on past data. (The word iterative stands for both my brand and the iterative process by which the metric evolves).
  4. The limit of 5 is not a shortcoming given the specific action and the time horizon. If for a particular brand we find the need for higher scale, it can easily “iteratively” improved upon and still be consistent with past results.
  5. It is easy to validate its predictability and improve on the weighted calculation.

Overall, I believe iTRM provides a better measure of WoM with potential to use and easily validate as a predictor of growth.

How do you measure your WoM?

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.