Category Archives: Analytical Culture

Relational vs. Transactional

Analytical Culture, Customer Experience, DataBase Marketing, Driller Q & A, Marketing thru Operations, Measuring Engagement, Newsletters

Jim answers questions from fellow Drillers
(More questions with answers here, Work Overview here, Index of concepts here)

Q: I am hoping you can help answer a question for our team. By way of introduction, I am the CEO of XXXX. We are a specialty retailer / restaurant of gourmet pizza, salads and sandwiches. We would like to know restaurant industry averages (pizza industry if possible) for customer retention – What percentage of customers that have ordered once from a particular restaurant order from them a second time?  I am hoping with your years of expertise and harnessing data you may be able to assist us with this question. Look forward to hearing from you.

A:  Unfortunately, in those said years of experience, I have found little hard information on customer retention rates in QSR and restaurants in general (if anyone has data, please leave in Comments).  It’s just the nature of the business that little hard data, if collected, is stored in such a way that one can aggregate at the customer level. The high percentage of cash transactions doesn’t help matters much; there’s a lot of data missing.

Over the years, sometimes you see data leak out for tests of loyalty programs, and of course clients sometimes have anecdotal or survey data, but this isnot much help in getting to a “true” retention rate. More often than not you discover serious biases in the way the data was collected so at best, you have a biased view of a narrow segment. Often what you get is a notion of retention among best customers, or customers willing to sign up for a loyalty card, but not all customers. And the large “middle” group of customers is where all the Marketing leverage is.

What to do about this predicament?

There are really two issues in your question; the idea of using industry benchmarks when analyzing customer performance, and the measurement of retention in restaurants.

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Norms of Reciprocity

Analytical Culture, Customer Experience, DataBase Marketing, Measuring Engagement, Web Analytics,

Social Marketing Doesn’t Rely on Social Media

Do you believe human beings share certain fundamental traits that define “being human”?

If so, do you believe that human beings tend to behave in certain ways under certain circumstances?

If so, do you then believe since human behavior has these tendencies, it can often be predicted?

If so, then do you think perhaps the study of Psychology and Sociology might provide you some clues to creating successful businesses, campaigns, products, and services?  While your friends and competitors are all iterating their way into oblivion?

On the web, time and time again, we see the same themes repeating.  Yet with each introduction of a new technology, these themes tend to be treated like a new discovery, even though the theme has been well established in the past.

Norms of Reciprocity is a constant human theme.  You may know the expression of these norms as “Sharing”.  Web old timers will probably recognize this idea as “Give, then Take” from the I-Sales discussion list as early as 1995.  In various forms, this theme goes back to the beginning of human history, all the way back to the handshake and other greeting gestures.  This same theme is embedded in countless Religions all over the world: “Do onto others as you would wish them do onto you”.  At least a couple centuries old, this idea.

Norms of Reciprocity simply means this: When you do something nice for a human being, help them in some way, this human tends to feel Gratitude towards “the doer” and tends to do something nice back.  Gratitude drives the desire to Reciprocate, because it’s just what humans do, it’s normal, a “norm”.

Norms of Reciprocity.

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Analyze, Not Justify

Analytical Culture, Web Analytics

Does this issue affect the Web Analytics Maturity Model?

A conference call with a Potential Client last week jogged my memory on a couple of events that happened during the flurry of Web Analytics conferences this Spring.  Here’s a portion of the call…

PC: “We’ve tried proving the profitability of our Marketing efforts and can’t seem to get the numbers working correctly.  So Jim, what we’d like you to do is take all this data we have, and justify the Marketing decisions we’ve made by proving out the ROI.”

Jim: “I’m sorry, did you say justify?  To me, justify means “find a way to prove it works”.  Is that what you are asking me to do?  Wouldn’t it be more beneficial to analyze the results, and then optimize your Marketing based on these results?”

PC: “Jim, around here we’re pretty clear our Marketing works, and Management knows this.  But Finance is asking for some backup, some numbers to justify the spend, not to analyze it.  We don’t need analysis, we need your ‘expert credibility’ to help us out with this.”

Jim: “I see,” thinking this is not a job I’m going to enjoy.  It’s the old ‘buy an outside expert’ routine, which I detest.

PC: “Jim, the team is united behind this mission, are you on board?”

Jim: “Well, perhaps I could be on board, as long as what you want is an analysis, which may also justify the decisions you have made.  But it might not, so I just want to be clear on what…”

PC: “You  know what Jim?  I don’t feel we’re going to have a fit here, I’m getting you’re not a team player.  Thanks for your time”.  CLICK

Sigh.  I’m actually grateful they hung up, I really dislike explaining to people why I won’t work with them.

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