Category Archives: Measuring Engagement

LTV, RFM, LifeCycles – the Framework

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

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

Q: I visited your website because I am trying to understand how to develop a customer LifeTime Value model for the company that I work at.  The reason is we are looking at LTV as a way to standardize the ROI measurement of different customer programs.

Not all of these programs are Marketing, some are Service, and some could be considered “Operations”.  But they all touch the customer, so we were thinking changes in customer value might be a common way to measure and compare the success of these programs.

A: Absolutely!  I just answered a question very much like this the other day, it’s great that people are becoming interested in customer value as the cross-enterprise common denominator for understanding success in any customer program!

If I am the CEO, I control dollars I can invest. How do I decide where budget is best invested if every silo uses different metrics to prove success?  And even worse, different metrics for success within the same silo?

By establishing changes in customer value as the platform for all customer-related programs to be measured against, everyone is on an equal footing and can “fight” fairly for their share of the budget (or testing?) pie.  By using controlled testing, customers can be exposed to different treatments and lift in value can be compared on an apples to apples basis – even if you are comparing the effect of a Marketing Campaign to changes in the Service Center.

Continue reading LTV, RFM, LifeCycles – the Framework →

Acting on Buyer Engagement

Customer Experience, DataBase Marketing, Measuring Engagement, Web Analytics

Over the years I’ve argued that there is a single, easy to track metric for buyer engagement – Recency.  Though you can develop really complex models for purchase likelihood, just knowing “weeks since last purchase” gets you a long way to understanding how to optimize Marketing and Service programs for profit.

Which brings me to the latest Marketing Science article I have reviewed for the Web Analytics Association, Dynamic Customer Management and the Value of One-to-One Marketing, where the researchers find “customized promotions yield large increases in revenue and profits relative to uniform promotion policies”.  And what variable is most effective when customizing promotions?

The researchers took 56 weeks of purchase behavior from an online store, and used the first 50 weeks to construct a predictive model of purchase behavior.   Inputs to the model included Price, presence of Banner Ads, 3 types of promotions, order sizes, number of orders, merchandise category, demographics, and weeks since last purchase (Recency).

The last 6 weeks of data were used to test the predictive power of the model, and the answer to which variable is most predictive of purchase is displayed in the chart below, click to enlarge:

Weeks since last purchase dominated the predictive power of the model, controlling not only the Natural purchase rate (labeled Baseline in chart above, people who received no promotions) but the response to all three different types of promotion.

Continue reading Acting on Buyer Engagement →

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.

Continue reading Relational vs. Transactional →