Want to see the answers to previous questions?  Here’s the blog archive; the pre-blog newsletter archives are here.

Q: One of the first things I am doing in my new job is to identify the Customer Lifecycle pattern – how many periods (month, year) will it be before a customer is likely buy again.  In enterprise software industry, where software cost easily 6 figures, # of years is a reasonable time frame.

A: Yes, one would assume this.  But these notions would most likely be based on a feeling of the “average” behavior, and on average, it probably does take a long time.

What is not known is this:  if the “average” is composed of short-cycle and long-cycle buyers, who are the short cycle buyers, and what are they like?  What industry SIC code, for example?  And can we get more of them, or at least focus more resources on them, if they are the most profitable?  So the challenge is not only to look for the “average”, but then understand how this average is composed.  If you can break down the average by industry, or by salesperson, for example, this might be highly directional information.

Q: From my internal analysis, however, I discerned from the sales figures something quite counterintuitive – the period between first and next sale is much shorter than I would have thought for the SW industry in general.

A: Pleasant surprise, eh?  I don’t know what kind of figures you are looking at, but make sure the data is in fact what you think it is.  For example, if you want to study software purchase itself, do the “sales” figures you are looking at include transactions involving not the sale of software, but also service, like installation or modification fees?  It would make sense that a “software sale” would be followed pretty quickly by an “installation” sale, for example.  You need to know this to properly segment.

Q: Would you be able to point me to some studies on how often customers wait after the first purchase before contemplating an upgrade of software or something you personally have done in consulting projects for SW companies?  This industry benchmark will then shed some light on whether this trend is something peculiar to our company or not…

A: I am not aware of any published study of this type.  And as you might imagine, these numbers would vary quite widely in the industry and the nature of the information would be a highly guarded corporate secret.  So I don’t think you will find any “benchmark” studies of this type, and sorry, I can’t share client data with you!

Q: The next step for me then is to map out the drivers for this behaviour and then calculate the LTV (LifeTime Value) and take a look at the actual LifeCycle events creating this LTV.

A: Yes, but to be precise, LTV is typically a forecast when working with current customers; it’s not known until the customer actually defects, marking end of  “Life”.  What you are probably looking for is more accurately called “Life to Date” (LTD), the actual sales of a customer from start of relationship until present.

Also, when segmenting customers by LTD, of course look for temporal bias – a 10-year customer is likely to have higher LTD than a 2-year customer.  If there are enough customers, it might be a good idea to first segment by start year, then LTD.  This way you have cohorts of customers who are going through the same experiences together and differences in LTD will be more significant in terms of predictive power – you don’t have to hunt around for external bias (e.g. competitive changes) that might affect LTD.

Think about what I said above about breaking the “average” down into different groups, because this will likely provide the Eureaka! moment and turn the data you are looking at into information.  For example, if you find the LTD of the “average customer”, this is very interesting information indeed, but not highly actionable – what “action” do you take knowing this information?  Can you point to or predict which customers are “average”?

However, if you were to find out the LTD differed dramatically by industry, by salesperson, by country, by time of year, by type of software module installed first – this is highly actionable information, because it provides very direct instruction on where the most profitable areas of business are.

If you lack thoughts in this area, try segmenting by variables that directly affect the experience of becoming a new customer.  At least one of these is most always predictive of LTD, when directly tied to the acquisition of a new customer:

1.  Campaign media (e.g. trade show versus magazine, online versus offline)

2.  Campaign content / offer

3.  Salesperson / Service teams

4.  Product or Category of first purchase

5.  SIC code / Industry (proxy for Product suitability to customer needs)

For example, if you find LTD differs by salesperson, you will find salespeople who create high LTD customers and salespeople who create low LTD customers; the company should study how each salesperson sells and teach the others based on results.

Or perhaps likelihood to purchase again is determined by which customer interface team installed the software – one team does such a good job the customer re-orders the next module very quickly, as compared with other teams.

Knowledge of this type would be extremely valuable to the company – you can use LTD to discover “best practices” hidden within the “average” data, and by spreading those best practices throughout the company, create enormous benefit and increase in profitability.

The secret to creating meaningful customer analysis that delivers high impact is this: always think about what you would **do** with the information you uncover.  If you can’t **do something** with the numbers you evolve, you probably need to drill down a little further and uncover the true meaning of the underlying data.

Hope this helps.  My personal guess based on what I know about behavior would be this: the type of software installed first and the sales / installation / after the sale care team are the two most likely variables predicting speed to next purchase, followed by the industry the buyer comes from.

This is a very interesting project; please keep me informed of your progress and I will help you in any way I can.

Jim

Have a question on Customer Valuation, Retention, Loyalty, or Defection? Go ahead and send it to me here. If on the topic above, you can leave a comment on the post:

Segmentation by LTD & LifeCycle

Want to see the answers to previous questions?  Here’s the blog archive; the pre-blog newsletter archives are 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.

But are you sure you want to use LifeTime Value for this application?

Q: From what you stated on your website, I will not be able to develop a LifeTime Value model unless I understand the customer Lifecycle.  The customer lifecycle is something that I could get a good understanding from using doing a RFM analysis.

My question is, once I complete the RFM analysis, what would be my next steps in developing a customer LifeTime Value model?   At this point in time, the hardest thing that I am trying to wrap my head around are the variables to include in the model.  I visited Arthur Middleton Hughes’ website:

http://www.dbmarketing.com

and he suggests the following variables (download spreadsheet, if interested):

http://www.dbmarketing.com/special_ltv.htm

Jim, could I simply use those variables going forward to calculate the LifeTime Value of a customer at my company?  I would appreciate any assistance you may be able to provide to me on this matter.  Thanks.

A: Well, that’s a big tangle of related issues!    Let’s unpack first, then answer the question.  First, the relationships between these ideas:

Lifetime Value versus Lifecycle – LTV is a number, LifeCycle is a trend over time that contains trigger events.  You don’t need the LifeCycle to develop (calculate) LTV, you need the LifeCycle to most efficiently and profitably act on and manage LTV issues.

RFM versus Lifecycle – RFM is a tactical model that is a “snapshot” of customer state at a point in time, the customer’s likelihood to respond.  Frequently used names for these customer states include active, lapsing, lapsed, defected.   Lifecycle is the “movie” one might put together from these snapshots of RFM states; the migration from one customer state to the next are the Lifecycle trigger points.

Now, let’s make sure we understand each one of the ideas:

LifeTime Value

Strictly speaking, LTV is not a very flexible concept and is best used for determining how much you can spend to acquire a customer and still make a profit.  This is the equation that Mr. Hughes has provided, a man by the way that I have a lot of respect for.  His model is quite detailed and useful for the purpose of finding break-even cost to acquire a customer.

To use Arthur’s LTV model, you have to find historical values and plug them in.  You could assume nothing will change and the LTV of certain segments of past customers will be the same; this is great for “benchmarking”, for example.  However, this approach is not measuring LTV, it’s predicting LTV based on historical data.  This is fine, and a valid method for certain types of analysis.

But, the premise of your question is you will be testing, and testing implies something new will occur.  So while you could use LTV to estimate results, you’d have to wait quite a while to prove the results one way or another.  LTV is really “forensic” in this way – you won’t know the final answer until the customers defect.

You could certainly go back 2 – 5 years after the tests, and prove one group had higher LTV than another, but that’s not typically a very useful approach when doing testing.

RFM (Recency, Frequency, Monetary)

RFM is a predictive model that takes a “snapshot” of the customer base and gives you a score for each customer, a prediction of likelihood to respond relative to all customers.

By itself, RFM doesn’t tell you if you are making money or not.  It is used to classify the “state” of customers at a point in time, usually for targeting purposes – are they active, lapsing, lapsed, defected?  In other words, it’s a customer segmentation tool.

For example, RFM could be used to choose your test and control groups for a campaign using Lift measurement – you would want test and control to have the same range and balance of scores.  In fact, one of the tragic campaign measurement mistakes people often make is not taking into account the likelihood to respond when selecting test and control groups, resulting in biased test results.

Customer LifeCycles

One of the great features of RFM is the idea of “ranking” customers relative to each other; this gives allocation of budget and success measurement a standard to follow.  A single  customer can have many different scores over the course of their LifeTime, with the likelihood to respond the score at a specific time.  In fact, if you looked at RFM scores over time for a single customer, you would have a clear understanding of the LifeCycle of a customer – the most powerful segmentation available in terms of message and offer targeting.

The problem with looking at RFM scores over time is complexity; the beauty of individual customer scores at a single point in time becomes unbearable when you are talking 125 different scores on 50,000 customers over 6 months.  That’s the internal or analytical problem.  Externally, this kind of information is extremely gnarly to present and explain to senior managers, it’s presentation hell.

The way I solve this problem is with a tool I call LifeCycle Grids.  The Grids takes the same fundamental drivers used in the RFM model and instead of ranking, uses thresholds or “hurdles” to classify customer states.  This creates a standardized customer LifeCycle “dashboard” so comparisons of customer value between different segments can be made more easily.  It works for both short and long term observations and is easy to represent either numerically or graphically.  And because it uses finite thresholds for activity rather than ranking, the same calculations that create the dashboard can be used to actually drive or trigger actions.

So the dashboard is actually the controller as well.  This is extremely beneficial in terms of linking presentations, plans, and results. People can literally point to a segment on the LifeCycle framework and say, “Let’s deliver message X to each person from segment Y who enters this cell” and see the results right where they pointed when the dashboard is updated.

Once you test some ideas and find out which approach generates incremental profits for a cell in the Grid, you can automate delivery of the program as customers enter that cell of the Grid.  This is the classic “sense & respond” approach to marketing communication – right message, right person, right time.

The LifeCycle Grids are demonstrated in a lot of detail for different applications in the series here, but probably of most interest to you as it relates to customer analysis, see here.

And now, to answer your question:

Which approach above, if any of these, would be best for standardizing measurement of ROI in widely diverse customer programs?

LTV would be appropriate if what you want to know is breakeven cost to acquire.  Since we are talking about customer programs, I doubt that’s what you want to use.  Plus, if you want a hard number rather than a prediction, you could be waiting a long time for the answer.

RFM is a “snapshot” model and so not really suited to long-term studies of customer value.

Customer Lifecycle models are more likely to be involved in the execution of a program, not the success measurement.  LifeCycle tracking could be (and often is) used to predict the financial success of campaigns before they have run their course, but you’re only predicting success, not delivering numbers into an ROI model the CFO would accept as “fact”.

Answer: None of the above.

What you need is an approach designed for the task, which in this case, is:

Lift Measurement or Near-Term Value

Lift is a measure of the performance of a test group of customers compared with a control group of similar customers who are not exposed to the test.  You can read more about control groups here.  In the analysis of value contributed by each group, many of the same values from Arthur’s LTV model are used – product margin, costs of program, fulfillment costs, payment parameters, etc.  However, if you are talking about a program to existing customers, cost to acquire is probably not relevant, though you might use source (campaign) to segment your test approach.

Lift is typically measured at intervals, say every 30 or 60 days, to see how test versus control populations are tracking, and can continue after the test is over to pick up residual value created in the customer.  However, this is not a Lifetime Value measurement, Lift models measure incremental contribution to LTV created by the Marketing, Service, or Operations program execution.

This means if you get lift from program test versus control, when you go back 2 – 5 years later and measure true rather than predicted LTV – after the customer has defected – you should in fact see the LTV in the test group higher than in the control group, barring any radical downstream difference in customer experience between test and control.  In this way, Lift models are actually predictive of changes in LTV.  That’s why the output of Lift models is sometimes referred to as the measurement of “Near-Term Value” and used much more often than the forensic approach of waiting for customers to defect.

Summary

All the above are core concepts in customer value measurement and management.

LTV is a measurement of net financial value contributed by a customer, and Lift measures  are like a “time slice” of the overall LTV curve.

LifeCycles are a management framework for programs designed to affect LTV, and models using Recency, Frequency, and Monetary are used to look at a “time slice” of the LifeCycle.

LTV can generally be increased in two ways: by creating more value during the existing LifeCycle, or by extending the LifeCycle.  Marketing (including Product) is typically used when doing the first, Service and Operations – customer experience and satisfaction – are largely what affects the second.

So it is completely appropriate to establish a unified approach to the measurement of customer programs intended to increase the value of a customer across all these disciplines, in order to ensure the allocation of  scarce resources to highest and best use.

A great question, and for a great cause!

Jim

Update:

Listrak asked me to do a podcast with them on these and related topics, check it out (MP3 link) here, or see list of all their Email Marketing Today podcasts here (I’m on Episode 42).

Have a question on Customer Valuation, Retention, Loyalty, or Defection? Go ahead and send it to me here. If on the topic above, you can leave a comment on the post:

LTV, RFM, LifeCycles – the Framework

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.

The  Natural buying rate (here, as much as 50% of campaign response) has tremendous implications for the measurement of Campaign profitability, and can also be used to measure the success of customer-centricity / experience / social programs.  These are the issues I cover in my review of the article.  If you’re interested in that take, you can read it here.

But for this post, what I’d like to do is  explore the Recency measurement idea itself, because I suspect a lot of people may not understand what it really means.  And since many Marketing folks are not used to taking action on this kind of data, also talk about what you can do with this information.

Most people think of time in a linear way.  A graph that includes time typically starts at some point in the past and churns through time in a sequential fashion.  Not so with the graph above, which is looking at Purchase Cycles.

In this style of cycle measurement, customers are moved back to the time = zero segment (left side of chart) as soon as a purchase is made, and time starts all over again for these customers.  If they don’t make a purchase, they continue to slide out down the curves to the right.  Can you picture this activity in your mind?

You can have customers who stay at the top end of the graph, rapidly cycling round back to zero weeks each time they purchase.  You can have customers with longer cycles that loop back to zero weeks in slower purchase cycles from the middle.  You can have customers who purchase only once and every week just slide out further way from zero until they fall off at the right.

That means the same person might be in different  places on a curve in different weeks.  The same person can buy 2 weeks after last purchase or 4 weeks after last purchase, or a person can buy every week, or every month.  All of these purchase cycles summarized produce the series of likelihoods you see on the chart.

The point of the chart is, no matter which promotion customers are exposed to, no matter when their previous purchase was made (2 weeks ago or 20 weeks ago), their likelihood to purchase again can be very simply and accurately predicted by knowing one simple data point: weeks since last purchase.

Said another way, because this is a core concept to customization using behavior:

Customers with all kinds of different purchase patterns, demographics, categories of purchase, campaign exposure, and so forth tend to behave in the same way, that is, their likelihood to purchase at any given point in time from this  online store is primarily a function of how long it’s been since they last purchased from the store.

There are some pretty significant online marketing implications from a statement like that.  But how do you act on this information?

You’ve probably heard of the “sales pipeline” idea from B2B.  Sales management gathers data to inform them on which deals are likely to close and when, and build a flow chart of expected revenues.  This helps management take action on any deals that seem to be “floundering” -  special exec attention, discounts, bundling, etc.

You can do this in B2B because the value of the customers is usually quite high, and you have sales people or account managers who are close to the customer and can provide this data.

In B2C, you can’t afford to have account people for each customer, but using Recency you can predict which groups of customers are most likely to purchase again, and then build the same kind of sales pipeline.  And then, customize your Marketing action based on whether the customer seems likely to buy or is ”floundering” and drive increased profitability.

Building a sales pipeline model can also be used to predict how well the business will be doing in the future, and what kinds of products or tactics are really driving future profits.  Like other kinds of optimization, moving focus or resources towards products and tactics that are driving value, and away from those destroying it, results in a more profitable business.  But using Recency, instead of optimizing the Present, you are really optimizing the Future.

Look at the chart above.  There is a discount promotion and a free shipping promotion.  The coupon promotion outperforms the free shipping promotion as long as the customer has purchased in the past 6 weeks.  After this point, free shipping outperforms coupons.  That is something, as a Marketer, I think I’d like to know.  It means to optimize this system, I should deliver campaigns not based on my calendar, but based on the customer’s calendar as evidenced by their purchase cycle behavior.

Similarly, around week 8 since last purchase, coupon performance drops below the baseline performance of people in the loyalty program.  And finally, at 20 weeks, coupon performance is basically equal to the Natural buying rate, meaning virtually everyone using a coupon would have purchased anyway without the coupon.

Please understand, I’m not saying these Recency curves will be the same for your commerce site – they will depend on the type of products you sell, how good your service is, and so forth.  You have to do your own analysis.  What I am saying is the Recency effect is universal and can be the most important variable you could ever use for segmentation if you are concerned about campaign profitability.

For a practical perspective however, data in the format above is difficult to use and explain to other folks.  I much prefer what I call the LifeCycle Grid format below, click to enlarge:

People are more used to seeing data in a format where “up and to the right = better” so I have flipped the zero Recency boundary to the right side.  The customers with the lowest future value are in the lower left (Pink) and highest future value are in the upper right (Green).  I have also cross-tabbed Recency with Frequency so we have an idea of the value of a customer; the value of the customer helps decide how to approach the customer.   For Recency, we have chosen “hard breaks” rather than a smooth curve.  This creates specific populations so we can target certain groups and measure results.

Example:  If I send a 10% off promotion to all customers, you will see dramatic differences in response and profitability across these different cells.  Working the grid this way with various offers, you will find that allocating the same Marketing budget and promotions evenly across all the cells is truly a suboptimal approach.

Additionally, the general location of the cell gives clues to customizing campaign content or angle of attack as well as customizing the offers.  In general, for the four colored segments:

Green: Best customers who are Engaged – this is a segment where aspirational messages and services are extremely effective.  Think ”Special VIP treatment” in campaign copy and offers.

Orange: Best customers with declining likelihood to purchase again – if you are truly customer-centric, it’s time to analyze (or survey) these customers for broken products, processes, and service.  Why is a best customer dis-engaging?  Can we help you?  Did we do something wrong?  Would you recommend us?

Yellow: Potential Best Customers – new customers and those who are “floundering”.  What can you do to turn them on?  This is a group that benefits from category or affinity analysis to inform campaign content; help them try new product ideas.

Pink: Defected Low Value Customers - high value, broad discounting (30% off anything) is probably the only thing that’s going to drive response from this group – is it really worth it / do you actually generate profits here?

From a management perspective, feeding specific populations through the Grids can inform strategic decisions.  If you believe the Grids essentially represent a sales pipeline, then how do the pipelines for different customer segmentations compare?

An obvious place to start is Campaigns – what do the sales pipelines look like for different Campaigns, which Campaigns generate the highest percentage Green segment 1 month after Campaign drop?  What about at the end of month 3?

Run Product or Category analysis through the Grids.  For example, new customers whose first purchase is in a certain category – does this category create customers with high pipeline value?  What about customers who continue to buy in the category?  Softgoods versus hard goods?  Software versus hardware?  Shouldn’t we feature products that drive high pipeline value in campaigns and on the home page, as opposed to products that generate 1x buyers?

How about channel analysis, which sources generate new customers with the highest likelihood to continue purchasing?  Are most of our PPC customers in the Green segment, and most of our Affiliate customers in the Pink segment?  Where do the Social customers end up?  At 1 month after first purchase?  At the end of month 3?

The beauty of this approach is it can be used over and over, on any platform, in just about any situation, to answer the same question: which activities generate customers with the highest future value?  The Grids provides a consistent way to compare investments in all types of activities – products, campaigns, service initiatives, usability, centricity.  Just take the population exposed to the test, run them through the Grid, and compare to average (or better yet, control).

Most Marketers grew up with a linear view of execution – just keep Pushing, the more impressions the better.  Taking this approach in an Interactive environment completely ignores the fact that many customers will come back and Purchase again without any Push at all - and especially so if you are nailing all the centricity angles.

The trick is to optimizing Interactive commerce for Profit is:

1.  Understand which tactics create customers with high pipeline value – those likely to re-purchase on their own - then,

2.  Take Marketing action based not on a linear calendar, but a cyclical one – the calendar defined by the customer’s own behavior, customizing the message by location of the customer in the purchase likelihood Grid.

Execution Tips: List selection for this customization program is easily automated, right?  Just use the Grid cell boundaries as selection variables.  Many people decide to keep a regular generic “Brand” email communication to all customers while running the hyper-targeted communications based on cycle behavior underneath.  In this case, consider backing off discounting in the Brand communication and stick to new products, new hires, content marketing, etc. and let the cycle-driven email handle the behavioral discount program.  Test for the optimal balance / frequency between the 2 different emails by tagging e-mails with Grid cell.

Questions on this?  Also, with this background you might now want to read my review of the study.

Have a question on Customer Valuation, Retention, Loyalty, or Defection? Go ahead and send it to me here. If on the topic above, you can leave a comment on the post:

Acting on Buyer Engagement

Want to see the answers to previous questions?  Here’s the blog archive; the pre-blog newsletter archives are 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 is not 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.

As far as industry benchmarking, two things:

1.  Annual reports for publicly traded eateries may be of help.  Customer loyalty info may be disclosed in these documents or conference calls with Wall Street.  Still, it will probably be of the quality referenced above – narrow in scope or behaviorally biased.

Sometimes you can put snippets of different conversations into an equation that allows you to guess at repeat purchase rate; hospitality analysts often want to understand repeat behavior and do this kind of forecasting.

2.  Ignore the industry benchmarks.  If you have the capability to track repeat rates, simply establish what they are now and use them as internal benchmarks to not fall below or create programs to improve against them.  

Frankly, I tend to discourage using “industry benchmarks” because the kinds of businesses that can really leverage repeat behavior and retention (customer-centric model) are usually *different* from the industry, so using a benchmark (say, from Domino’s) is probably low-balling your potential.  

Not that Domino’s is a “bad” operation, mind you, but they are what they are, they tend to be more on the operational excellence side of the game than customer intimacy (that’s what we called the customer-centric / social approach back in the early 90’s). 

Product leadership, the 3rd value discipline, is pretty much table stakes for anyone in the restaurant biz, and I assume from your business description you just might consider this a primary focus which you then leverage to create power in the intimacy area.  This is essentially the Apple Strategic model.  If the product is not great, the love will not come.

My point is this: without understanding the value discipline and Strategy of a competitor, you can’t know if any benchmark is something you want to compare to, because the business may have a completely different focus than yours.  Worse, using industry averages simply hides any real information you might gain that is actionable for your business.

For example, even though Walmart and Nieman Marcus are in the same business, I don’t think anyone would say they have the same Marketing Strategy or core value proposition.  Walmart is of course the poster child for operational excellence with the end result being value pricing, which flows to the advertising content.  There’s nothing “wrong” with this approach, it simply is what it is, and customer intimacy / relational / social marketing simply doesn’t really fit here.  You certainly can try to be as intimate as possible; but it must be done within the constraints of the model and not reduce operational excellence.  Importantly, this is a “mass” concept, so Push media is the most effective.

Sam’s Club is an example of how one might accomplish this mix.  A “membership” is certainly more customer intimate and allows customized communication, a key component of customer intimate execution.  Again, this flows into the advertising content.  Sam’s gets to leverage the Walmart infra, so they can at the same time maintain a decent level of operational excellence.  Remains to be seen if they could do so without Walmart.

Nieman Marcus on the other hand uses a customer intimate value proposition, and their execution reflects that.  Value pricing is traded off for a high level of customization and personal service, where repeat business is very important since the number of customers this proposition attracts is smaller than the “mass” approach;  you have fewer, but each more valuable, customers.  In this model, mass media is not very effective because the audience is not mass; instead, you rely on the intimacy to Pull customers in, and much more of the Marketing budget is invested not in Advertising, but on in-store (employees, fixtures, locations) and individual communication. 

This relational or customer intimate model is the root of  ”social marketing” and why any attempt to turn online social activity into some kind of mass media advertising opportunity is a complete Paradox.  A step by step example of optimizing the relationship marketing / social model is here: Marketing Bands Series.  To optimize the social model, you divert Marketing budgets away from Mass Advertising and Push into Pull areas like Usability / Store / Interfaces / Packaging, Customer Service, and Customer Retention.

Given the above, would Nieman Marcus ever consider using Walmart’s customer retention rate as a benchmark?  I think not; this approach would make no sense at all.  The mass model can’t leverage customer retention because it’s not intimate; if you can’t act on the metric, why measure it?  This is not to say Walmart “doesn’t care” about repeat business, of course they do.  But they can’t really lever it because it’s more operationally efficient for them to use the mass approach.

That’s a very long explanation for why I dislike using industry benchmarks but many, many people don’t realize how important this idea is; it’s why on a core business model basis some companies will not be able to realize significant benefits from “going “social”.  So on the whole, I would much rather use internal benchmarks that I can improve on that are aligned with the business drivers and are controllable through my own execution.

From looking at your web site, I’d judge you a Nieman as opposed to a Walmart, so customer retention can be a powerful tool for you.  So let’s talk about measuring retention.

“Retention” is a very time-specific concept – over the course of 3 months?  A year?  Five years?  A 20% retention rate over a 5 year period and a 60% retention rate over a 3 month period might both be stunning achievements, if you know what I mean.

So, if you are able to do the analysis, I would pick some marks – 3 month, 6 month, 1 year, etc. – and see what you get for repeat buyer or retention rates.  The slope of that curve will determine where any danger points are that you might take action on.  

For example, if retention falls dramatically from 3 to 6 months, then you know that you should be watching for people who have not transacted in over 3 months, and for  those people you should craft mail / e-mail promotions designed to bring them back.

As often happens with restaurants, there’s probably a good chance that if the person is still living in the area (more on this below), the reason they are not coming back is probably  controllable – they had a bad experience.  A promotion like “We’ve missed you” or “Give us another chance” that is tightly targeted to known defectors will usually pay back quite handsomely in both the short and long term. Defected customers not only visit once on the promo but also (hopefully) have a better experience and re-engage as a repeat visitor.  If your value prop is customer intimate / social, you absolutely must invest in superior customer experience so repeat experiences are rewarding.

If you see some success with this approach, you could then fine tune the analysis to find out if the dropout has a peak in month 3, 4, or 5.  This fine tunes timing of your drop; the closer you can get to the behavior with the message the more effective the campaign will  be.  There is a “peak profitability” timing in one of these months.  

Then the program can be automated, for example: if we don’t see a transaction from this person for 120 days, drop the message.  This way, you end up mailing every month but the audience is completely different and very highly targeted each and every time.  You will find this “right message, to the right person, at the right time” approach is much more profitable than mailing all customers because it directly leverages the customer intimate value prop.

Speaking of mailing all customers, the people who are still active within this 4 month time frame are probably still loyal and you can improve overall margin by not sending these special promotions to those people until they “slip” out of the 4 month window.  There’s no reason to discount to people who are highly likely to purchase anyway.  This is the Pull part of a relationship or social  execution.  What you should be really concerned about are the people who are dis-engaging, where there has been product or service failure.

In fact, in a relational marketing scenario, there is no real need to market to these people at all, you’re basically “preaching to the choir” (example) and doing so is a waste of resources (and often margin).  You will be far better off taking the money you used to spend marketing to the choir and allocating it to in-store, core value proposition ideas.

Many marketing people (especially of the Push variety) find this difficult to understand, but there no more powerful Marketing tool than your value proposition when communicating to the active customer base.  It’s why they are coming back, your Pull is already strong with them.  Why beat them over the head with messages when they are telling you by continued transacting that they like what you are doing?  Wasteful.  (more detailed example)

Finally, in a location-based scenario such as restaurants (and since you are the CEO and not running a single store), you might consider factoring in local uncontrollable churn into any metrics you create as internal benchmarks.  

Households in different areas have different natural churn (move) rates.  Since you have stores in different states, for example, one would expect a lower retention rate from stores that have a higher natural household churn rate.  These stores might be doing very well with controllable churn (product, service) but without the household churn adjustment, they could be unfairly benchmarked “bad”.  HH churn numbers are generally available free from city / state government or the Census.

Hope that helps!

Jim

Note to blog readers: Do you see the parallels above to a lot of what is going on in online publishing / advertising / marketing?  If not, see Jonathan Mendez’s Reaping the Ads You Sow for a more direct analysis of the same concept online.  The strength of the web is in Pull, in converting demand, not Push or creating it.  Use offline for Push; that’s what it’s good at, and synch the two to optimize the entire Marketing ecosystem.

Have a question on Customer Valuation, Retention, Loyalty, or Defection? Go ahead and send it to me here. If on the topic above, you can leave a comment on the post:

Relational vs. Transactional

When you’re in the business of measuring the effects of Marketing programs, certain patterns begin expressing themselves over and over.  One of the oldest in the contribution to success of various parts of a Marketing effort, sometimes called the 60-30-10 rule:

60 percent of success is determined by the audience quality
30 percent of success is determined by the offer
10 percent of success is determined by the creative

Where do these stats come from?  Continuous improvement testing.  Over the years, if you run a lot of different tests, you just begin to see this pattern.  And the pattern holds across a very wide variety of business models – online and offline.

The key takeaway here: audience quality is the most important component of success in a results-oriented Marketing campaign.  This is why the CPM’s for niche Magazines, for example, are so high.  These Magazines are tremendously efficient marketing vehicles because they have high audience quality, which drives end behavior – results.

And the primary reason the audience quality is so high?

People pay for these Magazines.  When people pay for something, they value it with more Attention. Why? Simple.

In a magazine like Hot Rod or Concrete Decor or Vogue, the percentage of content that is interesting to the niche audience is very high. In fact, the Advertising is viewed as content.

Smaller audience, very high quality. Ads work like gangbusters.

Clearly, there are other ways to run a media model.  At the opposite end of the media spectrum, there is free.

You produce content that appeals to a very wide, least common denominator audience, one where very few people are interested in the Marketer’s product at any particular time.  But because the advertising is so cheap on a CPM basis, this media can be effective for products with extreme distribution and universal demand.

That’s why creative is more important in broadcast.  Because in Broadcast, there’s a very small number in the success formula above where the 60% for audience quality used to be.  All you have to work with is offer and creative, because the audience quality stinks by definition – it’s a broadcast, and Reach is the driving metric, not Quality – you have to turn over a lot of rocks.

Enter the Web.  Just think about it for a second.

Given the two media models above, which model will most likely succeed in an environment like the web? Where the very nature of the usage is defined personally?

I don’t know about you, but the “Broadcast” model on the web just makes no sense to me; it’s anti-consumer behavior. And Behavioral Targeting is the right idea using the wrong tool – we should be creating platforms for customizing content, not advertising delivery. It’s back-asswords.

My old boss Barry Diller thinks a paid model will succeed, one more like magazines. And that makes a lot of sense to me. Not that there won’t be free content.  You will always be able to read free content from:

* People who have opinions about a certain topic, whether very insightful or clueless

* People pitching you to buy something, whether the pitch is overt or “social” in nature

* People who are trying to build a reputation for themselves or a company, deserved or not

The real question for this free segment is, what advertisers will want to reach these audiences?  The answer, if there exist paid content sources with quality audiences, is nobody but the CPA folks.  And that will probably put a lot of the free content operations out of business.

Because the Brand folks, the ones with the big money, will go to where the (paid) audience quality is, because that model works for them. This is not about online or offline, the transmission mode of the content is irrelevant.

It’s about advertisers wanting a quality audience. Just like what happened (over time) with Cable TV versus Broadcast. Smaller, niche audiences dramatically improve advertising performance.

But for this paid content model to work, it will also have to be about people not wanting to waste so much time combing through the crap to look for quality content.  It will be about the Net Meaningful Audience, the people who self-define their interest in a topic by their willingness to pay for it.

Meaning a much smaller, but much, much more profitable audience for many web sites. If the site-centric model survives. Vertical sites and networks seem like the right idea, but in practice people just go buy Reach, so they trash the model, turning it from Cable right back into Broadcast in terms of ads as content.

Or, someone like Google will finally make micropayments work.

If you can think past the tool to the behavior, I bet you might see why this could ultimately be the best idea for Display – essentially, the aggregation of a personal Magazine you pay for, article by article. A magazine like Hot Rod or Concrete Decor or Vogue. One where you get the best content on your topic from any source and you want to read every bit of it.

One where the Ads become content. Like they are in Search.

Kind of like the way people buy songs instead of albums, and create a personal collection of only songs they like?

Look, I know content wants to be free and all that.

The problem is, most of that content is worth very little from an advertising perspective, it lacks audience quality. And let’s face it, most of the real investigative reporting and expert commentary is generated by the offline media, which online simply passes on.

And that’s fine too, but this work has to be paid for somehow.

If online ever expects to get “its share” of the media budgets out there, what’s needed is a second tier for Display to pay for this work, whether the work is done by an offline or online entity.

One like Cable on top of Broadcast. One where you get access to high quality content before anybody else. One with a focused, high value audience advertisers will drool over.

One where people expect the ads and read them like content.

Then the market will bifurcate, just like Cable and Broadcast, and you choose which online tier to use based on your business model.

Micropayments are not just about paying for content, folks.

They’re about delivering a high quality audience that won’t have the slightest problems with also viewing ads – because for virtually the first time on the web, the ads will be content.

Have a question on Customer Valuation, Retention, Loyalty, or Defection? Go ahead and send it to me here. If on the topic above, you can leave a comment on the post:

Net Meaningful Audience

I say weakest because Advertising cannot fix a poorly thought out Product, Pricing Strategy, or Distribution system.  It just can’t.  Yet huge amounts of money are wasted trying to do exactly that.

Perhaps this why someone feels they need to publish a book that tells people Product is important in Marketing.  To me, that’s the most circular or redundant idea for a Marketing book I’ve ever heard.

Marketing starts with Product, which should include all the audience or market segmentation studies (People) that drive the creation of the Product - defining the need.  If you do this first and develop a Product which truly fills the need, AND you get the Pricing and Distribution right, the Product will literally sell itself to the core audience.

If you can make it that far, THEN the Product can perhaps be sold to the next segment out from the core through Advertising.  All “Marketers” should know this.

So why is the online Marketing space so focused on Advertising?  I can think of a few reasons:

1.  Most online Marketers don’t really have a Marketing background, they come from Advertising or the Technology work closely tied to online Advertising.  Since all they do is online Advertising, the distinction doesn’t matter.

2.  In some companies, Marketing has been “downgraded” as a Strategic function to become “Marcom”, which isn’t really Marketing, but Communications - Advertising + PR.  So the people working in “Marketing” simply are not required to have Marketing skillsets.

3.  Many people just have no idea that Advertising and Marketing are different; they simply don’t know.  This often creates confusion, because concepts that seem brand new to Advertising people are often well understood by Marketing people, even though Advertising is a part of Marketing!  Examples would include any concept or activity considered “customer centric”.

I have in the past encouraged people in online Marketing to audit some Marketing classes and find out what the bigger picture is, because if you understand how all the pieces fit, you end up being a much better Marketer.  This is especially true with online where there is so much integration and measurement.  And when we get to real offline –  online integration, understanding the more global concept of Marketing becomes really important.

No time for classes?

If you’re interested in broadening your understanding of Marketing, there is a book that provides a framework for understanding the Marketing issues you are not familar with but skips over a lot of detail you probably do know about Advertising.  It came out a few years ago and I think it was largely missed by the online community whose primary focus was (is?) Advertising.

Now that the web is coming together nicely as a channel, perhaps it would be a good time for online Marketers who lack a Marketing background to pick up a copy of Marketing Champions and learn what’s ahead for you: owning the customer relationship, company-wide.

That is, if you decide Marketing is the career path you wish to follow.

There is so much more to Marketing than Advertising.  If you really want to change the outcome, transform a company, Marketing is the medium.  Advertising is just the message.

Note to Web Analysts: You will find Marketing Analysis – Customers segmented by Product Affinity, Channel Preference, Service Experience, etc. at least as exciting as Traffic Analysis!

Thoughts?  Any other reasons why the online Marketing space is so focused on Advertising and ignores the rest of the P’s?

Have a question on Customer Valuation, Retention, Loyalty, or Defection? Go ahead and send it to me here. If on the topic above, you can leave a comment on the post:

The Other 3 P’s

This area of  Psychology is more generally referred to as Cognitive Dissonance.  Along with Norms of Reciprocity, Dissonance is one of the most important pieces of Psychology for today’s Marketing folks to understand.   This is doubly true if you are serious about using a two-way Social model in Marketing.

Here’s why:  The Social sword has two edges.  If you are going to use a two-way Relationship Marketing approach, you will create higher expectations with those who Engage.  If you fail to perform, or just act like an Advertiser would, then you will end up creating more damage than if you had simply ignored the two-way idea.

For Marketing, the important idea to understand is the human brain always questions actions taken, however briefly, and tries to resolve conflict.  Any unresolved conflicts tend to taint the action, they create Friction, and drive down the Potential Value of the experience.

The important action item for Marketers is to know this will happen beforehand, and take steps to counteract the Dissonance.  The result will be customers who have generally better experiences, and you know what that means, right?

In other words, by planning for Post-Action Dissonance you are using a Prediction that increases Profits or cuts Costs down the road.

For example, in the early shopping carts, there was rarely any “confirmation” of a successful transaction.  Merchants found over time this made customers uncomfortable and caused additional customer service load.  When the confirmation was added, a lot of these service problems went away and satisfaction rose.

A “discovery” of sorts, but totally Predictable, if you understood the concept of Post-Action Dissonance and planned for it.  Other examples from the Lab Store, including the increased Profitability that comes from Predicting Dissonace, can be found here and here.

In fact, think about this: many of the online “discoveries” that have to do with Marketing usability and performance - use of headlines, copy treatments, landing pages, pathing / navigation, button layouts, location signaling, all of it – are rooted in the Psychology of Post-Action Dissonance.  And the web is full of these opportunities, because it’s a remote environment, often lacking a feedback loop.

Post-Action Dissonance tells you these lessons can be applied offline as well.  It’s not about the channel, it’s about the receiver – humans.

Humans must, they have a drive to resolve the outcome of an action taken with their expectation of taking the action.  This is an incredibly powerful idea to know.  Next time you are designing a Campaign, Interface, or System, keep Post-Action Dissonance in mind.  Why?

Just think about how much proft will be lost when Post-Action Dissonance has to be “Discovered” rather than being Predicted.

Have a question on Customer Valuation, Retention, Loyalty, or Defection? Go ahead and send it to me here. If on the topic above, you can leave a comment on the post:

Post-Action Dissonance

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.

The Gratitude cycle doesn’t depend on what the technology is, or if there is any at all.  If anything, technology simply extends the number of humans you can engage in reciprocal behavior with.

I first heard of this theme back in the 1970’s related to the CB radio communities, and it existed before that in ham radio.  Since then, we have been through Compuserve Forums in the 80’s, message boards as early as 1985 with The Well, then e-mail discussion groups, to hybrids like Yahoo Groups, and on into Social Media. 

And in every case, the same rules of successful interaction within these communities always applied, even though the technology was different.  No matter what communications technology the “community” uses, humans find a way to organize it with certain rules. 

And the primary driver of these rules is always Norms of Reciprocity.  Give, then Take.  The rules of successfully participating in any of these communities have not changed at all.

In fact, these reciprocity norms define the meaning of ”community”.  If a “Give, then Take” attitude is not present in a message to the community, then what you have is a message called Advertising.

Advertising has no “Give”, only “Take”.

What does all this have to do with Marketing?

In mass Advertising, it’s extremely difficult to measure the effects of a campaign at the level of Individuals.  You can measure the effects on an Audience as a whole, but not on Individuals.

But when you can measure the impact on Individuals, as you can in many forms of Direct Marketing and on much of online Advertising, now you have the ability to step through a doorway and take advantage of human behavior, including Norms of Reciprocity.

And I think this is where people are getting stuck, including the proponents of everything Social. 

These folks are trying to use Audience measurement models to define the success of (Social) Campaigns targeted to Individuals.   “Social Media” is an oxymoron; it can’t be Social and Media at the same time.

The bottom line is, if you are going to embrace a two-way Social model in Marketing, you must measure the success of this effort differently.  Impressions, reach, size of audience, none of that matters in a model where Relationships - driven by Reciprocity – are the goal.

The above metrics are one-way, broadcast advertising measures.  If “Social” or “Relationships” are to be Marketing models, what’s needed is a way to measure a 2-way exchange, a Relationship.  If it’s the Relationship that’s important, why would you use a “media metric” to measure success?  What you need is a social metric.  A  measure rooted in Psychology, one that addresses Norms of Reciprocity directly.

The question you are trying to answer in Relationship Marketing is not “how many people did I Reach”?  “Influence” or any version of Reach is a crap metric in a Social model; it’s measurement for the sake of measurement.  If it’s Reach you are pegging to, then you’re not Social, you are Media, you are All Take.  There is no Exchange in Reach; Influence is a Social metric Paradox. 

There’s nothing wrong with being Reach-based entity, but just stop calling it Social.  You’re a broadcast tower, a magazine, a newspaper.  Un-Social; Media.  Personally, I don’t think it’s a very good business model, as I said several years ago, unless it goes hyper-vertical to provide context.  That means admitting the “Social as Media” business is much, much smaller than everyone thinks it is.

But let’s say you truly want to be a Social entity or use Social techniques to faciliate Marketing.  Then the real question you need to answer in this Relationship Marketing scenario is: What is the state of my Relationships – Growing, Strong, Weakening, or Failed? 

Why?

Because unless to can define “state”, your Social Marketing efforts are not actionable and you are simply Media.  What you need to know to make Social Marketing work is this: How likely are people to interact with me in the Future?  Because if you know the answer to that question, then you can take the appropriate action against a Growing, Strong, Weakening, or Failed prospect or customer state.

That’s a Relationship.  It’s about the future, not the past.  It’s about Norms of Reciprocity; what I do for or with you today defines what you are likely to do for or with me in the future.  The past is over with; the most important issue is this: where’s the Relationship going?

The question you need to answer in a Social Marketing scenario is not “did they interact with me”, because that’s in the past and there is no Social Power in the past.  The power of Social, the value of ”Give, Then Take”, is in Tomorrow.  Right?  Potential Value.  How much Reciprocity have I earned, what is the Value of this Gratitude in the Future?

The power of Social is not in how many connections you have.

It’s understanding how to make important connections more valuable.

Fortunately, if you use metrics from Psychology rather than Media, the Value and State of your Relationships – Growing, Strong, Weakening, or Failed - are metrics that are not very difficult to measure (example).

Using these Values and understanding Reciprocity, you can then leverage Gratitude and create campaigns that not only Surprise and Delight customers but make a ton of money at the same time.

When you see how well that works, you will want to start segmenting by Relationship State instead of by demographics or other non-Social “Media Metrics” to increase profits by reducing Relationship Friction.

Once you start seeing the cause and effect of true Social or Relationship Marketing, you might even get good enough to see the value of correcting Relationship mistakes before they happen.

Social = Relationship, Relationship = Psychology, not Media.

If you want to do or be Social, then by all means, get on with it already.  There’s already a Model for all this as it applies to Marketing and this model drives profits.  The measurement of success in Social is not unknown and does not require continued mystical thought grazing.  It simply requires you to decide if you are in fact a Social entity and not in reality a Media outlet with fancy new clothes.

If you are starting up a Social entity, the phrase “Norms of Reciprocity” is your gateway to decades of research and testing on humans as Social animals.  This knowledge could save you years of iteration.

If you are already a functioning Social entity, stop gazing into that navel of yours and start publishing quantifiable Metrics from Psychology and Sociology, not Media.  You’ll soon find out whether that Social thing you are doing has Marketing value to anybody or not.

If you are a Marketer trying to leverage the Social in all of us to create and strengthen Relationships, stop looking at Social like Media and demand your vendors do the same.  

Then everybody can skip the million monkey iteration thing. 

Your thoughts on the above?

Have a question on Customer Valuation, Retention, Loyalty, or Defection? Go ahead and send it to me here. If on the topic above, you can leave a comment on the post:

Norms of Reciprocity

Want to see the answers to previous questions?  Here’s the blog archive; the pre-blog newsletter archives are here.

Q:  First of all thank you for your help.  I have some questions I would be pleased if you answer them for me.

A:  No problem!

Q:  1. RFM analysis – is it possible to use some other ranking technique rather than quintiles? Using quintiles for bigger databases will cause many tied values, isn’t it a problem?

A:  Sure, you can use it any way it works best for you.  There is no “magic” behind quintiles, you can use deciles or whatever works best. It’s the idea of ranking by Recency, Frequency, and Value that is the key concept in the model.

I’ve seen dozens and perhaps hundreds of variations on the core RFM model, depending on how you classify a “variation”.  One change that’s common is changing the scaling, as you mention above, to accommodate the size of the database.  Smaller databases use quartiles or even tertiles.  Larger databases, choose the ordered distribution that meets the need.

A more common modification is to convert “M” to different types of “value” depending on the business model.  Instead of Sales, people fine-tune the financial side by using Net Sales, or Gross Margin, net out discounts, etc.  Or they use non-sales representations of value tuned to the business model – ad revenue per visit, total days of activity, that kind of thing.

Further, what can happen is the analyst or marketer will begin  to see patterns underlying the RFM cells – in sales, response, location, merchandise, source, or some other customer variable.  This leads to cross-tabbing RFM score with other variables, and discoveries are made which lead to customized versions of the RFM model.

For the most part, I envision this work really as segmentation, meaning the scoring is not really modified – it’s the population the scoring is run on that is modified.  So for example, you run separate RFM scores for customers who are primarily  hard goods buyers versus primarily soft goods buyers.  This approach to scoring is sometimes referred to as RFM-C, where C = category. 

Or for large, ongoing campaigns, you can cross-tab RFM score by source of the customer.  This leads to “weighting” the value of campaigns not by Sales or Response, but the long-term profitability of the customer – you see campaign sources “clustering” in high or low RFM scores.  Some campaigns generate weak customer profiles, but the volume justifies doing them, as long as they are kept “reigned in”.  Other campaigns generate high value profiles who are “slow starters”, and might be killed if you only looked at Response and not RFM Score.  So the scores begin to play more of a role as a “standard” way to view customer value across categories, campaigns, channels, etc.  

This approach to scoring can eliminate a lot of the “gut feel” legacies that can happen in marketing and merchandising.  Sure, go with your gut, but let’s use a standard way to compare the results of your gut feel and produce a “gut check” comparison.

Q:  2.  I am planning to add user complaints and suggestions to RFM analysis.  Each complaint will decrease the user score and then cause to organize promotions just for users who had a complaint recently.  Is it a good approach to add it to RFM analysis?  (I am not sure but some are using this method.)

A:  I’m not exactly sure I know what you mean by “add”, but I think I get the gist of what you’re trying to accomplish.  In fact, this project sounds like an example of a company actually trying to “do something” about customer engagement and experience instead of the usual navel-gazing.  I have done these kinds of “apology campaigns” before and they can be very profitable, especially for most valuable or highly engaged customers.

The scores only are predictive on a single behavior being scored, so I would not involve 2 different behaviors (purchase and complaint) in the same score, since the result would be defeating to the purpose of the score.  I would not “adjust” a score directly based on a different behavior; I would score this behavior separately – and then use the scores in tandem to make adjustments in execution.  If you really want to use multiple behaviors simultaneously in a model, you need to move up the modeling food chain to regression.

As an analyst, you can of course “add” to the RFM scores any way you wish.  You can add any characteristic as a “tag” to a score but I would not involve these characteristics in the scoring itself, unless they *are* the score.  But from the perspective of a Marketing person who has to use the scoring, I would not want you to “corrupt” the scores themselves, but rather to segment by other variables and then examine and use the scores to act.

For example, if these complaints are in the customer account, you could score the customers on some other behavior such as purchases and include the RFM score in an account field, then cross-tab score to complaints.  For example, “Give me every customer with a high RFM score AND at least 2 complaints”.  Or lever off the complaints, “Of customers with at least 2 complaints, what are their RFM scores?”

Or, as discussed above, the complaint idea is an opportunity to create a custom RFM-style score for complaints.  Recency and Frequency are still important, but there is no Monetary Value.  Time frame may also be different for complaints than purchases, for example, past 30 days or past 3 months as opposed to a full year or longer.  You could generate this “RF” score and then use it in combination with the RFM score to drive different messaging to people by both:

1.  How engaged they are in some behavior

2.  Intensity and level (overall Frequency) of complaints, where the more Recent a complaint has been made, the more likely it needs to be addressed in some way.

Customers with high scores in both areas would be both most valuable to the company in the future AND at highest risk for defection.  This is, of course, an extremely valuable target from a Marketing perspective and one that should be addressed with great care.  Sending these people “normal” e-mail communications, for example, is much more likely to accelerate to defection than retain the customer.

Depending on your business model, for these highly valuable and likely to defect customers, you might want to skip e-mail or snail mail and get the President of the company to phone them!

Have a question on Customer Valuation, Retention, Loyalty, or Defection? Go ahead and send it to me here. If on the topic above, you can leave a comment on the post:

Hacking the RFM Model

The latest shiny object where Coupon Proneness comes into play is the “shopping cart recapture” program.  Mark my words, if it is not happening already, these programs are teaching customers to “Add to Cart” and then abandon it, waiting for an e-mail with a discount to “recapture” this sale – a sale that for many receiving the e-mail, would have taken place anyway. 

The best way to measure this effect is to use a Control Group.

When I hear people talking about programs like this (for example, in the Yahoo analytics group) what I hear is “the faster you send the e-mail, the higher the response rate you get”.

That, my friends, is pretty much a guarantee that a majority of the people receiving that e-mail would have bought anyway.  Hold out a random sample of the population and prove it to yourself.  There is a best, most profitable time to send such an e-mail, and that time will be revealed to you using a controlled test.  The correct timing is almost certainly not within 24 or even 48 hours.

That is, if you care about Profits over Sales, and trust me, somebody at your company does.  They just have not told you yet!

When you give away margin you do not have to give away on a sale, that is a cost.  Unless you are including that cost in your campaign analysis, you are not reflecting the true financial nature of the campaigns you are doing.  If you are an analyst, that’s a problem.

If you are using cart recapture campaigns, please do a controlled test sooner rather than later.  Because once your customers have Discount Proneness, it will be very painful to fix.

For that matter, if you are an online Marketer in a multi-channel company, you should be regularly using controls because they are the gold standard in Marketing Measurement / Campaign Attribution.

At some point, your boss will be more concerned about attributing Profit than attributing Sales.  Would be nice if your response to this question was, “Yea, we’ve been looking into that”, wouldn’t it?

If you’d like to hear more about this topic and see some example data on what these scenarios look like, you can attend this webinar:

What Online Marketers Can Teach Offline Colleagues (and vice versa)
May 19, 2009  noon ET     Jim Novo, Kevin Hillstrom, and Akin Arikan

A WAA event, open to both members and non-members.  Web analysts are not the first to grapple with multiple channels.  Traditional marketers have always had to illuminate customer behavior across stores, call center, direct mail, etc.  So, rather than reinventing the wheel in each camp, what proven methods can you teach each other?  Three different but aligned approaches on solving the multichannel puzzle, should be something for everyone here.

Have a question on Customer Valuation, Retention, Loyalty, or Defection? Go ahead and send it to me here. If on the topic above, you can leave a comment on the post:

Got Discount Proneness?