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

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 the excellent book!  I’m really excited about digging into our own customer data to see what we’ll learn.

A:  Thank you for the kind words!

Q:  However, when you’re creating the RF Scores, what is the standard timeframe you should use?  I have access to about 5 years worth of purchase data – should I create RF scores based on the last 5 years, 3 years, 2 years, 6 months?

Our sales are quite cyclical, so I think the baseline should probably be at least a year, and I’m considering doing two years.  It seems as though if I get too much larger than that, my results will be too watered down. 

I’m also planning on generating “historical” RF scores by filtering my data to reflect the purchases only up to a certain point.  So, to generate a Q1-09 score, I’d create it from sales data of Q1-07 through Q1-09.  The Q2-09 score would be from Q2-07 through Q2-09, etc.  Does this make sense?  It will allow us to see the changes that have been happening in our company even though we’re only just now looking at the data.  It will give me a picture of what it would have looked like, had I looked at it back then.

A:  I think you have accurately understood the situation and have the right approach!  This type of analysis is very sensitive to time frame.

There are really 2 broad types of customer analysis.  There is analysis for action in the present, a Tactical approach driving towards a “we should do this now” result, and the more Strategic analysis, which is informational and says “this is what we should have done then” and / or “this is why we should make these business changes”.  The shorter time frame is Tactical, the longer timeframe Strategic.

So, for example, a 2 year timeframe could give you the answer to this question: which of our best customers are becoming unlikely to buy from us again?  This leads to immediate activation of some kind of marketing outreach or discount / incentive program to get another purchase from this group.

Add a timeframe that ends 4 years ago, then one ending 3 years ago, then one ending 2 years ago could highlight changes in the business over time, for example, best customers with high intent to purchase 3 years ago clustered in certain segments or SIC codes; now customers with this same definition are clustering in different segments or SIC codes. You will see migration of segment focus, if any.

Another way to think about this is time frame for the RF analysis determines sensitivity to new customers.  Long time frames tend to rank customers who have been with you a long time higher than new customers; this is just a function of how the ranking methodology works – these long-term customers have had more time to increase the Frequency or Monetary component.  This can mask important rankings in Frequency with newer customers, what you might call “future best customers” or “up-and-comers” who are accelerating their purchase behavior.  These folks are ideal targets for soft recognition-style rewards (not discounts) – VIP treatment, bonuses, etc.

You could even use this kind of analysis to prove the strengths (or weaknesses) of the RFM methodology for your business: given an RFM score of XXX 3 years ago, what behavior did the customer engage in during the following years?  Does the score in one year predict behavior the next year?

Or, perhaps rather than a ranking approach, the fixed activity threshold approach (like LifeCycle Grids) is more appropriate to our business.  LifeCycle Grids are basically the same idea as RFM, only sometimes more accurate for businesses with known cyclicality; it’s easier to build that cyclicality into the model if you abandon “ranking” and use thresholds.

In fact, this idea was born from an exercise like the one you propose: let’s re-score and re-rank customers each quarter, and track the RFM score over time.  Nothing wrong with this really, except there is the fundamental problem of scores changing due to outside influences, for example, a large new customer campaign.

When such a campaign is executed and then the database is re-scored, the RFM scores of customers can change even if their behavior has not because you are re-ranking a customer file that has changed in composition. Due to the new customer campaign, it is now “heavier” with Recency = 5 customers, which can push down the other customer scores even though behavior has not changed.

This is the primary reason I invented the LifeCycle Grid idea.  If you use thresholds or Hurdles for behavioral segments rather than ranking, the “score” of someone does not change when the database composition changes.  Someone deemed “best” and likely to buy if R = 30 days and F >= 25 purchases is still “best”, no matter how many records you add to the database.  These thresholds define the customer status by putting them in a fixed position box on the Grid, not a ranking.

And that is why RFM tends to be used as the Tactical, “we are doing a campaign right now” valuation method, and LifeCycle Grids tend to be used for the more Strategic analytical exercises.  However, the Grids can also be used for Tactical execution.

For example, any customer with F >= 25 over past 2 year period, who drops in R past 90 days, automatically should receive a call from their salesperson.  These reports could get run on a weekly basis, and of course can be segmented many different ways depending on the population you run through the Grid.  Because you’re using thresholds rather than “ranking”, a customer will appear in the Grid at the same location no matter what the size or segment of the input population.

So for example, you can run only customers  who responded to a campaign and see where they end up in terms of Recency and Frequency over time.  With a series of such runs, say monthly, you can create a “movie” that shows the evolution of the customers over a time frame and begin to judge the long-term effects of certain campaigns.  An  example of this approach is here.

Overall, I like the Grid approach much better.  Not only do you avoid the “population problem” of ranking when using RFM, but you can use the same approach over and over (good for management understanding) for many different kind of analysis, both Strategic and Tactical depending on needs.  You can use all kinds of visual aids such as color in the grid to represent different segments or campaigns, making presentations much easier for management to understand.  Decision making with execs can be much more of a challenge when all you have is RFM scores.

All that said, RFM is still probably the easiest  approach for specific, usually campaign-related tasks such as predicting campaign response or profitability.  Same data but a different, more short-term oriented way to look at the world probably best kept out of the boardroom but still has a place in the analyst’s toolbox.

Hope that helps!

Jim

Questions?  Does anyone think there is value in predicting which customers will become best customers and which customers are defecting, by campaign source or product line?  If you knew this information, could you act on it?  Would management care about this?

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RFM versus LifeCycle Grids

This research dovetails nicely with a lot of the topics discussed here on the blog lately, so I thought I’d use it (with a nod to Godin’s post on Strategy vs. Tactics today) to provide some fodder for thought.

First, the importance of Psychology in Marketing.  So many of the “discoveries” arrived at through  brute force testing of Online Advertising are already well known in the greater discipline of Marketing through Psychology.  For more on this read “The Other 3P’s” and if you’d like to do something about lack of knowledge in this area, make sure to read this comment on source books.

Second, this research is a great example of isolating the true drivers of behavior.  The idea of looking at baby names to isolate the real behavior from “technology and other commercial effects” while including “symbolic meaning about identity” results in a broad, Strategic-level answer to the question, not a Tactical one. 

Why is this important?  It means the results can be applied across a host of different Marketing situations, rather than only a specific one. 

Much of the “research” done on web topics suffers horribly from pointing to rare, specific successes as a model for everyone else to follow.  Might be OK for Advertising people, gives them a low risk excuse to play with a Tactic.  Useless for Marketing people, who have the Strategic need to describe results before they happen. 

For the analysts out there, Strategy is the Hypothesis.  Do you just create tests aiming for brute force pass / fail, or do you follow the scientific method and have a Hypothesis before you design the test?

Third, the whole issue of web business models, which always seem to be built on the concept of Quantity versus Quality as the Strategic vision.  These models are about the fastest growth rates, total sign-ups, and traffic.  The problem with this approach is this: it’s only really meaningful if “Reach” Advertising is the core business model. 

That’s where the trouble is: successful Advertising on the web is not about Reach and Audience, it’s about Preference and Individuals.  This is the paradox of Display Advertising in Social Media; it’s exactly the wrong approach as defined by everything people say is “Social”.

And, this is why you find that over time, almost every “new” business model that starts as some kind of a mass concept fails until it turns into a vertical concept – the exact opposite of the Quantity / Reach model.  By going Vertical, the model moves from Quantity to Quality and then often succeeds – by serving a smaller, select group of people with certain preferences, building Relationships.

Why?  Because, as stated in the Wharton piece, “the adoption velocity has a negative effect on the cumulative number of adopters”.  Begging the question:  Is your product more like a disk drive, that lacks any cultural identity?  Or is your product “in a domain where people use it to communicate to others” like Fashion?  Auto?  Decor?  Social Media?

The former begs rapid adoption, the latter, slower adoption.  Anything Social, it seems, would benefit from a slower adoption rate.  Paradox, again, right?  That’s the difference between Strategy and Tactics, the difference between Marketing and Advertising.

I can hear some of the cat-calls now.  Jim, we’re all about scale, the VC’s say we have to grow rapidly, it’s the way the business model works.  Network effects, you know.  Really?  Is a larger network always better than a smaller one? 

What if you (and they) are wrong?  What if the Reach model is the wrong one for the web?  After all, it’s an offline, one-way model.

What if rapid growth actually destroys the value of the business, by attracting the “me-to” crowd that abandons the trendy in favor of the new?  What if the early adopters provide a false read on what the important business drivers are, and in fact are your worst customers?

How many of those millions of accounts are dormant?  How long has it been since the early adopters came back?

What’s your Adoption Strategy?

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Adoption and Abandonment

Got a question about Customer Measurement, Management, Valuation, Retention, Loyalty, Defection?  Just ask your question.  Also, Feel free to leave a comment.  Want to see the answers to previous questions?  Here’s the blog archive; the pre-blog newsletter archives are here.

Q: I received this article (Norms of Reciprocity, measuring value of Social Marketing) via a friend’s Twitter account.  Very interesting.

A:  Glad you enjoyed it!

Q:  It has made open up my ACT! database, and my Outlook databases and add the metric of Growing / Strong / Weakening / Failed to my normal Sales and Business progress metrics.  If I group those categories and correlate to traditional metrics, it’s impressive how they reflect each other.

A:  Yes, most people are surprised.  It’s a very, very simple idea that seems to work across just about any human activity including crime, attendance, and so forth.  

The more Recently someone has done something, the more likely they are to do it again.  Conversely, the longer since an activity last took place, the less likely the person will do it again.  Often called Recency in Psychology and studied quite a bit.

Q:  Now I have to think about how I really use and apply this. : )

A:  Well, if I can guess you are in Sales from your title, typically one of the best applications is in what Strategic Marketing folks might call “allocation of resources”, which probably translates into “lead nurturing” for you.

Most experienced people in Sales have a sort of “sixth sense” when it comes to thinking about the likelihood of a close happening.  They worry about certain prospects more than others, and a sort of “ranking” or “scoring” happens in their mind.  One of the triggers that frequently comes up in this is “how long” it has been since there was any contact activity with the prospect, and the feeling the longer it has been without sales activity, the less likely the sale is to close.  Sales Managers will often allocate resources based on these kinds of “feelings” they or salespeople have.

The problem with all this “gut feel” is, newer sales people don’t have it, and so probably are not as productive as they could be.  The other is since a lot of this is not tracked in any way, there aren’t any firm “guideposts” and it may be that sales are lost that otherwise could have been made due to a lack of urgency or misdirection.

So, given limited resources, a sales force would generally like to focus on the leads most likely to close, and not work on the less likely leads until the most likely leads have been addressed.  This is the idea of scoring, let’s rank all of our prospects by likelihood to close.

Now, as far as what you might do in ACT! or similar (and knowing nothing about your business), here is what I would do.  Just start informally comparing prospects that close and those that don’t close in terms of these timing issues, “how long since contact” or “how long between contacts” for each case.

Typically you will start to see patterns of some kind, for example:

1. “Prospects who have not made it to 2nd sales appointment within 30 days of 1st contact are less likely to close”

2. “Prospects who take longer than 25 days to respond to proposal are less likely to close; prospects who take less than 10 days to respond to proposal are very likely to close”

and so forth.  Look at important events in the sales process and note the “time since” or “time between” and look for such patterns.

Now, as I said, many salespeople, especially experienced ones, have some sense of these ideas, but they have never been quantified. The advantage to quantifying them like this is you can move to a “triggered contact system” based on them, which I think you can do in ACT! if you have the data.  This conserves salesperson resources and helps them always be focused on where they are most likely to close the business.

So, for example, salespeople (sales managers, if more appropriate) receive a communication each day about any prospects who are coming close to any of these triggers above.

In scenario 1 above, a counter starts on 1st contact and if another sales call has not been scheduled within 20 days of 1st sales call, a reminder goes out saying “you have 10 days to get a 2nd appointment or you may lose this sale”.  In scenario 2 above, sending the proposal triggers the counter, and a sales contact is suggested at 7 days later and 15 days after that.

The optimal timing of these contacts is something discovered over time, and of course depends on the business. But having these triggered messages available to guide salespeople towards which contacts they should be most focused on that day or week is a lot better than nothing.

So instead of a salesperson thinking this:

“Gee, it’s ‘been awhile’ since I talked to prospect George. Maybe I should call him”.

you get this thought:

“I sent the proposal to prospect George 7 days ago, and I need to close him in 3 days, or he becomes less likely to close at all.”

The difference in those two thoughts and the action taken can be a lot of sales – especially with newer sales people, who don’t have enough experience to understand the “rhythm of the sale” in this specific business yet.  If you’d like a more detailed example, there’s one here: B2B Software – Latency Tripwire.

Spreadsheets are usually a great tool for this kind of discovery work.

Hope that helps!

Jim

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Lead Scoring and Nurturing

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?

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Norms of Reciprocity

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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?

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

Q:  Most CRM experts agree that discount is a terrible way to attract new customers.  They seem to all agree that these “transaction buyers” are money-losing customers and have no loyalty.

A:  I think using discounts profitably for customer acquisition depends a lot on your “Brand Personality” and your business model.  That said, often people screw this up and attract the wrong kind of customer.

Q:  But, I have seen a  lot of different opinions on the use of discounts to increase loyalty and retention among current customers.  I have seen experts contradicting themselves on this subject saying that discount is a terrible way to reward gold customers or to move up customers to a “better segment” and after some time they contradict themselves mentioning a successful discount case study (points are a common method used).  Jim, what is your opinion about using discounts as a weapon in a retention program?

A:  First, we have to define “discount”.  Price discounts have the effect of reducing margins, but so do “better service” ideas like “VIP phone lines” and loyalty programs.  So you can take your discount on the top line or the operational line, the fact is it costs money to provide good service to best customers in hopes of keeping them.  I mean, what’s the $10 million you spent on a CRM system?  Choose your poison, it costs money to retain customers.

The real question is this – can you make money doing it, in any of the above cases.  If by giving a customer a discount I increase their overall profitability, in excess of what I lose on a discount, then I made money.  Same with the costs of a loyalty program, a rebate program, a newsletter, a special room, a lead management system, etc.

End of story. Whatever you do, it has to make more money, or it’s silly.

Problem is, most people don’t know how to *measure* any of this properly.  This is the topic of the Chapter in the book “Expense and Revenue You May Not Be Capturing” (Ch 29).

Discounts aren’t bad by themselves.  What screws people up is not offering them at the right time to the right customers with the right value of the discount or operational expense.

Discounting to best customers can be very dangerous – something most people don’t know, let alone measure correctly.  You can lose money very, very quickly.  I often rail against this; you have to understand subsidy costs and how to measure them or you get burnt very quickly.  I got burnt for over $1 million in a single promotion doing this – and it was the exact same promotion I made over $1 million on 6 months earlier.  Difference?  LifeCycle stage of the customer.  Or, if you prefer, the process of dis-engagement.

Here’s a real world example.  *ACTIVE* (engaged) HSN customers spent about $320 a month, buying 8 $40 items.  If I send them a coupon for $10 off, they spend $310 buying 7 items at $40 and one $40 item at $30, so I lose $10 *plus* the cost of the promotion.

This is subsidy cost.  They would have bought anyway, and I let them do it for $10 less.  Said another way, this is the “Pull” effect – some people will buy without any Marketing at all.  These folks are overwhelmingly active best customers, those who are “engaged” and have interacted with you Recently.

Perhaps you have put brand new products up on your web site and found they are selling even before you promote them at all?  This would be evidence of engaged customers, and the value of those sales is the tangible result of the “Brand  Engagement” your company has created (at least for the week or month).

So, the $10 per customer sale loss represents a devaluation of the Pull value embodied in your Brand, Service, Products, and Execution.  It literally is equal to the amount of loss you sustain by “over-Marketing” to a customer who is loyal and already engaged.

I suspect it is this issue – known as subsidy cost – that draws the ire of the experts who are saying “discounts are a terrible way to reward gold customers”.  They are correct.

The best – meaning most profitable – way to reward loyal customers is with non-discount aspirational offers that drive loyalty.  These offers could be anything from simply thanking them for their business (surprising how well this can work if done correctly) to highly specialized services or access to the company.

Now, if I take this same group of HSN customers and send them a coupon for $10 off any purchase over $50, they spend $400 for the month, and increase of $80 from the average of $320.  Why?  Because on that coupon transaction, the average transaction value is $120 – 3x higher than the average without a coupon.  Did I make money on this campaign?  You betcha.  At an average 30% operationally loaded margin, I spent $10 to make $24 ($80 increase x 30%) – a profit of $14 on the transaction before promotional costs.

The same discount of $10 to the same customer segment can generate completely different behaviors.  The trick is to understand  these behaviors through careful testing and measurement using control groups to measure the real net lift in profits.

So now, let’s change customer segments, move later in the LifeCycle to Lapsing customers, those who are on their way to defecting.  These are customers who have stopped visiting or purchasing and have not had interactions with you Recently.

Lapsing HSN customers don’t respond well to $10 off $50 coupons – it’s too late in the LifeCycle, average price falls over time and they won’t “buy up”, the average purchase price won’t rocket to $120 from $40 like it will with active customers. 

But if you look at what they like to buy (category affinity), and send them a $10 off coupon for a specific category, and tell them when you’re going to have a cool show on that category, you make a ton of money on the promotion.  Why?  Because a huge number of them buy when they wouldn’t have (as demonstrated by the lack of buying behavior in the control group), and a few “restart” as active customers who continue to purchase for several months – the “re-engaged“.

Different segment, different timing, same offer that lost money with active customers generates profits with Lapsing customers.  How do you know when a customer is Lapsing, when you should switch from $10 off $50 to $10 off a specific category?  You test it; a Recency analysis and test is a great place to start with this idea, full story here.

You can also take advantage of known LifeCycle purchase transition behaviors.  Many best customers start out buying in one category and then switch to another; you can run a simple analysis to discover these patterns.  Many moderate value customers simply never make the transition.  But if you know there is a “likelihood” of the transition and help it along a bit, you can turn a moderate customer into a best customer with a well-timed category discount.

At HSN, a 60 day old “average customer” who started buying in jewelry, if sent a $10 off jewelry coupon, loses you money.  Why?  They already have enough jewelry, response is low, they are at the end of the LifeCycle for the category.  You net no “lift” – they just spend $10 less that month.  But if you send them a 20% off fashion coupon, you make a ton of money over the next 90 days.  Why?

Because if you study HSN best customer buyer behavior, you find they start in jewelry and migrate themselves to fashion.  So what you are doing here is taking a moderate buyer and “helping” them to discover a category with a high likelihood of long-term satisfaction.

You’re modifying the LifeCycle.  Instead of defecting, a portion of them become heavy fashion buyers – the longest LifeCycle, highest margin customers.  You may lose money on the first fashion purchase.  But you end up converting a bunch of them to a new higher margin product line where they will continue to purchase for years.

Over time, you continue to refines segments and discounts until you optimize the entire system for maximum profitability; you are using the LifeCycle to manage margins by applying discounts very precisely.  Example of this can be found here: The Discount Ladder.

Every business I have done marketing / customer analytics for works the same way.  But for interactive, these effects are amplified and become very significant.  This is one reason why interactive is different, and why it’s a bad idea to treat interactive like offline – say, by blasting out the same e-mail to every customer.

So yes, discounts can be bad.  But they can be very good.  They are the ultimate motivator, and so are very effective.  You just have to know the who, when, and what of using them.

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:

Use Discounts for Customer Retention?