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Q: I purchased your book and have a few questions you can hopefully help me out with.

A: Thanks for that, and sure!

Q: We have 4 product lines and 2 of them are seasonal. i.e we have customers that year in year out purchase these items consistently but seasonally, for example, every spring and summer.  Then they are dormant for Fall and Winter.  Should I include these customers along with everyone else when doing an RFM segmentation?

A: Well, it kind of depends what you will using the RF(M) model for, what kinds of marketing programs will be activated by using the scores. If you know you have seasonal customers and their habit is to buy each year, AND you wish to aim retention or reactivation programs at them, I would be tempted to divide the customer base so that seasonal customers are their own segment.  Then run two RF(M)  models – one for the seasonals, and one for everyone else.

Q: If I include seasonal customers, and I run RFM say on a monthly basis, these seasonal customers will climb / fall drastically with time depending on the season, so it seems like it may complicate the scoring process.

A: Sure, and you could segment as I said above.  Or, you could run across a longer time frame, say across 2 – 3 years worth of data. This would “normalize” the two segments into one and take account of the seasonality in the scoring – perhaps be more representative of the business model.  However, the scores would become less sensitive due to the long time frame so the actions of customers less accurately predicted by the model.

Q: Can you provide me with some examples as to how segmentation is carried out?  Let’s say I being with RFM and all my customers are rated 5-5, 5-4, 4-5 etc.  What are the next steps, do we overlay with other characteristics like age, gender, etc?  Or are the 5-3 etc. our actual segments?

A: This goes back to what you want to use the RF(M) model for.  In the standard usage, each score will have roughly the same number of customers in it, those with higher scores will be more likely to respond to marketing and purchase, lower scores less likely.

Another way to say this is: since the lower scores are less responsive, they require higher value discounts or promotions to activate them.  So you can customize offers based on score, which will provide the maximum response for the minimum discount exposure.

Try testing different discount levels with different scores to see where you get maximum profit; an example of that kind of testing and math is here:

Using Recency to Drive Promotional Profit

The above assumes you are focused on response and profit, two of the more common objectives in data-based marketing ;)

However, other valuable information can be discovered using this framework, see chapter 20 about adding customer characteristics to RF(M) scores.  This is not needed if you are just concerned about response / profit for campaigns – the segments are the segments.

But if you’d like to know, for example, what kind of merchandise is appealing to 5-5 (ultra-best, highest responding) customers, you could run the scoring, take the 5-5’s, and then cross-tab with whatever else you have – find out what kind of merchandise they buy, what their sex is, etc. – any kind of data you have or can get on them.  This kind of work can help develop creative, decide what to feature on the home page, etc.

Q: Should we provide them a segment name like High Recent / Low Frequents, Low Frequents / Low Recents etc?  How do the other characteristics come into play with respect to the naming conventions, segmentation etc.  I keep reading of different segments like ‘Loyalists’, ‘Laggers’, are these defined through our subjectivity?

A: Generally yes, subjective labels; you may  find it is easier to communicate using labels rather than scores, so you could suggest that certain ranges of scores be called something like ‘Loyalists’ or ‘Laggers’.

The High Recent / Low Frequents segmentation is the quadrant approach from LifeCycle mapping, see the chapter 22 in the book.  This is an alternative way to use the RF(M) variables to map your customer base in a more Strategic way than RF(M) affords.

In other words, you could use RF(M) to score for Campaigns and the LifeCycle maps to report on your progress over time to management, since both approaches are based on the same variables – great way to “connect the dots” for manic-ment.  Also, RF(M) is a bit hard to visualize just using the scores; the LifeCycle maps allow you to plot campaign results in a very visual, easy to understand display of the data.

See these newsletters for a deeper explanation of Strategic versus Tactical use of RF scoring:

RFM versus LifeCycle Grids

LTV, RFM, LifeCycles – the Framework

Hope that helps!

Jim

===================

Q: How do I establish the optimal number of segments / buckets I should have when analyzing a customer database?  I was using a method of dividing them into deciles and assigning Decile 1 to Segment A, Decile 2 & 3 to Segment B, Deciles 4 – 6 to Segment c and Deciles 7 – 10 to Segment D.  However, the absolute number in each is not practical as I will not be able to manage that number of customers in the way I need to.

A: Well, it would help to know what Kind of business this is, but in general, the most effective place to break behavioral segments is where behavior changes.  You can force people into Deciles, which often does not create meaningful segments, or you can look at the data and see where there are “bulges” or changes that seem significant.

So, for example, look at this LifeCycle Grid:

Current / Potential Value Matrix

Note the low Current Value breaks of 1, 2, 3 because there are large groups of customers there.  Then, 4 – 9, because each of those has many fewer customers and their behavior is similar.  Same with 10 – 24, that’s a pretty good customer comparatively, and after 25 purchases, well, they’re crazy-good customers and behave similarly.

These cutoffs start out as somewhat arbitrary, but over time and testing you find out, for example, that a 9X buyer behaves more like a 5X buyer and not so much like a 10 – 24X buyer, so the cutoff is 9X.

Here’s another example:

Freemium Customer Conversion

The segments graphed in this post are actually deciles of 1 year customer value; but the data plot is of the first 14 weeks of weekly spending.  Note how there are clear inflection points where the  behavior changes, and this is where I would generally create the “boundaries” for a segment.  Doing it this way means the segmentation already has some type of powerful behavioral trait, making it by definition significant and “actionable”.

Hope that helps!

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:

Defining Behavioral Segments

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Q: We’ve been playing around with Recency / Frequency scoring in our customer email campaigns as described in your book.  To start, we’re targeting best customers who have stopped interacting with us.  I have just completed a piece of analysis that shows after one of these targeted emails:

1. Purchasers increased 22.9%
2. Transactions increased 69%
3. Revenue increased 71%

A: There you go!

Q: My concern is that what I am seeing is merely a seasonal effect – our revenue peaks in July and August.  So what I should have done is use a control group as you described in the book – which is what I am doing for the October Email.

A: Yep, that’s exactly what control groups are for – to strain out the noise of seasonality, other promotions, etc.  But don’t beat yourself up over it, nothing wrong with poking around and trying to figure out where the levers are first.

Q: Two questions:

1.  What statistical test do I use to demonstrate that the observed changes are not down to chance

2.  How big should my control group be – typically our cohort is 500-800 individuals

A: Good questions…

On a group that small, you are probably not going to get anything “statistically significant” without ruining your total profit, e.g. might have to use 50% in control.   If you have the leeway to do it, that’s what I would do.

On the other hand, in some cultures people will go bonkers over giving up sales to learn something really important.  OK, so take 10% as control and repeat it 3 times; if the results are stable then you have your proof.  Do another control every once and a while (every 6 months?) just to make sure it tracks.

Either way, you don’t really need statistics.

Practically, confidence is the likelihood a sample represents the population.  This can be a really useful idea when you are forced into very small sample sizes or the event is highly risky to repeat.  But here, if you are testing a really large slug of the population, confidence is less useful.  Or if you can repeat the event (because essentially, you are in control of it and it’s low risk), do you really need to force yourself through the screw of complying with the statistical math?  It’s like using a 727 for crop dusting, overkill for the situation, methinks.

If you were running a drug manufacturing line, statistical concepts like confidence and significance are absolutely valuable.  But for a marketing program?

That’s why I love the idea of “beefy controls” in start-up projects because I *do not* have to rely on statistics that the audience likely does not understand and provide room to question the results, e.g. “Yea, but what if the result is an outlier?”  Very appropriate in high risk situations, with giant populations and a lot of money on the line.  For this situation, perhaps not.  But, if you’d like to go that way, there’s lots of calculators on the web that let you play with some of the numbers anyway.

Here’s one, make sure to read the descriptions of the variables underneath the calculator:

http://www.surveysystem.com/sscalc.htm

Nice work on the core campaign idea, by the way!  Now we just have to tighten it up a bit…

(3 months later)

Q: We decided to tighten the targets and do a “best customer defection” email program. Basically, we look at customers who has an RFM score of 555 in the previous scoring period who have dropped out of that score.

A: Interesting!  So instead of targeting by  guessing the current score of a defecting best customer (say 355), you are looking for all customers who were formally best customers, regardless of current score.  This is a subtle difference, but much more of a LifeCycle approach and frankly why I prefer these kinds of ideas over “straight” RFM.

An example might be helpful.  Let’s say the acquisition folks run a huge new customer campaign in between the prior RFM scoring and the scoring done before your campaign drop.  A big inflow of new customers can artificially “force” certain groups of customers down in score – even though their own behavior has *not changed*.  In this case, the new score is not reflective of actual behavior, so increases noise in the system.

That’s the problem with the “Snapshot” or date-specific view of Customer State – it’s a single point without reference.  By using prior score, you are acknowledging behavior over time and the primary importance of the former State, as opposed to the current State – a Movie as opposed to a Snapshot.

In other words, from a  Marketing perspective, I’m more interested in the path they are taking through the LifeCycle than any particular point in time during the LifeCycle represented by a single RFM score.

Q: Good news on your advice.  We ran a 50% control (500 purchasers in each group) and the results really nailed the issue for us. The actual number of purchasers remained unchanged at 20% but Total Revenue and Average Spend increased by 40% compared to control.

(Jim’s Note: for those not following, a very precise target group of 1000 was split into 2 groups of 500.  One group received this  campaign, the other did not.  People who did not receive the campaign purchased at the same rate as people who did receive the campaign, but the people who received the campaign averaged 40% higher spend).

A: Awesome.  So what you are seeing is Customer State makes a huge difference in terms of what offers / timing can be most effective for this “Recently defecting best customers” cohort.  If I’m reading your numbers correctly, no lift in response versus control but a huge lift in revenue.

To me, that means these customers are early in the process of defection – still buying, but without a special treatment, slowing down the monthly spend.  After all, they are very Recent (former 5XX), so highly likely to purchase again, which is why lift in response was flat – they likely would have purchased anyway.

Not a bad time to hit them.  Offers to a very Recent State should focus on increasing order value, not generating response – you don’t want to spit into the wind, but go with the natural flow of the behavior.

In other words, these customers likely would have purchased anyway, but at lower price points if they had not received the campaign.  The common way this is addressed is with  “threshold” discounts – if average order is $50, then something like “$10 off any purchase over $50″ – test different thresholds to maximize profitability.

Looks like you gave them the right offer ;)

On the other hand, a straight discount to this specific best customer group – $10 off anything, and especially when their normal category of purchase is promoted to them – almost ensures that you will lose money.  Why?  Most of these customers would have bought at full price anyway, as demonstrated by equal buying activity whether the customer received the campaign or not.  So the discount turns into a loss versus no campaign at all.

Unfortunately, I see a lot of this exact type of campaign delivered to best customers because all customers get some version of the same offer.  “Hey Jim, we’re not sending the same message to every customer, we send different messages by segment”.  Sure, the copy and art are customized for different segments, but the segmentation is not by Customer State, so the offers are mismatched and suboptimal.

This is the value of using control groups; they drive understanding of Marketing concepts like opportunity costs and subsidy costs.  These two concepts are the reasons why ignoring Customer State is suboptimal: by not segmenting using State, you will get lower than possible profit or sales at most customers, depending on Customer State.

Had you not delivered a campaign tailored for prior Customer State, money would have been left on the table by way of lower order size.  And 40% Revenue lift sounds like it might have covered the cost of the campaign ;)

Q: We tried to run a Student T test on the results but our new statistician informed me that the distributions were not normal – so on her advice we ran a Wilcoxan Test which gave us a highly significantly p = 0.016

A: Oh, so you still went the stats route?  Well, the fact you HAVE a statistician tells me the culture there is more familiar with interpreting these ideas, so more power to you.

Glad it worked out and keep me informed on how things go downstream.

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:

Increase Profit Using Customer State

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Q: I was wondering if you’ve done any work with, or given thought to, companies who have a cloud based Freemium business model?

Should they be tracking usage (or anything) at the free level?  Should they be tracking usage at the paid level?  I’m sure defection rates are a big problem, but I’m wondering how many focus on engagement thru mass marketing versus trying to keep what they’ve got, or influence the free users to make the leap to paid.  Any thoughts on this?  Maybe you could do a blog post on it.  It seems like a good fit with your brand of analysis but I’m just starting to think it through…

A: I just finished an analysis that’s a good example of this problem.  Behavior during the Freemium period can predict who is highly likely to become a paying customer, who will need marketing efforts like additional sampling / package discounts, and who will not become a customer no matter what you do.

So the answer is you need both, analysis of paid and free.  But in particular, what you need to do is understand the transition from free to paid by comparing the behavior of known converters versus non-converters over time, preferably using events that create value for customers.

Typically the differences will be volume / persistence related, generically, low Recency high Frequency.  Also, likely converters to paid will tend to use a wider variety of features consistently.  In the analysis, the question to answer is which of these value-creating behaviors is predictive of becoming paid?

Said another way, you tend to see a fairly fast drop-off among *all* new Freemium customers after the initial burst of activity, but the ones that are not going to stick tend to drop off even faster in their activity.  Then, there is a “bounce” in activity where the ones who are most likely to end up as paid begin to cycle behavior more quickly and begin to use more features, and the others simply drift off the map, with no “bounce” as Recency becomes extended.

Classic LifeCycle analysis – customers tell you what they will become in the future by what they do today.  Having the very detailed behavioral information typically seen with interactivity just multiplies the ability to do this kind of prediction.  More on the Freemium model, including determining appropriate cost to acquire, is here.

Q: Do the standard analytics packages allow a business to look back at the “free” behavior of paid subscribers?  I’m thinking of Freemium cloud based solutions and how they would track this.  Do products like Crazy Egg get you there or do you really need something more sophisticated to do this kind of analysis?

A: I’ve never used Crazy Egg so I don’t know about that one specifically, but in general you can do quite a lot with the basic tools that support customizable segmentation.  The challenge with going that way is you have to be super-technical with the implementation to capture important event data points, you have to create many different segments, and then the killer problem – you can’t “re-analyze” a different approach with these tools, for the most part.  If that’s what you mean by “look back”, it’s highly unlikely you could accomplish what you need to do.

So it’s possible, but these tools are not really designed for “behavior over time” work and certainly don’t allow for much “exploration” of the data – any change in analytical approach is likely to be a “going forward” type of measurement, not looking back.  So there would be lots of iteration before you even knew if the analytics set-up was correct or what events are meaningful.  In other words, it’s possible but could waste a LOT of time.

I’d much rather find the system that contains the key elements of activity – when did they sign up, what features are they signed up for, when did they add other features.  This data probably resides in whatever system manages the account.  Dump that data off into a spreadsheet or database, try to figure out what’s meaningful, look for correlations.

Then, once you have a grip on some solid ideas, then maybe you go into the front end and try to align traffic and behavior with known “events” that seem to predict upgrade to pay, if that’s what the mission is.

Otherwise, you will be setting up a ton of tracking on all kinds of events not knowing what is meaningful, and then dealing with a really poor interface for the analysis of those events.

The other way to go, of course, is to use one of the advanced web analytics tools, which sit on real databases and can be queried.  But assuming that’s not an option, I would try to look for hard data points in the backend first, then knowing key behaviors, look for what might cause those behaviors in the traffic side.

Example

Let’s say you have a project management application that has a 60-day free trial then converts to paid.  Value is created for the customer when they use the functionality of the app – say create project, comment on project, upload file, or any other actions you deem critical.  “Traffic” in a situation like this may be only marginally indicative of value creation; rising activity could even be a negative indicator (frustration with application).

So, you want to create a situation where you can analyze these important behavioral events by account, and (ideally) you want to know the source of the account creation – campaign code, referrer, etc. That’s all you need for data, simple table, maybe a dozen columns.

Then, compare average account that converts to paid with average account opened at the same time as the converters but does not convert, over the 60-days before trial end.  For each of converting and non-converting, aggregate each of key events by week, divide by number of accounts to get average behavior per account, and you would have 8 weekly average data points for each of the events, both for non-converting and the converting accounts.  Maybe a dozen simple line graphs with 8 weekly data points, one set for accounts that paid, one set for accounts that did not.

Converting and non-converting graphs should look different for some variables.  Both will typically start out with high levels of activity, then for some variables you will see them diverge.  This not only predicts which variables affect conversion, but reveals to you the best time during the 60-days to intervene with surveys, help, or other marketing programs to re-engage the accounts that appear to be headed for defection.  If you have campaign data, also which campaigns tend to create accounts that convert and which don’t.

One of the event graphs may look to be more predictive than the others, with abrupt changes in direction going into the conversion event.  For example, perhaps it will look like this:

(Click pic for larger image)

This is the behavior of 10 different 1st year spend levels (deciles) over the first 14 weeks of their Life, engaging in an event that creates value for them.  The dark blue line represents average top spender.  Note how for top spenders, the profile is quite different.  The graph tells you that by week 4 or so, you can probably predict who will become a best customer and who will need intervention based on this activity.

You can run this kind of event profile for any variable – events, campaigns, etc. as long as you know complete / non-complete goal or end value of the customer.  In your case, since the goal outcome is binary, there would be 2 lines instead of the 10 spending deciles above: converters versus non-converters.  Create a converter versus non-converter chart for each key activity variable (create project, comment on project, upload file, or any other actions you deem critical), and look for this kind of divergence.

Drilling down more deeply by excluding all but 3 lines so we can see the behavior “in the middle”, we find some interesting patterns:

(Click pic for larger image)

Here, we see patterns that provide clues to the testing targets one might want to address to see if “middle” customers could be turned into better customers.  The blue segment, showing a series of higher highs and higher lows after it “bottoms” for this behavior, is most likely to benefit from intervention of some kind.  The pink segment looked promising, but then put in lower highs and lower lows – these customers lose momentum quickly and have trouble self-sustaining.  The yellow segment was never really in the game at all.

Yes, the comparison to stock market charting is intentional!  It’s an expression of group behavior.

If I had to pick the segment with the best potential, I would try the blue segment first, and the data points could be used for automated triggering of different types of campaigns. For example, “If by week 4 activity for Variable X  falls below 60, trigger Campaign A.  Then if by week 11 activity for Variable X is not above 40, trigger Campaign B.”  Remember, these are averages, so not all customers in the segment are below threshold.  The idea is to target a specific behavior with a specific message.

Just to be clear, you don’t need the goal value of the customer to put a model into practice, only to prove the initial model – certain patterns in behavior predict high value customers.  Once you know the end value of  customers – convert or not, monetary value, any goal – you can run the LifeCycle movie “backwards” like the charts above and find out which early  behaviors are predictive of high and low value customers.

If you want to go further, you could show these graphs and data to a modeler and see if they can create a more precise mathematical model, which can be developed much more quickly with this kind of evidence to review.

Once you fully understand what this LifeCycle landscape looks like, THEN you could go back and instrument the web site and analytical tool to monitor some version of this data in a more automated way.  But trying to guess what’s going to be important beforehand and work through a study like this using a vanilla web analytics tool is the very, very long way to get where you need to go!

Jim

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Freemium Customer Conversion

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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

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Segmentation by LTD & LifeCycle

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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

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