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

Q: I have recently purchased your book Drilling Down and going through the many interesting concepts.

A: Thanks for that!

Q:  I work for a membership Organization and we would like to conduct some analysis into who we may lose and approach them even before their membership lapses.  But the only problem here is that we carry data only on the purchases made (though many of our members do not purchase our products and stay a member) and web site visits.

A:  Are you *sure* that’s all the data you collect?  I once worked with a professional membership org that thought they only had one data source, but turns out they had 8 – from 8 different areas of the org – that nobody really knew about.

Q:  How do I know if a particular member is going to resign and lapse soon with this limited amount of behavioral data.  Recently it’s been a concern that we are losing members who have been with us for more than 10 years and who are in their mid career profession (aged between 30 to 45) and indicated no specific reason for resignation. 

This has been going on for the last few months and now we would like to strategically target these customers and approach them even before they react negative.  What concepts could help me to do this? Your guidance would be much appreciated.

A:  OK, my answer will be in two sections: if you (hopefully) find you have more data than you think, and if you really don’t have any other data to fall back on.

The first thing you should do is make sure you don’t have any other data sources.  Purchases and web sites visits might be the most obvious, but do you have:

Membership Campaigns
Conference registrations
Customer Service incidents
Local or regional meetings
Training sessions
Speakers
Orders for training materials

and so on.  In some orgs a lot of this is siloed  or even outsourced, but the data is still there – it’s a question of whether you can access it.   Think through the business model, and think about any possible interaction points with a member.  Then ask, Where would this data be?  You might be surprised at what you have.

Once you have more data, subscription relationship analysis of this type and predicting churn come down to three main thrusts:

1. Number of activities – similar to banking customer analysis, we often find that the number of different areas a member has tangible contact with is predictive of retention or defection.  For example, some members attend the annual conference and others do not.  Some members participate in local meetings and others do not.  Some buy training materials and others do not.  Some members do all three, some two, some one.

On average, the members engaged in all three activities are the most likely to remain members relative to those engaged in only two.  And members engaged in only two activities are the most likely to remain members relative to those engaged in only one.  This is a “run rate” kind of retention, an expectation.  From a Marketing perspective, this means you want to always be adding to the number of activities a member engages.

2. Change in number of activities – when you see a member drop from 3 to 2 activities, this is a clear signal that there is a problem of some kind, it’s a dis-engagement from the org.  You want to take action when you see these events, find out what is happening and if there is anything you can do to correct this.

The reason driving this downgrade may be a soft incident, say a content problem, or may be a hard incident, like payment problems. Either way, the org needs to find out if the issue can be addressed.  If you start to see “clustering” of these kinds of downgrades in relationship quality, it’s likely something more systemic is going on.  Often you will see certain segments who exhibit similar problems.

For example, members acquired through a certain publication may exhibit similar downgrade behavior at roughly the same interval from joining.  This is evidence of a systemic problem – something about folks from this source is unique, and for whatever reason, the org is not satisfying their needs.

3. Predicting change in activities – if you want to go further down this road and actually *predict* a change in the number of activities before it happens, you can look at the Recency within that activity.  A member who goes to conferences on a regular basis who then skips one is in danger of defecting from that activity – for some reason, the conferences are not providing the value they used to.

Or, something has changed with the member, their position in the LifeCycle has moved.  The conferences still provide the same value as they did before, but this value is shrinking for the member who (perhaps) needs more challenging or different content.

The same could be said for attending local meetings or buying training materials, etc.  For a known user of a specific activity, how long has it been since they used it?  Does this non-usage break an established pattern of usage?  If so, you have a triggering event for a marketing / membership intervention.

OK, so what if you really don’t have any more data?  You can create it.  One of the easiest and least intrusive ways to do this in a member org is with surveys.

Why?  Membership orgs have embedded permission to interact with members; it’s the nature of being part of such a group. What I mean by this is asking members to take part in a survey is not only quite natural, it’s often expected and perhaps even appreciated.  After all, what could be more aligned with a membership org than asking the members where the org should be headed and where they would like to see it go?

By thinking through and properly crafting such a survey, one should not only get a sense of potential friction points in the org overall but also get a sense at the individual level of which members are becoming dissatisfied and more likely to defect.  Implementation of a program like this means, of course, that the survey responses are tracked at the individual level so that action can be taken at the individual level. You can’t just do a random popup on the web site to make this work.

Moreover, in the out years, one can reverse engineer the reasons for defection.  Each year, analyze the population of members who defected in the previous year, and ask yourself, How are these people similar?  Do they come from similar backgrounds or industries?  Did they join in the same year? Come from the same marketing source?  You may already have such survey data and simply had not thought of using it for analyzing and predicting which members would defect.

Finally, I am fully aware that often, bringing these kinds of issues to the table can be painful for a membership org.  In my experience, many of these orgs are highly politicized and there is a certain “we’ve done it this way for 100 years” attitude.  When you present data that contradicts long-held beliefs there can be tension.  And this is fine - when the org is on board with the project.

So my final advice would be this – whether you have the data or generate it, the success of a project like this can revolve around the strong commitment of the org to actually do something about member defection. Ask this question of management first:

If we find members are leaving because of something we are doing or not doing, will we change this, even if the problem contradicts long-held beliefs?  The answer will show you how committed the org is to fixing member retention.

“It depends” is not the answer you want to hear from these senior players, because this means they don’t have a retention problem, they have an acquisition problem.

Unless they are willing to change, they need to recruit more members that are not like the members they have if they want to decrease defection in the membership.

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:

Member Retention in Professional Orgs

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

We looked for the most active member of the cluster (our “thought leader”) and asked them if they would help us with a “member get a member” program.  Would they be willing to distribute discount coupons to their friends, especially ones who were not already customers?  Time after time, the answer was:

“Honey, all my friends are already customers of yours”.

We launched the program anyway, because it was a pet project from upstairs  – I was a junior marketer at that point so I couldn’t kill it ;)  The program never, ever worked, no matter how hard we tried.  It generated very few new customers while giving lots of discounts to people who were already active buyers.  Basically,  the cost of those discounts overwhelmed the value of the new customers generated.

Apparently a similar thing happens online with Social marketing.

As part of a WAA program that reviews academic research for WAA members, I was able to take a look at a paper titled:  Firm-Created Word-of-Mouth Communication: Evidence from a Field Test by David Godes and Dina Mayzlin.

Here’s my Executive Summary:

The authors investigate the effectiveness of a firm proactively managing customer-to-customer communication.  In particular, they are interested in proving how, if at all, a firm should go about effecting a meaningful word-of-mouth (WOM) communications program.  This is done through two different data collection schemes: a large scale, 15 market test through BzzAgent with a client restaurant chain, and also through a controlled online experiment.  The results are somewhat counterintuitive and may change the way web analysts and Marketers should be thinking about WOM and social analysis, particularly if there is a hard monetary investment in the WOM program.

Specially, the researchers are trying to answer 2 questions:

1.  What kind of WOM maximizes incremental Sales?

The answer: WOM created by less loyal (not highly loyal) customers, and occurring between acquaintances (not friends).  Though perhaps surprising, this result is often found in Marketing program measurement; Sales would occur anyway without the program, especially among best customers.  These results demonstrate the pitfalls of not using control groups (people not exposed to the campaign) to measure Marketing effectiveness.

2.  Which people are most effective at creating the WOM above?

The answer: “Opinion Leaders” or “Fans” are not as effective in spreading WOM that drives incremental Sales because these efforts are “preaching to the choir”, per #1 above.  The networks that opinion leaders or fans have are likely to already knowabout the Product from pre-existing conversations, and spending money on creating a campaign to reach these people is ineffective because the social communication has already taken place.

In sum, if you want to invest in a WOM program that will drive Sales you would not have received anyway, you want the WOM conversations happening, as the authors say, “where none would have naturally occurred otherwise”.

Here’s a portion of my Review, where Awareness versus Persuasion is used as a framework to help explain the results:

The discussion of the difference between the need for a persuasive argument versus building awareness is something web analysts should keep in mind so they can make sure they understand the real needs of the Marketer or Product Manager.  For products with high awareness already, what is really needed to increase Sales is persuasion of the people already aware, not more awareness.  New products obviously need increased awareness.

Per this study, this persuasion versus awareness question affects the choice of who to recruit for WOM campaigns. Loyal customers are the best persuaders and are best used when the product already has high awareness.  If you want to drive sales through increased awareness – the goal of many WOM campaigns online – you should be recruiting less loyal customers and encouraging them to talk not to their friends, but to their acquaintances.  This approach appears to be contrary to the “opinion leader” or “fan” approach now thought of as best practices.

As is typical of academic research and testing, the paper contains an extensive review of the results of other WOM Marketing studies all the way back to the 1970’s upon which the hypothesis for this test was formulated.  If you are a WAA member, you can get a copy of the peer-reviewed paper from the journal Marketing Science (other versions are floating around) with all these footnotes listing previous test sources.   Instructions on how to do this are at the end of the Review.

————–

I’m not going to post the rest of my Review on the blog.  Rather, I’d like to ask some questions and get some discussion going on this topic, because I think this test and paper shines light on a fundamental flaw in the way people think about Marketing on the web.  So read the rest of the Review here if you’d like, then please come back and offer your opinions on these questions:

1.  Test results indicate if you’re going to maximize an investment in WOM, you should target less loyal customers talking to acqaintainces rather than loyal customers talking to their friends.  Do you understand this idea?  Believe in it?

2.  Do you think this is an important discovery?

3.  Do you think this general model – the idea that to maximize Sales, best customers should be treated differently than other customers - might apply to many different types of web / interactive marketing?

4.  Will you do anything with this information?  What’s first step?

Update:  Prompted by the questions in Comments, I offer these additional links to previous posts.  Here is a more detailed explanation of  “lift” and “incremental”.  Those who might want want to go “up” to a macro model for how to think about Marketing in a Social or Relationship  mode should see Framework for Engagement.  If you want to go “down” to analysis or execution and are interested in “How do I Measure and take Action on these Ideas”, see the Measuring Engagement Series.  If you want to understand how this model integrates with the traditional offline Push model, see the Marketing Bands Series.

 More questions?

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:

Awareness versus Persuasion

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

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

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

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

And the primary reason the audience quality is so high?

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Or, someone like Google will finally make micropayments work.

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

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

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

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

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

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

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

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

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

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

Micropayments are not just about paying for content, folks.

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

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

Net Meaningful Audience

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

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Q:  I’m involved in a loyalty program analytics project.  This client is a local pharmacy.  All sales are done directly in store, the web site is just for communication purposes.  The general problem we are trying to solve is the manager doesn’t have any detailed ideas about shoppers behavior apart from human observation. 

The idea is to launch a card-based loyalty program which will track sales activity and give insight into customer behavior.  The program will be points-based calculated on amount spent.  Points can be redeemed as rebates, coupons, gift certificates, or use points to buy items in loyalty program catalog.

The task is to segment customers according to their recent purchase behavior and determine the customer lifecycle.  I’ve been able to do some basic analysis using the R package and MySQL database, but am unable to detect customer lifecycle. 

Can you please give me guidance on this?

A:  What is the Objective of detecting the LifeCycle, to create a more “active” customer retention program?  Loyalty programs can be quite “passive” and often benefit from a more active overlay.  But there can be many reasons to want to understand the LifeCycle…

Q:  My 2nd task is to use the behavioral data with demographics to  build a direct marketing strategy and provide management with insight into the customer base, for example: percent new customers, % of Gold customers who passed to Silver in last quarter.

A:  Again, it would be helpful to understand how management would take action on this data.  But I suppose you are in the common position of not knowing the tactical approach, and nobody will lay it out for you (a.k.a. they are clueless)…and you don’t know the right questions to ask or how to ask them.

Let’s infer that the Objective for the loyalty program is to increase customer retention, the Strategy is to customize communications based on behavior to increase relevance and response.  This is typical for a loyalty program.

So, the metrics required for the Objective would show which customers are active and which are in the process of defecting.  To execute the Strategy, you need segments such as New, Silver, and Gold customers.

I use LifeCycle Grids for this purpose.  See this post on Measuring Customer Engagement for examples of what this approach looks like.  You can use % instead of counts in the grid segments if you wish.

Customers tend to move from right to left in the grid over time if they are inactive and defecting.  If active, they stay over on the left. Generally, the customers of most concern are those in the orange upper left Quadrant (Q3), best customers who are becoming former best customers.  These are the folks who should be targeted with double-points offers and so forth to reactivate them.

The most profitable grid cells to target for  reactivation can only be found by testing, but I suspect for a pharmacy it’s generally in the middle of the Recency scale – 60 days no activity, 90 days no activity, 120 days no activity.  You can’t wait too long, but you don’t want to do expensive promotions to customers who would buy anyway.  Wait for them to show signs of lapsing, then promote.

As far as “how many Silver turn into Gold”, if you line up the Frequency parameters in the grid for that boundary, you can use the grid for that as well.  Or, create a custom grid to reflect just movements between levels by using a selective population input rather than “all customers” in the grid.

The great thing about using the LifeCycle Grid method is it’s the same analysis for any population or segment, so management gets used to it and this drives consistency in judging the value of Marketing programs.  So you could run the same grid for Men, or people 50+, or people within 5 miles of the store. 

Comparing what the grids look like for different populations produces insight you can use to further sharpen your targeting ability.

Q:  How do I determine the value of a point for the different segments, say Gold versus Silver?  How do I optimize the ROI in each different segment given differing point values, what is the formula I should use?

A:  Hmm, you’re really out in the cold on this, aren’t you?  Are there any Marketing people involved in this, or are you supposed to use the “magic of analytics” to tell the Marketers what they should be doing?  Don’t tell me, let me guess – this is a “MarCom” project and the person who normally buys online banners and sends out press releases will be in charge of loyalty communications…

Here’s the problem.  The question you are asking is a Marketing question, not an Analytical one; there is no “formula”.

First, a good loyalty program budgets 3 – 5% of sales to points / overhead, meaning a point could be worth in real money between 2 cents and 4 cents, or you are planning to award double / triple points in certain segments. 

By a “good program” I mean a proactive effort that changes behavior, as opposed to running as a faith-based initiative, the old “we have a loyalty program so people are more loyal” approach.

Companies try to do loyalty programs in the 1 – 2% range but it’s rare they actually work because there’s not enough leverage.  You can’t really provide decent rewards that truly motivate behavior in that range, unless you can provide a lot of intangible benefits though other arrangements outside the point formula.

For example, you have a movie star under contract and part of that contract is to attend company functions.  You then arrange for such a function and customers bid points for one of 10 available slots.  That’s an intangible benefit; there’s not a 1:1 financial correlation between the value to the customer and cost of points.  It’s “insider access”, recognition that is  emotionally rather than financially driven.  Inviting best customers to sporting events has long been used the same way.

If your budget is limited to a 1 cent a point, you really need to think through how you will create excitement for the program by adding these intangible benefits.  Not that you have access to movie stars, but how about unique discount programs?  Exclusive product Previews?  Paid services provided free?  And so forth.

Second, you really can’t change point values across program segments without getting into a lot of trouble on the finance side, it becomes too difficult to audit and you lock yourself into all kinds of execution problems.  For example, what happens when a Gold Member drops to Silver if the point values are different?  Further, what if an item is bought when the member is Gold and returned when the member is Silver?  How will the accounting work when the net differential in point value creates a value gap?

If you want to boost offer value for certain segments, you award more points - points are still worth the same amount, but you give double or triple points, not change the value of a point.  Points end up on the balance sheet as a liability so this is deadly serious stuff, you do not want the value of a point to fluctuate, trust me.

Ask any CFO.

What level is selected generally depends on the margins in the business, and how the program will be paid for.  Example: you decide to kill all promotional coupons and use the savings from those discounts to fund points, which would tend to improve your margins, because point discounts are targeted, coupons are not.

But there is no way to determine a point value that is optimum for ROI, it’s how the points are applied and used in the program, the Marketing Tactics – the Targeting, Messaging, and Timing driving behavioral change – that determine what the program ROI will be.

The best advice I can give you on this is to TEST before you decide on anything – point levels, budgets, segments, etc.  Use the LifeCycle Grid to find out what works in terms of offers and behaviors.  Can you induce loyalty and repeat purchase from lapsed buyers?  What level of discounting or special services is required to change behavior?  Then translate these results into budgets, point values, and programs. 

I realize you probably don’t control this decision, but I would raise the question if you can: what if we don’t really need a Loyalty Program?  What if a less complex and less expensive Retention Program (based on testing results with the LifeCycle Grids) would be more appropriate for our business? 

For example, what if we could get best customers to raise annual spend 10%, and at the same time induce just one more shopping trip before a customer defects?  How much would that be worth on annual basis?  How much can we afford to pay for these revenue increases?

The LifeCycle Grid gives you a “roadmap” for testing your Targeting, Messaging, and Timing  consistently across different segments as your customer population churns over time.  You can use the results to develop either a simple Retention Program or a more complex Loyalty Program, whichever the test results seem to indicate is the best direction to go.

Loyalty programs do not create “Loyalty” – your products and service create Loyalty.  Loyalty programs are an incentive to change behavior, and if analyzed and executed that way, can be very profitable, as can simple ongoing Customer Retention campaigns.

Jim

Readers: Additional questions or thoughts?

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Loyalty Program Structure & Tracking

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

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

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

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

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

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

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

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

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

No time for classes?

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

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

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

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

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

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

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

The Other 3 P’s

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

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

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

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

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

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

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

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

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

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

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

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Post-Action Dissonance

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