Tag Archives: Marketing thru Operations

Using Multiple, Related Customer Models Across the LifeCycle

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

Topic Overview

Hi again folks, Jim Novo here.

So you have all these simple but powerful customer models – Recency alone, Latency,  RFM, or LifeCycle Grids – how do you know which one (or ones) are best to use for your business? Guess what – it depends on the specific features of your business and also how you run the business. Now, while that might sound a bit scary, it’s really not that big a deal and in the end, the great news is you’ll end up with an approach customized to your business. So how do you accomplish this? Just segment and analyze your customers; they will tell you, my fellow Driller, which direction is the best to follow. You dig? Let’s go ahead and see what that looks like …


Q:  We recently purchased your book

A:  Thanks for that!

Q: and we are ready to start building some RFM analysis.  We are a search marketing business – we have a large customer /prospect base.  We have limited knowledge about them and we are keen to start on the journey.

A:  OK…let’s see what you’ve got.

Q:  We are hoping to extract database (approximately 25k names) of the last 6 months records and do some RFM analysis on key customer groups.   Specifically:

TEST GROUP A – people who initially purchased one of our trial products – we want to know what is their RFM score.

TEST GROUP B – subscribers to our “tool kit” product at $50 / month – we want to know what is their RFM score.

Q:  What kind of data are we talking about?  Is it web site visits, clicks on emails, transactional / subscription data, all of the above?

A:  Before setting up the model we have a couple of questions we hope you can shed some light on:

1.  How do we treat subscription – our business has a mix of one-off and subscription business – if someone “buys” every month with a subscription, is that included in Recency & Frequency?  Any insight you can provide us would be great – we found some info on this in the book but unsure given ours is a mix of subscription and one off.

A:  Well, let me start off by saying that your primary goal is to understand the customer LifeCycle so you can use it to increase your profits.  If you can understand the LifeCycle, you can discover the most profitable ways to acquire customers and stimulate demand from them.  Which behavioral model (RFMRecency alone, Latency, or LifeCycle Grids) is best suited for this task is something you will have to discover.  You’re probably jumping the gun a bit (I’m guessing) to hop right on to using the RFM model.  Before thinking about the models, you should probably do some thinking about segmentation – what logical groups of customers do you have, and what is likely the best model to score each segment with?

I’ll walk you through some of the thinking…

First, it is perfectly acceptable to use different models on different segments, in fact, often more desirable.  The one-off business – if you mean the ongoing purchase of products sequentially over time – is suitable for RFM analysis, it’s a “retail” business segment.  The most Recent and Frequent buyers of these one-off products are the most likely to purchase another one.

As you map promotional response to RFM score, you will see that there is a score that represents customer defection – response falls off rapidly below this score.  This score is your “trip wire”, it basically tells you that unless you get people with this score to purchase again, you have likely lost them.

Further, if you include the original source of the customer in the scoring process, you will find certain campaign sources tend to create customers with consistently high RFM scores and others tend to create customers with consistently low RFM scores.  This fact should allow you to better balance marketing spend, shifting spend from low score to high score customer sources.

The subscription business is something different, and probably requires a different approach.  For example, you might look at “average subscription length” to determine a “trip wire” for customer retention efforts.  If the average subscription length is 8 months, you know that in the eighth month of any sub’s tenure, you should be proactive in retaining them.  So for example, subscribers in their 8th month should be offered a renewal “bundle”:  “Renew for another 3 months and get 20% off”.  This will in effect drag a bunch of potential 8 month defectors into 11 months, where they have a better chance of “sticking” with subscription.

Two models for two distinct segments.

But, if I understand your business model, you have a fairly typical direct marketing of services approach – you acquire customers through free or low priced offers (these are also the one-off products?) and once customers gain confidence, you attempt to upgrade them (these are the subscription products?).  So what you really should be paying attention to is the “flow” of the customer through this expected one-off to subscription LifeCycle, then use the models to alert you to opportunities to increase profits along the way.

One way to do this is to deconstruct the LifeCycle of best customers.  Extract your best customers and look at the event sequences.  What series of events occurred, and what was the timing of these events, that lead to the creation of these best customers?  

Then, use this real world LifeCycle pattern to improve the profitability of your marketing efforts.  This approach is the basis of my LifeCycle Grid model, and it’s extremely powerful because you are using the two rules of High ROI Customer Marketing to your highest and fullest advantage:

1. Don’t spend until you have to

2. When you do spend, spend at the point of maximum impact

So, to follow through with the example above:  Let’s say you are running your two models on the two segments – the RFM model / one-off segment and the Average Subscription Length / subscription segment.  If the LifeCycle is as described above, best customers probably come in through the one-offs, migrate to being high RFM score customers in the one-off segment, then convert to a subscription and end up in the subscription segment. This conversion to subscription is clearly a “tipping point” of some kind – the point of maximum impact.  Well then, when does this occur?

Further analysis of best customers shows that the conversion to subscription typically occurs when a one-off customer achieves a RFM score of 444 or higher, and the conversion to subscription takes place on average within 30 days of a customer achieving this 444 RFM score.  Knowing this, what can you do?

Think about it.

First, when a customer achieves a 444 RFM score, you know this customer has high potential to become a best / subscription customer.  So it would be worth it to you to focus on these customers, do something special for them, stroke them in a special way beyond the regular e-mail newsletter.  How about calling them and thanking them for their business?  Sending them a special e-book?  Adding a new software service free?  Divert resources from low ROI marketing activities to pay for this High ROI marketing activity.  Same budget, higher profits.

Second, you know there is a clock ticking.  You have 30 days to convert this customer to a subscription customer.  After 30 days, they will start becoming less and less likely to become a subscription customer.  It’s now or never.

You now have all you need to fulfill the two rules of High ROI Customer Marketing:

1. Don’t spend until you have to

2. When you do spend, spend at the point of maximum impact

You have just created a dynamic, action-oriented, custom-built, LifeCycle-based behavioral model for a particular segment.  A model customized to your business and one that self-adjusts to customer behavior dynamically.  What does that all mean for you?

It means that if it takes somebody a year to hit 444, that’s fine, you don’t waste a lot of effort marketing subscriptions to them – they are not ready anyway.  If someone hits 444 in 2 months, you are tipped off to act on subscription marketing in a big way.  Instead of treating every customer the same, regardless of their behavior and potential value to the company, you are allocating resources to customers based on a model that guarantees to increase your profitability. 

And because it is all driven by simple numbers and time stamps, you can completely automate not only the scoring and “trip wires” but the marketing as well.  Same marketing resources, deployed in a smarter, more profitable way.

Then you take the next segment, then the next, then the next.  Let’s say you find that if a one-off buyer has not bought a subscription within 1 year of first one-off purchase, they are highly likely not to *ever* buy a subscription.  Well, stop marketing subscriptions to them at the 1 year trip wire.  Market one-offs, or create a new product that would appeal to this group.  Develop a model for each segment, and automate.

Q:  2. Do you capture RFM score monthly and track – if so does that mean you need to apply a date stamp for e.g. to the score?

A:  You can look at RFM scores on a sequential basis if you have a plan of action to take advantage of that knowledge.  Rising scores indicate rising potential value to the company – these are best buyers in the making.  Falling scores indicate the beginnings of customer defection.  This is the customer LifeCycle playing out in front of your eyes.

You could use this data to manage marketing activity at the individual level, though it’s pretty granular and would take a lot of resources; pretty advanced stuff and in the beginning, better to stick with segments and learn.

However, in the aggregate, you could create reports that look at the “delta” from month to month, basically predicting the future value of the business – is it rising or falling?  With this, you could answer this question: Is our marketing creating customers with high future value?  The approach is similar to the “delta grids” in the LifeCycle Grid part of the book, it’s a “migration” report showing you how customers are moving though the LifeCycle.

For example, you could create a report that answered this question: 

Of all customers with a score of over 55X (rocket fuel customers) 6 months ago, where are they now?

Answer:
35% 55X
25% 4XX
15% 3XX
15% 2XX
10% 1XX

This result isn’t particularly significant **by itself**.  However, when tracked on a monthly basis over time, if you saw increasing defection in the high end and growth in the low end, trending towards something like this (compare with above):

15% 55X  (down 20 % points)
15% 4XX  (down 10 % points)
20% 3XX  (up 5 % points)
25% 2XX  (up 10 % points)
25% 1XX  (up 15 % points)

that would literally mean the future sales (potential value) of your customers is falling; something you are doing (probably in marketing or service) is working against you.  Put another way, sales in the future will be lower on a per customer basis than they are today.  However, if you saw retention in the high end and defection in the low end, trending towards something like this (compare with first chart above):

40% 55X  (up 5 % points)
30% 4XX  (up 5 % points)
10% 3XX  (up 5 % points)
10% 2XX  (up 5 % points)
10% 1XX  (up 0 % points)

that would literally mean the potential value of your customer base is growing – you are acquiring higher value customers and  / or retaining a higher percentage of high value customers.  Sales in the future will be higher on a per customer basis than they are today.

That kind of crystal ball on future sales and profit can be very valuable indeed!

Jim

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Segment to Best Determine LifeTime Value (LTV)

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

Topic Overview

Hi again folks, Jim Novo here.

LTV has to be actionable.  If  you can’t take action on the information, it’s not relevant anyway.

There you go, the most universally true rule when attempting calculation of LTV.

And the best / easiest way to accomplish this is to identify similar customer behaviors and segment the customers by these behaviors – THEN figure out LTV by segment.

If you can’t actually take action on the information, then why spend countless $$ and hours fussing over all the reasons the number you come up with might be wrong and trying to solve unsolveable data or corporate issues? The best idea to implement when developing / using LTV is consistency – let’s get the team to agree on what LTV is and how to measure it, stick with those ideas for at least several years, test and take action on the results to uncover value, THEN (perhaps) discuss improvements!


Q:  I have just been reading your series on Comparing the Potential Value of Customer Groups. I am having trouble calculating the lifetime value of our customers.

A:  Yes, well, everybody does for some reason!  Often the problem is too much
focus on trying to look at the “average customer” as opposed to segmenting
customers.  By segmenting first, it’s both easier to get to LTV *and* more useful since it’s easier to take action on  a segment than the “average customer”.

Q:  Our company provide accounting software solutions to small to medium sized owner operated  businesses.  Because of what we sell and who we sell to, a lot of our customers are most likely to just buy one or two of our software products and unless they sign up for support (only around 15% do), we may never here from them again.  It is therefore very difficult to determine an average / standard lifetime that customers use our product.

A:  Sure.  First, the 15% segment that does sign up for support sound like good customers to me.  So that’s one segment.  How long do they typically stay signed up?  That’s the average life for this segment.

Then there are probably people who upgrade over time, right?  I can’t imagine an accounting product that people would not upgrade – perhaps not every cycle, but every 2nd or 3rd cycle.  That’s another segment.  Then there are probably some who both follow the upgrade cycle and pay for support.  These are probably the “best customers” and they are a unique segment as well.

And finally, you have the buyer who makes one purchase and you never see again.  These people are also a segment.

Q:  What should I base it on, how long our customers use our products (which would be almost impossible to determine), or how long they spend money with us?  So I measure on average the time between the first and last transaction of customers who have the highest Recency???

Continue reading Segment to Best Determine LifeTime Value (LTV)

Modeling Defections – When is a Customer No Longer a Customer?

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

Topic Overview

Hi again folks, Jim Novo here.

Metrics are not usually also Models; the metrics have to be fine-tuned / combined and built up into models. And executing this process usually depends alot on what type of business is being analyzed, and what kind of problem is targeted for a solution. So while it’s pretty simple to define a metric, creating a version of the metric that specifically addresses the challenge at hand can be a bit more difficult. Not hard, mind you; it mostly just takes a decent understanding of how the business works. Want some examples? Read on, O Fellow Driller …


Q:  Is Latency, as a metric, out of the question when the spread of the number of days in a latency period is so wide that to average them out and call the resultant figure “Acceptable days to date of predicted purchase” would seem meaningless?  I am thinking about the disparity in latency between customers who are Heavy, Moderate and Low users.

A:  I’m not sure I have enough context to understand the question (what are you trying to accomplish by using the metric?) but Latency is what it is.  In other words, you take your clue from the existing behavior itself.  If the average Latency for a certain segment is 2 years, well, it is, and that’s not too long or too short, it just is.  Whether you can act on that information is another story; it depends on what you are trying to accomplish.

For example, average Latency on major home appliances, depending on brand, is anywhere from 5 to 10 years.  Is that too long of a “spread” to make the metric useful?  No.  It just is what it is, and you deal with it. Typically these ideas are used to reallocate marketing spend away from waste on unresponsive segments towards segments that will generate incremental profits.

Continue reading Modeling Defections – When is a Customer No Longer a Customer?