Archive for the ‘Measuring Engagement’ Category

A Framework for Engagement – e-Mail Example

Tuesday, February 5th, 2008

Let’s take a specific example of what I was talking about in the last post in this series to show you what a Relationship Marketing Strategy looks like in action.  I have stated, for example, in this interview, that email is both taking credit for sales that would have happened anyway and generating more sales than you think it is. 

These statements are both accurate at the same time.  When you use Control Groups to measure the incremental behavior generated by e-mail campaigns, what you find is both effects occur at the same time but each one happens with a completely different behavioral segment – Engaged versus dis-Engaging.  Unfortunately, in most cases, the net effect is e-mail falsely takes credit for more sales than it doesn’t get credited with accurately, because the Potential Value (likelihood to buy) of the Engaged is far greater than that of the dis-Engaging.

Against a graph of Response by how long ago the last click in an e-mail occurred, these dual effects on the actual response rate of an e-mail drop versus control group look something like this (red line).

Click picture to enlarge:

On the left we have the highly Engaged, and moving to the right, the pattern of dis-Engagement.  On the left, we have a much higher percentage of “would have bought anyway”, which decreases as we move to the right.  The portion of the blue bars above the red line represent buying activity in the control group – they bought without receiving an e-mail.

As we move to the right, this effect decreases, until the blue bars are now below the red line.  The space between the blue bar and the red line represents sales made because of your e-mail that were not tracked back to the e-mail drop.  Often, this is a result of simply not tracking the “campaign tail” for long enough, which is difficult to do without using a Control Group to find the long-term lift.

The implication: for commerce, you should be sending a different message to these different behavioral segments depending on where they are in the LifeCycle if you want to maximize profit.  In the segments with highest likelihood to buy, you should take it easy on the discounts; one way to optimize commerce profit across the entire engagement spectrum is to use a Discount Ladder

For all other business models, it’s highly likely that you could benefit from the same approach, if you have clear value KPI’s and understand this dis-Engagement process.

Now, I am well aware the above sounds insane to offline retail folks.  Most if not all of you lack the data to measure these effects, but that doesn’t mean they don’t exist.  For the folks that do have the data, the day will come.  Hey the web is interactive, the web is different, right?  Well, yes it is, so why measure the effect of promotions like they do offline if you have a superior method of optimizing for profit?

When a visitor / customer is highly Engaged, they often generate visits or sales without needing any Marketing at all.  That’s what the Relationship Marketing Strategy is all about – the Relationship drives the business.  That’s why, for example, people have found that including a lot of relevant and customer-focused content in a commerce newsletter gets higher response rates than just sending people coupons.  It’s why creating a new customer kit drives higher repeat purchase rates – it’s the Relationship building.  And that’s why you will find (if you use Control Groups) that for the highly Engaged, your e-mail program is taking credit for sales it did not generate, and that if you are providing discounts to the highly Engaged, you are probably wasting money on them.

A portion of the Engaged segment would have bought anyway, and the fact that you dropped a coupon in their lap with e-mail is simply coincidence.  Or, if you send a coupon every week on the same day, the customer simply waited for the coupon they knew they were going to get so they could make a discounted purchase they would have made anyway at full margin.  That’s how e-mail takes credit for sales it does not generate, and anybody who is managing to ROI / profit should care deeply about this.

Now, if you’re a “share” thinker, this subsidy cost related to “would have bought anyway” doesn’t matter to you, because any sale the other guy didn’t make is a good sale.  But last time I looked, you can’t put share in a bank account, and the logical extension of this share mindset is you can get 100% share by selling product below cost, so I have never understood it.  If your directive is increasing gross sales, that’s pretty much the same thing – you get there by unproductive ad spending, which in the end is the same thing to the bottom line as selling product below cost.

By the way, I’m not saying the Engaged should receive no communications, but they should get a different kind of communications tailored to their behavioral state.

On the flip side, no matter what your directive, you should care about not getting credit for sales your e-mail generated.  E-mail to another segment, those in the process of  dis-Engaging, almost certainly generates sales you are not tracking and not crediting to e-mail.  And that’s because the dis-Engaging are changing their behavior with the company for some reason.  They are seeking alternate channels, for example.  In other words, they are responding to your e-mail but they are not responding through your e-mail, they are not using whatever devices or links you give them in the e-mail but are still making a purchase because of the e-mail.  Again, you don’t see this unless you use Control Groups.

A third segment, the dis-Engaged, doesn’t respond to your e-mails at all.  And they’re not going to, because your company is now irrelevant to them.  The company has not been tracking the dis-Engagement process so it didn’t take any specialized action to slow or stop the dis-Engagement.  In fact, the company is probably just damaging their Brand by sending these folks any e-mail at all.

This is Relationship Marketing Strategy; it completely redefines how you communicate with customers based on where they are in the Engagement / dis-Engagement cycle.  And it works amazingly well.  The bottom line is customers remain customers longer – this Strategy tends to extend the LifeCycle – with the result customers end up with higher LifeTime Value.

Are you surprised?  You shouldn’t be.  People talk about this incessantly on the web all the time, don’t they?  Relevance?  Customer centricity?  Customer experience?  Blah blah blah? 

Then how come so few people are using a Relationship Marketing Strategy?  How come so few are using Control Groups to measure the true net influence of e-mail?  Why are people blasting out the same irrelevant message to all their customers once a week?  A lot of talk and very little action, methinks.  Perhaps you just needed a framework to put everything into perspective, a roadmap to getting it done? 

Now, I realize many folks in the community don’t have the tools they need to measure dis-Engagement; typically only the high-end tools have metrics like Recency and Latency and even though Google Analytics tracks Recency, it isn’t easy to do much actionable segmentation for that metric in the tool. 

There’s a very simple reason for this, if you think about it – it’s a lot harder to measure something that doesn’t happen than measure something that does happen.  After all, servers are all about requests, they’re not really thinking about “did not request”, if you know what I mean.  That job takes a database that’s remembering the last date a request was made forward in time, and calculating “did not request”.  That capability is a lot more expensive, at least for now.

But, I hope the “tool problem” doesn’t mean the community will ignore the concept of dis-Engagement while screaming to the skies about how important Engagement is to measure.  Those of you with access to a transactional database don’t have to wait for web analytics tools, you can profile customers for Engagement and the dis-Engagement process right in the transactional database with a simple query tool.  And I hope you now have a Strategic framework to think about why dis-Engagement is so important, at least from a Marketing perspective, so when you get your hands on that high-end tool, you will know exactly what to do with it.

If you really take some time to think about the ramifications of the Relationship Marketing Strategy that Engagement is a Tactical part of, you just might come to believe that dis-Engagement is even more important to measure than Engagement.

To summarize this series, the idea of Engagement, and a lot of notions surrounding it – customer centricity, relevance, and customer experience - are concepts within a Marketing Strategy known as Relationship Marketing that tosses out calendar-based communications in favor of communications based on the customer’s relationship with the company.  The ability to do this depends on an understanding of the Customer LifeCycle, the results of each interaction between the customer and the company over time.  The LifeCycle is tracked using various Engagement metrics, including dis-Engagement, which typically is the first sign of a problem with the customer Relationship.

If your company is having trouble understanding why Engagement is important to measure, perhaps it’s because senior management lacks the context of the Customer LifeCycle for taking action and the strategy of Relationship Marketing as a game plan.  Maybe you should send a link to the Wikipedia definition of Relationship Marketing to the CMO or CEO and ask, “Is this what you want?  Because if you do want this, I know how to measure the success of it”.

Questions or comments on e-mail engagement and response? 

Have you ever heard of the strategy called Relationship Marketing?

A Framework for Engagement – Implementation

Monday, February 4th, 2008

Like I said in the last post, I’m sure there are quite a few different reasons why folks want to measure “Engagement”, and not all of them have to do with Marketing.  But if you are talking about Engagement as a metric to be used in Marketing, now you have the complete framework for why (as opposed to how) it is so important to measure Engagement – to define the LifeCycle of the customer, in order to communicate and act in the most customer-centric, relevant way possible.  Logically, this approach drives higher profits.

The LifeCycle is about both Engagement and dis-Engagement.  If you are in the Marketing camp, you can’t just talk about measuring Engagement.  After all, if Engagement is really important and valuable, then dis-Engagement has to be really important and valuable in the opposite way – it’s a bad thing.  Dis-Engagement means, literally, that your company is no longer relevant to the customer. 

In some businesses, online display advertising for example, it’s not clear that dis-engagement really matters, at least in the current model from the perspective of the advertiser.  Hey, an impression is an impression, right?  Who cares what happens after that.  At least they’re talking about some kind of engagement metric – Duration – which should relate to the quality or the likelihood of an impression.  Not much more they can do, in my opinion, for that business model.  But from the perspective of the site owner the ads run on, dis-engagement should be a big deal – especially if you paid something to get that visitor to come to your site in the first place.

So, we have Engagement, and we have dis-Engagement, which it seems nobody ever talks about.  I sincerely hope that changes in the future as we move forward.

Now, how do we track the LifeCycle, how do we actually implement?  It’s really very simple in concept:

1. Define / Measure Engagement – any way you want to, as appropriate for your business; whatever activity or combinations of activity you feel appropriate

2. Measure dis-Engagement – the absence of Engagement, as in the visitor / customer stopped doing whatever it is you define as Engagement for your business model

3. Take some kind of Marketing or Service action to slow or reverse the dis-Engagement with dis-Engaging folks

That’s not very hard, is it?  No.  If you’re looking for some kind of model to follow for planning and managing LifeCycle communications, take a look at Satama’s REAN.

However, even when you get the LifeCycle and learn to react to it, the system is not optimized yet.  The Relationship Marketing Strategy is not optimized until you start predicting dis-Engagement, and taking action to try and re-Engage the customer before they completely dis-Engage.  Because once your company becomes completely irrelevant, it’s very hard to change that for the visitor / customer – much harder than if you act before or when the dis-Engagement is occurring.  You can’t have an “annual re-Engagement campaign” and fix this – you have to fix it as it is happening, meaning you throw out all calendar-based communication and communicate based on where individuals or segments are in the LifeCycle.

Fortunately, dis-Engagement is usually a process – unless the company screwed up in a really big way.  And this dis-Engagement process is fairly uniform and actually quite easy to predict with simple tools.  The company most often becomes irrelevant to the visitor / customer over time.  In other words, the company gets second chances, the customer often gives the company leeway to become relevant again.  So as a company or analyst, the key is to:

1. Recognize dis-Engagement has begun with a customer or segment

2. Have a re-Engagement plan and implement the plan before the company becomes irrelevant to the customer

I’m pretty sure most people reading this know what comes next – how to measure dis-Engagement and act on it – given I have plastered this information all over my blog and web site.  If you don’t know how to predict dis-Engagement and the triggers you can use to take action, this is a good place to start.  Depending on your business model, you should probably also take a look at what Theo proposes in terms of Kind and Degree for survey work once you have dis-Engagement behavior as a trigger for the survey.

For most web sites, regardless of what you are using as a metric for Engagement, a good clue the dis-engagement process has begun is when a visitor stops visiting, posting, commenting, buying, or whatever is key to generating value on your site.  The challenge is you have to recognize this non-event has occurred right away, because the longer you wait to try and re-Enage the visitor / customer, or ask why they are dis-Engaging though a survey, the less likely it is you will be successful.  And in case you are wondering, those of you with e-mail tactics that consist of relentlessly pounding your list with the same messages and offers regardless of visitor / customer behavior are not addressing the re-Engagement issue – trust me.  Think about it.

I’ll get into how a Relationship Marketing strategy affects e-mail marketing and measurement tomorrow in the next post.

Does the above make sense to you?  Questions?  Criticism?  Problems?  Let me know, leave a comment.

A Framework for Engagement – Background

Sunday, February 3rd, 2008

There has been a tremendous amount of discussion around the Engagement topic that started here and fragmented into a bunch of chunks and related topics, including here, here, and here.  I have also had a lot of one-on-one correspondence in and around these topics through e-mail lately, and while thinking through all this, realized there’s a lot going on but there may be a “can’t see the forest for the trees” scenario building here.

In other words, the discussion is so focused around Tactical measurement issues that folks may not be seeing how “Engagement” is an important part of a much bigger Strategic idea, and that fact should influence the way people think about Engagement, if this whole concept is ever going to be more than just a pile of exotic Tactical metrics lying around.

Now, I realize people want to measure Engagement, particularly on-site Engagement, for lots of different reasons that may not have to do with Marketing Strategy.  I think it’s the nature of people with technical backgrounds to think very Tactically.  Many of these folks are more concerned about usability or something closer to “customer experience”, or the general appeal or stickiness of a web site.  And that’s certainly OK with me, and important work. 

But this post is not for you folks. 

This post is for the more Marketing-oriented analysts and managers, many of whom have technical backgrounds and have done an amazing job of learning about Marketing.  Really.  I wish Marketing folks would take the time to learn as much about Technology as these technical folks have learned about Marketing.

Marketing is much more than the Tactical stuff, like Advertising, Branding, conversion, and all that.  At least to some of us old folks.  Marketing has traditionally had a Strategic role, which means it sets up the business model and ensures that the “product”, whatever that is, directly addresses the marketplace and audience, that the product is built to sell – and stick.

Those of who are interested in Engagement from this more Strategic level of Marketing, this post is for you.  And just as an aside, we’re not talking about fuzzy Branding and social media Engagement stuff here (You’ve got to engage your customers!  Really!  It will be good for business!), we’re talking about real world, provable, profit and loss Database Marketing (here’s how to increase profits by measuring and acting on Engagement).

In the mid-eighties some academic folks published thought pieces around a data-driven marketing approach they sometimes referred to as Relationship Marketing.  In 1992, a pretty famous Silicon Valley guy named Regis McKenna published a book called Relationship Marketing, which put forth a framework and real life examples of how it all worked, mostly focusing on B2B. 

Essentially, the idea behind Relationship Marketing is that rather than segmenting customers by age, income, product, and so forth, you segment them by where they are in their relationship with the company; you create a Marketing Strategy that is customer-centric instead of business-centric.  The core Strategy was this: if you communicate with customers based on where they were in their Relationship with the company, the communications would be more relevant to the customer, so your marketing would be more successful and profitable over the long run. 

Customer-centric.  Relevant.  I’m not kidding.  Check it out on Wikipedia if you don’t believe me; you will be shocked at the concepts and language used.  It all should sound very, very familiar to you when you read it…

It gets better.  To do this Relationship Marketing thing properly, you have to understand what the Customer LifeCycle is.  Lifecycles are literally a measurement of engagement and dis-engagegment between the customer and the business.  The idea was if you could measure these engagement / dis-engagement cycles, you could tell when things were going well or poorly between the customer and the business, then proactively take action to correct problems and issues.  Further, if you got very good at recognizing these cycles and patterns, you could actually anticipate / predict problems and take corrective action before the customer had a negative experience.

So it’s 1993, and I’m sitting in the middle of the greatest interactive experiment to date on the consumer side – Home Shopping Network.  And I’m thinking, if this Relationship Marketing Strategy works so well in B2B, why wouldn’t it work for B2C?  The data will be different, the metrics different, the trigger behaviors different, the Tactics different, but the Strategy, the Relationship Marketing Strategy – wouldn’t that work?  Particularly when we’re so interactive with the customer?  Shouldn’t it work even better when there is direct and sustained interactivity with the customer?

You wouldn’t believe how well it worked, at many different levels of the company.  Customer Marketing programs with unimaginable response rates.  90-day ROMI numbers of 400% or better on a consistent and sustained basis – using offline marketing.  That’s how well it worked.  The magic of using Control Groups to measure campaign profitability allowed us to capture every bit of incremental profit that was derived from re-engaging the customer – even if they didn’t respond directly through the campaign itself, but through another channel.

So, these conversations about customer-centricity, relevance, and engagement have been taking place for over 20 years in Marketing Strategy.  The challenge with this model - and probably why it isn’t more widely known – has been the data, it’s a very analysis-intensive model, as you might expect.  

In B2B, McKenna’s ideas grew into what is now known as Contact Management or Sales Force Automation.  On the B2C side, I guess CRM was supposed to be the ticket, but somehow (until recently) these folks forgot about how critical the analytical part of the equation was to the success of the Relationship Marketing concept.

As far as online goes, we have the data,  but until fairly recently the online space has been so all about Brand and Advertising, not Relationships.  Now the Web has decided it will be all about Relationships, which is very cool.  The web is perfect for that approach, (um, can you say interactivity?) and it’s perfect for a Relationship Marketing Strategy.

I’ll get into the Implementation of a Relationship Marketing strategy tomorrow in the next post.

Interview-Podcast w/ Jim Novo

Friday, February 1st, 2008

Friend and fellow blogger Alan Rimm-Kaufman spent some of his valuable time asking my opinion on various online marketing issues in a far-ranging interview and podcast.

We met in person for the first time doing a presentation together at the DMA show in Chicago this fall, and because he used to work at Crutchfield – a truly customer-driven remote retailer – we share some experiences and beliefs.

For those of you who might be wondering where a lot of the Marketing Productivity ideas I post here come from, this interview-podcast is probably a pretty good backgrounder.  We talk about a lot of stuff, including:

Monetizing customer experience

Importance of Control Groups / Source Attribution

Multichannel Marketing Strategy

LifeCycle Contact Strategy versus Calendar-based

Retail Business Models / Lab Store

Search box or not? / Serendipity

How to tell if online customers are really engaged – without web analytics

Here’s another link to the Interview-Podcast.  Enjoy! 

That was lots of fun, thanks Allen!

Messaging for the Apathetic

Sunday, January 20th, 2008

Recall from the Messaging for Engagement post we generally have 3 states of customer in the database:

  • Engaged – highly positive on company, very willing to interact - Highest Potential Value
  • Apathetic – don’t really care one way or the other, will interact when prompted – Medium Potential Value
  • Detached – not really interested, don’t think they need product or service anymore – Lowest Potential Value

Combine this messaging approach with a classic behavioral analysis, (the longer it has been since someone purchased, clicked, opened, visited etc., the less likely they are to engage in that activity again) and you get different messaging for each group, what I call Kiss, Date, and Bribe.  Click image to enlarge if you want…

Kiss Date Bribe

Please note “Months Since Last Contact” means the customer showing interest / taking action and contacting you in some way (purchase, click) not the fact that you have “contacted” them by blasting out e-mails.  Behavioral analysis is about customer behavior, not yours.

We’ve already gone over an example of Kiss Messaging, so lets provide an example of Messaging for the Apathetic. 

Recall the tactical background with Apathetics:

Apathetic – Date Messaging: We’re not real clear where we stand with you, so we’re going to be exploratory, test different ideas and see where the relationship stands.  Perhaps we can get you to be Engaged again?  In terms of ROI, this group has the highest incremental potential.  Example: this is where loyalty programs derive the most payback.

The most consistently successful (meaning profitable) messaging for this group generally looks at what their past behavior is and tries to drive it just a bit higher with a carrot / stick combo. 

In commerce, if you were looking at a behavioral segment such as “No Purchase in 180 days” (month 6 on chart above), you find their average purchase price and then discount for purchasing over that average price threshold.  So, for example, if a segment (or individual customer, if you can go that far) has an average purchase price of $80, you do a promotion like $10 off any Purchase over $100.  This approach tends to preserve margin on the customer while driving new activity, thus setting up the customer to become re-Engaged on a longer-term basis. 

Why re-Engaged?  A new purchase moves them to the 1 month column in the chart above, so they have a much higher “natural” likelihood to purchase again.  They are now Engaged again, and their messaging should change to Kiss, if you want to really leverage their state.

The values I have chosen above are not a “formula”, you have to test and optimize the thresholds and discounts for your business.  For example, sometimes people don’t trade up to just over the threshold, they’ll respond to a $10 off purchase over $50 discount by generating an average purchase price of $125.  Now you’re talking some severe latitude on your margins and you can try for incremental response with a higher discount or try to drive margin with a higher threshold.

The trick with Apathetics is this: unlike the Engaged, they probably need some incentive to act on.  But unlike the Detatched, they still have some Potential Value you would like to unlock – you don’t want to just all out bribe them because you’ll lose some of that Value. 

After all, in an always-on sales environment like the web, some people are going to purchase anyway – without an incentive - no matter what segment they are in.  For this 180 day case (chart above) a healthy portion of the 7% are “buy anyway” kind of folks.  The more Recent the action, the more likely it is to repeat.  That’s why you give ‘em a threshold – to ensure you don’t give away more margin in discounts than you are making from the rest of the promotion. 

Does this “threshold approach” depress response?  Sure.  But are you trying to drive response (gross demand) or profit?  Those of you whose success is judged by ROAS don’t need to answer; profit doesn’t matter in your world.  You’ve never used a control group.

If you were working on my business, I’d want you driving profit.

*** Where Are Your Brand Manners?

Thursday, January 10th, 2008

Here’s an article giving quite a few examples of companies taking the route I covered in the CMO: Strategic Seat is CCO post.  Interesting that the majority are direct marketing companies and at one, customer service reports to Marketing.  This Marketing department views their job as “service to the customer”.  Now that’s a Marketing department I would feel at home in.

Marketing is indeed a much greater force to be reckoned with when it is strategically and operationally integrated into the brand promise and product offerings and not just a Meatball Sundae.  But somebody has to think that through and make it happen. 

How about you?

Here’s the link:  Where Are Your Brand Manners?

 

KFI’s: Key Forecast Indicators

Sunday, November 11th, 2007

As I said in my presentation at the eMetrics / Marketing Optimization Summit, if you want to get C-Level people to start paying attention to web analytics, you have to get into the business of predicting / forecasting.  Let’s face it, KPI’s are about the past, right?  You don’t know “Performance” until it has already happened.

But C-folks don’t really care much about what has already happened, because they can’t do anything about it.  What they really want to know is what you think will happen.  For example, ideas like “sales pipeline” – a forecast.  If you start forecasting – and you are right - you will get attention from the C-folks pronto.  The web is a great forecasting tool because it’s so frictionless; it tends to provide tangible signals before many other parts of the business.

So: Do you have any KFI’s – Key Forecast Indicators?

I have one for the Lab Store, and it tripped about 2 months ago.  It’s the Unwanted Exotic Index (UEI).

As part of the Lab Store, we run a moderated board where people who want to give up exotic pets can post the availability, and people looking for exotic pets can post requests.  Typically, the ratio of people giving them up to wanting them is about .25 - for every post looking to give an exotic up, there are 4 posts looking to adopt.

A couple of months ago, this ratio starts popping higher.  A couple of weeks ago it hit 1.25 – for every 5 posts looking to give up an exotic there were 4 posts looking to adopt.  The last time something like this happened was prior to the mini-recession of 2004, when the Unwanted Exotic Index tagged 1.0 for a short time.  After this happened, our sales got soft about 2 – 3 months later.

Why is the UEI predictive?  Let’s go through the logic – my logic, anyway!

Keeping certain types of exotic animals can be a strain on a family, both from a time and money perspective.  They can be high maintenance.  On the margin, as the economy gets tougher and people look to manage household budgets, these pets can get some scrutiny – particularly if kids have lost interest or gone off to college.  So more go up for adoption.  At the same time, requests to adopt fall, as families who might have considered an exotic pet put the “owning decision” on hold.  Taken together, these decisions cause the UEI to spike higher.  Both giving up and deciding not to own exotic pets affects Lab Store revenues “expected” in the future.  So the UEI ends up being predictive of future demand.

Makes sense to me.

Now, I’m a pretty good student of macroeconomics and pay attention to many economic indicators, especially predictive ones like the ECRI’s US Weekly Leading Index.  If you’re an analyst, you should too; economic indicators provide context for any analysis you might have to do, and clients often want to understand the impact of these external issues on their business.

As far as the Lab Store specifically, I don’t usually pay much attention to the macroeconomic cycles.  The pet business tends to be insensitive to the economic cycle; people don’t stop caring for pets as the economy wobbles up and down.  That’s why it’s such a good business – if you can find a niche.  So I don’t get too concerned when I see these predictive macroeconomic indexes forecasting a slowing economy.

However, what we have here with our Unwanted Exotic Index is a confirmation of the broader economic forecasting tools that is specific to our exotic pet business.  That makes me sit up and take notice!  Looks like our business is setting up for a repeat of the 2004 slowdown - the last time the UEI spiked like this.  Why is this important?  Because I can do something with this knowledge.  I can re-allocate and re-prioritize based on this knowledge.  For example, I can move from a “grow bigger” to a “grow smarter” mode.

And please note: this KFI has nothing to do with traffic or sales on the web site; traffic and sales are “rear view”.  By the time you see the sales slow down it will be too late to do anything about it.  And that’s why the C-folks don’t care much about web analytics reports.  

You could track an index like the UEI with a web analytics tool, but you’d have to come up with the idea first.  My point is you will probably have to look outside the usual “rear view” metrics to find one with forecasting ability.  I caution you not to substitute a “survey” for a predictive model; people’s opinions are a notoriously lagging indicator.  You’ll be up to your ears in the slowdown before people start turning bearish.

So: Do you have any KFI’s – Key Forecast Indicators?  Tell us about them. 

If you don’t have any KFI’s, now is the time to start looking for them.  What can you see now that predicts what will happen in the future?  Think about the business, think about the data sources, and put together a bunch of different ideas.  Track them back a couple of years and post them monthly going forward.  You’re bound to find something predictive.  Perhaps something about posting, like the UEI.  Recommendations / comments as a percent of visitors or something like that.

If you’re stuck, start with a simple “engagement” idea – percent visitors / members / customers who visited / logged in / bought in the past 90 days.  If this percentage is falling, so will your business in the next 3 – 6 months.  If your business has a lot of seasonality in it, look to year-over-year comps of the same metric.

If you’ve never played this game before, you won’t have proof your KFI’s work until after the business is in the soup, but you’ll be ready with accurate and actionable KFI’s the next time around!

What’s the Frequency?

Wednesday, October 31st, 2007

The following is from the October 2007 Drilling Down Newsletter.  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?  The pre-blog newsletter archives are here, “Best Article” reviews here.

Q:  I ordered your book and have been looking at it as I have a client who wants me to do some RFM reporting for them.

A:  Well, thanks for that!

Q:  They are an online shoe shop who sends out cataloges via the mail as well at present.  They have order history going back to 2005 for clients and believe that by doing a RFM analysis they can work out which customers are dead and Should be dropped etc.  I understand Recency and have done this.

A:  OK, that’s a great start…

Q:  But on frequency there appears to be lots of conflicting information – one book I read says you should do it over a time period as an average and others do it over the entire lifecycle of a client.

A:  You can do it either way, the ultimate answer is of course to test both ways and see which works better for this client.

Q:  Based on the client base and that the catalogues are seasonal my client reckons a client may decide to make a purchase decision every 6 months.  My client is concerned that if I go by total purchases , some one who was  really buying lots say two years ago but now  buys nothing could appear high up the frequency compared to a newer buyer who has bought a few pairs, who would actually be a better client as they’re more Recent?  Do I make sense or am I totally wrong?

A:  Absolutely make sense.  If you are scoring with RFM though, since the “R” is first, that means in the case above, the “newer buyer who has bought a few pairs” customer will get a higher score than the “buying lots say two years ago but now buys nothing” customer.

So in terms of score, RFM self-adjusts for this case. The “Recent average” modification you are talking about just makes this adjustment more severe.  Other than testing whether the  “Recent average” or “Lifetime” Frequency method is better for this client, let’s think about it for a minute and see what we get.

The Recent average Frequency approach basically enhances the Recency component of the RFM model by downgrading Frequency behavior out further in the past.  Given the model already has a strong Recency component, this “flattens” the model and makes it more of a “sure thing” – the more Recent folks get yet even higher scores.  

What you trade off for this emphasis on more recent customers is the chance to reactivate lapsed Best customers who could purchase if approached.  In other words, the “LifeTime Frequency” version is a bit riskier, but it also has more long-term financial reward.  Follow?

So then we think about the customer.  It sounds like the “make a purchase decision every 6 months” idea is a guess as opposed to analysis.  You could go to the database and get an answer to this question – what is the average time between purchases (Latency), say for heavy, medium, and light buyers?  That would give you some idea of a Recency  threshold for each group, where to mail customers lapsed longer than this threshold gets increasingly risky, and you could use this threshold to choose parameters for your period of time for Frequency analysis.

Also, we have the fact these buyers are (I’m guessing) primarily online generated.  This means they probably have shorter LifeCycles than catalog-generated buyers, which would argue for downplaying Frequency that occurred before the average threshold found above and elevating Recency.

So here is what I would do.  Given the client is already pre-disposed to the “Recent Frequency” filter on the RFM model, that this filter will generally lower financial risk, and that these buyers were online generated, go with  the filter for your scoring.

Then, after the scoring, if you find you will in fact exclude High Frequency / non-Recent buyers, take the best of that excluded group – Highest Frequency / Most Recent – and drop them a test mailing to make sure fiddling with  the RFM model / filtering this way isn’t leaving money on the table.

If possible, you might check this lapsed Frequent group before mailing for reasons why they stopped buying – is there a common category or manufacturer purchased, did they have service problems, etc. – to further refine list and creative.  Keep the segment small but load it up if you can, throw “the book” at them – Free shipping, etc.  

And see what happens.  If you get minimal  response, then you know they’re dead.

The bottom line is this: all models are general statements about behavior that benefit from being tweaked based on knowledge of the target groups.  That’s why there are so many “versions” of RFM out there – people twist and  adopt the basic model to fit known traits in the target populations, or to better fit their business model.

Since it’s early in the game for you folks and due to the online nature of the customer generation, it’s worth being cautious.  At the same time, you want to make sure you don’t leave any knowledge (or money!) on the table.  So you drop a little test to the “Distant Frequents” that is “loaded” up / precisely targeted and if you get nothing, then you have your answer as to which version of the model is likely to work better.

Short story: I could not convince management at Home Shopping Network that a certain customer segment they were wasting a lot of resources on – namely brand name buyers of small electronics like radar detectors – was really worth very little to the company.  So I came up with an (unapproved) test that would cost very little money but prove the point. 

I took a small random sample of these folks and sent them a $100 coupon – no restrictions, good on anything. I kept the quantity down so if redemption was huge, I would not cause major financial damage.

With this coupon, the population could buy any of about 50% of the items we showed on the network completely free, except for shipping and handling.

Not one response.

End of management discussion on value of this segment.

If you can, drop a small test out to those Distant Frequents and see what you get.  They might surprise you…

Good luck!

Jim

On Engagement

Wednesday, October 17th, 2007

I’ve had some bad luck with connecting to the web lately, trying to catch up on blog posts as the latest trip winds down.

The panel on Engagement at the WebTrends customer meeting was a lot of fun, probably best described as “productive friction” if forced to describe it with a phrase.

Based on comments from the audience, the panel was quite useful in terms of vetting some of the ideas floating around out there and answering their burning question, “Am I missing something here?  Why should I care about this engagement thing?”

This in itself is an interesting issue: generally, the audience perceives “engagement” as yet another buzzword of the week that like most buzzwords, is simply another word for stuff most of the audience deals with all the time, namely customer service and retention – or customer “experience” if you prefer last week’s buzzword.  This was the insight I gained from the well-lubricated crowd at the party after the panel, so please take this fact into account as well.  Do people tend to say what they really think after a few drinks?  Or were they just tired of talking about web analytics the whole day?

Some of the more interesting discussion among the panelists actually took place right before and after the panel, when we had a chance to really first explain our positions and then challenge each other to defend them.  Great conversation.

For what it’s worth, here’s a breakdown of what I thought I heard being said.  My perception and reality may of course be different and I encourage participants to correct any misperceptions I may have had…

Andy Beal – as the only “generalist” on the panel, I think Andy was a bit steamrolled by the hard core “get the facts” thing web analytics folks do.  He maintained web analytics could measure only one area of customer engagement with a company (the web), and that you would never get the full picture of engagement because some of it is unmeasurable.  Probably true in a strict sense, though I bet there’s a lot that can be measured on the web through customer conversations and so forth.  However, we left this “can’t be measured” question to simmer, because the rest of the panel and the audience wanted to talk about web analytics so that was what we were going to do.

Anil Batra / Myself – I’ll go out on a limb and say our positions were very similar; I’m sure Anil will chime in.  Basically, the formula is this:

The difference between Measuring Activity and Measuring Engagement is Prediction.

In other words, when you start using the word Engagement, you are implying “expected” activity in the future, with this expectation or likelihood being valued or scored with a prediction of some kind.  Activity without an implication of continuity is simply Activity, it’s history and stands alone.  Same stuff web analytics has always done, nothing new.

Jim Sterne – Jim was a bit more global in his thinking as you might expect, and seemed to be concerned more about how Engagement fits into the greater Marketing picture rather than looking to hang parameters on it.  How Engagement is related to Customer experience and Brand, how it does or does not turn into Loyalty, and so forth.

Gary Angel / Manoj Jasra – not sure either of these fine folks fully buy into the “prediction” requirement Anil and I support, though they might be talked into it.  Gary and I had a long conversion which included June Dershewitz after the panel, where we traded examples and generally wrestled over what I would call the “advertising / duration conundrum”. 

I maintain advertising is an outlier in this discussion, which is strange since those folks basically started this whole engagement thing and stoked the fire hard with the Duration variable that got web analytics folks in general so pissed off.  Not sure Gary or Manoj will ever accept Duration in any form as a measure of Engagement, where I maintain that if you isolate Advertising as a unique conversation, it makes a lot of sense.  The reality of buying online display ads is you need an absolute standard or the networks and buying process absolutely fall apart; you simply cannot look at a unique Engagement metric for every site or the buy would never get done.  So you hold your nose, say Duration is important to advertising as a metric, and do the deal.

In other words, there is a huge difference between being Engaged with a site and being Engaged with an ad on the same site.  These are two completely different ideas and unless you believe that Engagement with a site always spills over to Engagement with the ads on the site (I do not) then these two ideas deserve two different treatments.

June wanted to get into it all over again at the eMetrics Summit…feel free to post your comments here June!

What Would You Pay for Web 2.0?

Friday, September 7th, 2007

I would like to “measure your engagement” with Web 2.0 applications.

Would you pay $10 a month for your Facebook, MySpace, Twitter, or other Web 2.0 app account?  

Things to consider including in your answer:

1.  Why you would pay $10 a month, what is your “can’t do without” functionality?

2.  Would advertising have to be banned from the app for you to pay anything at all?

3.  What alternative would you use to achieve the same “can’t do without” functionality if you had to pay $10 a month for your account and there is NFW you are going to pay?

Please comment.