Marketing Productivity Blog

I’ve spoken in the past about our local CBS affiliate WTSP-10 and their Moms Tampa Bay effort as a great example of “old media” that gets Social and has created something quite powerful using that ‘ol stick of theirs.

WTSP’s 6 PM newscast now features “news pics” sent in by viewers in almost every show.  They headline the story, run through the video they have, and on the way out, the anchor says,

“Here’s some pictures of the scene sent in by our viewers”.

Nice effort to be more interactive.  If I was going to Optimize it, I’d like to see it a bit more personalization, which increases the prep time, of course.  But it would be nice to hear the anchor say “Jody in Seminole sent us this picture” or something similar. 

The challenge with that is someone has to probably respond to the e-mail address or phone number, ask for name and location, and probably (to be on the safe side) get permission to use the name.  You could take care of a lot of this with some automation on the Submit with an auto-responder or similar technology.

This weekend I witnessed evidence another station in this area might also get it, public station WEDU-3.  What was great about this execution is it ‘worked” from a TV perspective; in other words, it did not feel “forced” as these cross-media attempts often do. 

Aside: Doesn’t it drive you crazy when TV people say “Log In to our web site for more info”?  Log in?  Why make it sound like a chore or you need an account to access the expanded info?  Can we please say “Visit our web site” instead of “Log In” you TV folks?

Anyway, I had this local political talk show on in the background while reading the Sunday paper (Yes, I actually watch TV and read the Sunday paper, I enjoy the Serendipity).  The moderator winds up a segment, then pitches the premise for the next segment.

Then he says:

“Before we hear from the panelists, let’s listen to what you have to say on this topic”.  They cut to audio recordings of 4 callers to their “Comment Line”.  A couple of calls on one side of the argument, a couple on the other side.  As the audio plays they have the spoken words in type on the screen, with the name and location of the caller.

Coming out the other side of this viewer commentary, the moderator looks straight at the camera and says ”Thanks very much for your comments, keep them coming” and promos the Comment Line.

Whoa.  Nice package.  Very matter of fact delivery, not all breathless and “We Care” crap.  The callers were very well-spoken, obviously someone had gone through and pulled the plums for the topic.  Sliding in the promo was seemless due to the immediate context, and these 4 short audio pieces were a fabulous set-up for the panelists, who often referred to the callers by name in their follow-on discussion.

Very, very smooth for TV.  If you’ve ever been involved in TV studio production, you know that stuff like this looks easy but it’s also easy to screw it up, to have it come off fake and plastic-looking.  There is a huge difference between having the talent say “We got an e-mail, here is what it says” and the using the actual Voice of the Customer.

Pun intended.

In other words, I think someone actually thought it all through, every step, the timing, the shots, the language, all of it.  I don’t think it was an “accident” this worked so well.  And that’s really good news, because it means the TV folks are starting to get it.

Don’t underestimate the power of the “the stick”, it has tremendous reach.  All they have to do is figure out how to Optimize the Interface a bit, you know, Band 3.  And this effort was a great example of just that; the audio bits, and the opportunity to participate, are the Pull.  I thinks it’s a good bet they will get a lot more callers to the Comment Line next week, don’t you think?

Hey, people might even rehearse what they are going to say to improve the likelihood their bit will make the cut and be on TV.

So, how are the TV stations in your market doing with integrating Social elements?  For all I know, the stations in my market are late-comers, not leaders in this area.

Or, perhaps the more relevant question is: Do you watch TV anymore?

Optimizing Individual Communications

Where the Band 4 Optimization optimizes general communications like newsletters, the Band 5 Optimization is all about hyper-targeted communications to individuals.  We’re talking mostly about special circumstance stuff here, more exotic ideas that may actually fall outside what you might traditionally think of as “Marketing”. 

If Band 4 is the “Air Cover“, Band 5 is Special Ops (see Band Model).

In Band 5, you basically have algorithms of various kinds that are ”sniffing” the databases looking for special situations that have exceedingly high ROMI.  Often, these ideas deal in one way or another with high value customers that appear to be dis-Engaging; many of these scenarios related to Marketing, Service, or Product in one way or another.

The tactical Marketing idea is this: you have all these sequenced communications in Bands 1 - 4, and some of them can be customized down to a certain level, but you are still dealing with fairly broad segments.  There are certain situations where you want to reach out at the micro-segment or individual level that can’t be handled by the “air cover” media, no matter how customized / personalized it is.

Our largest program in Band 5 was FIPS - the Future Intent to Purchase Score.  Small audience, large impact; the ROMI on this thing was unbelievable.  To give you an idea of scale, the mass customization Magazine dropped to about 2.5 million customers.  The FIPS drop usually ran about 100,000 pieces during the cycle between Magazine drops. 

Yet, FIPS generated more incremental profit per drop cycle than the Magazine, which dropped all at once.  FIPS dropped when the customer was “ready”.  More on this idea below.

FIPS was based on a hand-built multiple regression model.  The data used to build the model came out of exotic ideas we just came up with and tested based on results from the Magazine and a fundamental understanding of Interactive behavior.  Lots of these special situation tests failed financially, but they ultimately provided the data needed to build the FIPS model.

The personalization data we used for the Magazine was driven by an Engagement model based solely on purchase behavior.  FIPS added lots of other behavioral data points.  One of the most mind-blowing, and ultimately the most revealing idea, had to do with the use of Tootie - the Interactive Voice Response Unit.

Tootie began life ignored by our best customers - the same folks who specifically asked us for “a way to order without waiting on hold for a Net Rep”.  In other words, these customers craved more control of the Interactive system we offered them.

After Marketing decided to go in and Optimize what those smart engineers had built, Tootie rapidly gained support from best customers.  The “personality” of the VRU was so popular she would get Holiday cards and gifts from customers!

What we found buried in the FIPS model was this: changes in use of the VRU predicted eventual customer defection by defining a tremendously important concept - Peak Engagement.

Here’s a simple explanation of this part of the FIPS model: if the percentage of orders placed using Tootie was rising, the customer was Engaged, and accelerating.  If the percentage of orders placed using Tootie was falling, this best customer - even though she had been purchasing Frequently and purchased Recently - was dis-Engaging, and on the way to Defection.  For those of you who might care, the actual calculation was more like a “Rate of Change” idea. 

Think about this for a second.  The customer, by all “normal” ways you might view behavior, was “Active” and still purchasing.  But there was a change in the associated behavior which predicted the customer was in Defection mode, that Engagement had Peaked.  It signaled the customer was beginning to dis-Engage even though the customer was still Active.  And it tuned out that Peak Engagement was the absolute highest ROMI opportunity for a Marketing intervention, a classic “TripWire” or trigger for taking Action.

For the web analysts out there, this idea would be similar to a visitor who has a history of posting  comments or reviews evey week who continues to visit at the same rate, but all of a sudden posts less frequently, say every other week.  FIPS would look at this change and call it a “trigger”, predicting the visitor’s post frequency would start dropping, and then pretty soon, the visitor would simply stop visiting.

The question is, if you knew this scenario was going to play out for a whole segment of Buyers, Pokers, Frienders, Posters, Reviewers, or whatever your site’s primary Action is, what would you do?  How would you go about redesign or adding new functionality?  How would you treat them differently, what would you say? 

For those of you that are into surveys, after this analysis, at least you would know who specifically to get feedback from.  Nothing like knowing exactly who they are behaviorally to get to Root Cause.

The first time we heard about this Peak Engagement idea, everybody in the room (about 15 analysts / Marketers) gasped out loud, followed by a chorus of “NO WAY!” (this was the mid-90’s, remember).  The question, as with all great behavioral analysis, was “why?”  What explains the relationship between Tootie use and Peak Engagement?

We took customers who tripped Peak Engagement and ran their history backwards to look for clues.  We came to a perfectly mundane conclusion: when we could find something tangible, the root cause of this change in Tootie behavior often was what we called Friction.   In today’s language, you would probably call these causal events ”customer experience issues”. 

Oooh, call the Chief Customer Officer…

In particular, for you segmentation fans, in order of magnitude:

1.  Customer bought a certain product or from a certain category.  If you followed this track down deeply enough, you usually found poor quality, misleading copy, high return rates, etc.

2.  Customer participated in a Marketing campaign.  Typically, these were run by well-meaning affiliates but not always; some were from other divisions of the company or mistakes we made.

3.  What appeared to be poor service experiences, probably related to #1 above, that could have or should have been handled in a more appropriate way, given the value of the customer.

The customer’s response, the “signal” they gave that Friction had entered their relationship with us, was a decline in direct interactivity with the system - declining rate of Tootie use.  The giving up of control over the system they had initially chosen to control.  In other words, it wasn’t as much of a pleasure to be in control anymore; Peak Engagement with the Interactive system had passed.

These customers, upon experiencing Friction, decided they would rather use a live rep (and probably wait on hold) than the Interactive interface to place orders.  Something fundamental had changed; the customer was slipping out of Interactive mode into a more catalog-like relationship.  They were moving from a lean-forward, participatory experience to a lean-back, detached experience.  What’s worse, this change in VRU usage creates a more frustrating customer experience (being on hold) than they were used to with Tootie.  So now they’re in a “Friction Loop”, creating more and more frustration.

Peak Engagement - the beginning of the end of the relationship.

Customers expressed this idea in their own words as “not as much fun anymore to shop”.  In most cases, they continued to shop, but with declining momentum and ultimate defection.  We had failed them, broken the Interactive bond.  Now we were just a “store”, like any other store or catalog.  The thrill of Interactivity was gone.

This effect is what I tried to describe back when the Engagement discussion started as the difference between Physical and Emotional Engagement.  There is a real difference.  The fact that someone is taking Actions (Physical Engagement) does not mean they are Emotionally Engaged, that they are deriving increasing benefits from the Interactions they Engage in.

This analysis drove the realization that Interactivity was truly a different thing from a Marketing perspective; it had a different emotional set surrounding it.  This is the idea an interactive system creates “Pull”, a desire above and beyond the utility it provides.  When this emotional layer is stripped away, you’re left with the utility of the experience.  And the utility of it the experience - Physical Engagement - is often not enough to maintain a relationship.

Marketing Productivity Conclusion: in an Interactive system, from a customer Marketing perspective, dis-Engagement is more important to measure than Engagement.  Engaged customers, well, they just truck along quite nicely all by themselves; the Interactivity itself is the Marketing, the Pull.  It’s when they start to dis-Engage from that system that you need to take Action; that’s the highest ROMI Tripwire.

Based on the above, the creative idea for the FIPS mailer was very simple - Recognition.  There were lots of versions, but the format was the same.  Plain white envelope with the HSN logo on the outside.  Short, very carefully written, one page letter.  Primary message idea:

“We just wanted to take the time to thank you for being one of our very best customers, and let you know we appreciate your business.  If we can be of service in any way, please let us know”.

As an aside, when a product or category was clearly the root cause of Friction, we turned this “evidence” over to the Business SWAT team, along with our calculation of lost customer value - nice touch, huh?

Understand, as is often true in database marketing, it’s not the creative that’s most important here - it’s the timing.  The right person, at exactly the right time.  With an appropriate message.  That same message anywhere else in the LifeCycle would not have been nearly as effective at “Pulling” these people back into the system.

In case you are wondering, yes, of course Customer Service knew all about this program.  In fact, they helped design the program, since the logical conclusion was some of these customers - if they had not given up on us completely - were going to call.  And call they did.  We even managed to get a flag into the rep screens so when a rep pulled up the customer, they knew the customer was in “FIPS Mode”.

Just to be crystal clear, not all of the FIPS customers had some kind of service issue.  But if they did have one, we wanted to try and resolve it in a manner appropriate to the customer’s value.

You just can’t do this kind of thing with a “calendar drop” mentality, it won’t work.  You have to drop according to where each customer is in their LifeCycle - regardless of date.  In a really large operation, that might mean you drop almost every single day of the year.   When the customer is ready, you have to drop - it’s a Tripwire thing.

FIPS was a completely automated, “lights-out” program - nobody had to touch it once it was built, except for occasional model tuning.  When the customer told us she was ready through her behavior, the mail went out automatically, directly from the lettershop with all the other customer service communications.

The right customer, at the right time, with the right message, all completely automated.  Next time you hear that phrase and think “that’s a bunch of crap, it will never happen”, remember the FIPS example.  It not only works, it works far better than you can imagine.

Any questions on this?  Anyone done similar stuff they can talk about?

(A post by post index of this Marketing Bands Series is here.)

After the lessons we learned in the Band 1 and 2 Optimizations (see Band diagram) for HSN, we were able to reallocate that budget to invest in Band 3 - Optimizing the Interface.  We realized during the previous Optimizations we were already getting a tremendous amount of traffic through channel surfer / clickers, but this traffic was not “converting”.  In other words, we really needed to Optimize the “Landing Page” for this existing audience - the TV show itself.

Don’t suppose the above scenario sounds at all familiar to the web analytics folks out there - you know, “more traffic, any traffic” is the answer?  Oops, maybe not, what about higher conversion?

I won’t bother providing the Band 3 example for the web; you all know what Optimizing Landing pages / web sites is about, or can certainly find that info elsewhere.  However, you might find the Optimization of a TV shopping channel interesting…

A little background first.

You probably don’t know how interactive HSN was, at least back then; the show just looks like somebody talking about products.  What you don’t know is there is a huge multi-screen war room setup steering the ship.  Every competitor monitored live in real time, along with all major TV networks.

Somebody is burning a flag on the steps of the Supreme Court, and every TV network is covering it?  Start selling American flags.  Folks on the Today Show say “blue is the new black”?  Put on blue clothing all day.  Competitor selling a product we have in inventory?  Put the same product up at a lower price.  The war room was a wild place - and especially so with 3 of these networks, all live, going full blast at the same time, 24 hours a day!

So first, HSN was interactive with the “environment”, followed trends rather than trying to Market against them.  Customer demand (sales per minute) declared what to put on a show; if they wanted to buy it, we wanted to sell it.  If they didn’t want to buy it, we tried to get rid of it by cutting price.

Sound familiar?  A bit like keyword phrase / Search analysis, perhaps?

HSN was also interactive with customers.  There are the callers you hear on TV, of course.  What you probably don’t know is all TV Shopping customers are absolutely chock full of comments about the products and presentations, and these are tracked and reported on the very next day.  Every day we had a sales meeting to review the prior day - how could we have done better - and as part of this meeting, we reviewed customer comments from the previous day, and tried to take action on these comments. 

To put some context around these daily sales meetings, the President of the company ran the show, and  the attendees were all VP or above.  It was the most important meeting anybody had.

Every day.

The kind of meeting a company might start having to really leverage the power of Interactivity and the output of Social Media, perhaps?  We’re talking about close to a billion dollar in sales company at this time, not a start up.  We sold management on the importance of an Analytical Business Culture.  You can too.

So, we have this pretty interactive system despite the one-way technology of TV, and Marketing now has budget to try and Optimize it.  What would you do, how would you improve this?

Here’s where we ended up, in some cases after years of working through the testing:

1.  Feed customer comments from the call center to the show host live.  We invented a system to send customer comments in real time to the host of the show.  They scrolled on a screen as the host was pitching the item, and could be tremendously effective in improving sales.  When I heard of people Twittering about speakers at conferences, that reminded me very much of this functionality.

2.  Capture successful interactions in the product database for review later.  After the show, the hosts would enter notes on which customer comments “worked” for specific products and allowed them to sell at a higher velocity.  These “best of” ideas could then be reviewed by any host who would be selling the product.

3.  Make real time testing actionable in the future.  There was a lot of testing in real time besides the host pitch - changing displays, lighting, formats, etc.  However, there was no “system” for capturing these ideas, it was all “gut” and learned by practice.  So we did the same as with the comments above - got these “tips and tricks” loaded into the product database so show prep folks and producers could get a leg up using the experience of others. 

Not exactly A / B testing, but there’s no A / B in live TV, you have to look at historical performance in the same Dayparts - and we did.  More on this in the next initiative…

4.  Program to the audience.  The very difficult thing with selling product on TV Shopping versus the web, of course, is that you are selling one product at a time.  How do you Optimize that? 

We studied the buying patterns - rowing machines sell best on Sunday mornings, Fashion on Tuesday nights, kitchen stuff at 3 PM weekday afternoons.  In other words, Dayparting. 

The audiences change throughout the day, and they have a different composition on Weekdays versus Weekends.  Optimize the product presentation to the audience.  Using Nielsen ratings for this Optimization would be a “Push” approach.  By using buying behavior, we were using a “Pull” approach - sell them what they have told us they want to buy using their actual behavior.  By the hour.

 5. Implement the best performance metrics and controls.  In the early days, Gross Sales per Minute  were everything.   Analysis proved that return rate and price had a very high correlation, which in this case was indeed causation - and we proved this with price testing. 

So we came up with the idea of an “average price per show” which would minimize return rates based on Daypart.  Literally, we found that the time of day a high priced product was sold influenced the return rate.  This was an improvement, but the system could be gamed by smart hosts looking to drive bonuses.

So, we converted from Gross Sales per Minute targets to Net Sales per Minute targets - net of returns, actual if available or forecast based on like item history, all in real time.  But in the case of some high volume categories with thin margins, this was not Optimal either.  So ultimately we made it to Net Gross Margin per Minute as the final sales metric.  Very similar to moving from ROAS to ROMI.

This series of “Business Rules” linking price, return rates, margins, and time of day basically ensured that even in a completely dynamic, real time environment, the whole system was Optimized for profit.

Do you wonder if a Dayparting approach could improve your commerce profits on the web?  Based on what I’ve seen, I’m pretty sure it would, as long as the costs of implementation are not prohibitive.

Question:

Look at these 5 ideas above.  Ask yourself if any of this is what you think of as “Marketing”.  These ideas are in reality a series of very intensive IT projects (on MainFrames!) involving telecommunications, core systems architecture, programming / code, and more.  Why was Marketing involved?  None of this is about ”Creating Ads”.

Marketing was involved because we insisted on being involved.  In an Interactive system, you can’t Optimize Marketing until you Optimize the systems delivering the Interactivity.   Those of you Optimizing web sites are quite familiar with this concept by now.

It’s about removing Friction.

Said another way, these ideas were all directly related to Voice of the Customer, Customer Experience, and long-term Customer Satisfaction.  The better HSN was on these issues, the more successful Marketing programs would be in terms of increasing Customer Value. 

Engineers are smart people, but if they knew everything, then why would web sites, VRU’s, or countless other machine-human interfaces have to be Optimized?  For best results, you really need both brains working together on these systems from the beginning.  iPod / iTunes / Mac is the most recent example - the Marketing Strategy is built into the product from the very beginning.

Failing this ideal world (and many companies are not there yet, for sure) analysts must get these Interactive systems Optimized before addressing the next challenge - Optimizing Customers - which begins in Band 4.  Otherwise, Marketing success will be undermined by Friction instead of driving incremental Customer Value.

For more on Measuring and Managing Friction, See Chapter 5 in the book sample PDF.

Any questions on the approaches / testing / decisions above?

(A post by post index of this Marketing Bands Series is here.)

Now that we’ve had some discussion on the ideas behind these charts, let’s dig deeper into the Marketing Bands chart and review details at each level (click for a larger image in a new window):

What we have here is a chart of the optimized interactive Marketing Bands system we developed at HSN, with an overlay of comparable online Tactics in red (Band numbers in Blue).

After nearly a decade of testing, we arrived at the place where each dollar of marketing spend - at each level - yielded the highest profitability to the company.  Many of the examples here pertain directly to web marketing, and I promise I’ll be specific on that.

Band 1: Public Relations / Partnership Marketing - at the widest end of the funnel, we tried all kinds of Mass Advertising - TV, radio, newspaper, TV Guide(s), all of it.  Even tried different combinations of media using geographically controlled test groups / media mix models.  None of it worked.

Core realization:  We are interactive TV.  We can’t buy mass media that is more powerful than we are when we do a great job with selecting products and presenting them properly.  The power of the click - in this case, people channel surfing with the remote control - is how we generate Awareness (Attention / Interest in the AIDAS model) with potential buyers.  Interactivity itself is the power.

Optimized Result:  Use the power of our TV and customer base to gain access to broad audiences through no cost / low cost Marketing Partnerships where HSN is featured. 

Example: Maxwell House cross-promotion, coupons on coffee cans.

BTW, Maxwell House was absolutely thrilled with the unit volume they blew though on this promo.  There were field reports of stock-outs in some areas  :O  This intense activity was primarily driven by current customers, of course.  You could say they were, um, Engaged by this offer, to say the least!

Web analog: Offline advertising has never worked particularly well for online properties with the possible exception of local media for new launches.  Use the power of interactivity itself to get the word out - focus on the product and the customer experience.  If you can get press / stimulate Social at very low costs, great.  Try to form partnerships with targeted packaged goods companies; this may be difficult if you cannot offer ”media” in exchange - high traffic.

Band 2: Affiliate Marketing - the necessary evil of distribution in the HSN case.  We paid commissions to cable systems based on sales from the zip codes they served.  There were also levels of extortion beyond that insisted on by some cable systems: forcing us to pay for “marketing” consisting of buying the cable system’s own media - TV spots, guide ads, statement stuffers, etc.

Core Realization:  We found through analysis that literally, the closer our channel was on the cable system to the “high traffic” areas of network (broadcast) television channels, the higher the revenue per household was.  For example, if ABC was on Channel 4 and CBS was on Channel 6, HSN on Channel 5 would generate max revenue per household for the cable system. 

It was that simple; closer adjacency to the most popular programming meant we got more “click traffic” from channel surfing.  This is pretty much identical to an SEO effort, where you are simply getting more exposure for your content during “seek” behavior (search on the web, channel flipping for TV).  None of the cable system marketing resources we bought made a difference in any combination using controlled tests.

Optimized Result: Killed as much of the system marketing as we could and used the money to pay higher affiliate fees for these premium channel positions near network TV channels.

Web analog: Traditional media approaches to affiliates need to be carefully scrutinized and tested through; look for environmental effects that affect performance (on the web, that they get SEO).  “Quality” in the affiliate world is everything.  Some affiliates will want to play ball and others will not.  There will be “super-affiliates” who really get it, and those affiliates should be supported in any way possible.  Don’t overspend on affiliates that don’t get it.

We’ll continue on down through the bands in the next few posts.

Any questions on the approaches / testing / decisions above?

(A post by post index of this Marketing Bands Series is here.)

So, we had some good ”translation” discussions on the HSN Marketing process document, and the idea that there are a couple of ways to look at “Segments”. 

It’s my belief that if you start with Communication Segments (an idea we finally arrived at with the HSN Optimization in 1993) and then move to Visitor or Customer Segments, you will end up with a clearer, more actionable picture in the end. 

If each Band has a single Objective, and you Optimize to this single Objective, you will end up Optimizing the entire system because Visitors / Customers naturally flow down through the Bands as they pass through the LifeCycle.

There’s really no concrete benefit, on either side, to send the same message to all the folks in these different Bands.  That approach is inefficient at the least and irritating to the customer at the most!

Given that, let’s take a look at the same kind of chart for a business that is primarily web-based (you can click the image to enlarge):

Again, the first 3 Bands generally address non-transactional participants (on the web, #3 is hybird) and are sub-segmented by ideas like Personas, demo or psychographics.  The bottom 5 bands are sub-segmented using transactional data, and how “deep” to Market down through these Bands is a Business Model decision.

For commerce models you probably Market most of the way down through the Bands.  Media models, probably only a couple of levels, because the Value of a ”Customer” doesn’t support going any deeper.

This approach - basing Contact Strategy on the “Band Status” (um, Engagement?) of a Visitor / Customer rather than simply by a Calendar Date - generally creates a Marketing Cloud that’s much more Relevant to the needs of the Marketing recipients.

This is true no matter which Band Customers are in, and provides for greater overall Relevancy if Customers ”hop around” between Bands - as some will do if your Marketing is successful. 

The primary Hop you are looking to encourage is to get folks in the process of Defection - Bands 5 to 8 - to hop back up to Band 3.  This is known offline as ReActivation. 

For Online Marketing purposes, we could call these same Hops up to Band 3 from the lower Bands Re-Engagement.  If you can go that far, the logic follows that Bands 1-2 are a pre-Engagement State, Bands 3-4 are the Engagement State, Bands 5-8 are a dis-Engagement State.  Remember there is a natural tendency for people to drop down through the Bands - this is the ”Process” (AIDAS) part of the model.

The great thing about this approach is it doesn’t really matter what metrics you decide represent “Engagement” for your site, what matters is if the Visitor / Customer is demonstrating pre- , full, or dis-Engagement behavior.  That’s how you put them in a Band, and that’s how you target the Messaging.

In other words, however you define Engagement, the Engaged Visitor / Customer is in Bands 3-4.  Measuring pre-Engagement for you is probably very similar to the web analytics thinking you do now on Conversion or (to reference Gary on lead gen) the likelihood of Conversion.  Measuring dis-Engagement is simply “how long since they were Engaged”, by whatever Engagement defintion you use.

Finally, I’d just like to be very clear on this idea of Analysis and Action by Communication Band and Customer State - the only thing you care about for Marketing is what the Customer State / Communication Band is now, because now is what’s most Relevant.  Those of you thinking about tracking “State Paths” or State History (and I know there are probably some) can put that aside, unless previous State is part of your Engagement Metric, or you’re in the Really Advanced Class. 

Depending on your Engagement Metric, you might have to look at History to establish what a State looks like, the “boundaries” of a State, so to speak.  But once you establish the elements of a State, you run the analysis once a week or month and Communicate based on Current State.  State is a Snapshot, not a Movie.  A Trigger.

The reason History is not very useful: in this Model the Band / State is Predictive.  There is a natural tendency for the Visitor / Customer to fall down through the Bands.  The Bands tell you where the Visitor / Customer is going, what message will be most Relevant to them tomorrow - and that’s why it’s so incredibly valuable as a Marketing tool.  Marketing Relevance improves dramatically.

I realize many analytics folks are working hard on the Band 3 concepts right now.  If I was to pick a next target with the highest potential for ROMI, it would be the Band 4 / Band 5 transition, where you need at least 2 different e-mail streams - one the “business as usual” stream in Band 4, one that recognizes predictive Behavior in Band 5. 

Here is an easy to get started example:  If an e-mail recipient doesn’t open your e-mail for 2 months, recognize that Behavior and change the e-mail stream for this customer to a Band 5 Communication Strategy.  Many of you already use vendors or systems with the capability to do this; all you need to do is think through the Customer State, create a Band 5 message, and Test versus the Band 4 message.  Test the same idea with different Behavioral windows; no open for 3 months, no open for 4 months, no open for 5 months, etc.

Need help with understanding Messaging by State?  You can get a good Overview here, with further info here and an awesome, detailed real world Band 5 example here.

Comments or questions on this?  Do you generally accept or reject this line of thinking?  Care to point out flaws, loopholes?

(A post by post index of this Marketing Bands Series is here.)

Not sure if all the web analytics folks out there will appreciate this post, but I’m pretty sure at least some of you are interested in how all the things we talk about in the Web Analytics / Database Marketing world fit into the larger world of Marketing.  So following on a question from Judah on depth of segmentation, and a post you should read from Gary on joining behavioral and demo / psycho segments, I thought I would offer this example.

One of the challenges people seem to have with behavioral segmentation is finding a way to organize it in their mind.  It just seems too overwhelming to think of Marketing to individuals at the “right time” based on behavior as opposed to some “calendar” idea where you communicate to everybody at once. 

If you want to create a “structure” or “process” to hang behavioral communications on, try this one.

Below is a chart I created in 1993 to explain what Marketing looked like at HSN (you can click for a larger image in a new window):

The format for each level in the chart is:

Type of Marketing
Purpose of Marketing
Tools / Tactics

I’d like to point out a couple of things.

1.  Note that I used the word “Process” in the title.  This is reflective of a real step by step road the customer takes, as opposed to a “Marketing Blast” kind of idea.  Each step has a specific Goal and Outcome and is measured that way; some measurements are more precise than others.  For example, in this world, “Brand-ing” has a role but it’s a specific role, it’s not “everything”.  By the way, “Affiliates” in this case are cable systems carrying the HSN signal.

If you like, this is what the AIDAS Strategic model looks like when you fill it with Tactical Action programs.  It’s also representative of the “cascading metrics” methodology covered extensively in Akin’s book.

2.  I suppose no web analyst out there missed the fact this chart looks like a funnel.  It is.   The difference between this funnel and a “Scenario” funnel is you don’t really have knowledge of “who” in the top 3 layers, these targets are “audience” as opposed to individual visitors or customers. 

In terms of segmentation, these top 3 layers are the psycho / demo ideas; as soon as you pass Level 3 you are into the first purchase, where the segmentation becomes behavioral.  Since at this point we have real data on the customer, the behavioral segmentation is more actionable than demo / psycho in terms of increasing customer value. 

The widest “Reach” is at the top layer, but it’s the least targeted.  The finest segmentation is at the bottom layer, where the Marketing is absolutely hyper-targeted and very specific to the individual customer.

3.  You can see on the far right two continuums (continnua?) - Mass to Targeted Marketing and Push to Pull Marketing.  The transition area between the two is the 3rd to 4th layers (dark bar in the Push / Pull continuum), where you get into Retention and Continuity Tactics; now that you have customer data you can engage in these activities.

Generally, these shifts happen pretty much together, so Mass Marketing = Demo / Psycho Segmentation = Push Marketing, and Targeted Marketing = Behavioral Segmentation = Pull Marketing.

4.  Another way to view this chart is as the Customer LifeCycle, which starts with general Awareness and ends with Customer Defection and a final tally of LifeTime Value and ROMI.  At HSN, the Catalog business generated the most incremental value as a Retention Device, so it kicks in on Levels 6 and 7, first as a general merchandise idea, then as a very targeted category idea. 

For those of you wondering where the web is on this chart, we bought the first web property in 1994, after this chart was created.  But on the web, the core idea is much the same.  Different tactics have different primary purposes and they should be treated and measured that way, not just “Blasted” out in one continuous Marketing puke.  

The impact of these tactics should be measured at the customer level (not campaign level) whenever possible to really understand the net value added.  That’s how you optimize the system.

Now, I have two questions for you:

1.  Did this chart help explain at all what the heck I’m talking about on this blog, or some other Marketing / Analysis issues?

2.  Do you have any questions?

I could go on and on about this chart in my language, but I would rather have you (especially the tech-oriented folks) ask questions in your own language, since:

* It seems like we get better knowledge transfer that way!

*  I don’t know what you don’t know; stuff that’s obvious to me after 25 years of Marketing is probably completely opaque to you.

So, any questions?

(A post by post index of this Marketing Bands Series is here.)

I got e-mail on the review I did of Akin’s new book.

“How is this book different from your book or Kevin’s books?”

Fair question.  Both Akin and Kevin read this blog and are free to add their voices and describe their books here in their own words.  I don’t presume for a second to be a “judge” of other people’s work - at least in this case.

Fundamentally, I think the difference between the books is the writer. 

I’m a Marketing guy, Kevin is an Analyst, Akin (I believe) was / is a Software Engineer.  So even through we talk about a lot of similar things, we approach these topics differently.

The intent of my book is to explain how very simple customer models can be used to drive tremendous increases in profitability, in virtually any business.  The book is about Marketing, it talks about how to create and measure Campaigns that maximize customer value while lowering costs.

Kevin’s books focus on multichannel retailing specifically, bringing varied and deep, often complex analytical insights to bear on this business model.  His books are about Analysis, models you can use to bring strategic insight to the business.

Akin’s book defines and explains a way to think about, measure, and execute Marketing in a complex multi-channel communications environment.  His book describes a System or “RoadMap”, a step-by-step way to break down this challenge and understand it.

There are similarities and differences between all these approaches.

The really interesting thing to me is this: across all three books, there isn’t any directly conflicting information or guidance, yet there isn’t a lot of redundancy either.  There are preferences for certain ways to approach Marketing issues, to be sure. 

But like I said, I think that’s simply based on where the writer comes from, what their background and experience is.

While we’re talking about Database Marketing books, any further suggestions on good books?  Please give a brief recap of the book.

Metrics and Methods for On and Offline Success, so goes the subtitle of this book.  This is a fantastic piece of work by Akin, who I have known for quite some time - since the early days of eMetrics.

What’s so good about this book?  Well, this is a tough space to write for, this seam where Marketing and Technology meet.  There’s an audience on either side and you’re writing down the middle.  Akin has done a great job producing a work that should have both sides paying attention and hopefully will provide a platform for better communication with each other.

The framework he chose for the book is a brilliant approach.

First, a dissection of Online, Direct, and Brand Marketing.  What are the metrics and methods that drive each of them?  How is each of these Marketing disciplines handling the multichannel challenge within their own silos, and what are they probably missing because of silos?

This first part of the book I think will be widely appreciated, especially on the Technology side, for laying out in a logical way what the various Marketing factions are up to, why they do what they do, and how they look at measurement.  I find in the web space particularly lots of people have 1999-era notions of what “Measuring Marketing” is.  Akin provides really great background here, lots of detail on where the various measurement approaches come from and how they are used. 

This Online, Direct, and Brand Marketing structure becomes the backbone of the book, it continues throughout the entire work and provides the reference point, the base for understanding.  Very smart idea, it brings everybody to the party.

In Part 2, Akin looks at why the various factions should be sharing their metrics and methods, how fusing the various multichannel measurement approaches developed by Online, Direct, and Brand Marketers results in a whole better than the sum of parts.  This section really digs into which metrics and measurement approaches are best for different situations and levels of available data.  I really like this “cascading” approach to data.  Got a little data?  Look at measurement this way.  Got more data?  Think about measurement this way.  Got a ton of data?  Here’s the best way to look at measurement.

This section really gets into the whole control group issue, and why, if you can, you should Measure Customers, not Campaigns, to determine your success.  Response is one thing, lift can be quite another.  Once this idea becomes fully embraced by the web analytics community, it’s going to be very disruptive.  But using controls is standard procedure in the BI world, so trust me, it’s coming to web analytics.

In Part 3, Akin flows it all together, providing sequential examples using the Attract & Acquire, Engage & Covert, Grow LifeTime Value metaphor.  What does truly integrated (no silos) multichannel Marketing look like in practice?  What do you do and how do you measure the results?  Now we’re cutting waste and improving Marketing Productivity throughout the entire Customer LifeCycle.

This section is notable for the use of case studies and detailed examples of what it looks like when you are actually maximizing value in an integrated way across all the touchpoints.  What Marketing looks like as it morphs from the ancient offline calendar-based Campaign model into the “right person, at the right time, with the right message”, Measure Customers not Campaigns approach.

This book is a significant addition to the knowledge base, particularly in the area of integrating Brand Measurement into the overall customer management picture.  He also provides a fabulous aggregation of Brand Measurement research sources I found very useful. 

Notable brand new ideas that I’m not aware of seeing anywhere else are the Maturity of Multi-channel Profiles idea and the Cross-Channel Funnel Report.  There are numerous other concepts that may not be brand new to the reader but are expressed in new or unique ways that are better than what has come before.

Fantastic job, really.  I’d call the book a must read, the kind of book I absolutely would not hesitate to hand to Senior Marketing folks and say, “Read this, it’s about where we are going”.

Probably wouldn’t use those exact words, but you get the idea…

As for the relevance of the picture below, see page 144 in Akin’s book!

The Education Committee of the Web Analytics Association is pleased to present the Knowledge Required for Certification document to the Web Analytics community for comment.  This document contains a detailed overview of what a candidate should know and be able to do to pass the Web Analytics Association Certification Test:

Knowledge Required for Certification Page

The document is available as a 37 page PDF or you can view it online as a series of web pages organized around core topics:

Site Optimization
Marketing Optimization
Analytical Business Culture

Feedback on this doc is welcomed on the WAA Blog post for the document; you do not have to be a WAA member to leave a comment.  An overview of the Certification Test project and projected timeline info are provided here.

We’re hoping to do a trial run of the Certification Test at the eMetrics Optimization Summit this fall in DC to uncover problems and issues, with actual testing to begin some time in 2009.

Many thanks to the more than 60 WAA member volunteers who worked on the various projects that have resulted in this document, including the development of the WAA / UBC Courses.  You don’t have to take the Courses to sit for the Certification Test, but all the Knowledge Required to pass the Certification Test is covered in the 4 WAA / UBC Courses.

Any comments or questions about the document itself (what is or is not included, for example) or the WAA Certification in general should be posted to the WAA blog rather than here.

Frankly, I’m relieved this document has finally been published!

Those of you interested in where web analytics is headed might check out the series of posts Jacques Warren in doing from TDWI (The Data Warehousing Institute) conference.  He’s in for a pound - an exhausting 6 days of high order brain-stuffing, much of it very technical in nature.

I believe most web analysts, if they didn’t come from DW / BI in the first place, would benefit tremendously from the kind of exposure Jacques is receiving at TDWI.  There is a larger scope sitting out there that WA fits into, and the DW / BI world has been around a lot longer.  Those folks have all the arrows in their backs already, and there is a lot to learn from them.

For example, the extent you believe what you see in web analytics reports actually happened, or whether you understand it is often an approximation of what happened, more like a model.  At least from a Marketing / Behavior standpoint.  A dose of reality like Jacques received can put this in perspective.

The very next question on the table is how do we get WA data into BI systems?  The answer, I believe, is Events.  There is really no point in stuffing page views and visits into a data warehouse; not enough value and won’t mean much to the broader Optimization picture. 

What the WA folks will have to do is decide what constitutes a significant Event (which could be a series of smaller actions) and then figure out how to mark that Event with a customer ID and get it into the warehouse. 

Some web analytics applications can already track Events (example), so that’s not the issue.  The question, as always, is what are you going to do with the Event?  Otherwise, it’s not worth tracking.  What’s needed is a Strategy for using high value Events first.

Otherwise, we’ll just end up with that many more junk reports.

At the same time, I think the more exciting prospect than what BI brings to WA is what web analysts can bring to BI, which continues to suffer from a focus on the technology instead of what they can do for the business.  While many WA folks understand the need to annotate and evangelize their work, many BI folks don’t see “being proactive” as part of their role.

I have to tell you, if you think WA and Optimizing web sites is exciting, wait until you get your hands on the entire business and start optimizing it.  Your first A/B test with a call center script, for example.  Fulfillment testing.  Packaging.  The list is endless.

That experience, my friends, is pure adrenaline.

I know some of you out there are already wearing both the WA and BI hats.  Got any killer Business Optimization stories (that you can tell?)