Jacques Warren @ TDWI

DataBase Marketing, Marketing / Tech Interface, Web Analytics

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?)

eMetrics 08 (SF)

Analytical Culture, DataBase Marketing, Web Analytics

As opposed to eMetrics 08 Toronto, don’t you know…

A really big shew, for sure. 

With the tons of WAA EdCom stuff going on, and the tremendous opportunities to just run into people in the halls and have hour long spontaneous “shootouts” (thanks for your help with “The Cluelessness of Crowds), it can be difficult to get to all the sessions I want to see.  

Still, I always try to catch sessions outside of the mainstream that look interesting.  Often nobody comments on these overlooked sessions, so I like to bring them to the surface.

The presentation by Egan van Doorn of the Dutch Automobile Association (ANWB) called Connecting Web Analytics with Decades of Marketing Metrics was such a session. 

Here, the beauty was in the simplicity and purity of the approach.  Classic Database Marketing – the targeting, the pacing.  No breathless monthly or weekly blasting of the same message to every customer.  No, to each customer the right message at the right time.

ANWB works with the understanding the calendar doesn’t matter nearly as much as the customer’s individual behavior.  When the customer is ready, they say so.  It’s all about Pull – gently bringing them to you, not beating them over the head.  Context, relevance; what they want, when they want it, while they are interested in it.  Like Search, right?

Web analytics folks often view multi-channel ideas as too complicated, and they’re really not – if you are using the right methodology and if you have some discipline.  Apparently, ANWB has both.

From a Marketing perspective, ANWB pays close attention primarily to high value online events.  Forget page views, visits, etc.  What they want to know is this: what action was taken for which we have a related product?  They store these events in the customer record, and then play out the online / offline Marketing stream accordingly.  If they can reach them online, that’s obviously cheaper.  If they decide to go offline (in the mail) they have their timing issues down and they make it happen. 

Very efficient, highly productive.  Huge increases in response rates, even offline when using online behavior to trigger the Marketing event.  Classic Database Marketing.  And there’s a reason they are so good at this – they’ve been doing the same thing offline forever.

If you can make money doing this offline, you can make an absolute pile of money doing it online because the Marketing is so much cheaper.  The problem is, most online folks don’t have access to that Database Marketing background, the understanding of how to optimize remote relationships.  So instead of playing it as Database Marketing, they play it like Media (Push) Marketing.  And they get unremarkable results.

How simple is it to do multi-channel right? 

Here’s an example, courtesy of ANWB.  Customer comes to the web site.  Customer searches for and finds info for “bike and hike” trails.  When this happens, customer is shown banners offering a “Bike and Hike Trails of the Netherlands” book during the rest of the visit.  Customer maybe buys the book.

Or not.  If they don’t, and waiting a reasonable amount of time for the sale to occur online, ANWB goes in the mail with an offer on the book, and then later on, a modified offer in the mail if there is no response.  Customers buy scads of these books.  Enormous ROI, both online and offline. 

Then repeat this scenario with every product line – what is the trigger event, what is the timing?  Man, that’s a beautiful business they’ve got going there.  Just printing money.

They do have one advantage – as a membership org, each customer has a unique ID, offline and online.  This was raised as an “unfair advantage” in terms of their success.  Disagree. 

Megan Burns of Forrester said as much in the 2nd half of the presentation.  The reason people don’t usually factorize to do this kind of stuff is they can’t project the ROI, they don’t know what they would do with a unified view of the customer to generate incremental profit.  So they can’t justify spending the money to make it happen.

This is really the same Push versus Pull issue I mentioned before – as long as you batch and blast, as long as you keep using the offline Push model, there’s no point in understanding any of this multi-channel stuff.  When you get ready to accept that the behavior of the customer is your key to relevance, and test through a couple of scenarios (as all offline DB Marketers have done), the ROI of the offline / online join becomes self-evident and justifies the spend to set up for it.

Wait a minute, you say – there’s no reason anyone would want to log into our web site.  Oh.  But now you are into Marketing Strategy.  Not the same issue.

Why won’t they log in?  Let’s say you don’t think you can get people to “log in” so you can create a database match.  Here’s the real question – have you conceived an experience for your web site that is worth a log in?  If the experience is worth it, people will log in, and you will have a database match.

That’s Marketing, my friends.  It’s not just about the “Push” MarCom stuff.  It’s the Strategy, the whole picture that creates the Pull that is so incredibly powerful. 

This is what makes interactive different.

Let’s assume you think I am wrong, that what I’m saying couldn’t possibly be true.  After all, how could so many people get interactive wrong?  Wisdom of Crowds, right?  After all, look at all the folks who got it so right in 2000 (not).  THAT was a Crowd.  This Jim Novo, he’s just pushing the ideas in his book.

OK, fair enough.  Here’s another voice that has been added to mine.  More on Akin’s book in the future – I’m only 1/2 through!

Acting on Desirability

DataBase Marketing, Marketing thru Operations, Measuring Engagement, Web Analytics

Now that we know how to Measure Desirability, we need to act on what we learn.

Many web Analysts and Marketers are pretty hip to Optimizing for Actions.  What they have a hard time thinking about is Optimizing Against in-Action.  It’s the mirror image of what people usually pay attention to.  If you’re having a hard time wrapping your head around this idea, try this analogy:

In the early days of web site funnel analysis, most people focused on the Active traffic, that is, the traffic making it to the next step, “step conversion”.  The focus was always on optimizing “for Action”, on getting people who made it to Step 2 to Step 3, etc.

One problem with this mindset, of course, is that the percentage of traffic making it through the funnel steps is often quite small.  So by optimizing “for Action” you are dealing with a small, probably biased group and the potential impact versus total traffic is going to be relatively small.

At some point, people began to realize the tremendous value of the mirror-image question – this traffic that is falling out of the funnel, where did it go?  Because if you could optimize against in-Action you would hit a much larger cross-section of the population and have a larger total impact.

In other words, the most important question to ask is not “Why is this small group of visitors converting”, it’s “Why is this huge group of visitors not converting?”  Further, if you knew what non-converting traffic did just before the in-Action, you could infer from this “Previous Action” why they were not converting.

This thought process is what convinced the web analytics vendors to start creating the “leak diagram” version of the Funnel, where you can see exit paths by funnel step.  This functionality allows you to target efforts not based on what people were doing, but what they were not doing, and infer why they were not doing it by looking at the Previous Action (funnel Exit path).

I challenge anyone to argue it’s easier or more effective to optimize a funnel by Action rather than by in-Action with Previous Action.  Previous Action shouts “why”.  Knowing that 80% of the Funnel Abandonment at Step 2 goes to the “Shipping Policies” page is like all those visitors screaming at you “I need more info on Shipping!” 

It just makes too much sense.

Likewise, when we see dis-Engagement we should read un-Desirability.  And we should look to the Previous Action for clues on what is un-Desirable.  Previous Action Clues such as:

a.  They bought the same product or products
b.  Products bought were from the same vendor or category
c.  Responded to same campaign / traffic from same source
d.  They talked to the same salesperson or service agent
e.  They were formally Engaged with the same kind of content

and on and on.  Find the dis-Engaging visitors or customers, then cross-tab by Previous Action.   Just like a Funnel Analysis with Exit Paths.  Attack the high volume ones first.  If you need help starting, perhaps you should ask Customer Service for a whole list of un-Desirability opportunities.

Here’s what needs to be understood.  Interactivity demands that these issues are somebody’s problem.  For as great as Interactivity is as attracting customers, it tends to be quite weak at holding them.  There is a tremendous ramp in Engagement early on in the cycle, which drops off just as fast on the other side for most participants except the very hard core.  Why?  Interactivity drives very high expectations on the visitor / customer side, and it doesn’t take much to screw up that relationship.

Interactivity is relentless like that.

So somebody has to do this job: finding the root cause of dis-Engagement and fixing it.  Why?  Because even more than with the typical web site optimization, very small changes can produce enormous increases in Profits.  Why?  Because you are dealing with much larger populations – those who did not Act, as opposed to those that tool Action of some kind.

What does this all mean on the ground level?

For Web Analysts: There is an exciting and challenging world waiting for you in this dis-Engagement data.  You may or may not be able to access this data through your web analytics tool.  If you can’t, find out where it is – in the customer service systems, help desk systems, commerce systems – and start exploring.  If you have a BI unit, find somebody in BI who wants to work on these ideas with you.

For any given free cycle, you should resist the natural tendency to “Go Deep” in your own world, spending your precious time probing the inaccurate.  Instead, “Go Broad”, and try to start connecting some of these un-Desirability ideas.  This can be hard work, but I know you’ll enjoy it, and the payoffs in terms of profitability are huge.

For Strategic Marketers:  Somebody has to do this job.  Will it be you or the “Chief Customer Officer?  Given Marketing causes a lot of these un-Desirability problems in the first place, it seems to me the Root Cause folks should be in charge of this effort, rather than those catching the flack.

Now I know what you’re thinking – this Desirability thing ain’t my job.  I Push.  I generate Sales, Awareness, etc.  “Desirability” is not on the List.  Poor service?  Not on the list.  Faulty products? 

Please, not my area.

OK.  Let me ask you something.  When you lose a customer, what needs to happen on your side?  You have to replace that customer just to stay even, right?   So, to grow sales – which I think you are in charge of – you have to not only replace the lost customer but also add another customer.  That means, for a fixed budget, that you’re not going to be able to grow sales as fast as you could if you were better at keeping customers, if you were attacking un-Desirability.

Do you think your sales goals for this year are “cake”?  That you have “easy” targets?  That you are absolutely confident you’re going to hit the numbers?  If you answered Yes, then fine, you don’t need to care about Desirability.  Just churn ’em and burn ’em, my friend.  I guess you’re not the kind of person who would like to absolutely smash your sales targets to bits.

For Both Analysts and Strategic Marketers:  If you are going to talk the customer experience talk, please start walking the walk.

A couple of suggestions:

1.  Yes, this un-Desirability work often requires (demands?) cross-functional teams, because un-Desirability problems often start in one silo (Sales, Marketing, Product) and end in another (Service).  Is that an impossible barrier to overcome?  Start looking for partners.  Better yet, start formalizing the idea of a Business SWAT team.  More real world examples here, here, and here.

 2.  Wikipedia defines Experience as “observation of some thing or some event gained through involvement in or exposure to that thing or event”.  Event.  Behavior.  Stop with the demographic segmentation already, it’s just obscuring everything that’s important to customer experience.  Save the demographics for the Push end of the funnel where they mean something.  Once you get to Action and move over into Pull mode, you’re now into Behavior. 

Desirability is about Behavior, not Age and Income.

So that’s the whole model, front-end to back-end.  This model incorporates many of the ideas floating around out there right now – Customer Centricity and Experience, Engagement, Reputation Management – into a single Data-Driven, Optimization-friendly, Customer-Aware, Accountable Marketing process.

In short, Measure Customers, not Campaigns.  That’s the secret to unlocking the power of Interactivity and making it work for you.  Otherwise, Interactivity can work against you.

Your Comments and Questions are appreciated.  Your challenges as well – why can’t you do this?  What will it take to change that?

Example: At HSN, I started by forming the Business Swat Team at the Director Level – IT, TeleCom, Customer Service, Marketing (me), Merchandising / Presentation, Fulfillment, Finance, and (of course) BI

Our first mission was this one.