Monthly Archives: April 2008

*** Ad Engagement & Silo Busting

Analytical Culture, DataBase Marketing, Web Analytics,

On the heels of the Desirability Series we have two related articles:

1.  This first piece is by Lester Wunderman, one of the “fathers” of Direct Marketing (so many fathers, so little time).  To me, it’s significant Mr. Wunderman would feel the need to come out and provide us with his definition of Engagement, at least as it relates to Advertising.  If he didn’t see Engagement as an important idea thrashing around looking for clarity, why bother?

His statements are necessarily broad I think, because he’s coming at it from the top level, the Strategic Layer, and in doing so has to cover a very wide range of industries and media.  Nonetheless, if you take the time to really read what he’s saying and think about it, he’s setting up a new kind of approach to Advertising similar to what I defined here.

Here’s the article link:
Engagement – A New Information-Based Form of Advertising

2.  In contrast, I’m not sure whether Roy Young is the “father” of anything but he is the President of MarketingProfs.com and coauthor of Marketing Champions: Practical Strategies for Improving Marketing’s Power, Influence and Business Impact – something I talk about a lot.

His topic:  Silo Busting, which is so critical to really driving a customer-centric Strategy.  Roy provides 5 solid tips on how to get started if you want to Take Action on Desirability.

Here’s the article link:
Five Tactics for Busting Silos in Your Company

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.

Measuring Desirability

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

Why do we want to do a 2-Step acquisition?  Because the conversion rate is going to be higher per dollar of media spend.  It’s the equivalent in Online of the difference between buying single words and buying phrases in PPC.  The former generates a lot of traffic, but the latter gets higher conversion and is much more Productive.

In other words, a 2-step customer comes into the Relationship with higher Potential Value and higher Momentum.  And that’s important, because it means you spend less in Marketing over the longer term as the customer will, on average, keep interacting for a longer time.

If you’re not sure what that all means, perhaps it will become clearer as we dissect Desirability (Satisfaction), the last component of the AIDAS model.  Here’s the core issue:

Offline, we know people come back to Brands or Businesses “by themselves” because they like the Product or Experience.  We also do Advertising to these same people, as well as those less likely to come back or not likely to come back at all.

So how do we know what percent of the resulting activity is due to people just coming back because they enjoy the business, and how much is due to the Advertising?  How do you calculate ROI?

A Very difficult task.  Even if you could identify the “likelies”, you generally can’t exclude them from offline media.  So this whole issue of “likelihood to come back” offline has been completely ignored, because there’s no way to act on it.

Online, and in much of Offline Database Marketing, we don’t have this problem.  It’s a pretty straightforward and common analytical task.

We can measure quite accurately how much of “coming back” is from Advertising and how much is from “Experience” or the more global concept of what Forrester calls Desirability – the fact the customer simply enjoys interacting with the business, and wants to interact again.   And, online we can target specific individuals with specific messages based on their likelihood to come back.

But, most people in Online marketing are not acting on this intelligence or targeting capability; they’re ignoring the idea largely because it didn’t matter offline.  Are these the same people that keep saying “Interactivity is Different”?

I hope not, because they’re certainly not acting like it is!

Why should this concept of “likelihood to come back” really matter to Online Marketers?  Because it is much, much more powerful than you think it is.  Orders of magnitude larger.  However, once you screw up, the downside is also quite powerful – “not likely to come back”.  This brings up two important and powerful areas to consider:

1.  Over-spending to get people to come back who would have come back anyway
2.  Under-spending to get people to come back who are less likely or unlikely to come back

In most cases, you will find the budget mis-allocated in this way.  To optimize, you will want to reallocate budget from #1 into #2.

Online, there is a powerful “Pull” that brings people back, over and over – without needing to provide incentives or begging them.  This Pull is the very fabric of Interactivity.

What’s more, you can measure this Pull quite precisely and take action where appropriate.  Here is how:

1.  If you don’t try anything else new this year, do a controlled test with your e-mail program.  This is the simplest, most direct way to prove to people you’re not (I’m not?) crazy about how powerful this Pull idea is.  Please do not use whatever demo / product segmentation you normally use with e-mail for this test.  If you want to analyze this Pull behavior, you have to segment using behavior.

Most of the big e-mail vendors can do this for you, tell them you want to do a “Recency Test with 30-day segments and a Control Group for each segment”.  The most universal “last interaction” (the base for Recency) for many folks will be “last open”.  You could also use “last click-through”, but of course you will have smaller active base.  If you’re in commerce, use “last purchase date” if you can, since that is what really matters.   Just send whatever your default creative is so you keep a baseline with prior campaigns.  You will probably end up with results that look like this.

If you want to know more about these ideas or set the test up yourself, there are detailed explanations  in this series and this series.  Questions?  Just comment below.

2.  Perhaps more importantly, you can measure the decline of Pull, the absence of Pull, and take action on that as well.  Pull is your measurement of Desirability.  Where you find lack of Pull, you will find un-Desirable experiences you can take action on.

Now, a lot of people talk about being “customer-centric” and customer experience and all that.  Makes perfect sense, and has made sense since probably the first barter transactions, right?

What you don’t hear people talk about is how to measure the profitability of a customer experience or Desirability effort.  How to identify Desirability problems – even if the customer doesn’t say a word about them.  How to isolate and fix these Desirability problems.  And how to measure the increased profitability directly attributable to fixing these Desirability problems.  Wouldn’t you like to identify these un-Desirability problems before they go Social on you?  Why be reactive when you can be proactive?

That would be a pretty neat trick, don’t you think?

Here’s how you do it.

Once you have proven how powerful this Pull (come back by themselves) concept is with your own data – and it is especially powerful among your best, most Engaged customers (is that a surprise to you?), start asking why, for other groups, Pull is declining or absent.  What is the commonality among visitors or customers with the lowest “”likelihood to come back”, where Pull is declining or absent?

Here’s what you will find:

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.  Behavioral segments.

Visitors or customers who “did the same thing”.

Basically, you will find out where Desirability is lacking, literally, what you are doing every day in Sales, Marketing / Product, Service, or Operations to drive away customers and prospects.

And then you can decide what you are going to do about it.  That’s a whole other challenge I will address in the next post.

Your feedback and questions are appreciated.