Marketing Productivity Blog

There’s been another eruption of discussion on Engagement.  If (like me) you’re more interested in the higher level ideas not so oriented towards the “tool” aspects of this discussion, make sure you catch this post and hefty comments.  For more, also here, here, and here.

At a high level, there really are 2 kinds of Engagement, and I think it would be helpful for us to start differentiating between them.

One is “intra visit” Engagement, which as far as I can tell is really mostly relevant to the world of Advertising.  Some action or series of actions indicates the visitor is “engaged” with the site or the Brand.  In this model, whether the visitor comes back or not is not particularly important to the Advertiser, as far as I can tell (it still may be important to the Publisher).  But as long as people find value in ”impressions”, then the issue of the visitor coming back or not seems to be irrelevant.  And so this type of engagement is really nothing new; it is simply the accounting of a variety of “past actions”, though perhaps in more complex ways.

The other kind of Engagement, the one the matters when your business model thrives on repeat business or visits, is the more complex idea for web analytics, because it involves measurement of activity that “has not happened” - a neat trick if your analytics app depends solely on parsing “requests”.  What you need to know is which visitors or customers “did not request”, and optimally, how long it has been since they last requested.

If you think about it, this is quite similar to the idea of separating the analysis of New versus Returning Visitors, which is as old as cookies.  Many folks think this kind of analysis is important, because Repeat users tend to behave differently than New users.  The difference between New / Returning and the second form of Engagement is this: you are also looking at who is not repeating, not returning.

In other words, you are measuring Friction, and looking for ways to reduce it.  Just like one looks for ways to reduce Bounce rate.

Friction is the force that causes the opposite of Engagement; it’s the reason Engagement ends, causes dis-Engagement.  Friction is really about the likelihood a customer will continue to do business with you. 

The actual causes of Friction are created on the business side, and manifest themselves on the customer side as impatience, frustration, and finally lack of loyalty.

Customers encounter varying degrees of this Friction in their business relationships, and become more or less likely to do business with you as this Friction changes.  They already have low tolerance for poor customer service, processes that don’t work as they should, pricing that changes unexpectedly or is confusing, interfaces that make it difficult to accomplish tasks, communications that are sloppy, not delivered in a timely way, or irrelevant.

All of these Friction points tend to create increasing levels of frustration and ill will, which over time mutate into dissatisfaction and defection.  Friction accumulates to the point the customer simply decides to start seeking alternatives, and once alternatives are found, the customer terminates the prior business relationship.

This may not sound new to you, but here is something that might be.  The Friction effect is especially true and is more pronounced as “customer control” of the business relationship increases.  All the talk about “customer experience management”, which usually lacks any kind of effective and rational measurement system, is fundamentally a discussion about Friction.  And you can measure Friction.

Customers are demanding and taking more control of business relationships themselves, as is true with web retail, or have been forced to take control, as with the practice of pushing customers to serve themselves though the web or a telephone interface.  As the ability for the customer to exert control in the business relationship increases, customers become less and less tolerant of Friction.

And, as Friction rises, the customer becomes less and less likely to do business with you in the future.  If a customer is becoming less and less likely to do business with you, the value you could realize from the business relationship with the customer in the future has to be falling.

In other words:

Rising Friction = falling Potential Value;

Falling Friction = rising Potential Value

So, if you can measure Friction, you can measure Potential Value, the value of visitors or customers in the Future.  By any segment: the Campaign they came from, the Products they buy, the Content they visit.  And, by comparing them, you can re-allocate budget away from Campaigns, Products, and Content that create low Potential Value towards Campaigns, Products, and Content that create high Potential Value.  You can literally map Friction across the visitor / customer base:

Measuring Friction is exactly what LifeCycle Metrics like Recency and Latency do.  By measuring Friction, these metrics also measure the likelihood of a customer to do business with you in the future, and so also measure the Potential Value of the customer.

Visitors and customers will “signal” their Friction levels through their own behavior; LifeCycle Metrics organize and codify this behavioral data for you, and allow you to create reports and trip wires that flag increasing or decreasing Friction.

And how do you reduce Friction?  By applying the grease - your innovative selling and service campaigns are the grease that will hopefully reduce Friction and increase the Potential Value of the customer.  Fortunately, you will have your LifeCycle Metrics to tell you precisely who needs the grease, when it should be applied, and even when it should be applied a second time.

These Potential Value metrics will also tell you when your relationship with the customer has already “seized up” and it’s too late for the grease.  You only have so much grease and the grease is expensive, so you want to apply it only when and where you think it is likely you can reduce Friction and prevent the relationship from seizing up.

By the way, customers are not the only folks who experience Friction, people trying to become customers experience it also.  An easy way to measure this want-to-be-a-customer Friction is to look at the visitor conversion rate on your web site, or the opposite - Bounce Rate.

Navigational design and layout determine “physical” Friction and copy elements determine “emotional” Friction.  Design and layout testing will reduce physical Friction; persuasive copywriting will reduce emotional Friction.  Success at reducing want-to-be-a-customer Friction is measured by an increased rate of visitor conversion to goal on the web site and offline as well.

It is measuring and Acting on Friction that makes this second form of Engagement truly a new thing.  It brings web analytics into the world of Prediction rather than being always backwards-looking, which is what you need to get the C-Level to really pay attention.  This approach also pushes the analysis into customer service, tools / products, policies, and all kinds of other areas. 

As an analyst, when you can show (examples) Campaigns, Visitors, and Customers are experiencing Friction, that they are literally being pushed away by specific Products, Content, Sales People, Service Agents, and Policies, you will be embracing the full potential of what visitor and customer analytics has to offer across the enterprise.

It’s always a treat to work with bright, committed people and I’m happy to say this was the case with the folks at the Oriental Institute.  These higher ed environments can be exceedingly complex from a Marketing perspective, and the OI is way up there on the complexity scale.  So much to do, so few resources to do it with.

That said, we came up with a crackerjack plan that should significantly boost paid Membership at the OI without additional time or money resources.  How?  Path Analysis.

Personally, I have never understood why many web analytics folks don’t care for Path Analysis; I can only surmise these folks are simply not doing it correctly.  For one thing, Paths don’t make any sense without the context of a behavioral segmentation - entry page, campaign, etc.  Just like any other web data, Path is useless without segmentation.  Or perhaps these folks don’t know how to interpret the data they see because they can’t survey a Path for the answers. 

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I’ll be spending next week at the Oriental Institute in Chicago leading a good ‘ol Marketing Makeover featuring Database Marketing.  While non-profit environments can be challenging from a resource perspective, fortunately there are grants available to these Institutions, and very fortunately for me sometimes these grants can be used to increase Marketing Productivity.

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Just wanted to add a quick piece about the results of Optimizing the Bands (see Band Model) - this is the Marketing Productivity Blog after all!  Thanks Moe for the reminder. 

As we Optimized, there were changes in budget allocation by Band, and as a result there was an increase in Net Customer Value - the goal of the Optimization program in the first place.  For those of you not following the whole story, the budget remained constant, we simply allocated it to the highest and best use through testing.

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In the Band 5 Optimization for HSN, we looked for high ROMI special situations in the database.  This is really classic database marketing stuff, you’re looking for segments, and you’re looking for ways to Optimize those segments.  You could spend the rest of a career doing this kind of thing; there are always new segments like FIPS being revealed if you have an active analytical staff.

There were other programs in Band 5 based primarily on product-related transition phases in the LifeCycle; I won’t go into these here.  If you are interested in these ideas, I wrote one detailed example, which combines Customer Experience Management / Band 3 - Customer Comment Analysis / Math / Product / Marketing right here.

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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.

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In the Band 3 Optimization, we’re concerned about the Interface, the moment of truth when people who have Awareness, Interest, and Desire generated in Bands 1 and 2 are ready to take Action.  If we are successful at Optimizing the Interface, people take Action and become Customers, entering Band 4 of the model.

At HSN, we looked at customer communication this way: as soon as a customer makes their first purchase, they begin the defection (dis-Engagement) process.  I’ve referred to this idea before as “customers naturally fall down through the bands“.  The approach assumes every customer interaction we have after the first purchase directly affects how long this new customer will remain a customer.

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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…

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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.

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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!

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