Last Exit Westend

1. You fail to recognize that your web site is disconnected from your business.
2. You want all things different, but nothing to change.
3. You don’t know why your web site even exists.
4. You believe that your web site content attracts customers.
5. You hope you are buying the right technology with the redesign.
6. You think you don’t need a concept.
7. You are producing all content in-house.
8. You aim at outsourcing content maintenance.
9. You think your business is essentially the same as twenty years ago – just with computers.
10. You consider to establish a Web Analytics culture in your company through monthly reporting of web site visits, visitors, and page views.

The term originally refers to landscape architecture, where it is describing a path in a park or green field that isn’t designed by the architects, but is created by people finding their own trails. They usually manifest as short-cut foot paths, eroding away the sward, and are often fought with fences and signs: “Keep off the grass!”

Looked at differently, these paths are documenting the perceived inadequacy and rejection of a plotted spatial design.

The article outlines the implications of “desire path” inscriptions for the field of web and service design. Let’s dive briefly into history first.

“In the beginning there was the homepage” could be a valid starting point for a history of web design. The homepage was a starting point for the user journey, a safe harbour for users surfing the deep waters of the Internet.

From there not much could go wrong originally. In the early Nineties homepages were barely more than linear text, with occasional headings and subheadings; a linear stream of words, only disrupted by underlined words in blue which marked connections to other pages, servers and sites. These connections were paraphrased as the auguries of a new type of non-linear text (“hyper text”).

Back then, all of these links on home pages were pointing to points somewhere in the unchartered terrains of the World Wide Web. For students pondering with markup language at the time they constituted a proof for their open-mindedness and connectivity within a multidimensional academic universe.

A lot has happened since then.
These days, commercial web site owners would rather change their web agency than to let any of the site visitors disappear from their carefully crafted home pages into any distant vault on a server far far away. The predominant idea today is to drag visitors deeper into a web site, comforting them with prominent unique selling points of products, with irresistible discount offers in web stores, and with positive customer testimonials – all supporting sales, or at least fueling an interest in a company’s products.

Yet web site visitors don’t really seem to appreciate the nicely laid out paths. Just as park visitors make their own trails by carving out footpaths offside the official tracks, web site visitors don’t follow the carefully crafted navigation paths either. More and more often we see users rather refining their search in Google than to bother with the navigation of a consumer web site.
As a result, web site visits are getting shorter and shorter, lasting for less than three pages on average, and with much shorter time-on-site values than two years ago.

The countless “Buy now” buttons that we can see on commercial home pages more and more often may make us believe that a quick and instant conversion is what people are looking for.
The underlying Web Analytics figures tell us a different story: Rates for purchase conversions stagnate well below ten percent across industries, Search Engine visits do most often end up on deeper navigational levels than the home page (which is a pity! Nobody sees the beautiful “Buy now!” buttons and teasers down there), and only occasionally these visits are traversing the site’s home page. And mostly, if they do: exit.

The “desire paths” web site visitors seem to have in mind surely are centered around taking the shortest possible path, but as Christoph Alexander has pointed out in his book “A pattern language” (in 1977!): “The layout of paths will seem right and comfortable only when it is compatible with the process of walking…” (pattern 120, Paths and Goals).

From here we dare to formulate a hypothesis: people promenading in a park are less likely to cut corners than people who try to reach their bus stop at the other side of it. We can tell the promenaders from those traversing by watching their behaviour. And we will see, undoubtedly: the “process of walking” has many faces.

In a similar manner we need to consider the web site promenaders as separate from the traversers and we need to make specific offers to accommodate both their needs.

Yet there is only one home page on each web site, and still the idea may prevail that there must be an ideal path layout. Sure – there are possibilities to prevent people from cutting the corners, but as Are Halland has laid out in his brilliant presentation “Core and paths” in 2007 the crystal clear design principles are too often obfuscated by the “Seven Deadly Sins of IA”, particularly by increasing the amount of choices.

In Alexander’s words: “To lay out paths, first place goals at natural points of interest. Then connect the goals to one another to form the paths. The paths may be straight, or gently curving between goals; their paving should swell around the goal.”

This approach, of course, presupposes a web site owner’s ability to cater for the most obvious short cut towards the goal. But on plenty of web sites we see the contrary: overcomplicated navigation patterns with multiple layers, flanked by category teasers, special product offers, obfuscated contact points, unclear pricing options hidden deep within the “place you order here!” funnel, etc.

Just as fifteen different brands of margarine don’t support easy decision making in a supermarket, offering five different ways to reach the same page on a web site doesn’t support easy decision making, either.

To design a web site from the home page may have been considered appropriate in the mid-Nineties. In 2011 you are wasting your money (as a business owner) and your time (or that of your agency) on this task.

Further reading:
Are Halland: Core and Paths (On Slideshare)

Christoph Alexander: A pattern language (New York, 1977). Available through a book store near you

There are plenty of blog posts out there labeled “How to increase your newsletter open rate” – and depending on from the blog post’s date you get advice that “40-50 per cent makes a great open rate” (or: 20. Or 10. It depends on which industry are in, largely. On the maturity of your market. Things like that).

Usually these claims are followed by advice along the line of: 1. Have a great subject line, 2. Make sure you send your newsletter on an appropriate day (usually: end of the week/weekend for private persons, Tuesdays and/or Thursdays for B2B), and 3. always include a personalized greeting: “Hello {First name}”.

Details vary, but most advice centers around the content, and some meta tasks like timing, dynamic field population, and subscriber base tidying.

I am slightly surprised how little effort is put on the improvement of newsletters as-a-service. After all, most newsletter recipients are bound to get largely identical repeating sales messages à la: “Our offer this week: 20% off on all products”, or: “Be the first of your friends to get the new {enter product name here}”.

So: being told over and over that you can now buy for less is a message that tends to wear off even with your most loyal subscribers. “Repetitio non placent”, as they say in Latin.

It is easy (well: is it really?) to imagine that newsletter subscriptions and user activities are following similar principles as any other life cycle model does.

Recap: after a honeymoon period shortly after signup where everything is nice and dandy some fatigue creeps in (be it related to the feeling of “same old, same old” with regard to the messages received, or a general change in user interest), before finally the likelihood to defect is becoming so high that no longer any look is thrown at your newsletters. Such an inactive subscriber is very close to one that never subscribed to your service in the first place.

If we can take this model for granted we can try to come up with particular actions in the newsletter program architecture and timing. The idea with this is to either prolong the honeymoon phase, or to decreasing the marginalization effects that the messages are generating over time.

For doing that reasonably we need to understand what makes the specifics of each phase. Which basic considerations are appropriate and what data is available to support our findings?

Let’s start with some basic considerations about the newsletter tool you’re using.

Assuming that you are using a newsletter tool you access via a browser (as opposed to the email client you have on your own computer) it is very likely that some built-in functionality is at your service, helping you to make the needed distinctions.

You should be able to follow the signup date for any particular user – that is helpful to determine the amount of newsletters a person has already received.

For any newsletter and any subscriber you should be able to see which links were clicked. That helps you to find the most prominent links for each newsletter, and it will help you to determine the activity level and fields of interest per each subscriber.

Finally, you should be able to see if certain email addresses produce bounces – for a set of different reasons the newsletter cannot be delivered to your subscribers’ email addresses – they “bounce”.

Let’s continue with some considerations about the particular hurdles you have to cross along the life cycle. I am modeling the life cycle stages in a generic way for any particular user/service here.

1. Right after signup a certain level of interest from any subscriber can be assumed. This interest can be increased or decreased over time, depending on the subscriber’s perceived value.

2. If all goes well, the subscriber will find your messages relevant and interesting. In other words: the post-signup dissonance is minimal. (I made this term up, deriving it from the term “post-purchase dissonance”, normally used for describing often-occurring mixed feelings about the usefulness of a purchased product in relation to its price).
During that phase you may gain important insights on which items in the newsletter were clicked by a new subscriber. Assuming a user’s explorative mindset after signing up for a newsletter this helps determining which topics are likely to resonate with him/her.

3. Sooner or later the increase in interest will get smaller. Lower click rates and response frequencies are the result. This point marks the beginning of a transition phase where finally…

4. … marginality kicks in.
This marginality can have different reasons (but they are all “situated” in the subscriber’s mind. They cannot be monitored directly): the feeling that the user “has seen it all”, a change in user’s interest (or life situation), a feeling of redundancy in the messages you are sending out, or a feeling of a lack of relevance.

5. As this deterioration in user engagement continues we will see longer and longer periods of inactivity. At this “fatigue” stage we will see newsletters which have no click activity for a given user at all, paired with high click frequencies on other newsletters. We as well can expect a longer latency time (newsletter sendout on Friday at noon, but clicks on it are only made on Sunday evening).

6. This oscillation in user’s response to newsletters may continue for quite a while and may vary according to the moon phase, user’s resistance against bad weather, or to the topics presented in the newsletter. However:

7. Sooner or later subscribers will no longer bother to open or read the newsletters received. At this stage the user has defected from the service.

These seven stages are not marked by clear boundaries. Tendencies over time may show emerging or arbitrary patterns – and in some phases it may only take very little effort to re-activate the subscriber. Other users may rush from phase to phase or show completely erratic behaviour.

The point is: the actions to be taken are very specific in any phase of the life cycle. To tell the phases from each other (they are not identical with the stages) I have included a graph below which models the seven stages of the user life cycle on a so-called “cusp surface”, adapting ideas of the French mathematician René Thom, and two authors named Zeeman and Renfrew (from their book “Transformations. Mathematical approaches to cultural change”).

Both horizontal dimensions of the graph are marking user perceptions of Interest and Marginality (the “parameter space”). The resulting three-dimensional surface depicts the relevance perceived by the subscriber. The path drawn on the twisted surface marks the user life cycle in time. Literally subscribers are “walking the line” Not all on identical paths and with the same timing, but pretty much along similar marks.

The projection of the cusp path to the two-dimensional I/M plain shows two things: (1). a significant rectangular “U-shape” (of the path), and (2) a greyed area which is labeled as “Bifurcation set“.

While the U-shape consists of three clear stages (I will use them later to group the counter measures into them), the “Bifurcation set” is a bit trickier to grasp.
Without even try to be tangent to the mathematical principles behind it, I am sure that a very pragmatic and “graphical” explanation will do for our purposes. In case you are familiar with the concept anyway, just skip the next paragraph. If you want to read more, get Renfrew/Zeeman’s book. It’s really interesting.

Within this bifurcation set, the maximum level of subscriber’s indeterminacy is given. On the three-dimensional surface we see that this area marks a set where for any unique coordinates in the parameter space (I/M) multiple points are given on the cusp surface. The “oscillation” between different relevance levels is to be taken literally, as the “true position” of the subscriber cannot be properly determined within the bifurcation set.
At the same time the subscriber may be considering the newsletters as “highly relevant” and “barely relevant” – it simply marks the assumption of certain indistinguishable criteria and probabilities for clicking or not clicking newsletter links. The randomness of the subscriber action is the point here – and this shall best not be confused with increase or decrease in user interest.

The three distinct stages on the U-shape can be labeled as following:
(A) growing user interest (either purely driven by curiosity and willingness to explore, but most often fueled by the first received newsletters themselves), (B) increased user marginality, and (C) increased likelihood for user defection.

As mentioned before, certain actions from the newsletter publisher can accelerate or slow down the transitions from phase to phase. Picking up the distinctions between strategic, tactical, and operational decision-making along with the stage definitions will give us a valid contrast folio against which we can formulate and valuate our attempts to minimize subscriber defection.

Honeymoon phase
The strategic goal can be defined as “minimizing the post-signup dissonance”. On the tactical level we concede a need for “building interest with regard to message content and message context.”

After all, the subscriber is about to learn about your offering and products (content), as well as about your service, about the terms and conditions of delivery, and about the sidekick offerings your company has in stock, i.e.: What other subscriptions are there? Why should users create and maintain their user profile? (this would be the context).

What would be operationally the worst thing to do was the repetition of identical messages and similar offers.

After all, a novelty factor deteriorates pretty quickly, if the only changing variant in your communication would be whether you have “Sunglasses for sale with a 20% discount” this week, and “Winter boots for 20 % less!” next week.

If you, in other words, are repeating both the communication scheme and the benefit week over week people will learn that pattern quickly. Instead of ordering at your shop after having seen the repeating message for a couple of weeks, they may rather go and check whether your discounted prices are any lower than the prices of a well-established competitor.

Increased user marginality phase
Strategically, the goal for this phase would be “to minimize the value perception decrease”.
Tactically, the appropriate question is: “How can we re-focus the subscriber?”, and the corresponding answer would be: “By offering strong selection criteria to re-gain relevance”. These selection criteria are to be strongly centered around content and categories.

Operationally, the worst thing you could do is to do random line-extension offers.

Imagine: you have tried to build a connection with your subscriber by focusing on your core competence of selling fashion. A lot of related featured fashion products can be thought of, but with a line extension offer for “winter tyres” you surely wouldn’t support the recipient’s focus.

In other words: if you are adding arbitrarity to your communication through contextless contents in this phase the odds your message will be well received are strongly against you.

Defection phase
Undoubtedly the trickiest phase, the strategic goal would be formulated as “regaining significance through a strongly personalized core offering”. Tactically, it would make perfect sense to now grant significant personalized incentives with clear benefits.

Operationally, the worst you could do in this phase is to grant generic “lawn mower” discounts to all of your subscribers of a certain age – and even worse it would be if you got caught with a discounted offer which isn’t better than the street prices paid for the goods you are discounting on.

Well – all of that is pretty obvious, once you think about it. Specific situations requiring specific actions, not a general de-valuation of your offers, products, or communication.

One question remains, however: how the hell could we tell one phase from another?
Simple answer: by utilizing usage data per newsletter and historic data per user.

Your newsletter tool should give you plenty of information about any of your users’ actions (if not: consider a different tool!) – and if you are doing ROI tracking on your newsletters (both in the newsletter tool and in your favourite web analytics system), you should have a valid purchase history on newsletter level and on user level.

So: start from what you know. You should know from your users at least: 1. at which point in time they signed up for your service, 2. how many emails they have received/opened (although the latter is a somewhat obscure metric!), 3. which links they have clicked in any of the newsletters, 4. which of the clicks led to a purchase.

As you are having that data both on an individual subscriber level as well as on the level of any particular newsletter issue you have two data sources to compare. Cutting through these four metrics groups, and knowing that all reasonable newsletter systems offer tools for maintaining your subscriber base already gives you a pretty decent toolkit for segmenting and filtering your newsletter recipient group and for targeting your messages related to the phases outlined here.
Start using that. Tailor your contents to your insights and filters. Experiment and play with it. Make your subscribers matter.

Finally: some examples on what to look at. Consider a user receiving a monthly newsletter from a travel agency. Having special offers for flights and hotel packages makes a central pillar for the commercial success for such a newsletter program.
Imagine one of your subscribers is always looking at the flight offers you have in for London (or for Luton. For Lisbon. Whatever!). My advice is: use that historic data set for future offerings – but not right away. Do it in the “marginality” phase.

Group your offerings by world region then. By destination country, if you want. By city, if you have to. Give those who have booked a trip to London through one of your newsletters more than once a related discount offer in a later stage of their life cycle (Defection).
Look at the most common city destination you have in your click/revenue statistics. Does this make a sufficient user base for a “special” newsletter sent to those a bit further down the road of their life cycle?

What’s the point constantly offering flights from Europe to Asia to those who never ever have clicked on any destination link which lies outside of Europe? Well – leave it up to them whether it matters or not. Let them refine what offers they are interested in. Help them select based on their previous choices. Provide relevant content with respect to their preferences.

Although you may argue about missing a sales opportunity with the “flights to Asia” thing – keeping a user retained through the relevance of your offering matters a lot more these days, I believe.

… and Five Things to Learn” is a brilliant roundup and an excellent outlook for the current and emerging state of Social Media Measurement.

For brands it becomes more and more of a commodity to have a Facebook presence. Companies even start giving up their own web presence altogether (so did Grey in Stockholm a while ago) – and who has ever played around with Facebook Insights for a while starts wondering: why would that happen?

Better web statistics surely can’t be the reason. Yet.

For those who don’t happen to be an Administrator on any Facebook “Fan” pages (or whatever these pages may be called these days) may perform a quick image search for screen shots on “facebook insights” with a search engine.
Similarly as with the Flickr profile stats (coming with a “Pro” account) these stats are repeatedly said to be “just awesome”, but if you have ever have taken a closer look at any contemporary Web Analytics solution, the term “superficial” may come to mind when looking at the Flickr stats or an Insights statistic.

Uh – did I really say that? Well: let me put this into perspective.

Your average web analytics solution has a multitude of ways to track conversions. At the same time conversion tracking is the single-most fucked-up item for nearly any web site I was happening to assess so far. Getting conversion tracking right is painful, error-prone, and more often than not some nasty enhancements to the back-end are needed which was done originally by the hairy programmer the company has fired six years ago.
So most companies have to live with the fact that we cannot get data on funnel dropouts between the funnel stages and just need to accept the fact that we know about the 0.26% visit-to-purchase conversion rate – but don’t know about any more details.

Compared to that, conversion tracking on Facebook is super-easy and super-simple. Doesn’t require custom coding and comes right out of the box. Somebody pressed the “Like” button: voilà – a conversion. Somebody posted something to the wall: voilà – another one. One person switched from the custom landing page tab to another tab: voilà… errrm. Wait a minute.

It seems as if the small chance for meaningful interaction is slightly different on Facebook. Any interaction there is possibly meaningful as there only are a few unique ones anyway. Combine that with only a very small amount of pages (and compare that to the document dumps you can create with a proper MOSS system), and add the promise of sociodemographic data as a secret sauce – and here you have very easy, simple to follow metrics: amount of daily likes and unlikes, total lifetime count of likes, pageviews per tab, etc.

The major difference between a company web site and a company (or product) presence on Facebook is that company web sites (and blogs) are not coming with social reach from the start. Gaining (and keeping) traffic on a web site usually is a slow and painful process (unless you are comingthru.com ).

“Social reach” is the predominant meme for enhancing a company web site with a Facebook presence. The connection between both can be reached by placing a “like” button on relevant elements on your company’s site – these items will then as well appear in the Facebook stats. Though this is – technically spoken – a double tagging, it doesn’t hurt the traffic acquisition strategy for your web site and enhances your Insights stats with higher numbers of feelgood metrics: indeed you may instantly like the “like”.

The implications of this trend are slowly gaining shape: instead of following an increasingly scattered anonymous audience on your web site which prefers multi-tab browsing more and more (a pain for session analysis) and shows no consistent behavioural patterns across visits, it is of course a lot easier to concentrate on small pieces of ad hoc interactions which indicate a sentiment rather than the conclusion of a complex consideration process which may culminate in a newsletter sign-up, a purchase, or a demo download.

Concentrating on increasing audiences and social reach (500 million active users in July 2010, growth rates well above 100 percent per year) makes us “return to the icon” (Marshall McLuhan):
The rapid audience growth in Facebook allows companies to indulge in a constant stream of success stories for their online presence (“22% increase in August, 58% increase in September”).

And consider this: The average amount of friends a Facebook member has is 130. Mostly we look at smaller networks which are linked reciprocally, but you can imagine the reach potential that service provides, if only 30 friends for each user were unique.

And, yes: – the sociodemographic data on Insights is so far a placebo only. All the tiny interaction bits and pieces cannot be sliced and diced into demographic segments.
This means that I currently cannot check whether members of my most favourite target audience (the infamous “over eighty-year old males living in tents in Iceland”) are the ones clicking all the tabs and likes – or whether their grand daughters in Toronto, London, and New York are doing it.
But it’s only a matter of time until we can segment this activity data, I’m sure.