Last Exit Westend

“You jump into the mirror / and you’re invisible” (Kate Bush)

In March 2012, I have pulled myself into a one-year experiment on search engine visibility: for the introductory slide of a presentation about Web Analytics I googled my name to see whether any of my work as a Strategist/Analytics expert would be visible.
Not much there, really. I haven’t been marketing myself actively.

So I have taken a screen shot of the first result page, and wondered: “What if I wouldn’t actively produce any content for my blog or produce any tweets?” “How would my visibility on Google change if I withdrew from creating content such as blog posts (visible for Search Engines) for a year?”

Pretty much a year has passed. Here are the results from my little “visibility” experiment.

In March 2012, the aggregated values for results linked to my person was 26, the aggregated value for results linked to contents I’ve created was 15:

Nearly one year later, the respective aggregated “person” value was 34, the aggregated “content” value dropped to 2:

What had happened in between?
During the year, I have created an account on Slideshare, and one on Vimeo. Both sites are ranking pretty high in Google (my Slideshare account doesn’t have any contents, my Vimeo account two barely viewed videos I did for/with a friend of mine).
My Facebook settings were and are so that search results related to my person are not publicly visible.

All other content-related previous entries (the bold ones in table 1) have disappeared within the past year, as well as my “person” entry for Drupal.

How to interpret these results?
Needless to say that this experiment is of course not providing statistically significant results. Nonetheless it triggers some ideas (and speculations) about search engines.

1. Results from Social networks generally rank very high in search engines for people searches. I would have had no idea whether there is any such thing as a “people search” classification, but adding the term “blog” to the search query simply brought up a similar “people” reference from LinkedIn. I have listed my blog’s URL there, but couldn’t find any reference to the actual blog. Adding “westend” instead to the search query brought up the site you are reading content from at the moment, so I would assume: there is no classification for searches in categories of “people” or “contents” – just stochastics.

2. Consequently, everything that has your first name / last name in the title or the URL will be regarded as highly relevant for the corresponding name search. Social networks use vanity URLs (of the format FirstnameLastname), and are thus considered highly relevant for searches like that.

3. As a matter of fact: Being found for content you want to be associated with (like: blog posts you have written) has become a lot harder. The “people results” are ranking so much higher they are obstructing those “content results”. The latter are thus only accessible indirectly.

4. It is tough to tell whether “own” channels are really dependent on fresh contents. I would suppose so but need to resume content production to verify.

5. Google Plus is showing a growing influence. Search results from Google Plus members are showing a small portrait photo along with them – in Google search, of course. And, certainly: not for me, as I yet didn’t tap into Google Plus.

6. XING has become popular beyond their traditional German-speaking market (huge increase in their rankings on google.com). Or, then: there are more people with the name “Michael Dlugosch” registered these days. On a side note: kudos to my namesake Michael Dlugosch, a German film critique who runs and constantly publishes on rottentomatoes.com. He ranks high for his presence on XING. But it’s not me. I have deleted my account when I moved to Finland.

7. Facebook’s highest profile number for users with the name “Michael Dlugosch” is 73 (format: /michael.dlugosch.[number]). Both Facebook’s own search and Google are only showing a fraction of this. Either people are keeping their profile access limited (as I try to), or they have deleted their accounts.

8. The “Flickr Photostream” result page has disappeared completely. This goes at least for me. Not for Miemo, who actually has a crowd of people following him and commenting on (which he deserves – see Miemo on flickr).
Within the “Google Image Search” results, there are still plenty of photographs taken by me. There, however, images accessed through applications using the Flickr API (such as picssr.com or lurvely.com) are ranking higher than the Flickr images themselves. Predominantly the thumbnails seem to have been indexed from flickr, while the API services are caching ‘ze real zing’.

9. When repeating this search on google.fi, on google.co.uk, and on google.de im March 2013, the results differ somewhat, but to no significant extent from my perspective. Just for the sake of completeness:
On google.fi the aggregate values were 38 for the person and 5 for the contents, on google.co.uk I have found 38 and 6, and on google.de the result was 12 and 0.

10. While not doing things visible for Google, I continued my regular activity on Facebook (but kept away from Twitter, largely). As a result, my Klout score went up from 33 to 41 within that year. The Twitter score contribution was as low at 6% (as expected), Facebook 61%, LinkedIn: 34%. The latter came as a surprise, as I didn’t post a single thing there when writing these lines. I didn’t count in that endorsements seem to be posted automatically, and might be miscounted as “activity”.

Take-home lessons:
While Google’s mission statement still is “to make the world’s knowledge accessible”, this little experiment seemed to show me that – duh – searches for people’s names are more likely to bring up results featuring people profiles than contents these people have produced (here is where the micro formats tried to tap in a couple of years back, but they didn’t quite succeed).

That doesn’t come as a surprise, as two things are largely corresponding: (1) profile pages allow for automatic generation of content displayed in result pages (esp. with vanity URLs). (2) The more popular the social network in question, the more likely it will produce a well ranking search result.

This creates a self-amplifying mechanism: a social network becomes more popular by providing search results ranking high, and those high-ranking results are thus becoming more likely to be clicked, eventually producing a higher ranking.

Unless you are a celebrity, chances are high that you can find yourself online for simply any profile page you create in any social network (the bigger, the better). This seems to be perfectly suited for “vanity googling” – and as long as it is all about the person (Hey! Look! It’s me!), people will eventually appreciate this.

Figuring out what a person does (for a living, or: as a mission), is undeniably harder. Certainly: profile pages do allow people to show up the way they want to, but despite the term “social search” being widely used these days, we are still looking at a staged presentation that has nothing to do with “social”.

If you look at your amount of friends (Facebook) or connections (LinkedIn) to determine your standing in Social Media, you just can see how many people have yet acknowledged that you exist.

If you take the matches for your name on Google as a measurement for your online presence (or, as in my original approach: for my degree of invisibility), you predominantly see the breadcrumb traces you’ve left behind for yourself. If you were lucky enough to do that with services having a high number of participants, you are misattributing the network success as your own.

There is hope, though: at the bottom of Google’s result page, there is a section labeled “Searches related to [search term]“. With a bit of luck, you’ll find these entries reflecting a bit of what you do, as it is said to relate to what other people have searched for.

And, as with all other things social: it’s not about what you think you can do or control. It’s whether others give a damn about what you do or don’t do. And with that regard, I am more than lucky.
Happy 11th wedding anniversary, my Dear! Thanks to you, I don’t go unnoticed!

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.