Back into the fray comes Joseph!
Proving that Serendipity is doing it’s job, I’ve had in my mind that it’s time to return to these thoughts and several people contacted me to find out if I was going to return to this blog.
Okay. Into the deep end first.
My time away has been due to busyness. Perhaps some readers have heard, NextStage Received its first patent on its Evolution Technology. For years we’ve been intentionally below the radar, now we seem to be becoming a recognizable object rapidly approaching from the far horizon. Now that we’ve left nap-of-the-earth flying I’m able to discuss things more openly, me thinks, hence some of my responses now and in the future.
Are the visitors happy?
One of the things I did while I was away was talk with a few people (about 100 so far) about what I’ll call The Purpose of Web Analytics. I did this research because of something I wrote in this thread above, “…all these analytics are worthless unless they create happy, satisfied visitors, yes?”
I’ve talked with upper management in education, politics, at national telecoms, financial institutions, transportation, recreation, … a pretty diverse group. Most of them were involved in marketing products or services or some other form of gaining marketshare. None of them were web analysts or involved in web analytics except that they received reports and were expected to act upon them. None of them were particularly happy about being made accountable to a system that (they believed) wasn’t measuring … and here’s where the challenges really made themselves known.
What was being measured? Lots of money was being spent and lots of people were being told that the measurements mattered and as one fellow explained, for the amount of money they were spending they expected some consistency.
“What do you mean by consistency?” I asked.
He pretty much didn’t know. He and those with him said lots of things and it could be distilled to a general dissatisfaction that there wasn’t a single model that they could consistently use and derive actionable meaning from. The dissatisfaction grew geometrically when the discussion got into executives making decisions based on sales presentations rather than a given product’s specific informational abilities.
At one point I leaned towards a speaker and quietly said, “Remember, Joseph friend,” and everybody laughed because the tension in the room was broken.
I reference these anecdotes because one of my original hopes for this platform was an increase in understanding and acceptance of some mutual goals regardless of discipline or tool platform.
In the end, doesn’t it all come down to “…all these analytics are worthless unless they create happy, satisfied visitors…?”
If I can’t act on it, it doesn’t exist
The next item I wish to thread into this discussion comes from an online conversation I had with Critical Mass’s Christopher Berry about why web analytics seems to be a harder sell in Canada than in the US. You can follow my side of the conversation in Canadian Based Business Differences — Responding to June Li, Christopher Berry and Jacques Warren, Responding to Christopher Berry’s Vexing Problem, Part 3 post, The Language of Web Analytics - The Hard(er) Sell in Canada, Responding to Christopher Berry’s “A Vexing Problem, Part 4″ Post, Part 1, Responding to Christopher Berry’s “A Vexing Problem, Part 4″ Post, Part 2 and Communicating Science to Business and Vice Versa and links are provided to Christopher Berry’s side on the conversation in those posts. I’ll invite people to pay particular attention to Communicating Science to Business and Vice Versa because (and as Mr. Berry noted) the summation is what counts, “Business is different. Business (me thinks) tends to be more ‘Tell me how to use this’ hence most business proposals and reports start with Christopher Berry’s nuggets then go into explanations.”
My research is convincing me that (what I recognize as traditional) web analytics is going to be losing its authoritative power in the coming years. I think web analytics (and yes, this does go back to my original hopes for this blog) will evolve (just as anything will if it is going to survive in a given changing environment). What will it do and look like? I have some ideas, of course. Just ideas at present, though. More things to research before putting down on paper (or in a blog) at present.
This does tie into my comment re Avinash Kaushik’s “…we shouldn’t use ill defined engagement metrics as a proxy for something solid like a sale.” I’ve been an oft-times unwilling father-confessor to businesses frustrated by ill-defined metrics of any kind and wanting something that is justifiable a) financially, b) scientifically, c) arithmetically (forget mathematically) and d) produces some kind of “do A, get B”, “this-equals-that” link between action and outcome.
The comment I love about this is “If I can’t act on it then it doesn’t exist”, ie, it’s noise, a distraction at best and something best ignored. This was a wonderful statement used in a business practices discussion.
I’d really enjoy being involved in a web understandability/measurement/future usability discussion that has as its theme “If I can’t act on it then it doesn’t exist.”
“To measure and analyze on and offline behavior and then try to predict who to market to by figuring out what they think is not doable with one tool or one metric.”
I responded earlier to this comment. People who attended either the Toronto ‘08 or SF ‘08 eMetrics conferences are probably well aware by now that NextStage has patented a technology that can determine how someone is thinking through any programmable device. I won’t go deeper into the topic here except to offer a comment I posted on Jim Novo’s blog about the {C,B/e,M} matrix and its use in marketing and analytics.
Picking up where I left off with Jim Novo’s comments in this thread…
I finally had an opportunity to read Jim Novo’s Measuring Engagement and its related Framework for Engagement posts. I truly enjoy Jim’s writing style and the points he makes.
I especially enjoy and appreciate his referencing Relationship Marketing because it places people center stage. Understand people and you can both understand and predict what they’ll do. Watch only what people have done and you can only understand their actions in a specific historical context, you can only predict what they’ll do when the confluence of events that led to their original actions repeats itself. Exactly (and don’t hold your breath). Relationship marketing works at the question “…all these analytics are worthless unless they create happy, satisfied visitors, yes?”
Jim writes “The challenge with this model - and probably why it isn’t more widely known - has been the data, it’s a very analysis-intensive model…”. Yes. Agreed. If Jim (or others familiar with these concepts) is reading (or perhaps at the next conference we meet at), I think this is where being able to substitute cognitive heuristic models makes sense (see Liberation and Heuristics or Responding to Christopher Berry’s “A Vexing Problem, Part 4″ Post, Part 1. I’ve also written elsewhere that I often wonder why more businesses don’t make use of cognitive heuristic models).
For example, I’ve recently been applying heuristic models to helping adult second language learners increase their language acquisition abilities. That’s a traditionally very tough nut to crack and (so far, anyway) I’ve been able to isolate neural activity that tends to make adult language acquisition challenging. Example 2, using heuristic models in the above grew out of learning which heuristic models are used (non-consciously, of course) by which personality types in their decision making processes. This non-conscious heuristic model selection process is being integrated into NextStage’s Rich Personae. These and some other areas of my studies are intensely data-rich models that can be reasonably simplified via cognitive heuristics.
I also strongly like your concept of dis-engagement, although I tend to use a methodology that incorporates “satisfaction” into the scaling system (see Meet Online Engagement’s Little Friend, Satisfaction. I shared that the complete form of this during a discussion at the SF ‘08 eMetrics. It looks something like the figure on the right.
Some definitions to help in understanding; the x-axis is Engagement and is a measure of the amount of pleasure or pain an activity is giving you. If something is giving you either pleasure or pain to any degree your attention is focused on it, hence you are engaged by it according to the definitions documented in Attention, Engagement and Trust: The Internet Trinity and Websites. The y-axis is Satisfaction and is a measure of acceptance and rejection of some internal state and/or external event.
I believe what you are referencing as “dis-engagement” is what we recognize as the slide from high acceptance to “0″ acceptance. Note that this is not rejection (as rejection is an active negation of acceptance) it is a lack of acceptance. I appreciate that the difference might be subtle and I believe that difference is significant. Rejection — the active negation of acceptance — can be thought of as someone pushing something away. Zero-acceptance is the point where one can “take it or leave it” and the internal state and/or external event does not have any value assigned to it, hence doesn’t register strongly in the mind/brain.
Mapping this figure to real world experience, you always want visitors/consumer/etc to be in the first quadrant (where the green curve is). People are both positively engaged (they like what’s going on) and positively satisfied (they accept it gladly). Depending on what you’re selling you may or may not want people in those other quadrants. The second quadrant (bottom yellow curve) indicates someone focusing on painful experiences or information, the fourth quadrant (top yellow curve) indicates someone who finds pleasure in painful experiences or information. The third quadrant (red curve) is where visitors/consumers/etc often end up and marketers/businesses don’t want them to be — the former are actively psychologically and physically moving themselves away from a business/product/service.
I’ll offer that the above is also a reasonable representation of your:
1. Define / Measure Engagement – any way you want to, as appropriate for your business; whatever activity or combinations of activity you feel appropriate
2. Measure dis-Engagement – the absence of Engagement, as in the visitor / customer stopped doing whatever it is you define as Engagement for your business model
I think where the image above (and the math behind it) adds real value is with your “3. Take some kind of Marketing or Service action to slow or reverse the dis-Engagement with dis-Engaging folks” because it provides enough information to know how, exactly, visitors/etc are “right now” interacting with your marketing information.
I also agree whole-heartedly with your statements about predicting “dis-engagement”, etc. I would love to see the data you used in your example and apply it to the above. I’m willing to bet that satisfaction/acceptance was the real driver (and I won’t get into the depths of group satisfaction/acceptance states here (really, Joseph? You’re going to leave something out? Whatever for?)). I did get a kick out of your graph of email response rates falling over time. It was very similar to the results we found in our research on how to design an effective email newsletter. Bravo! I always love it when our findings match others’. Gives me hope we’re doing something right.
<ASIDE>
For what it’s worth, much of the rest of what you’ve written in your post is so close to what we learned in our email newsletter research that the overlap is astounding. Not surprising, I guess, as you’re listing an email-based experiment. It would be interesting to learn what else the rules we discovered pertain to. Let me know if would like to explore this.
</ASIDE>
You also list an implication about sending different messages to different segments. Yes, agreed. I believe the above allows for much more targeted and action-driven messaging (based on much of what I’ve shared above).
Perhaps, in the end, we’ve derived nothing more than a simplified mathematical model (complete with suggestions for better outcomes) of Relationship Marketing?
Whoosh!
Took me two days to put the above together folks. Sorry for the delay. More to follow. Soon.
Promise.
Steve Jackson added the following ...
Hi Joseph,
Good post. I read your article on NextStage’s technology. Could you explain the differences between Evolution technology and Behavioral targeting?
I would define behavioral targeting as changing a websites marketing materials based on a visitors actions. An example might be that the visitor types a keyword into Google and upon clicking the top SERP is dynamically served marketing materials relating to the keyword/phrase.
When I read the PR linked above I found myself thinking it sounded very similar. Is it this kind of technology or is there something different or additional with Evolution?
I see Jim has also responded and I have a lot of time for the dis-engagement/engagement model Jim has been discussing for years now.
In my opinion there is visitor analytics (goal:prospect conversion) and there is customer analytics (goal:customer re-conversion). These can be broken down further into customer lifecycle models if it’s useful. Nokia for instance have a customer lifecycle that has 4 phases and have different tactics and therefore measures for each.
In both prospect and customer cases we use a model I introduced called REAN (reach, engage, activate and nurture). There are different ways to reach, engage, activate and nurture prospects so that more of them convert. Similarly different ways for customers.
Jim in my view has pretty much focused most of his efforts to the customer analytics. Recency and frequency models work much better when you know who you’re dealing with and have permission to talk to them. I think it is also tremendously useful for segmentation purposes in visitor analytics.
Finally I would like to comment on why I think this post and the one you wrote about the technology NextStage has developed is interesting.
It goes back to a point I first read about ages ago on Eric’s blog (late 2006) and then when Eric published his engagement formula became the legendary “engagement” debate I’m sure we all remember, on Occam’s Razor, Jims site, my site and a bunch of others. It got quite heated at times as it should. Passions were ignited and people were drawing lines in the sand. At the time I took a step back and looked at what we all were saying and came to the conclusion we were largely debating semantics though we all agreed on some things.
What we pretty much all agree on is that the more ways we have of identifying ways to get more customers and take actions on our metrics the better. Note the “take action” part. I think most of us agree if you can’t act on the KPI then why use it.
We all agree that we have to use quantitative (clickstream), qualitative (voice of customer, attitudinal) and competitive (comparison) data to drive the best insights. Learning to combine these data sources is the way the industry will move in my opinion.
Indeed as an aside I am selling this concept now (as I was 3 years ago with Aboavista before we were acquired by Satama) as Conversion Assessments. 3 years ago Aboavista were looking at things like the Future Now PA model and applying similar thinking here in Europe but the process was not as well defined or clear to me then as it is now and that is down to conversations like this one.
So I am interested to know how your system works. It seems to suggest that two of the three data sources I feel the industry needs to work with (Quantitative & qualitative) could be somehow combined in one solution. Or am I wrong?
Christopher Berry added the following ...
Thanks Joseph for the citation and the dialogue.
I continue to be in awe of Jim Novo’s ability to speak both Sciencese and Businessese. I’ll echo the power of the “R” in RFM. Recency. The notion that a human in habit tends to stay in habit resonates. “Anchor and Adjust” is another way of phrasing it.
I’ll take a stab at the Future of Web Analytics —
You have physical technologies - Omniture, Google, Google Analytics, Summize - on the one hand. On the other, you have social technologies - the Scientific Method, Web Analytics departments, the conference call.
If you examine job postings the WAA site (plug) on the Demystified Yahoo Group (plug), you’ll find a long list of physical technologies listed. “Candidate must have 15 years of Omniture, 12 years of WebTrends, and 5 years of Crystal Reports” - all too often. So many of us, and HR professionals, are obsessed with physical technology.
(If you examine the Critical Mass postings (plug), you’ll only find “SPSS” listed, and that’s not even required. Shaina Boone and I made a decision, a long time ago, that we didn’t care if somebody had an interface memorized - if they didn’t have the soft social skills and a number of traits, we couldn’t grow them)
I don’t believe that most organizations have focused on the social technologies. Worse, it’s the social technology of analytics that’s the hardest part of the slog. Sure, if you think it’s a nightmare to install Google Analytics or Omniture in a very large organization (and it is) - just imagine how hard it is to socialize a culture of analytics in an organization (and it is).
First, there’s fear that data driven insight is going to suck the creativity right out of most of our jobs — that we’re all going to become slaves to the Algorithm in the end. I don’t believe that this will ever be the case. Data driven insights enable data driven learning. It many ways, through the magic of multivariate testing, we can try out high-risk creative without risking catastrophic failure. It’s an innovation enabler in certain risk-adverse organizational and national cultures.
Secondly, there’s a very good reason why the world is populated with few scientists, statisticians, mathematicians, forensic accountants and programmers. Math only really appeals to a minority of the population. So, we have a barrier of skills.
Thirdly, there’s the scientist - business communication gap, discussed at length in previous posts. It takes a scientific mentality, often (not always) to derive very deep insight from a mess of data. In many respects, being a web analyst shouldn’t be so much about running reports, but should be about running the scientific method on very expansive datasets. Then, that scientist has to tell a story that is accessible and usable.
Fourthly, there’s organizational inertia. Organizations are a lot like people, they anchor and adjust too.
Four barriers, all human generated: fear, skills gap, communication gap, inertia.
I predict that the real challenge in the Future of the Web Analytics is going to be more around the social technology implementation and maintenance, and not so much the physical technology. Physical technology will always play a role, it has to by default, but it will become much less pronounced in the future.
Blackbeaks Blog….All things Analytics - » Web Analytics and Visitor Engagement… Again! added the following ...
[…] is a recent excerpt I posted on WebAnalyticsDemystifieds’ Future of blog talking about the same thing (engagement); It goes back to a point I first read about ages ago on […]
Steve Jackson added the following ...
Chris you’ve hit the nail on the head. I agree entirely with those points. Building culture and winning the web analytics game is the hardest part. I am currently writing a book on the subject.
Marketing Productivity Blog » Blog Archive » Friction Model added the following ...
[…] the higher level ideas not so oriented towards the “tool” aspects of this discussion, make sure you catch this post and hefty […]
Christopher Berry added the following ...
@Steve Jackson
ORLY?
I think that would be a valuable contribution.
The Future of Web Analytics, Demystified » Blog Archive » Responding to Geertz, Papadakis and others 5 Feb 08 comments added the following ...
[…] Imagine what it’s like on my end. — I picked up the thread of this conversation at Back into the fray comes Joseph! and am planning on getting more involved in this blog again simply because some folks took the time […]
Stéphane Bouchez » Blog Archive » Web Analytics and Visitor Engagement… Again! added the following ...
[…] is a recent excerpt I posted on WebAnalyticsDemystifieds’ Future of blog talking about the same thing (engagement); It goes back to a point I first read about ages ago on […]
The Future of Web Analytics, Demystified » Blog Archive » Responding to Jim Novo’s 12 Jul 08 9:40am comment added the following ...
[…] (This post is a response to Jim Novo’s 12 Jul 08 9:40am comment. I’m posting it here as I’m including some images and I need to post rather than comment in order for images to show up. You can read the original comment here) […]
Joseph Carrabis added the following ...
Howdy,
Jim, I responded to your 12 Jul 08 9:40am comment at http://thefutureof.webanalyticsdemystified.com/2008/08/29/responding-to-jim-novos-12-jul-08-940am-comment/
Sorry not to get to it sooner. You gave me a lot to think about.
And I’ll be getting to the other comments once I finish a few projects after the holidays. - Joseph
Joseph Carrabis added the following ...
Responding to Steve Jackson’s 14 July 08 3:15am comment (which can be read here)
First, 3:15am? It’s good to know I’m not the only one who’s feverish mind keeps them thinking all night.
Second, you ask “Could you explain the differences between Evolution technology and Behavioral targeting?”. My first response is no, I couldn’t. I can’t because I’ve never encountered a definition of behavioral targeting that makes sense to me in the framework in which I study. I can offer some responses based on your offering of “I would define behavioral targeting as changing a websites marketing materials based on a visitors actions.”
The possible response I offer is from a demonstration we did in Feb 2000, before NextStage was officially NextStage; Evolution Technology Ontology on an eCommerce Site. I state openly that nothing has changed from the original presentation other than correcting a few typos, fixing some grammar, some formatting and using “Evolution Technology” or “ET” in place of what we originally thought to call it.
Let me also offer an alternative from not so long ago.
On the day I read this comment a real situation occurred which I’ve documented with a reader of these blog posts. (This is where I get to be educated) Could you explain to me what Behavioral Targeting could do with the following, please? I’m asking because when I read about Behavioral Targeting I often think to myself, “This is useful how?” and a) I appreciate people may ask the same about what NextStage does and b) that the question stems my lack of understanding, not necessarily a lack on the part of Behavioral Targeting.
An individual goes to five websites that they’ve never gone to before and this blog (which they frequent) in a single session (serially, not in parallel) starting with this blog. This blog is bookmarked, the other sites are accessed by typing their names into the location bar at the top of their browser. The sites and their order are this blog, Lowes, HomeDepot, WalMart, then two local (to them) recreational vehicle sales sites (let’s call them RV1 and RV2). The only time they go beyond the homepage on any of the sites is on RV2 where they follow a “Service” link.
Total time front to back is perhaps five minutes starting on or around 2:30pm local time, most of that time they’re scanning this blog. Once they follow the RV site “Service” link they close their browser session.
Given the above, ET would have determined that
the visitor was male
their age (±5 years or so)
during a pause in their browsing of this blog they received auditory stimulation
after that auditory stimulation they started thinking about food
the thoughts of food were focusing on 4-5 hours in the future
this individual wasn’t interested in traveling
this individual wanted to buy something right now, wasn’t doing research for a future purchase
was shopping by product first price second
and it would have started making conclusions about the price point. It would have known how much this individual was psychologically prepared to commit to the purchase and would not have been able to assign a dollar value at this point. At least I doubt it could have assigned a dollar value this individual was willing to commit at this point in the browsing session.
Connected to a sufficiently robust CMS system and with some business rules in place, ET would have determined that this individual wanted to purchase a price specific, outdoor type cooking supply not related to travel for use later that day and delivered appropriate content (it would have started showing grills, grilling supplies, propane tanks and refilling information. Our visitor’s goal was the last. Also, at this point ET could have made very accurate dollar value guestimates if it hadn’t done so already). We have pieces and parts of this scenario detailed in Reading Virtual Minds, Chapter 4 “Anecdotes of Learning” (I really need to finish writing that book one of these days).
Visitor Analytics, Customer Analytics
My belief is that such forms of analytics are too narrow. Knowing someone only when they’re a customer or a visitor robs the analytical process of understanding the individual as a person and is an objectification of the person that most people object to when asked. Simply put, the more you know about any individual the more you can plan, anticipate, provide, respond, prepare, …, for them when and should they return. To that end, you can engender them to return by knowing them well enough and properly exercising that knowledge.
Should social communications prevail, I suggest that this level of knowledge (hence demonstrations of trust, fair-exchange, respect, etc) will become mandatory. People want to be treated as individuals and special in their own right. Even people who claim to want to “only be one of the crowd” are stating that their singular (to them) identifier is their quest for (what they think of as) anonymity, thus the knowledge requirement is again mandatory. This is something I believe you point to with your “Learning to combine these data sources is the way the industry will move in my opinion.”
And note that what I’m offering is my opinion, nothing more. My opinions change rapidly when sufficiently convincing contradictory information is recognized.
“It got quite heated at times as it should. Passions were ignited and people were
drawing lines in the sand.”
I can’t share enough how much I disagree with the above as a solution methodology. I was not part of the above and don’t know much (if anything) about the discussions occuring at that time. What I can offer is that such demonstrations are indications that logic is not being used, instead emotion and ego are in play (see “We have too much data to analyze so we’ll just close our eyes, shoot and pray”). Most people demonstrating “heated”ness are doing so because of some form of boundary or territory violation (physical, psychological, … Your use of “drawing lines in the sand” is an obvious indication of this).
My suggestion to people with whom I’m having a discussion and when recognizing emotion and ego are coming into play (including my own ego and emotion) is to (basically) ask what’s going on, inviting them (or myself) to focus their (or my) rise in energy at its own cause. People able to perform that refocusing often discover quite a bit about themselves and also usually end up producing stronger, more salient arguments for their causes.
So, as a general question, Why were people getting heated? What was causing passions to be ignited? Were lines being drawn to keep something out or not let something in (these are demonstrations of two very different psychosomatic states)? Were they being drawn to keep something in rather than not let something out (ditto)?
As for the heat and sand drawings around Engagement, the reformulation of Eric’s original equation demonstrates that everybody’s definition (provided the three basic rules apply) can be used equally, that inclusion and exclusion of different elements only comes down to a question of increasing or decreasing accuracy. If the model currently being used is providing sufficient accuracy for a business purpose, excellent and why be troubled further? If it’s not providing sufficient accuracy, here’s a method for increasing that accuracy.
This methodology is no different than using a variety of astronomical tools to get an increasingly accurate understanding of the heavens. A backyard astronomer is excited investigating with a 10″ Schmidt-Cassegrain, someone wanting to research extrasolar planets needs much more to get the job done. The question are “How accurate do your measurements need to be?”, “What you want to look at?”, “What do you hope to find?” and so on. A more earthbound example of this is meteorology. Want to know where a hurricane is really going to go? Get as many weather modeling products as possible and do a least-squares analysis of their results.
You write “So I am interested to know how your system works.” So am I, sometimes.
Then “It seems to suggest that two of the three data sources I feel the industry needs to work with (Quantitative & qualitative) could be somehow combined in one solution. Or am I wrong?”
I’m much more comfortable answering if you are correct rather than if you are wrong (I’m not a moralist). My biased view is that any solution based on a limited parameter set has already declared itself a limited solution. Accuracy will depend on the degree to which the original parameter set can be duplicated. This is true of everything. NextStage’s models use over 80 “variables” right now and we’re constantly in contact with other researchers to validate new variable forms once they are recognized.
Can data sets be combined? I hope so and so long as conservation of units applies. We agree in that, I believe. Will they? Again, my belief is that they will. When it will occur and through what agencies I have no conjectures at present.
(and now, back to work…)
Joseph Carrabis added the following ...
Responding to Christopher Berry’s 15 Jul 08 8:26am comment which you can read here
Your welcome for both.
My thanks to and appreciation of Jim Novo, as well.
Recency…since that element was added to this discussion I’ve been doing some research on “recency”, the “notion that a human in habit tends to stay in habit” a you eloquently put it. The more and more reading I do the more and more I liken it to NSE’s Visitor Return Report. It definitely isn’t Loyalty although I’m thinking recency is often confused with loyalty (and this is probably already addressed by others in this discussion. I’m behind in my readings. What can I tell you).
There is a phrase used in my studies, “As soon as you’re ready to not put up with your life as it is any longer, it’ll change.” This concept seems to be the domain in which recency concepts dwell and are not where loyalty concepts dwell. From what I’ve read, recency only deals with (what one would hope would be) an inverse exponential — the more often someone returns the more likely they are to return, the less time between visits the less time between their last visit and their next visit.
There is nothing in the recency model to account for several social science elements, many of which are elements of habituation (sequencing or chunking are two that come to mind immediately).
Stabbing at the Future
To see if it’s done, perhaps?
I appreciate your “…there’s fear that data driven insight is going to suck the creativity right out of most of our jobs — that we’re all going to become slaves to the Algorithm in the end.” and “I predict that the real challenge in the Future of the Web Analytics is going to be more around the social technology implementation and maintenance, and not so much the physical technology.”
You just know I’m going to agree with you, yes? Let me push the envelope just a bit further; I’ve learned that people who are comfortable with their creative skills and creativity have absolutely no problem incorporating data-driven insight into their practice.
That “comfort” lends itself to being open to new methods and new technologies, especially if it means granting them the ability to improve their own processes.
Steve Jackson added the following ...
@Joseph;
3.15am your time - is early morning this side of the water. Probably about 9:15 in the morning. That’s not to say I don’t stop up all night thinking about this stuff. I am admittedly a proud geek.
::
Given the above, ET would have determined that….
(Long list of impressive knowledge about someones browsing behavior and motives)
::
I guess then that NextStage’s tools must use an opt-in approach? Or are you using “methods” IE; combinations of research with consulting like Future Now with Persuasion Architecture?
You currently can’t know which five sites they have visited unless;
a) you had some kind of technology which is reporting activity back to you.
b) you were watching the person (like in a usability test).
c) You’ve developed profiles based on how all people generally tend to behave and have a system to optimize it.
For instance on click Zeroeth;
“BeneVino, using ET and without using Cookies, has already gathered enough information from you to know if you’ve been to the site before, regardless of which computer you’re working on.”
At this point you need to have either asked or guessed based on potential personality types. Regardless of which computer you’re sitting on gives it away meaning that it has to be a method rather than a singular technology. If that is the case then yes, I might be getting it and the difference between NextStage and behavioral targeting.
What behavioral targeting does is simply target ads and offers based on your behavior. Depending on the behavioral network you will receive ads based on preferences you have pre-identified or clicks you have
made. In your scenario after the auditory stimulation they might be *thinking* about food and your offers are presumably designed around this potential situation.
In the behavioral targeting world you wouldn’t know that unless the behavior indicated it. (IE they went to a search engine and typed “burger” - in which case you could serve an advert)
Where your service appears to be different is that you’re building a psychological profile which reminds me of Future Now’s persuasion architecture, mapping needs according to logical or emotional decisions and depending on fast or slow personality types.
This in particular sounds very similar to Keirsey & Myers/Briggs modelling; “the way you think, the way you feel, the way you believe things should be.”
So is your method to design websites based on this kind of principle? If so I get it. NextStage sounds like a system that combines technology (maybe a panel of opt-in visitors) and scientific design based on personaility types as well as a lot of persona based research.
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Visitor Analytics, Customer Analytics
My belief is that such forms of analytics are too narrow.
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I agree. It’s why in my up-coming book I am dedicating a chapter to Persona based design and another process on how to measure it. Combined the three forms of analytics and scientific testing is very powerful.
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“It got quite heated at times as it should. Passions were ignited and people were drawing lines in the sand.”
I can’t share enough how much I disagree with the above as a solution methodology
::
I agree again. I said so at the time to some of the individuals involved via email, face to face and called for the subject to be dropped at one point because much of the debate revolved around semantics. As far as finding a solution to the problem was concerned the debate only increased awareness of the problem.
My comment on the Omniture blog was an observation around the passion that was demonstrated by everyone. Passion about your subject is required as I’m sure you’ll agree and what was really quite outstanding about the whole debate was how many people got fired up around the world. Without passion and fire in some of the discussions this would never have happened.
Lines in the sand just demonstrated the strength of the passions and yes egos.
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Why were people getting heated?
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One camp didn’t believe Engagement was a valid metric while the other camp did.
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What was causing passions to be ignited?
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The valid arguments on both sides of the fence added fuel to the fire.
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Were lines being drawn to keep something out or not let something in (these are demonstrations of two very different psychosomatic states)? Were they being drawn to keep something in rather than not let something out (ditto)?
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You are right about the egos/emotions in play. There were bruises and counter punches being thrown around which didn’t serve to help and made the whole debate a bit of a flame war after a while. But it calmed down and people got back to business
In my view lines were being drawn to prove or disprove the value of engagement as a KPI. There was one camp that didn’t want what Matt calls the “uber” metric and another that saw the value of it. The argument revolved around the uber metrics complexity and the fact that engagement was often a proxy for sales or outcomes.
There were facets of both arguments that were valid. If you put the engagement formula Eric designed in front of non analytics folks in an organisation they would leave the room in droves. For those guys conversion rate is a stretch. Eric states it’s not for everyone.
However that doesn’t mean it’s not valuable.
Jim Novo added the following ...
Joseph,
Glad to be “engaging” with you again on this topic!
A couple of comments that you might find helpful in our quest:
1. Probably the most tangible link you will find between my world and yours are the numerous studies done on Recency from the psych and related literature. From predicting which men will stand up when a woman enters the room to predicting the likelihood that someone will commit a crime, Recency is the most pervasive human behavioral model. It is everywhere. But like I said, don’t take my word for it, the academic documentation of the Recency Effect as it is sometimes called is extensive. And I think a more academic treatment of the topic, especially the discussions surrounding “why” and “how” the effect exists fro a brain perspective, might provide us some common language to work with.
2. The Recency model is not perfect, and clearly one can do better. However, the pure simplicity of the model is tremendously appealing, especially when faced with the challenge of trying to get people to analyze anything at all. What is also interesting is even when applying higher-level models to the same data (for example, various regression models) Recency nearly always floats to the top as the primary predictive force in the model. So you can improve the probabilities by adding other information – past products purchased, number of customer service calls, etc. – but the primary screener is still the Recency effect.
I see this “Recency with Modifier” situation as quite similar to the Pain-Pleasure, Accepting-Rejecting model and have witnessed the outcomes in action many times. I am totally on board with the idea of “lack of acceptance” and “take it or leave it”. In fact, I would argue that this zone down to zero acceptance is the only area where Marketing can operate effectively; Marketing will have no effect on people with “negative acceptance”.
Further, and this is the point I am so desperately trying to get people to understand, acceptance is not static, it decays over time. And that is why the timing is so important, you have you have to get in front of decaying acceptance (dis-engagement) and act before the customer slides into negative acceptance. Otherwise, it often becomes too expensive from a Marketing perspective to reverse the acceptance. There is probably psych literature on this idea of “resistance to reversing acceptance” though I am not aware of it. When I’ve seen this effect, it often looks physical to me, as in “a body in motion tends to stay in motion”. This is why I often refer to “Customer Momentum”, and it manifests in the parabolic nature of the graphs in the examples I provided.
3. Finally (at least for this session) I just want to make it clear that these Recency / dis-engagement / decaying acceptance effects are not limited to interactive environments or e-mail. I’ve seen them in cable television, catalog, wireless, TV Shopping, fund raising, online or offline, you name it.
However, the very interesting thing you start to see is the more control a customer has over the interactions, the smoother (and thus more predictable) the response functions are.
A concrete example of this is seen in multi-channel retailing. In store retailing, Recency-based models tend to produce “lumpy” (not so accurate) response curves. In Catalog, the curves get a lot smoother, though still bumpy. In TV Shopping, they become quite smooth, and on the web, they are scarily mathematical in nature.
The explanation we came to for this effect: the path from store retailing to catalog to TV Shopping to Web represents decreasing Friction for the customer. In other words, all else equal, there are more barriers to making a purchase when you have to get in your car and go to the store than there are when purchasing from a catalog. But you have to have a catalog to make a purchase. TV shopping removed that barrier because it was “always on”, but the linear format of the show still presented a barrier. The Web removes even that issue. As Friction decreases – or the customer has “more control” if you prefer – the Recency model becomes a more and more accurate predictor of dis-engagement.
I find when looking at this behavior it reminds me very much of the Stock Market – arguably a near Frictionless environment. In fact, you can apply the same kinds of “technical analyis” to interactive customer behavior as those often used in the markets with respect to price and time – moving averages, rate of change, and so forth. The Future Intent to Purchase Score (FIPS) we used at HSN was such a model, and the primary predictive variable in this hand-built multiple regression model was – you guessed it – Recency.
More on FIPS here, Peak Engagement:
http://blog.jimnovo.com/2008/06/22/peak-engagement/