Monthly Archives: February 2014

Will Women Make More Empathetic Marketers?

First published Feb 27, 2014 in Mediapost’s Search Insider

empathyAt the risk of sounding sexist, I wonder if women might make better marketers then men?

If you’ll remember, I proposed a new way of defining the job description of a marketer in last week’s column: to understand the customer’s reality, focusing on those areas where we can solve their problems and improve that reality.

If we’re painting with incredibly broad strokes here – which we are – and we had to attach that description to one gender, which gender would you pick?

I know I’m dancing on shaky ground here – or, in my case – thin ice, but I think we all agree that while equal, men and women are different. Men are better at some things. Women are better at others. Yes, there’s a normal distribution curve in both cases, but for some things, the female curve is going to be further to the right. When I look at the qualities that might make an awesome marketer in the new world order, I have to say it seems better suited to the natural strengths of women. That’s why I don’t believe it was coincidence that more women showed a positive response to my column last week then men.

Let me give you an example of a sex based difference we found in our own research that will help explain my reasoning. We looked at how men and women navigate websites using an eye tracking station. When we looked at aggregate heat maps, which showed all activity, there was little difference. But when we sliced the activity into half-second by half-second increments, there was a significantly different scan pattern between men and women. Men went right to the navigation bar and starting mapping out the architecture of the site. They made a mental wireframe to help them get around. Their first priority was how they were going to get things done. Women, however, first looked at images, especially people and the main content on the homepage. Their first priority was whom they were dealing with and what the site was about.

That, in a nutshell, sums up a crucial difference between men and women. Men are driven by tasks – they work to get stuff done. Women are empathizers – they work with people.  In the end, both often get to the same place. But they may take very different paths to get there.

The new world of marketing I’m proposing is all about nurturing relationships – true one-to-one relationships. It’s much more about “who” and “why”, and less about “what”.  It’s about sensing what the world looks like from the prospect’s perspective and moving an organization’s internal strategy closer to that perspective. I’m not saying men can’t do that, but I am saying that women can do it at least as well as men. And perhaps that can help bring more balance to the world of marketing. While total head counts of men and women in marketing are roughly equal (with some reports giving women a slight edge) the same cannot be said of pay scales. According to the latest Marketing Rewards Survey, published by the Chartered Institute of Marketing, the gap between men’s and women’s salaries has widened by 10% since 2012. This gap shows up most noticeably at the highest levels of the industry, where twice as many men (18%) reach director level as women (7%). This also holds true for marketing heads, with men almost doubling women again – 22% vs 12%. These numbers are out of the UK, but the Bureau of Labor Statistics has similar numbers for the US.  They’ve lumped in Marketing and Sales Managers, but the stats show that women earn about 67.7% of what men earn.

There are going to be some massive shifts in marketing in the coming decades. One of them might be between the genders in who holds the top marketing roles.

 

The Psychology of Usefulness – The Acceptance of Technology – Part Four

After Venkatesh and Davis released the TAM 2 model, Venkatesh further expanded the variables that went into our calculation of Perceived Usefulness in TAM 3:

TAM3

Venkatesh, V. and Bala, H. “TAM 3: Advancing the Technology Acceptance Model with a Focus on Interventions,”

Venkatesh divided the determinants of Perceived Ease of Use into two categories: Anchor determinants and Adjustment determinants. Anchor determinants where the user’s baseline and came from their general beliefs about computer and usage. In Bayesian terms, this would be the user’s “prior.” It would create the foundational attitude towards the technology in question.

Anchor determinants included:

Computer self efficacy – How proficient the user is with the current technology paradigm (i.e. how comfortable they are with computers)

Perception of External Control – How much organizational support there is for the system to be accepted?

Computer Anxiety – Is there apprehension or fear involved with using a computer?

Computer Playfulness – Spontaneity in computer interactions.

Then, Venkatesh added the Adjustment determinants. These factors come from direct experience with the technology in question and are used to “adjust” the user’s attitude towards the technology. Again, this is a very Bayesian cognitive process.

Adjusting Determinants included:

Perceived Enjoyment – Is using the system enjoyable?

Objective Usability – Is the effort actually required what it was perceived to be (resulting in either positive or negative reinforcement)

Over time some of the Anchor factors will diminish in importance (Playfulness, Anxiety) and adjustments will become stronger.

So now, in TAM 3, we have essentially the same process of acceptance, but with much more granularity in the definition of the determinants that go into the Perceptions of Ease of Use. However, with the division of determinants into the categories of “Anchor” and “Adjustment” Venkatesh starts to hit at the iterative nature of this process of acceptance. We create a baseline belief or attitude and then this becomes updated, either though external forces (the Subjective Norm) or our own internal experiences (the Adjusting Determinants). While the model indicates a linear decision path it’s now appearing likely that it’s a more recursive path.

As an interesting aside, Brown, Massey, Motoya-Weiss and Burkman (2002) said that they found variance in the importance of Perceived Ease of Use. Remember, in the original TAM model, Davis indicated that while Perceived Ease of Use does have impact over the original attitude towards a technology, he found that Perceived Usefulness is a more powerful indicator of usage intention. Brown et al found that this varies depending on whether the acceptance of a technology is mandatory or voluntary. If acceptance is mandatory, they found that Perceived Ease of Use may actually have a more important impact on system acceptance.

After TAM 3, there was a further attempt to round up the competing theories that all contributed to the evolution of TAM. This was the hopefully named Unified Theorty of Acceptance and Use of Technology (or UTAUT) – proposed in 2003 by Venkatesh, Morris, Davis and Davis:

UTAUT

Venkatesh, V., Morris, M.G., Davis, F.D., and Davis, G.B. “User Acceptance of Information Technology: Toward a Unified View,” MIS Quarterly, 27, 2003, 425-478

So, what began as an attempt to simplify our understanding of the acceptance of technology became a rather unwieldy beast. Bagozzi, who worked with Davis on the original TAM model, finally had step back in and comment:

The exposition of UTAUT is a well-meaning and thoughtful presentation. But in the end we are left with a model with 41 independent variables for predicting intentions and at least eight independent variables for predicting behavior. Even here, arguments can be made that important independent variables have been left out, because few of the included predictors are fundamental, generic or universal and future research is likely to uncover new predictors not subsumable under the existing predictors. The IS field risks being overwhelmed, confused and misled but the growing piecemeal evidence behind decision making and action in regard to technology adoption/acceptance/rejection.

Bu the biggest criticism of Venkatesh’s models from Bagozzi (2007)  pointed out the same fundamental flaw that I mentioned – the assumption that intention leads to usage. Here are Bagozzi’s main concerns with the evolution of TAM:

Is Behavior the End Goal? – Bagozzi says:

The models…fail to consider that many actions are taken not so much as ends in and of themselves but rather as means to more fundamental ends or goals.

TAM ends at behavior. Actually, it ends at intent, as it assumes that intent always leads to behavior, but we’ll come back here in a moment. Bagozzi’s point is that behavior is dependent on a broader context, and there could be an end-goal that will impact acceptance that is completely ignored in the model. For example, lets say that the specific technology to be accepted is a tool to analyze conversion data in online campaigns. The end goal is to improve ROI from all online campaigns. But, to reach this end goal, there are a number of contributory goals, including, but not limited to:

  • More efficient budget allocations
  • Improving Landing Page performance
  • Better tracking of performance data
  • Improve click throughs on online ads
  • Improve the online conversion path

The tool in question is a subset of the third item in the list. Because the decision to accept this tool is contingent on meeting a number of broader goals, it obviously becomes a critical factor in that acceptance. But Bagozzi’s point is the only place this is accounted for, presumably, is in the anticipated belief upstream in the model. Once again, TAM falls victim to its own quest for parsimony. Bagozzi argues a better approach is to understand that this is a process, and as such will include goal striving. And that is a recursive process:

In goal striving, intention formation is succeeded by planning (e.g, when, where and how to act instrumentally), overcoming obstacles, resisting temptations, monitoring progress to goal achievement, readjusting actions, maintaining effort and willpower, and reassessing and even changing goals and means. These processes fill the gaps between intention and behavior and between behavior and goal attainment and are crucial for the successful adoption and use of technology.

Finally! An acknowledgement that it’s not a straight line from intention to behavior!

The Gap between Attitude and Intention – First of all, Bagozzi disagreed with the elimination of Attitude as a preliminary step to Intention. Further, he says that even with Attitude back in place, there may be very compelling reasons why a person may agree that the technology is acceptable, but still choice not to accept it. To fill in the gap, he proposes borrowing from the Belief-Desire model. Here, even if the Belief is in place, there also needs to be Desire before the intention to act is formed. So, to recap, a User may have all the right determinants in place to decide that the technology in question is perceived to be useful (PU) and that it will be sufficiently easy to learn to use (PEU) but still not have any desire to accept the technology. Perhaps the decision to accept was made by her boss, who is an asshole and she’s resisting on principle.

In Bagozzi’s paper, he goes on at some length to outline the limitations in any of the proposed TAM models. In addition to the above points, he also suggests that things like the group, cultural and social aspects of technology acceptance are not adequately dealt with in the model, as well as emotions, self-regulation and other mediators that are common in our pursuit of goals. In short, he says that technology acceptance is too reliant on context to lend itself to general models and the decision path itself is much more complex than the models would indicate. He advocates a new foundation, based on his work of goal setting and striving:

37_10_1108_S1548-6435_2011_0000008005

Bagozzi 2007

This lays out the decision making core process, not specific to technology acceptance, but applicable to the striving towards any goal. But, in this model, there is the opportunity to “plug in” factors specific to the acceptance of a technology within a specific context. For example, inputs into Goal Desire could include any number of things: first of all, the goal itself (taking into account the entire goal hierarchy that leads to the focal goal – the acceptance of a specific technology), anticipated and anticipatory emotions, relative advantage, job fit, attitudes toward success, failure, outcome expectancies, and, of course, Perceived Usefulness and Perceived Ease of Use. The exact balance will be contingent on circumstance.

The arrow leading up to Action Desire represents mediating factors such as group norms, subjective norms, social identity, effort expectancy and attitudes towards an act.

A key addition to Bagozzi’s proposed model  is self regulation. Somewhere between desire and intention, humans have the ability to reflect on their desires and decide if they are comfortable intending to act on that desire. Specifically, in the case of technology adoption, we have to decide if the behaviors we would undertake would sit well with our moral and belief framework. Let’s say, for instance, that the technology adoption being considered would increase efficiency dramatically, allowing the company to decrease head count. You may be aware of the human cost of the decision to adopt and this may cause you to regulate your desire (increased efficiency) against it.

Below is an expanded version of Bagozzi’s model with all the inputs shown.

decisioncore

Bagozzi’s proposed model with inputs shown (Bagozzi 2007)

What is interesting in Bagozzi’s model is the chain of decision making which separates Goals desire from Behavioral desire. This is a further exploration of the transition from Attitude to Action, which was so deterministic in Venkatesh’s models. Bagozzi sets it in what, to me, is a more palatable framework. We have goals, which likely include broad goals and sub goals in some type of hierarchy. Our desire to reach these goals then have to be translated into intentions, where the actual execution required is begun to be planned out. This helps us understand the required behaviors, which then leads to behavioral desires. These desires then get translated into intentions. But throughout this chain, there are a number of mediating factors, both internal and external, that can cause reflection, resetting, outright abandonment or modification of both desires and intentions. There is no single arrow pointing to the right. There is, instead, an iterative process that allows for looping back to any one of the previous stages.

This post has become much longer and much more technical than I originally intended, so I think we’ll wrap this up and start fresh next time with a recap of the various models of Technology Acceptance, and my attempt to build a model that allows for iterative reflection and adjustment.

Now, That’s a Job Description I Could Get Behind!

First published February 20, 2014 in Mediapost’s Search Insider

I couldn’t help but notice that last week’s column, where I railed against the marketer’s obsession with tricks, loopholes and pat sound bites got a fair number of retweets. The irony? At least a third of those retweets twisted my whole point – that six seconds (or any arbitrary length of message) isn’t the secret to getting a prospect engaged. The secret is giving them something they want to engage with.

tweet ss

As anyone who has been unfortunate to spend some time with me when I’m in particularly cynical mood about marketing can attest to, I go a little nuts with this “Top Ten Tricks” or “The Secret to…” mentality that seems pervasive in marketing. I’m pretty sure that anyone who retweeted last week’s column with a preface like “Does your advertising engage your consumer in 6 seconds or less? If not, you’re likely losing customers” didn’t bother to actually read past the first paragraph. Maybe not even the first line.

And that’s the whole problem. How can we expect marketers to build empathy, usefulness and relevance into their strategy when many of them have the attention span of a small gnat? As my friend Scott Brinker likes to say when it comes to marketer’s misbehaving, “This is why we can’t have nice things.”

Marketing – good marketing – is not easy but it’s also not a black box. It’s not about secrets or tricks or one-off tactics. It’s about really understanding your customers at an incredibly deep level and then working your ass off to create a meaningful engagement with them. Trying to reduce marketing to anything less than that is like trying to breeze your way through 50 years of marriage by following the Top 3 Tricks to get lucky this Friday night.

Again, this is about meaningful engagements. And when I say meaningful, it’s the customer that gets to decide what’s meaningful. That’s what’s potentially so exciting about breakthroughs like the Oreo Super Bowl campaign. It’s the opportunity to learn what’s meaningful to prospects and then to shift and tailor our responses in real time. Until now, marketing has been “Plan, Push and Pray.” We plan our attack, we push out our message and we pray it finds it’s target and that they respond by buying stuff. If they don’t buy stuff, something went wrong, probably in the planning stage. But that is an awfully long feedback loop.

You’ll notice something about this approach to marketing. The only role for the prospect is as a consumer. If they don’t buy, they don’t participate.  This comes as a direct result of the current job description of a marketer: Someone who gets someone else to buy stuff. But what if we rethink that description? Technology that enables real time feedback is allowing us to create an entirely new relationship with customers. What would happen if we redefined marketing along these lines: To understand the customer’s reality, focusing on those areas where we can solve their problems and improve that reality?

And as much as that sounds like a pat sound bite, if you really dig into it, it’s far from a quick fix. This is a way to make a radically different organization. And it moves marketing into a fundamentally different role. Previously, marketing got its marching orders from the CEO and CFO. Essentially, they were responsible for moving the top line ever northward. It was an internally generated mandate – to increase sales.

But what if we rethink this? What if the entire organization’s role is to constantly adapt to a dynamic environment, looking for advantageous opportunities to improve that environment? And, in this redefined vision, what if marketing’s role was to become the sense-making interface of the company? What if it was the CMO’s job was to consistently monitor the environment, create hypotheses about how to best create adaptive opportunities and then test those hypotheses in a scientific manner?

In this redefinition of the job, Big Data and Real Time Marketing take on significantly new qualities, first as a rich vein of timely information about the marketplace and secondly as a never ending series of instant field experiments to provide empirical backing to strategy.

Now, marketing’s job isn’t to sell stuff, it’s to make sense of the market and, in doing so, help define the overall strategic direction of the company. There are no short cuts, no top ten tricks, but isn’t that one hell of a job description?

The Psychology of Usefulness: The Acceptance of Technology – Part Three

In Part Two of this series, I looked at Davis and Bagozzi’s Technology Acceptance Model, first proposed in 1989.

Technology_Acceptance_Model

As I said, while the model was elegant and parsimonious, it seems to simplify the realities of technology acceptance decisions too much. In 2000, Venkatesh and Davis tried to deal with this in TAM 2 – the second version of the Technology Acceptance Model.

TAM2

In this version, they added several determinants of Perceived Usefulness and demoted Perceived Ease of Use to being just one of the factors that impacted Perceived Usefulness.  Impacting this mental calculation were two mediating factors: Experience and Voluntariness. This rebalancing of factors provides some interesting insights into the mental process we go through when making a decision whether we’ll accept a new technology or not.

Let’s begin with the determinants of Perceived Usefulness in the order they appear in Venkatesh and Davis’s model:

Subjective Norm: TAM 2 resurrects one of the key components of the original Theory of Reasoned Action model – the opinions of others in your social environment.

Image: Venkatesh and Davis also included another social factor in their list of determinants – how would the acceptance of this technology impact your status in your social network? Notice that our calculation of the image enhancement potential has the Subjective Norm as an input. It’s a Bayesian prediction – we start with our perceived social image status (the prior) and adjust it based on new information, in this case the acceptance of a new technology.

Job Relevance: How applicable is the technology to the job you have to do?

Output Quality: How will this technology impact your ability to perform your job well?

Result Demonstrability: How easy is it to show the benefits of accepting the technology?

It’s interesting to note how these factors split: the first two (subjective norm and image) being related to social networks, the next two (Job Relevance and Output Quality) being part of a mental calculation of benefit and the last one, Demonstrability, bridging the two categories: How easy will it be to show others that I made the right decision?

According to the TAM 2 model, we use these factors, which combine practical task performance considerations and social status aspirations, into a rough calculation of the perceived usefulness of a technology. After this is done, we start balancing that with how easy we perceive the new technology to be to use. Venkatesh and Davis commented on this and felt that Perceived Ease of Use has a variable influence in two areas, the forming of an attitude towards the technology and a behavioral intention to use the technology. The first is pretty straight forward. Our attitude is our mental frame regarding the technology. Again, to use a Bayesian term, it’s our prior. If the attitude is positive, it’s very probably that we’ll form a behavioral intention to use the technology. But there are a few mediating factors at this point, so let’s take a closer look at the creation of Behavioral Intention..

In forming our intention, Perceived Ease of Use is just one of the determinants we use in our “Usefulness” calculation, according to the model. And it depends on a few things. It depends on efficacy – how comfortable we judge ourselves to be with the technology in question. It also depends on what resources we feel we will have access to to help us up the learning curve. But, in the forming of our attitude (and thereby our intention), Venkatesh and Davis felt that Perceived Usefulness will typically be more important than Perceived Ease of Use. If we feel a technology will bring a big enough reward, we will be willing to put up with a significant degree of pain. At least, we will in what we intend to do. It’s like making a New Year’s Resolution to lose weight. At the time we form the intention, the pain involved is sometime in the future, so we go forward with the best of intentions.

As we move forward from Attitude to Intention, this transition if further mediated in the model by our subjective norm – the cognitive context we place the decision in. Into this subjective norm falls our experience (our own evaluation of our efficacy), the attitudes of others towards the technology and also the “Voluntariness” of the acceptance. Obviously, our intention to use will be stronger if it’s a non-negotiable corporate mandate, as opposed to a low priority choice we have the latitude to make.

What is missing from the TAM 2 model is the link between Perceived Ease of Use and actual Usage. Just like a New Year’s Resolution, intentions don’t always become actions. Venkatesh and Davis said Perceived Ease of Use is a moving, iteratively updated calculation. As we gain hands-on experience, we update our original estimate of Ease of Use, either positively or negatively. If it’s positive, it’s more likely that Intention will become Usage. If negatively, the technology may fail to become accepted. In fact, I would say this feedback loop is an ongoing process that may repeat several times in the space between Intention and Usage. The model, with a single arrow going in one direction from Intention to Usage, belies the complexity of what is happening here.

Venkatesh and Davis wanted to create a more realistic model, expanding the front end of the model to account for determinants going into the creation of Intention. They also wanted to provide a model of the decision process that better represented how we balance Perceived Usefulness and Perceived Ease of Use. I think they made some significant gains here. But the model is still a linear one – going in one direction only. What they missed is the iterative nature of acceptance decisions, especially in the gap between Intention and Behavior.

In Part Four, we’ll look at TAM 3 and see how Venkatesh further modified his model to bring it closer to the real world.

So, Six Seconds is the Secret, Huh?

First published February 13, 2014 in Mediapost’s Search Insider

oreo-superbowl-blackout-adApparently, the new official time limit for customer engagement is 6 seconds, according to a recent post on Real Time Marketing. How did we come up with 6? Well, in the world of social media engagement it seemed like a good number and no one has called bull shit on it yet, so 6 it is

Marketers love to talk about time – just in time, real time, right time. At the root of all this “time talk” is the realization that customers really don’t have any time for us, so we have to somehow jam our messages into the tiny little cracks that may appear in the wall of willful ignorance they carefully build against marketing. The marketer’s goal is to erode their defenses by looking for any weakness that may appear.

Look at the supposed poster child for Real Time Marketing – the Oreo coup staged during the black out in the 2013 Super Bowl. Because the messaging was surprising and clever, and because, let’s face it, we weren’t doing much of anything else anyway, Oreo managed to gain a foothold in our collective consciousness for a few precious seconds. So, marketers being marketers, we all stumbled over ourselves to proclaim a new channel and launch a series of new micro-attacks on consumers. That’s where the 6 seconds came from. Apparently, that’s the secret to storming the walls. Five seconds and you’re golden. Seven seconds and you’re dead.

Oreo surprised us, and it wasn’t because the message was 6 seconds long. It was because we weren’t expecting a highly relevant, highly timely message. Humans are built to respond to things that don’t fit within our expected patterns. The whole approach of marketing is to constantly blanket us with untimely, irrelevant messages. Marketers, to be fair, try to deliver the right message at the right time to the right person, but it’s really hard to do that. So, we overcompensate by delivering lots of messages all the time to everyone, hoping to get lucky. Not to take anything away from the cleverness and nimbleness of the Oreo campaign, but they got lucky. We were surprised and we let our defenses down long enough to be amused and entertained. Real time marketing wasn’t a brilliant new channel; it was a shot in the dark – literally.

And there’s no six-second gold standard of engagement. If you can deliver the right message at the right time to the right person, you can spend hours talking to your prospective customer.  It’s only when you’re trying to interrupt someone with something irrelevant that you have to hopefully shoehorn it into their consciousness. Think of it like a Maslow’s hierarchy of advertising effectiveness.  At it’s best advertising should be useful. This sits at the top of the pyramid. After usefulness comes relevance – even if I don’t find the ad useful to me right now, at least you’re talking to the right person. After relevance comes entertainment – I’ll willingly give you a few seconds of my time if I find your message amusing or emotionally engaging.  I may not buy, but I’ll spend some time with you. After entertainment comes the category the majority of advertising falls into – a total waste of my time.  Not useful, irrelevant, not emotionally engaging. And making an ad that falls into this category 5 seconds long, no matter what channel it’s delivered through, won’t change that. You may fool me once, but next time, I’m still going to ignore you.

There was something important happening during the Oreo campaign at the 2013 Super Bowl, but it had nothing to do with some new magic formula, some recently discovered loophole in our cognitive defenses. It was a sign of what may, hopefully, emerge as trend in advertising – nimble, responsive marketing that establishes a true feedback loop with prospects. What may have happened when the lights went out in New Orleans is that we may have found a new, very potent way to make sense of our market and establish a truly interactive, responsive dialogue with them. If this is the case, we may have just found a way climb a rung or two on the Advertising Effectiveness Hierarchy.

How Can Humans Co-Exist with Data?

First published February 6, 2014 in Mediapost’s Search Insider

tumblr_inline_mpt49sqAwV1qz4rgpLast week, I talked about our ability to ignore data. I positioned this as a bad thing. But Pete Austin called me on it, with an excellent counterpoint:

“Ignoring Data is the most important thing we do. Only the people who could ignore the trees and see the tiger, in real-time, survived to become our ancestors.”

Too true. We’re built to subconsciously filter and ignore vast amounts of input data in order to maintain focus on critical tasks, such as avoiding hungry tigers. If you really want to dive into this, I would highly recommend Daniel Simons and Christopher Chabris’s “The Invisible Gorilla.” But, as Simons and Chabris point out, with example after example of how our intuitions (which we use as filters) can mislead us, this “inattentional blindness” is not always a good thing. In the adaptive environment in which we evolved, it was pretty effective at keeping us alive.  But in a modern, rational environment, it can severely inhibit our ability to maintain an objective view of the world.

But Pete also had a second, even more valid point:

“What you need to concentrate on now is “curated data”, where the junk has already been ignored for you.”

And this brought to mind an excellent example from a recent interview I did as background for an upcoming book I’m working on.  This idea of pre-filtered, curated data becomes a key consideration in this new world of Big Data.

Nowhere are the stakes higher for the use of data than in healthcare. It’s what lead to the publication of a manifesto in 1992 calling for a revolution in how doctors made life and death decisions. One of the authors, Dr. Gordon Guyatt, coined the term “Evidence based medicine.” The rational is simple here. By taking an empirical approach to not just diagnosis but also to the best prescriptive path, doctors can rise above the limitations of their own intuition and achieve higher accuracy. It’s data driven decision-making, applied to health care. Makes perfect sense, right? But even though Evidence based medicine is now over 20 years old, it’s still difficult to consistently apply at the doctor to individual patient level.

I had the chance to ask Dr. Guyatt why this was:

“Essentially after medical school, learning the practice of medicine is an apprenticeship exercise and people adopt practice patterns according to the physicians who are teaching them and their role models and there is still a relatively small number of physicians who really do good evidence-based practice themselves in terms of knowing the evidence behind what they’re doing and being able to look at it critically.”

The fact is, a data driven approach to any decision-making domain that previously used to rely on intuition just doesn’t feel – well – very intuitive. It’s hard work. It’s time consuming. It, to Mr. Austin’s point, runs directly counter to our tiger-avoidance instincts.

Dr. Guyatt confirms that physicians are not immune to this human reliance on instinct:

“Even the best folks are not going to do it – maybe the best folks – but most folks are not going to be able to do that very often.”

The answer in healthcare, and likely the answer everywhere else where data should back up intuition, is the creation of solid data based resources, which adhere to empirical best practices without requiring every single practitioner to do the necessary heavy lifting. Dr. Guyatt has seen exactly this trend emerge in the last decade:

“What you need is preprocessed information. People have to be able to identify good preprocessed evidence-based resources where the people producing the resources have gone through that process well.”

The promise of curated, preprocessed data is looming large in the world of marketing. The challenge is that, unlike medicine, where data is commonly shared and archived, in the world of marketing much of the most important data stays proprietary. What we have to start thinking about is a truly empirical, scientific way to curate, analyze and filter our own data for internal consumption, so it can be readily applied in real world situations without falling victim to human bias.

The Psychology of Usefulness: The Acceptance of Technology – Part Two

In my last post, I talked about how the Theory of Reasoned Action and the original Technology Acceptance Model tried to predict both intention and usage of new technologies. As a quick recap, let’s look again at Davis and Bagozzi’s original model.

Technology_Acceptance_Model

In aiming for the simplest model possible, there was significant conflation applied to the front end of the model – with just one box representing external variables, which then led to two similarly conflated boxes: Perceived Usefulness and Perceived Ease of Use. While this simplification was admirable in the quest for parsimony, in real world situations it seemed like it went too far in this direction. There was a lot happening between the three boxes at the front of the model that demanded closer examination.

Davis indicated that there was an interesting relationship between Perceived Usefulness and Perceived Ease of Use. One of the mechanisms at play that has to be understood is self-efficacy. In understanding adoption of technology, self-efficacy is a key factor. Essential, it means that the easier a system is to use, the greater the user’s sense of efficacy. They believe they have control over what they are doing. And control, especially on a work context, is a strong motivational driver. There is an extensive body of work exploring the psychological importance of control. If we feel we’re in control, we also feel empowered to mitigate risk. The concept of self-efficacy helps to highlight the importance of the Perceived Ease of Use box. But what about the other box: Perceived Usefulness?

Davis, in his accompanying notes and research, indicated that Perceived Usefulness is a stronger indicator of intention than Perceived Ease of Use. In other words, we are willing to put up with some pain to learn a new technology if we feel it will offer a significant improvement in our ability to complete a task. This balancing equation requires two heuristic evaluations on the part of the user: the allocation of cognitive resources required to gain proficiency and the expected usefulness of the tool once proficiency is gained. This is exactly the same equation used in Charnov’s Marginal Value Theorem, applied in a different context. In optimal foraging, we (and all animals who forage) balance expenditure of resources required to reach a food patch against the expected food value to be derived from that patch. In technology adoption, we balance expenditure of resources required to master a new technology against the increased usefulness that technology offers.

In this heuristic evaluation, there are four key marketing lessons for anyone who’s business model relies on the adoption of new technology:

1)   Lessen the intimidation of the learning curve. Persuade the user (and this is a key point that I’ll return to in in point 4) that this is a reasonable investment of resources. Build a sense of perceived ease of use. Provide visible links to intuitive learning resources. Often, marketers overplay the feature benefits of their products to show how powerful they are. But, as they’re doing this, they fail to realize that this upsets the balance between perceived usefulness and perceived ease of use.

2)   Provide clear examples of perceived usefulness in terms that are immediately relevant. Remember, this is the key factor in the equation the prospect is trying to balance. The more salient you can make the perceived usefulness, the more likely the user is to adopt it, even if a learning curve is present. Ideally, get that usefulness across with very specific, industry relevant examples that allow the user to visualize usage of the technology.

3)   Remember that the user is balancing the two factors. Ease of use is great, but it can’t come at the expense of overall usefulness. In fact, in calculating the right balance (which should be done with extensive testing feedback from target customers) it should offer a significant gain in usefulness (as measured against any incumbent technologies) with a relatively manageable investment of resources.

4)   Remember that you’re talking to a user. When trying to strike the right balance, remember that you’ll probably be talking to different people as the decision progresses. For the user, the right balance between perceived usefulness and perceived ease of use must be struck. But at some point, you’ll be talking to a buyer, not a user, before the sale actually is closed. This would be one of those external variables that fall outside the scope of the Technology Adoption Model. This switching of roles from “doers” to “buyers” is dealt with extensively in my book, The BuyerSphere Project.

In the next post, I’ll talk about how the Technology Acceptance Model has been modified over the past 2 decades so it better reflects real world decision making.