Book review: “Crossing the Chasm”

I recently read both Crossing the Chasm and Big Bang Disruption, which are two books covering a similar topic: how do you position and sell innovative products in fast pace market?

“Crossing the Chasm” by Geoffrey A. Moore was first published in 1991 and talks about how to bridge the “chasms” which can occur in the traditional “Technology Adoption Lifecycle” (see Fig. 1 below). This traditional model depicts the transition from a market solely for “innovators” and “early adopters” to reaching a mainstream audience.

In his book Geoffrey A. Moore, describes two cracks in this traditional bell curve. Firstly, a chasm between “early adopters” and the “early majority”. An approach to market that works for early adopters might not work for more mainstream users of the product, or vice versa. Secondly, Moore talks about a chasm between the early and the late majority, pointing out that each market has its own dynamics.

Moore’s advice with respect to crossing these chasms sounds fairly straightforward: “make a total commitment to the niche, and then do your best to meet everyone else’s needs with whatever resources you have left over”. I like how Moore then goes on to break down customer segments into related target-customer characterisations and scenarios (see Fig. 2 below).

Once you’ve established the specific market niche that you want to target, you can then start creating a strategy for developing this market. Moore provides a very helpful market development strategy checklist (see Fig. 3 below).

The other key thing which I picked up from Crossing the Chasm is the “Whole Product Concept”, which idea was first described in The Marketing Imagination by the late Theodore Levitt. This concept is quite straightforward: There is a gap between the marketing promise made to the customer – the compelling value proposition – and the ability of the shipped product to fulfil that promise. The Whole Product Concept identifies four different perceptions of product (see also Fig. 4 below):

  1. Generic product – This is what’s shipped in the box and what’s covered by the purchasing contract between the seller and the buyer.
  2. Expected product – This is the product that the consumer thought she was buying when she bought the generic product. It’s the minimum configuration of products and services necessary to have any chance of achieving the buying objective of the consumer.
  3. Augmented product – This is the product fleshed out to provide the maximum chance of achieving the buying objective.
  4. Potential product – This represents the product’s room for growth as more and more ancillary products come on the market and as customer-specific enhancements to the system are made.

Main learning point: Things have changed quite significantly since Geoffrey A. Moore first wrote “Crossing the Chasm”. The pace of new technology introductions has probably increased tenfold since the first publication of the book, with businesses concentrating hard on making technology products as accessible and mainstream as quickly as possible. “Crossing the Chasm” nevertheless contains a number of points which I believe are almost timeless: thinking about target users and their scenarios, and the concept of a minimum product configuration that fulfils customer needs.

Fig. 1 – The Technology Adoption Lifecycle by Joe M. Bohlen, George M. Beal and Everett M. Rogers – Taken from:


Fig. 2 – Geoffrey A. Moore’s template for creating a target user characterisation and scenarios – Taken from:


For  ____________ (target customer)

who ____________  (statement of the need or opportunity)

our (product/service name) is  ____________  (product category)

that (statement of benefit) ____________ .


For non-technical marketers

who struggle to find return on investment in social media

our product is a web-based analytics software that translates engagement metrics into actionable revenue metrics.

Scenario / A day in the life (before)

  • Scene or situation – Focus on the moment of frustration. What’s going on? What’s the user about to attempt and why?
  • Desired outcome – What’s the user trying to accomplish and why is this important?
  • Attempted approach – Without the new product, how does the user go about the task?
  • Interfering factors – What goes wrong? How and why does it go wrong?
  • Economic consequences – So what? What’s the impact of the user failing to accomplish the task productively?

Fig. 3 – “The Market Development Strategy Checklist” – Taken from: Geoffrey A. Moore – Crossing the Chasm, p. 99

  • Target customer
  • Compelling reason to buy
  • Whole product
  • Partners and allies
  • Distribution
  • Pricing
  • Competition
  • Positioning
  • Next target customer

Fig. 4 – “The Whole Product Concept” by Theodore Levitt – Taken from:  

original metaphor

Related links for further learning:


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Learning more about running A/B tests

I love running A/B and multivariate (‘MVT’) tests. These are experiments designed to evaluate different design or copy variants, based on actual performance data. Instead of comparing or deciding on product options based on gut feel, an A/B or multivariate test allows to you to compare alternatives based on objective data and predefined success criteria.

However, running these kinds of tests can be quite tough. These are some of the reasons why:

  • Insufficient traffic – Time and traffic are two important prerequisites if you want to be able to draw some meaningful conclusions from your experiments. However, what do you do if you don’t have a large user base yet or when you traffic starts faltering?
  • Not sure which metric(s) to focus on – One of the things that I’ve learned the hard way is the importance of being clear upfront about the exact goal of the test and ensuring that you’ve selected the relevant metric(s) to focus on.
  • Determine required sample size – Working out the sample size you need to in order to reach a “point of statistical significance” with your test can be tricky. Luckily, most A/B testing tools have an automated function for calculating this.

The other day I came across a great post by Optimizely titled “Stats with Cats: 21 Terms Experimenters Need to Know”. Reading trough this piece really helped me in understanding more about how to best design an experiment and tackle some of the common issues which I outlined above.

These are the main things that I learned from Stats with Cats: 21 Terms Experimenters Need to Know:

  1. Statistical significance – Significance is a statistical term that tells how sure you are that a difference or relationship exists. For example, if you want want to be able to confidently tell whether there’s a difference between version A and B, you need a treshold (e.g. 95%) to describe the level of error you’re comfortable with in a given A/B test. Significant differences can be large or small, depending on your sample size.
  2. Confidence interval – This is a computed range used to describe the certainty of an estimate of some underlying parameter. In the case of A/B testing, these underlying parameters are conversion rates or improvement rates.
  3. Bayesian – This is a statistical method that takes a bottom-up approach to data analytics when calculating statistical significance. It encodes the past knowledge of similar, previous experiments into a prior, which is a statistical device. You can use this prior in combination with current experiment data to make a conclusion on a currently running experiment.
  4. Effect size – The effect size (also known as “improvement” or “lift”) is the amount of difference between the original version (‘control’ version) and a variant. This could be an increase in conversion rate (a positive improvement) or a decrease in conversion (a negative improvement). The effect size is a common input into many sample size calculators. For example, Optimizely’s A/B Test Sample Size Calculator lets you enter an expected conversion rate for your control version.
  5. Error rate – The error rate stands for the chance of finding a conclusive difference between a control version and a variation in an A/B test, or not finding a difference where there is one. This encompasses “type 1″ and “type 2″ errors. A type 1 error occurs when a conclusive outcome (winner or loser) is declared, and the test is actually inconclusive. This is often referred to as a “false positive”. With type 2 errors, no conclusive result (winner or loser) is declared, failing to discover a conclusive difference between a control and a variation when there was one. This is also referred to as  a “false negative” (see Fig. 1 below).
  6. Hypothesis test – Sometimes called a “t-test”, a hypothesis test is a statistical inference methodology used to determine if an experiment result was likely due to chance alone. Hypothesis tests try to disprove a null hypothesis, i.e. the assumption that two variations are the same. In the context of A/B testing, hypothesis tests will help determine the probability that one variation is better than the other, supposing the variations were actually the same.
  7. Fixed horizon hypothesis test – The key thing with a fixed horizon test is that it’s designed to come to a decision about version A or B at a set moment in time, ideally after reaching the point of statistical significance.
  8. Sequential hypothesis test – A sequential hypothesis test is the opposite of a fixed horizon hypothesis test, as the underlying principle of this test is that the experimenter can make a decision on the test at any point in time.

Main learning point: Even though I’m not a statistician or a data analyst, I found it really helpful to learn more about some of the terms that experimenters need to know about. Especially given some of the challenges with respect to running successful experiments, I believe it’s important to think through things such as a null hypothesis or desired effect size before you design and run your experiment.

Fig. 1 – Possible outcomes of A/B experiments – Taken from: Error image

Related links for further learning:

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Posted by on February 14, 2015 in Agile, Measuring, User Experience


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The what and why of programmatic marketing

The term “programmatic marketing” is relatively new. Ben Plomion, VP Marketing at Chango, first wrote about programmatic marketing back in 2012. In this article he expands on the ‘what’ and the ‘why’ of programmatic marketing. Ben’s piece formed a great starting point for me to learn more about what programmatic marketing means and what its benefits are.

Let’s start with the ‘what':

Wikipedia provides a nice and concise definition of programmatic marketing: “In digital marketing, programmatic marketing campaigns are automatically triggered by any type of event and deployed according to a set of rules applied by software and algorithms. Human skills are still needed in programmatic campaigns as the campaigns and rules are planned beforehand and established by marketers.”

I’ve broken this down into some specific elements:

  1. Events – Marketers can set rules around specific ‘events’ which they expect to trigger specific marketing activities (e.g. a display ad or an email). An abandoned online shopping cart is a good example of such an event. For instance, I receive an email with a subject line that says “Do you still want to buy a white pair of Converse All Stars” after I’ve abandoned this product in my shopping basket.
  2. Automatic triggers – Once an event has been selected, an automatic trigger can be created. For instance, if I search for “blue cashmere” jumpers, I’ll be presented with display ads for the blue cashmere jumpers on other applications or sites that I visit or browse.
  3. Rules set by marketers – There’s a strong human element to programmatic marketing. Marketers need to fully understand the customer journeys and metrics related to their product or service. This understanding will help you to make sure the right marketing activity is triggered, for the right customer and at the right time.

Why? What are the benefits of programmatic marketing?

  1. It’s automated – By automating buying decisions, marketers remove the friction of the sales process (including humans placing buying orders) and reduce their marketing costs.
  2. Organising data – A programmatic marketing platform allows marketers to better organise their data and create highly targeted marketing campaigns. The goal is to avoid wasted clicks or impressions. Programmatic marketing helps to target those consumers who have (expressed) an intent to buy, and who are likely to covert into the desired behaviour.
  3. Targeting and personalisation – Programmatic marketing helps in targeting specific user types or segments, having a better understanding of user activity and interests. Programmatic marketing increases the likelihood of consumer action by showing each user a personalised message. The goal is to present users with a more customised call-to-action based on their recent browsing behaviour, for example, or other anonymised data that you know about them.
  4. Reaching consumers across channels and devices – Similar to marketing based on user behavioural data (see my previous point), you can use programmatic marketing to understand and tap into which channels and devices customers use as part of their experience.

Some programmatic marketing techniques to consider:

  1. Dynamic Creative Optimisation – Dynamic Creative Optimisation (‘DCO’)  allows marketers to break an online ad apart into individual pieces, and to create different pieces for different audiences. With these dynamic elements, you can easily rotate the layout of the ad based on user data (see Fig. 1 below). For example, if we know that a user has been looking at cheap flights to Orlando, we can tailor the ad accordingly (see the Travelocity example in Fig. 1 below).
  2. Shopping cart abandonment email campaigns – Every retail or transactional site collects data on users who don’t complete the checkout process. Abandoned shopping cart emails are sent to those customers who added products to their cart but failed to check out. Customers can fail to purchase for a whole a number of reasons, varying from deliberate (e.g. decision not to purchase) to circumstantial (e.g. the website crashed or the session timed out). Sending a users an email to remind them of their abandoned shopping cart is a great way for businesses to act on this data (see some examples in Fig. 2 and 3 below).
  3. Programmatic site retargeting - Programmatic site retargeting (‘PSR’) is designed to increase revenue from someone who has already visited your site or expressed an interest in your product. As the aforementioned Ben Plomion explains here: “PSR crunches all that data and creates a score that determines how much to bid to serve an impression for that user via an ad exchange, allowing marketers to target leads on the cheap”. It’s about using data such as resource pages on your site that a person has visited, or where the user came from, to serve a highly targeted and relevant ad on the favourite site or application of the user.

Main learning point: After having dipped my toe into programmatic marketing, I feel that there’s much more to learn about how programmatic marketing works and about how to do it effectively. Some of the programmatic marketing techniques seem fairly obvious. However, I guess the challenge will in collecting, understanding and selecting the right data to drive your programmatic marketing activity.

Fig. 1 – Good examples of Dynamic Creative Optimisation – Taken from:


Fig 2 – Example of an email to remind people of their abandoned shopping cart – Taken from:


Fig. 3 – Example of an email to remind people of their abandoned shopping cart – Taken from:


Related links for further learning:


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Book review: “Thinking with Data”

It’s oh so easy to get immersed in analytics or big data sets without a clear idea of the questions one wants answered through data. The book Thinking with Data – How to Turn Information into Insights by Max Shron talks about how to get the most of data and how to go about looking for the right data.

Max Shron is the founder of Polynumeral, a New York based applied data strategy consultancy. The title of the first chapter of “Thinking with Data” is aptly titled “Scoping: Why Before How” and it covers the main concept behind this book: “CoNVO”. CoNVO stands for context, need, vision, outcome:

  1. Context (Co) – Context emerges from understanding who we are working with and why they’re doing what they are doing. Who are the people with an interest in the results of the project? What are they trying to achieve and why? Shron offers some good examples of context (see Fig. 1 below). The context provides a project with larger goals and helps to keep us on track when working with data. Contexts include larger relevant details, like deadlines and business objectives, which help to prioritise.
  2. Needs (N) – It’s useful to see  how Shron looks at “needs” from a data perspective; “what are the specific needs that could be fixed by intelligently using data? If our method will be to build a model, the need is not to build a model. The need is to solve the problem that having the model will solve.” Shron goes on to explain that “when we correctly explain a need, we are clearly laying out what it is that could be improved by better knowledge.” I’ve included some good examples of needs in Fig. 2 below.
  3. Vision (V) – Shron describes the vision as “a glimpse of what it will look like to meet the need with data”. The vision could consist of a mockup describing the intended results, or a sketch of the argument that we’re going to make, or some particular questions that narrowly focus our aims (see Fig. 3 below).
  4. Outcome (O) – For a data scientist, the “outcome” is all about understanding how the work will actually make it back to the rest of the business and what will happen once it’s there. How will the data and/or insights be used? How will it be integrated into the organisation? Who will use it and why? Shron stresses that the outcome is distinct from the vision; the vision is focused on what form the work will take at the end, while the outcome is focused on what will happen when the work is done (see Fig. 4 below).

Main learning point: Even though I got the sense that “Thinking with Data” is more aimed at data scientists and analysts, I found the book very useful for me as a ‘non-data professional’. Despite it being a very short book, Shron gets his main “CoNVO” concept across very effectively. A good use of data starts with properly scoping the problem that you want to solve. An unstructured scope will make it hard to gather the right insights and to use large data sets intelligently. Using Shron’s COnVO model will help to gather and analyse data in very targeted and efficient kind of way.

Fig. 1 – Examples of Context – Taken from Max Shron – “Thinking with Data”, p. 3

  • This department in a large company handles marketing for a shoe manufacturer with a large online presence. The department’s goal is to convince new customers to try its shoes and to convince existing customers to return again. The final decision maker is the VP of Marketing.
  • This news organisation produces stories and editorials for a wide audience. It makes money through advertising and through premium subscriptions to its content. The main decision maker for this project is the head of online business.

Fig. 2 – Examples of Needs – Taken from Max Shron – “Thinking with Data”, p. 5

  • Our customers leave our website too quickly, often after reading only one article. We don’t understand who they are, where they are from, or when they leave, and we have no framework for experimenting with new ideas to retain them.
  • Is this email campaign effective at raising revenue?
  • We want to place our ads in a smart way. What should we be optimising? What is the best choice, given those criteria?
  • We want to sell more goods to pregnant women. How do we identify them from their shopping habits?

Fig. 3 – Examples of mockups and argument sketches – Taken from Max Shron – “Thinking with Data”, pp. 9 – 13


Mockups can take the form of a few sentences reporting the outcome of an analysis, a simplified graph that illustrates a relationship between variables, or a user interface sketch that captures how people might use a tool.

Example of a sentence mockup:

The probability that a female employee asks for a flexible schedule is roughly the same as the probability that a male employee asks for a flexible schedule.

There are 10,000 users who shopped with service X. Of those 10,000, 2,000 also shopped with service Y. The ones who shopped with service Y skew older, but they also buy more.

Argument sketches:

A mockup shows what we should expect to take away from a project. In contrast, an argument sketch tells us roughly what we need to do to be convincing at all. It is a loose outline of the statements that will make our work relevant and correct. While they are both collections of sentences, mockups and argument sketches serve very different purposes. Mockups give a flavour of the finished product, while argument sketches give us a sense of the logic behind the solution.

Example of the differences between a mockup and an argument sketch:

Mockup – After making a change to our marketing, we hit an enrolment goal this week that we’ve never hit before, but it isn’t being reflected in the success measures.

Argument sketch – The nonprofit is doing well (or poor) because it has high (or low) values for key performance indicators. After seeing the key performance indicators, the reader will have a good sense of the state of the nonprofit’s activities and will be able to adjust accordingly.

Summary of the differences between a mockup and an argument sketch:

In mocking up the outcomes and laying out the argument, we are able to understand what success could look like.

The most useful part of making mockups or fragment of arguments is that they let us work backward to fill in what we actually need to do.

Fig. 4 – Examples of an outcome – Taken from Max Shron – “Thinking with Data”, pp. 14 – 16

  • The metrics email for the nonprofit needs to be set up, verified, and tweaked. Sysadmins at the nonprofit need to be briefed on how to keep the the email system running. The CTO and CEO need to be trained on how to read the metrics emails, which will consist of a document written to explain it.
  • The marketing team needs to be trained in using the model (or software) in order to have it guide their decisions, and the success of the model needs to be gauged in its effect on the sales.

Thinking with data

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Posted by on February 4, 2015 in Book Reviews, Data, Measuring


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Learning from Roman Pichler about Agile Product Strategy and Roadmap

A few months ago I did a course titled Agile Product Strategy and Roadmap by Roman Pichler. Roman is a London-based Agile & Lean product management expert, who published the book Agile Product Management with Scrum a few years ago. Roman’s course was all about how to best create a product strategy and an effective product roadmap. These are the main things that I learned during this course:

  1. Why the need for a more iterative product strategy? – Roman kicked off the course by talking about more traditional ways to formulate a product strategy. Traditionally, a company would do lots of market research upfront, spend a lot of time on a solid business case and then create a long-term product roadmap. The perceived benefits of this approach are a sense of risk management and a sense of thoroughness. I would argue that a long winded product strategy formulation process can result in a false sense of reassurance. Speed to market is key and I believe that most companies simply can’t afford to spend loads of time and effort doing upfront research or planning. Roman made the case for a much lighter process; a quicker way of assessing market/product opportunities and an iterative process of testing product assumptions.
  2. ‘Just enough market research’ – Instead of spending ages carrying out market research and analysis upfront, Roman talked about doing ‘just enough’ market research. When you apply a ‘lean’ approach of testing assumptions and hypothesis (see my earlier blog posts about assumptions here), you should do just enough market research to test any key risks or leap of faith assumptions.
  3. Product vision – A number of the course attendees were keen to find out more about how to best create a product vision. Roman explained the main function of a good product vision: to provide the “why”, the underlying motivation for a product that you’re looking to develop. What’s the impact or change that you’re looking to achieve and why? A business vision or product vision provides you with guidance, a continued purpose. We talked about the often misused lean concept of the “pivot” (a term coined by Eric Ries, founder of the Lean Startup movement). The idea is that when a company pivots, it means that it changes its approach or tactics whilst staying grounded in its vision. Roman then went on to talk about the characteristics of a good product vision and running a vision workshop (see Fig. 1 and Fig. 2 below).
  4. Product roadmap – Roman spent a good part of the course talking about the value of a good product roadmap, which was very helpful. Prior to this course I had already used Roman’s template for a Goal Oriented Roadmap, which I’ve found to be a great tool for combining product strategy with specific business or product objectives (see Fig. 3 below). Roman explained that a product roadmap communicates how a product is likely to evolve over time. He suggested that the younger your product is, the shorter your roadmap horizon should be. For example, with young products, the time span of your product roadmap should be no more than 6-12 months. The other thing Roman mentioned, which I feel gets often overlooked, is that the creation of a roadmap is a collaborative process. One of the reasons why I particularly like the Goal Oriented Roadmap is that this template is very easy to extend to suit one’s specific purposes. For instance, if you’re looking to create a roadmap for a portfolio of products or for specific releases, then it’s easy to adjust the “GO” roadmap template accordingly (see Fig. 4 below).

Main learning point: Roman’s Agile Product Strategy and Roadmap Course is great for anyone who wishes to learn more about more ‘holistic product thinking'; creating a product roadmap and being clear on why certain products/features are on the roadmap (and others aren’t). The key thing for me is Roman’s goal-oriented product roadmap, a great tool for combining product milestones with specific business goals.

Fig. 1 – Characteristics of a good product or business vision by Roman Pichler – See

  • Think big – A good vision is lofty and aspirational.
  • A shared vision – Create a common sense of purpose which is shared widely across the company.
  • Motivating – Outlines the benefits that the product or service creates for others.
  • Short and sweet – A good vision is easy to understand and to communicate.
  • Use for decision-making – Use your vision as guide when making business or product decisions.
  • Distinguish between vision and strategy – A vision should not be a plan that outlines how to reach a goal.

Good examples of a compelling vision:

Toys”R”Us – Taken from:


Mozilla Firefox – Taken from:

Screen Shot 2015-01-27 at 08.36.04

Fig. 2 – Running a visioning workshop – See also:

  • Goals - (1) To get buy-in for the vision early on from a range of internal and/or external stakeholders and (2) to leverage the knowledge of the group to come to an agreed product vision
  • Have a rough idea of the vision beforehand – As a product person, it’s important to already have an idea of the what the initial product vision and direction should like like. This will help in facilitating the workshop.
  • Listen, but don’t end up making weak compromises – Especially when you’re doing a visioning workshop with a large group of stakeholders, it’s important to listen to the different viewpoints but not making weak compromises on the vision ‘just to keep everybody in the room happy’.
  • Consider stages of market maturity – What stage is your target market in? What is the competition like and what are their differentiators? What are the needs of your market segment?

Fig. 3 – Template for the Goal Oriented Roadmap by Roman Pichler – Taken from:


Fig. 4 – Ways in which to add to the Goal Oriented Roadmap template by Roman Pichler

  • Product portfolio planning – Add an extra layer to your product roadmap to outline dependencies between products, specific “portfolio goals” and to highlight where products fit within your portfolio. Roman suggested using the BCG Matrix to qualify products within the portfolio: “stars”, “cash cows”, “question marks” and “dogs”.
  • Product release planning – Add another layer to your product roadmap to indicate budget/costs per product release or milestone. This helps in assessing the so-called “project management triangle” per individual release (see Fig. 5 below). You can then link a backlog with features specific to each milestone on the roadmap.

Fig. 5 – The Project Management Triangle, managing the ‘triple constraint’ – Taken from:



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Posted by on January 30, 2015 in Startups, Product Management, Agile


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Book review: “Designing for Behavior Change”

This article was first published in August ’14 on

Behavioural economics, psychology and persuasive technology have proven to be very popular topics over the past decade. These subjects all have one aspect in common; they help us understand how people make decisions in their daily lives, and how those decisions are shaped by people’s prior experiences and their environment. A question then arises around what it means to change people’s behaviours and how one can design to achieve such change.

Stephen Wendel, a Principal Scientist at HelloWallet, has written “Designing for Behavior Change”, which studies how one can apply psychology and behavioral economics to design for behavior change. In this book, Stephen Wendel introduces four stages of designing for behavior change: Understand, Discover, Design and Refine (see Fig. 1 below):

  • Understand – The process starts off with gaining an understanding of how people make decisions and how our cognitive mechanisms can support (or hinder) behavior change.
  • Discover - The second stage is about a company working out what it wants to accomplish with the product, and for whom.
  • Design – The actual design stage can be broken down into two subtasks: (1) designing the overall concept for the product and (2) designing the specific user interface.
  • Refine – Analyzing data to generate insights and ideas for ongoing improvement of the product.

Stage 1 – Understand

The Understand stage is all about understanding how people make decisions and how the mind decides what to do next. There is a clear distinction between the deliberative and the intuitive mind. Our deliberative or “conscious” mind tends tends to be slow, focused and self-aware. In contrast, when people are in an intuitive or “emotional” mode they are likely to act on “gut feeling”, fast and unaware. Most of the time, we are not consciously deciding what to do next. Instead, we often act based on habits. Even when we do think consciously about what to do next, we actively try to avoid hard work.

“Designing for Behavior Change” stresses the importance of being very clear about the type of behaviour one is trying to encourage: a conscious choice or an intuitive response. Stephen Wendel proposes a simple but powerful model – “Create” – which helps to understand what products need to do to get users to take a particular action:

  • Cue – A cue for users to think about what to do can either be internal or external. External cues happen when there is something in our environment triggering us to think about a certain action. Internal cues are the result of our minds thinking about the action on its own, through some unknown web of associated ideas.
  • Reaction – Once the mind has been cued to think about a potential action, there is an automatic reaction in response. This reaction tends to be intuitive and automatic.
  • Ability - Assuming the choice has been made to act, the question arises whether it is actually feasible to undertake the action. Wendel suggests that the individual must be able to act immediately and without obstacles.
  • Timing - When should you take the action? The decision when to take action can be taken based on a sense of urgency, and by other, less forceful factors.
  • Evaluation - After an initial intuitive response, there might be room for a more conscious evaluation of the action and of potential alternatives. This happens especially when we are facing novel situations, and we do not have an automatic behavior to trigger.

These five mental events can be best summarized through the “Create Action Funnel” (see Fig. 2 below). The main point to make with respect to this funnel, is that people can drop out out at each stage. A person will most probably only continue through the funnel if the action is more effective or better than the alternatives.

“Strategies for Behavior Change” is the third and final output of the Understand stage. The book suggests three possible strategies to consider:

  • Cheat - If what you really care about is the action getting done, and it is possible to all but eliminate the work required of the user beyond giving consent, then do it.
  • Make or change habits - If the user needs to take an action multiple times, and you can identify a clear cue, routine, and reward, then use the “habits” strategy (see Fig. 3 below).
  • Support conscious action – If neither of the two aforementioned strategies is available, then you must help the user consciously undertake the target action.

Stage 2 – Discover

The second stage of Stephen Wendel’s designing for behavior change process is the Discover stage. The main goal is of this stage is to figure out what it is that one wants to accomplish with the product. Wendel identifies five distinct steps with regard to discovery:

  1. Clarify the overall behavioral vision of the product.
  2. Identify the user outcomes sought.
  3. Generate a list of possible actions.
  4. Get to know your users and what is feasible and interesting for them.
  5. Evaluate the list of possible actions and select the best one.

When thinking about “target outcomes,” you can think about what both the company and the user aim to accomplish with the product. You can then clarify this outcome by asking yourself some of the following probing questions:

  • Which type? – Does the product ultimately seek to change something about the environment or about people?
  • Where? - What is the geographic scope of the impact?
  • What? - What is the actual change to the environment or person?
  • When? – At what point should the product have an impact?

Within the Discover process, a lot of emphasis is placed on finding the “Minimum Viable Action.” This is the shortest, simplest version of the target action that users must take so that you can test whether your product idea (and its assumed impact on behavior) works.

At the end of the Discover stage, you should have detailed observations about your users, a set of user personas, and a clear statement of the target outcome, actor and action.

Stage 3 – Design

Wendel then goes on to explore the Design stage. The purpose of this stage is to create a context that drives action. There are three key aspects to this process:

  1. Structure the action – To ensure that an action is feasible and inviting for the user. Creating a “behavioral plan” can be a good way to outline the different steps users should take from what they are doing now to using the product and completing the target action. This can be a simple flowchart or a written narrative; the key objective here is to think about the sequence of real world steps a user needs to take to complete an action.
  2. Design the environment - To ensure that the environment is constructed in such a way that it supports the action. When talking about “environment,” Wendel means two things. Firstly, the product itself. For example, a web page or smartphone where a user takes an action. Secondly, the user’s local environment, which can be both physical and social. Wendel then goes on to identify a number of ways in which products can construct an environment, e.g. by increasing the motivation for people to act or by generating a feedback loop.
  3. Prepare the user - How does one prepare the user to take action? Stephen Wendel suggests three tactics which can help to prepare the user to take action, now or in the future: “narrate” (change how users see themselves), “associate” (change how users see the action) and “educate” (change how users see the world).

Stage 4 – Refine

Refine is the fourth stage of the behavior change process. This stage is all about learning about how people actually use the product, its behavioral impact and identifying areas for improvement. There are three main components of this stage:

  • Impact Assessment – Measure the impact of the product, based on clear target outcomes and well defined metrics for each outcome. Here it is important to set clear thresholds for success and failure.
  • Identifying obstacles to behavior change – Discover problems, develop potential solutions and generate additional ideas for how to make the product better. One can start this process by watching real people using the product (direct observation) and by gathering usage data. We can thus start getting a better insight into how people use the product, what the bottlenecks are, where the product is having the most impact on people, etc.
  • Learning and refining the product - Determine what changes to implement through (1) gathering lessons learned and potential product improvements (2) prioritizing potential improvements based on business considerations and behavioral impact and (3) integrating potential improvements into the appropriate part of the product development process.

“Designing for Behavior Change” offers good insight into how to best design in order to impact human behavior. Not the easiest of topics, Stephen Wendel provides a clear framework against which we can think about the behavior a product needs to impact and how to best achieve this impact.

Fig. 1 – Stages and outputs per stage of the designing for behavior change – Taken from: “Designing for Behavior Change” by Stephen Wendel, Preface-4

Stephen Wendel 1


Fig. 2 – The Create Action Funnel – Taken from: “Designing for Behavior Change” by Stephen Wendel, p. 40

Stephen Wendel 2


Fig. 3 –  The Habit Loop by Charles Duhigg – Taken from: “Designing for Behavior Change” by Stephen Wendel, p. 59



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Posted by on January 26, 2015 in Book Reviews, Design, User Experience


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App review: OpenTable

“The UK’s number one restaurant booking website” is what it says on the homepage of One of the reasons why I was keen to find out more about OpenTable is the fact that it serves two types of customers since its connects restaurateurs with customers. This is a similar mechanism which Carwow specialises in, a website which I reviewed previously.

This is my review of OpenTable’s iOS app:

  1. How did this app come to my attention? – I found out the other day that “TopTable”, a restaurant booking site which I’ve used in the past, had been taken over by OpenTable. It’s always good to have a restaurant booking site at hand, reason why I decided to give OpenTable a try and do a quick review.
  2. My quick summary of the app (before using it) –  This is a restaurant booking site. I expect to be able to discover and book restaurants in my local area through this site.
  3. How does the app explain itself in the first minute? – Straightforward; a list of restaurants that I can book (see Fig. 1 below). Because I’ve enabled location tracking, the suggested restaurants are all in the area from where I’m accessing the app. I can view available time slots per restaurants as well as star ratings and proximity.
  4. Getting started, what’s the process like (1)? – I first decided to change the default setting from “Table for 2, today at 12:00″ to 2 people on Sun 25 Jan at 12:00. I then select “Chamberlain’s Restaurant” at 12:00, after which I get presented with a nice landing page for Chamberlain’s Restaurant (see Fig. 3 below). By default, the “Info” view is displayed for a restaurant, which means that the view offers practical info such as price range, cuisine and parking. When I switch to the “Reviews” view, I see that the restaurant is rated based on four criteria: food, service, ambiance and value (see Fig. 4 below).
  5. Getting started, what’s the process like (2)? I then click on the red “12:00″ button, after which I click on the “Reserve as a guest” button (see Fig. 5 below). Signing up as a guest is simple, I just need to enter my first name, last name, email and phone – all the info I expect to submit when making a restaurant reservation (see Fig. 6 below).
  6. How easy to use was the app?  Very easy and intuitive. Granted, I didn’t do the whole Open Table sign-up process, but the design of the app is simple and provides the functionality that you’d expect.
  7. How does the app compare to similar apps?  I had a play with the iOS app of Bookatable, which felt very similar to Open Table. Whereas OpenTable seems to focus very much on displaying available time slots (see Fig. 1), Bookatable concentrates more on providing appealing visuals and highlighting good restaurant deals or “offers” (see Fig. 7 below). Another example, US oriented Resy, looks quite basic in comparison (see Fig. 8 below).
  8. Did the app deliver on my expectations? – Yes, although I expected the app to work harder on ‘drawing’ me in, explaining the benefits of OpenTable and really encouraging me to pick one of the restaurants recommended. The iOS app provides an easy to use and intuitive experience, but I believe it could provide a more compelling experience (see Fig. 9 below).

Fig. 1 – Screenshot of the first screen of the OpenTable iOS app, based on my location


Fig. 2 – Screenshot of date and time selector on the OpenTable iOS app

OpenTable 2

 Fig. 3 – Screenshot of the landing page for “Chamberlain’s Restaurant” on the OpenTable iOS app


Fig. 4 – Screenshot of “Reviews” view on OpenTable iOS app 

OpenTable 6

Fig. 7 – Screenshot of the step prior to making a booking through the OpenTable iOS app

Carwow 7

Fig. 6 – Screenshot of information required to make a reservation through the OpenTable iOS app 

OpenTable 8

Fig. 7 – Screenshot of the first screen of the first screen of Bookatable’s iOS app, not based on my location 

Bookatable 1

Fig. 8 – Screenshot of the first screen on Resy’s Android app


Fig. 9 – Some initial suggestions to make OpenTable more compelling

  • Highlight ‘hard to get’ bookings – Similar to US apps like Resy and Shout, it would be interesting if OpenTable were to highlight certain restaurants,e.g. based on reputation (and difficulty of getting into) or based on price.
  • Personalised recommendations – Once I’ve made a few bookings through OpenTable, I expect to see or receive more personalised recommendations. I can imagine that OpenTable are already looking at best ways to engage with and retain their users, encouraging ‘personalised discovery’.
  • Visually appealing – Some of the imagery currently available through OpenTable is fairly bland and nondescript. I feel that rich imagery can act as an first important pull to get the user to make a booking or to look at the available menu options or reviews for each restaurant.

Related links for further learning:


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