Monthly Archives: February 2015

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:


Tags: , , , ,

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:

1 Comment

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


Tags: , , , , , , ,

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:


Tags: , , , , ,

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

Leave a comment

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


Tags: , ,