Potential: Revealed

Strategic Thinking, Innovative Ideas, Growth Marketing, and Revealing of Potential

Archive for experimentation

Hammer in search of a nail?

With this post I will give a bit of plug to a good, relatively new blog on all the latest in the world of Payments.

There is plenty of buzz (and spin!) regarding Apple’s foray into “contactless payments” and how it might validate and accelerate an emerging trend. When I read CNN.com’s headline  “The end of credit cards is coming” my natural skepticism went on high alert. A post on Payments.com by Karen Webster, partially in response to CNN’s article and the issue overall, really hit the nail on the head.

It is fun and compelling to learn about a heretofore unmet – or better yet, unknown! – consumer need that has been splendidly filled by an innovative and heroic entrepreneur. Even better if it is Steve Jobs and Apple – the darling, so far, of the first decade or so of the 21st century. The foreseeing of the unforeseeable is often referred to as unlocking “latent” demand. Demand we didn’t even know existed or in ways we didn’t foresee. Sometimes it happens and I’ve written about it on this blog and elsewhere.

The Payments.com post, however, pointed out that both unlocking latent consumer demand for mobile, contactless payments may not have arrived just yet. Karen pointed out many industry factors, ranging from too many competing approaches to too few points of sale (POS) for acceptance (and daunting costs to enable the millions of POS devices functioning perfectly well today across the country without “contactless” capabilities).

The most glaring thing missing in my opinion is less technological and more fundamental: the lack of a compelling value proposition to the parties involved (made up of consumers, payments processors & networks, and merchants). Is there a compelling value proposition to be had? If not, is there really any latent demand? Are we all really, unknowingly so far, just waiting for a way to ditch our current payment methods (e.g,. cash, debit and credit cards, gift cards, checks) for one that uses our mobile phones instead? While none are perfect are the available methods broken and of low enough utility to be replaced?

My comments to Karen’s post (you can find them here):

“It should be noted that Apple’s business model and track record is to be closed (a profitable strategy, no doubt), and another key player the mobile networks are notoriously closed and seeking a way to corner any market for themselves and control / disallow other alternatives.

Along with the sheer steepness of the adoption Karen points out, I think these forces will make it hard to see any widespread adoption soon. Forecasts so far are mostly hype.

Personally I also don’t see the creation of a compelling value proposition which is always required to unlock the so-called latent demand for a mobile & contactless payment alternative (other than the “cool” factor, and for certain high traffic environments where checkout speed might have high marginal value). Current consumer demand for payment methods is well satisfied without NFC (Near Field Communications)-enabled phones.”

Spouting opinions is fun. I gave mine – what’s yours?

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Working Together: Great Potential Revealed

Spring and summer have been busy work-wise, and lazy otherwise. The combination of hard work and the opportunity, through abundance of summery weather and a relaxing time away with family, to do nothing much has also given me time to read some interesting books.

Recently I’ve gotten hooked on science and history – in particular the rise in the early 20th century of quantum mechanics in physics. I have been amazed at how individually brilliant these scientists were and how incredible their vision and discoveries were. Imagining and then doing the math and experiments to prove what they imagined, in a time with no computers, little funding, and few sophisticated laboratory tools is the epitome of the human spirit and thirst for knowledge and understanding.

What I’ve also learned that was true and critical to the discoveries made was the collaboration and sharing that occurred. There were plenty of rivalries and some conflicts but given the stakes – and the potential for fame – there was more openness than secrecy. These remarkable men and women – Einstein, Curie, Fermi, Szilard, Meitner, Oppenheimer, Dirac and many others – were of varying nationalities and located across Europe, plus America and Asia. Again in a time of no computers or internet, they made a conscious investment – which was non-trivial given the communication challenges of the age – in publishing their discoveries, writing to each other regularly, and attending formal and informal gatherings where theories, approaches and findings were presented and debated.

 
They seemed to know that their ideas were worth far less if they hid them. They knew they’d be more valuable if they invited others to learn about them, debate or challenge them and add to them. Or perhaps that their individual ideas and theories were just small parts of a huge body of unknowns that one of them could not possibly explain alone. If they wanted to be successful – be part of explaining the universe – they had to cooperate with others.

Together they were discovering more deeply how the universe works, at the atomic and then sub atomic levels. Imagining and then proving that atoms existed and contained electrons, protons and neutrons. Imagining and then proving that even smaller things existed such as quarks, gluons and other interestingly-named particles. Imagining and then proving that atoms could be split – and fused. Some, such as Einstein, at times wished they’d never had their great thoughts or published them — since it led in 1945 to the deaths of more than 100,000 Japanese citizens in a matter of seconds with dropping of bombs. Bombs with innocent sounding names like Fat Boy and Little Man.

Yet there is no denying that there have been many positive aspects to what these people discovered and helped the world to understand. It has and continues to change the world as we know it.

And their approach to innovation and knowledge sharing can teach us a great deal about what can happen when the potential of new ideas is fueled by a spirit of cooperation and sharing for the common good.

If you are interested at all in what I’ve been reading, here’s a few selected titles:

The Story of Science: Einstein Adds A New Dimension by Joy Hakim – actually a great middle school to early high school text book. If all children had books written by and teachers like Joy Hakim, we’d have more kids interested in science. Her writing is fun and informative.

Einstein: His Life and Universe by Walter Isaacson 

A Short History of Almost Everything by Bill Bryson

Predictive Analytics: How it Works (#2)

In the first post about predictive analtyics we learned about the essential building block of predictive analytics: the predictor. This is a value calculated for each entity (say, a customer) who’s actions or behaviors are to be predicted – for instance the recency, in months, since a customer’s last purchase.

Prediction power is enhanced if you use more than one predictor at a time. In doing so you are creating a model. Models are the heart of predictive analytics. In this post I’ll discuss how you can find the “best” predictive model. I put “best” in quotes because from a practical standpoint, unless you assume unlimited time and resources you may be best off finding a model that improves your results (e.g., reduction in customer churn) over previous experience. Today there is available very powerful modeling software and well-trained and talented statisticians, but the number of variables to consider in any predictive model (across demographics, transactions, behaviors) can be extremely large making determination of the “best” model cost prohibitive.

Fortunately, taking an incremental, continuous improvement approach can yield solid results for most any business and the promise that results will improve over time. A common tool is to develop a yield curve. For example, plotting the results of a predictive model for churn with amount of churn on the Y axis and percentage of customers contacted in a retention campaign on the X axis will show a curve the decreases to a point — i.e., up to a certain percentage of a universe of customers contacted, attrition rates will fall — but will bottom out and then move upward. Meaning that not all customers will respond to a retention campaign and you are best off contacting only those predicted to respond well. After that point, you are best leaving the balance of the universe of customers alone – either because they are not likely to churn anyway or because the predictive models say campaigns to retain them will be unsuccessful (and possibly other methods are needed – along with models that might predict how these approaches can be equally tuned to expend effort on just those predicted to be successful).

Now, although the model does not work perfectly, the socring and ranking of customers according to their likelihood to be retained provides clear guidance on how to invest in retention programs to yield the best results. It will prevent campaigns to retain customers that are too aggressive (trying to retain those that are not likely to respond positively, or wasting effort on those that are likely to stay).

There is a great deal more to predictive analytics than I’ve covered in the past two posts. But I hope one message is clear: you can gain practical improvements in marketing results or other customer touch points through the use of analytics that don’t need to be complex (at least to start) nor perfect. Commitment, willingness to experiment and continuous improvement are what’s really required.

Thanks for reading and I’ll look forward to comments.

Predictive Analytics: How it Works (1)

Sorry for the delay between posts. For past month or so we’ve been working on a very interesting project dealing with product ideas based on financial transaction data and powered by predictive analytics. While we are working to develop some early prototypes we have also been talking about challenges that need to be addressed when taking such products to market.

One issue over and over has been risk of market launch failure due to lack understanding of how analytics work (often lacking even rudimentary let alone deep understanding). A majority of key stakeholders – potential customers and internal business unit and functional area team – have heard of and are relatively convinced of the potential for analytics to optimize decision making. Whether that be to improve marketing effectiveness or precision of sales forecasts. Yet the basis for belief is often what they’ve read about or been led to believe by others. Analytics are not perfect and an important approach to achieving long term benefits from analytics is experimentation, challenging current results, and continual tuning of analytical models. We can foresee a gap forming where confidence in what is being developed and sold to clients falters due to lack of basic understanding of predictive analytics.

So, I thought I’d put together a brief series of posts (sort of like I did on “Practical Strategy” a little while ago) to explain predictive analytics.

The essential building block of predictive analytics is the predictor. It is a value calculated for each entity to be predicted – for instance the recency, in months, since a customer’s last purchase. Typically, the higher the calculated recency the more recent was the last purchase. As you’d expect, a good predictor is usually a reliable variable that consistently improves accuracy of some decision or action. Such as “customers with a high recency value typically have a higher response rate to marketing programs.”

There are other predictors that might work better with certain actions or decisions. For example, if you have an online subscription-based service, customers who spend less time logged on are less likely to renew annually. Tuning attrition or churn reduction campaigns by targeting customers who have low usage predictor values can boost effectiveness.

To make prediction even more precise you can use more than one predictor at a time. In doing so you are creating a model. Models are the heart of predictive analytics. Some simple models that might predict likelihood of a customer to renew their subscription:

– Linear – adding predictors together. For example: Recency + Household Income.

– Behavioral Rules – joining two or more behaviors with rules defining predictions of another behavior. For example: Usage (high or low) and Responded to Offer in Past 3 Months.

The best predictors will be predictive models that combine multiple aspects of a customer (e.g., demographics) and their behavior. A predictive model characteristically must be deeper and more complex than the above examples – uniting sometimes dozens of predictors. More on determining the best predictive model and harnessing rich sources of data to create powerfully predictive analytics in the next post. Thanks for reading and let me know if you have comments or can share your own experiences.

Imagination

Albert Einstein once wrote that in science “imagination is more important than knowledge”. That’s a powerful thought. I suppose you might expect nothing less from an intellectual giant such as Einstein.

What resonated with me, as someone who often not only wants to understand but who finds fully understanding something (i.e., “knowledge”) to be particularly satisfying, is the caution it offered about seeing knowledge as the only worthy end (to some research you’ve conducted, a project you’ve managed, a business problem or opportunity you’ve worked hard on).

Further, in reading more about the context of Einstein’s writing this line, he is saying bluntly that science like many pursuits in life is really just a journey, full of unknowns and unfolding unendingly. At any given point in time there are many truths or facts that are well-accepted and proven but an infinite number more truths and facts that are quite unknown and sometimes seemingly unknowable. Particularly in science there are many areas of study that deal with phenomena that are not readily or directly observable.

Einstein,and other great scientists, made many of their most astounding breakthroughs using their imagination rather than getting stuck trying to understand the seemingly unknowable. They would imagine some alternative reality to what was known at the time, think through how this alternative world might look and how it might operate if it were discovered to be true, then go about experimenting, searching and testing as if the alternate view were indeed true. This gave them great freedom to work creatively rather than be confined by the “known”. As a non-scientist, for me at least, this was very revealing and refreshing – creativity and science go together! I think I thought before this that they were mutually exclusive.

I began to relate this to my work with business clients where we might be talking about a new product or concept, or a new approach to promotional marketing and other challenges where some facts are well known and many others are for practical purposes unknowable. In such a situation how do you proceed? Einstein would say, if I may be so bold as to speak for him, to first beware of investing all your time into trying to know everything. This is similar to the common advice to avoid “analysis paralysis”. He adds to this common wisdom a more unique point of advice: use your imagination and then be bold enough to just try it out! Experiment. Try. Fail. Try again with another approach.

This is of course no guarantee of success. Your imagination might fail you. But when faced with a big challenge, using your imagination can be a powerful tool to spur action and overcome inaction. At the very least, doing so will give you a taste of how Albert Einstein thought and that alone will be fun!

Experimenting leads to Expanding

Recently I read an interesting research article on “The Contradictions That Drive Toyota’s Success” that I may blog a couple times on since it was full of, well, contradictions to conventional wisdom of what makes businesses successful.

In summary the authors describe three “forces of expansion” (defined as those that lead the company to instigate change and improvement) and three “forces of integration” (defined as those that stabilize the company’s expansion and transformation. The countervailing nature of these forces allow Toyota to be widely and sometimes wildly innovative, creative, and constantly renewing itself, without undo chaos or losing its very clear and constant cultural identity. First I’ll focus on the Expansion forces.

The Expansion forces are noted as Set Impossible Goals, Local Customization, and Experimentation. Each are interesting but the Experimentation force was of particular interest. First, it is an important tool to facilitate the achievement of Impossible Goals. The culture of Toyota is one of pushing the employees to move freely outside their comfort zone and into uncharted territories through regular experiementation — and learning from both successes and failures. There is an interesting illustration from the development lifecycle of the Prius hybrid vehicle. In 1993 (yes, 1993!) they began development and first came out with a car that had 50% improvement in fuel efficiency. This was summarily rejected by Toyota executives in favor of a goal of 100% improvement. This made them look beyond conventional technologies and experiment their way through a string of failures: engines that would not start reliably, ones that could only travel a few hundred yards, battery packs that would not operate in the heat — or the cold.

Two simple concepts that Toyota employs when in experimental mode leapt out at me:

– think deeply but take small steps
never give up

These sound trite on the surface — too simple to be truly useful. But in thinking about them further, they go together beautifully (and powerfully).

On the first concept, my experience is that many companies get caught up in what I call “mistaking action for progress”. The steps they take may be indeed small but they are not small on purpose. And regularly they admonish their employees to take steps, any steps, so that they can report on “progress” (typically upwards to those above putting the pressure on). Rather than thinking deeply (which takes time but can look like lack of progress) and purposefully breaking a goal down into small, purposeful steps, the action appears to be guided by ready-aim-fire in reverse.

The second concept also sounds too pat but again my experience is that contemporary short term business thinking precludes applying a “never give up” attitude. It is not that companies want their employees to give up at the first sign of duress but without the advantage of using a small-step approach, which carries with it the corresponding advantage of low costs for any failures, costs can mount and patience for success wanes.

Experimentation is one of the most useful and powerful tools an organization can employ. The growing availability of data on markets and customers, the open foundation of the Internet, the near instantaneous pace of all communications, and many other aspects of the current business environment make experimentation both possible — and vital.

Do you agree? Are there other ingredients to successful experimentation?

Good D

In a previous post I mentioned that D for Data was a critical component for revealing insights about your customers. In today’s age, that is as close to “goes without saying” as you might be able to get. So this post is intended to define a simple approach to improving your D … or in sports term, “playing good D”.

First, a “customer advocacy” mechanism is required. For some established companies customer-centric thinking (and ordering of data and systems to deliver insights) is second nature and a distinct “advocacy” role, department, program or such would be unnecessarily bureaucracy. For most companies though, such a formally designated entity is a key building block. A customer advocate can help both develop and drive the systematic approach for discovering and leveraging customer insights, and be a peer at the management table when priorities and on-going investments must be clearly proposed or vigorously defended.

Second, in the infancy stages of a customer insights journey, a world-class, competitively differentiating customer insights data set and deep analytics capabilities are too grand and far off to achieve in the near term. The risk and complexity in achieving this higher stage of customer enlightenment can overwhelm and obscure what are most likely low hanging fruit — or what I like to call “pumpkins” (pumpkins are actually a fruit, and they are not just low hanging but they can be found on the ground ready to pick up). An iterative-experimental approach can give both a good chance to take advantage of the pumpkins along the way and build momentum toward achieving the higher stages of enlightenment within a reasonable time frame. How to do this?

I’ve used a three-part, interconnected process that includes Research, Analytics and Experimentation. These ideally are set up to work together, under one leader. There’s no one best way to set this up and the choice depends upon the organization’s culture and management style.

Research: this is the “compass” function within the overall process. It helps to set the direction for where the keenest insights might be found. It develops an on-going portfolio of customer data and sources through qualitative and quantitative research.

Analytics*: this is the “targeting” function. Using analytics techniques and sound experimental design approaches, it generates testable hypotheses about the next best place to go, within an achievable distance, from the current understanding of customers.

Experimentation: manages the experiment through to implementation and measurement of results, working in partnership with other parts of the organization, external partners, vendors, etc. to get it done.

The key is setting up such a process and team to iterate quickly, gaining the benefit of pumpkin insights and the benefit of honing the skills of implementation of customer insights in the most rapid way possible.

* not to be confused with the general, pervasively useful capability of “analytics” (see Kyle McNamara’s blog for a clear view of analytics).