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 (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?

Smart People on Analytics and Segmentation

Recently I asked my network of friends and former colleagues about some work I am doing for a financial services client (“a top 10 U.S. bank”). On the surface they asked me to look at their approach to segmenting their commercial client base and recommend improvements or a different framework. Based on what I found, I essentially asked my network the following:

“Do you concur with my recommendation: the bank has done a good-enough segmentation of their client base but results have been poor due to poor execution. I have told them an increased focus on improving execution will yield better results and is a better investment than seeking an ‘improved segmentation'”.

To share my wealth, I got many good responses and you can see them below. Feel free to respond to any of them and where I could, I provided links to their web sites, blogs or LinkedIn pages.

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B2B is usually more stable than B2C, so the data gathered for the segmentation can still be used for future segmentation, if necessary. Segmentation (or Multivariate analysis) can be easily obtained by several different purchasing behavior metrics, or simply by addressing different purchase intent metrics (as well as awareness of services, need gap analysis, current service provider, and switching intention) for prospects. If they are just profiling their house file, and they need to validate their segmentation exercise, they should have held out a sample for control. Maybe the execution was not as bad as the context. It is recommended that a hold-out sample is put aside and no execution is applied to it to gauge what the actual lift is.
So I would combine your approach with a holdout (or control) to run the test (executions) at different levels. Cheers, Roger Ares
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Sounds like you’re doing well and are involved in some interesting work with your client. On the surface, your premise does seem reasonable. We have a predictive modeling focus, and we frequently work with clients to develop segmentation strategies, and would concur with your observed delimma of operationalizing and getting expected results from good statistical modeling. Rick Nichols

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Doesn’t “Good to Great” or one of those McKinsey books have lots of examples of how even a mediocre strategy well executed does better than a spectacular strategy implemented poorly? I think your intuition is correct; although there is a wide variety of segmentation approaches. A good segmentation doesn’t address all needs (which in itself is counter-intuitive). A good segmentation approaches 1-3 business objectives and optimizes the segmentation against them. The key question is what is the primary objective right now (and the answer isn’t grow sales). It might be lower churn, add users, increase usage, etc. A good segmentation will appropriately focus on the dependent variables that accurately predict that independent variable. Too many dependent variables (the objective) and the segmentation gets diluted.

On some previous work, I had the opportunity to work with a Brazilian beer company to implement a segmentation. The company grew revenues dramatically. While I wish we could take all the credit, I do believe that the good segmentation and the focus on implementing it was a big contributor. And it was hard. There was always pressure to do other things that had nothing to do with the target segments. While they were exciting, we worked hard to only do those things geared toward the target segments. Hope all is well with you, Matthew Hull

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I agree that execution is more important that perfect segmentation. If we take the example of a simple organization that focusses on one type of customer only, there is no need for segmentation. When the customer base is more complex than that, then we use segmentation to group similar categories of customers so that we can address their needs, prioritize, etc. So HOW we segment is not as important as WHAT we do after the segmentation. Thanks, Mohan

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We find at our company that reaching our clients (new and prospective) in the B2B area is very touch in go. For example, we delivered an IPOD containing our message to100 clients and saw our best ever response and sigh-up rate…yet a month later…we tried it again using the itouch (different audience) and saw half the success. Iteration and experimenting is key. Bill Zielke, VP Marketing, BillMeLater
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My two cents…. you are such a breath of fresh air! I agree with the underlying premise that without strong execution, even a great segmentation will fail. You already have a workable segmentation, I would focus on outling the execution strategy and how to get your critical players on board and turning the ‘flywheel’. (did I just say ‘flywheel’? 🙂

There are many examples of success that are a result of a simply ‘mediocre’ foundation, executed flawlessly…… Kellie Brody, Sales Excellence
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I generally agree with your thinking. My personal observation is that the evaluation of results from segmentation testing are not detailed enough and or the implementation is not taken to a detailed enough level either for budgetary or “know how’ reasons. Unless the evaulation uncovers a silver bullet, so to speak, the lift will initially be modest at best. What is required is the follow through execution to dig deeper into the reasons for certain behaviors, followed up by using those learnings to further capitalize on opportunities to trigger the desired client behaviors. And so on to zero in on the exact triggers that will change the behaviors in the way that produce the desired lift in results. Best regards, Deborah J. Ackerman
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You are correct, if they did segmentation properly but failed at execution then they need to look at what steps they missed. That being said if the segmentation is old (over 18 months then you do need to segment again). Many times financial companies look at a few buckets about customers, whose buying what, from where – when they should be looking at who has the highest margins by customer type and concentrate in going after higher margin customers and drop the volumes of ones that have low margin – same can be applied to products and services.

Dalia Quinones-Zayas
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Traditional Market Segmentation at a large bank can be little more than a waste of time in this market. You need deep credit, risk profile and behavioral analytics in order to determine profiles
that can accurately predict lift and better yet the development and expansion of profitable relationships. Think of it like one-to-one but with SOA-like reusable products and services so that you can both identify and execute against patterns within segments and sub-segments quickly and effectively.

Regards, Mike Rouse

Yes, and…

A little while back I took a management development class (at Duke, with props to their exec ed team) that was unique and continues to have impact on my work and management thinking. It was an “improv for business” course, taught by an great full time b-school prof plus a colleague of his who is a member of 2nd City, the famous improv troupe out of Chicago (John Belushi, Bill Murray, John Candy, Bonnie Hunt, and many othersn are alumni). Some surprises from that experience:

Improv while looking chaotic and without form (and at a given moment it may be) is actually governed by some clear rules and norms.

Rather than being at odds with business improv provides a framework and method for breakthrough thinking.

Improv is first about listening, then about acting. Intense listening with the intent to truly understanding the sender’s message is the key to being a good improv player.

While there are others, and for the experienced improv player many additional and deeper levels to the world of improv, here are the most basic of rules:

1. Say yes, and. That is, agree with your stage partners, and expand from there. On a superficial level this agreement can be literal, eg. “let’s go to the gym”, “yes, and I’m going to get you RIPPED for your wedding”, but on a more fundamental level it’s about agreement between the players that if they are truly open to knew information — and trust one another — they can create something unique by building upon each others input and ideas.

2. Treat everything your partner says/does like manna from heaven. Everything is a gift, and if you take the time to really listen to/explore what he/she has said/done, there is a bounty of treasure there for you to use.

3. Make bold choices. New improvisers tend to put the onus on the other improviser to add information to the scene rather than you putting it out there themselves. You learn however that it is more productive — and more fun — to be bold and add as much to the scene every chance you have.

4. Don’t try to control the scene. Look after your own character, and trust that your scene partners are doing the same. The best scenes emerge from your interactions with others, rather than any singularly funny or outrageous thing you or another character says or does.

We did many fun group exercises over three days where we practiced listening — ensuring we truly received the message from our partners. This is not easy and revealed all of our weaknesses in this area. We also did many exercises to practice being “bold”. This rule (#3) stretches — and hopefully breaks — your natural defense against experimentation and failure. If you are going to be bold you will fail ocassionally (and a lot, at the beginning). And lastly, rule #4 played itself out over and over as we worked together and practiced improv scenarios, reminding us that the most beautiful tapestry comes from the weaving of many threads.