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.

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.

Here’s a two-fer: a customer of mine gaining new, happy customers. Making my customer, of course, happy too! See the customer reviews on Salesforce.com’s Appexchange (like on the “Review” tab). DecipherTech is a provider of sales analytics solutions, offering a truly innovative “cloud computing” datawarehouse application, delivered in a Software as a Service (SaaS)model. DecipherTech really applied the approaches I’ve been writing about (hopefully not too preach-ingly) over the past year about being practical with your strategy (so you can understand it and act quickly), defining your target market clearly (so you know where you want to go), and having focus and discipline in your marketing execution.

An interesting aspect of their business is offering their SaaS solution on Salesforce.com’s Appexchange. It gives access to literally millions of potential customers but also requires that customers can write reviews of your product for all to see — similar to the consumer world of eBay and Amazon, but relatively new and possibly intimidating, in the B2B world.

But if you are executing your strategy and business plans with confidence – you’ve got nothing to fear!

This is the third in a series on developing a “practical strategy”.  So far we’ve looked at two of the five basic questions that can be used as a framework for building and testing the strategy of an organization. The last three questions we’ll cover in this post.

The first two questions are “what business are we in?” and “where is the market going?” These questions serve to both build upon the vision which was developed (described in the first post ) and to test it in a practical way. The final three are “Where do we want to go?”, “How will we win?”, and “How will we get there?”. If the vision and the first two questions are for framing and testing then these last three are to useful for building out the details and getting ready to launch.

Picking up from the last post where the market landscape and strategic choices were developed the next step is to make those choices and identify the possible outcomes in order to be precise with the strategy. It is easy to be wishy-washy (sorry for use of such a jargon-laden term!) or settle for being too-high level. After all this is just the “strategy” and details can come later, right? Not right! Sure more details will come later in iterative execution phases and over time but forcing out specificity at this point is very valuable. Otherwise you can easily develop an elegant and logically sound strategy that still fails in the real world.

For example, while developing long term strategy at CheckFree, the leading provider of outsourced online banking and bill pay to U.S. financial institutions, the market — of both consumers who used it and the banks who provided it to them — was rapidly coming to accept such applications as mainstream (a classic sign of  market “maturity”). But there was clear difference in the states of the two key market segments that made up the value chain for CheckFree. One segment, the bank market, was more mature and the competition was likely to force price into being a key competitive issue. Consumers, the other key segment, were still in the early stages of mainstream adoption. Plus a key variable was not simply adoption (what % of households were paying bills online) but penetration (what % of all household bills were being paid online — a sort of share of “bill payment wallet”).

The adoption metric was headed to and beyond 15% (and was at 30% at the leading bank in the U.S.) but the share of wallet was less than 5%. A clear choice on “where do we want to go?” was made: focus on the consumer. Clearly it seemed that there was both a significant unanswered challenge – how to get adopting households to pay all of their bills online through their bank — plus significant upside (increasing penetration offered a rich pool of latent, recurring revenue).

Turning to “how will we win?”: as with all of these questions they are best used in companion and with one another in an iterative manner. For instance, if we had chosen, instead, to give primacy to the bank market’s needs and compete on dimensions of traditional IT outsourcing — such as low cost, scale and quality — we felt we could win yet these were more mature areas and risk of commoditization was high (and price being a likely, and recurring, battleground). When we thought through our choice to compete with a consumer-focused strategy we were betting on this “pulling” through the banks and positioning us as clearly differentiated and preferred option in any competitive situation. The thinking was: if we could be the world-class experts in consumer adoption we were purposely choosing a more difficult yet competitively defensible path. We believed this competitive stratgegy would further raise switching costs and lock in market share with banks who chose us — and serve to help us avoid competing on price.

The last question, “How will we get there?” seems a little anticlimatic. This is by design. As I’ve mentioned previously a risk in developing strategy (amongst many!) can be that it is not practical (e.g., too high level, non-specific, hedges or is wishy-washy). If we’ve been thorough in answering and iterating through the vision and the first four questions the we’ll combat the impractical through the explicit development of a plan to accomplish the chosen strategy. The plan must include a clear set of discrete steps, time-phased and integrated across necessary functional or other organizational boundaries, assign specific accountable owners, and designate expected outcomes which become goals and metrics upon which to review and judge progress of the strategy execution and success of its outcomes. Wrappered around this methodology for developing practical strategy should be some sort of on-going strategic review, discussion and revision process (which I might blog about some other day). I like developing a 2 to 3 year strategy and then reviewing it every quarter on a rolling basis.

That’s it. I would welcome Comments from friends of my blog and from those just passing by and here for the first time.    Randy

Following on from the previous post, and the second in this series on developing a “practical strategy”, there are five basic questions that can be used as a framework for building and testing the strategy for an organization. I will cover two of them in this post and the rest in a post or two over the next few weeks.

The first two are “what business are we in?” and “where is the market going?” These questions serve to both build upon the vision which was developed (described in the first post ) and to test it in a practical way. The thinking being: the vision has to not just read nicely and seem logical but you should be able to deconstruct it and determine its practical applicability.

For example, here’s a real-world vision statement: We help mid-size businesses improve their Pipeline-to-Profitability (“P2P”) cycle. Our business intelligence solutions are easy to use, offer immediate value and require minimal investment, using existing systems and data sources. For this company, it was a significant turning point to re-define their business in this way. Previously they were more me-too as a business intelligence software provider delivering custom solutions in the “small to medium” (SMB) market. This was a good business but to grow it and to develop efficient marketing strategy and execution behind it was actually difficult because “what business are we in? “ resulted in an answer that was too broad and undifferentiated.

Above I underlined some key elements of their new vision. Each of these were chosen carefully and were backed by analysis, discussion and judgment to test whether they gave clear guidance about what business are they really in and whether data could be gathered which indicated where the market was going. As a product and marketing professional, having clear sets of facts and decisions about these two elements is a big advantage – and too-often they are not clearly available as marketing strategy is developed.

I’ll discuss just a couple of the key elements of the new vision from above to illustrate:

Mid-size – depending upon the definition of “small to medium size (SMB) business” there are at least 6.6 million (and some reports put the number at 20+ million if you include part time, SOHO and cash-only businesses) in the U.S. There’s a fair amount of hype about the potential for pursuing and selling to this somewhat untapped and very large B2B segment. Some iterative analysis and pondering of readily available data on this market showed us that the larger revenue size (what we came to call “mid-size”) businesses were more readily identifiable (e.g., segmented into industry categories) and still represented a significant market (625,000 in the U.S.).

Pipeline to Profitability – the company had developed some good off-the-shelf analytics that could be used by sales management to better understand their sales performance and provide insight that can improve effectiveness and results. The sales cycle though is a generic concept and varies widely across businesses due to product mix, complexity, price, market segments and channels. Some study of the marketplace indicated though that the sales pipeline – the narrow set of sales steps used to move a “qualified” prospect through to final sale – was a universal issue and the heart beat of any sales process. It was also well-defined and lent itself to simple analytics that yielded significant (i.e., high value) insights. Most importantly it was generally poorly served in terms of linking the management of the sales pipeline to profitable outcomes. Most solutions on the market totally ignored this critical component.

Immediate Value – later on you’ll learn that this was the chosen “key differentiator”. Every business or organization needs a key differentiator – ideally just one (that is so powerful that if well chosen and executed it is sufficient) to anchor the focus of the business, including technology and product investments, marketing messages and delivery or supply chain operations, as those apply.  Much of what happens, or more accurately doesn’t happen, in business intelligence solutions and particularly in the CRM or sales arena is that value is not immediately delivered. Rather data (e.g., reports, alerts) is delivered slowly after much effort (and investment) and typically is not exactly (in form or content) what is needed by end users such as busy sales managers and executive managers. So rounds of iterations and alterations take place in search of the value and satisfaction required to ensure the solution will actually be deployed and used. This lag in achieving value – some call it ROI — and reasons for the lag are too numerous to go into here. It simply became clear that if value could be delivered “immediately” (the initial goal is within two days and long term goal is truly immediate) there was a void in the market and competitive differentiation could be clearly articulated and achieved.

Using these two questions in an iterative fashion is the best approach. Take each key element of the vision, ask: “what business are we in?” if we use that element. Then gather some external market data to ask further: “where is the market going?” relative to this key element – and you will rather rapidly shape, focus and finalize the business vision and also build up the fact-base behind it. This should give any business confidence that it is on the right track and once we are done with our five questions, should give the business confidence to pursue the entire vision with high energy and the proper amount of investment to achieve success.

True to my intent, I have written somewhat eclectically about discovering potential, looking ahead, thinking critically and objectively and wanted to get back to a business-oriented mode for a few posts.

Many organizations (and individuals!) are scratching their heads trying to figure out how to deal with our current, unique and challenging circumstances. But they are also trying to plan for the future (with optimism that “this too shall pass” and wanting to be ready for the next set of opportunities and challenges). I applaud any form of optimism! And so, I have a practical tool for use in getting some strategic thinking and planning done, which seems especially useful in these times as an overdone, over-wrought approach will be overkill when “directionally correct” might be all that is needed until some of the uncertainties and issues of the current time pass. I would argue though that even in more certain times, the approach I’ll write about in this and subsequent posts is useful and gets most any organization beyond being stuck in the present and looking ahead with a critical and purposeful eye.

The approach I advocate is squarely focused on getting a specific vision and strategy down on paper – and will serve as a very powerful tool to also use in successive iterations (a critical component of the strategy process as a one-time vision and strategy exercise might not even be worth the effort).

What I also like about this approach is that it uses language and key words that were not “strategy double-speak” and won’t put off the executives and other participants who often tune out of a strategy exercise because of preconceived notions about strategy, consultants, etc. (i.e., ”too complicated”, “too high level”, “not executable”).  

The approach also ensures completeness without being overly complex and strenuous as a management team exercise. I often say when about to embark on this process that I want the team to “work out”, not “wear out”, their thinking capacity.

I call it Practical Strategy because of the definition of the word “practical”: \ˈprak-ti-kəl\, adj., useful and no-nonsense.

There are two basic steps to the process, with the second working through and answering a series of questions. I’ll summarize the first step in this post, and then work through the second part and the questions in a couple of subsequent posts.  The first step is to articulate a long range vision for the business. This can sound too simple on the surface. A good vision is not just a statement that gets put onto posters, inside annual reports, or laminated on cards handed out to employees and customers. Getting it right is hard work but needn’t be a too-long effort. It must be clear, specific and define the place for the business to aspire reaching (but with no set time horizon). A test will be that a good vision statement can be decomposed and set the boundaries for and guide the answering of the subsequent questions in this exercise. If it fails this basic test, the vision is not practical and should be refined.

I’ll give an example. The practical vision for Domino’s Pizza: “Make and deliver a fresh, hot, high-quality pizza to the customer’s home within 30 minutes or less.” Several things:
- this makes clear what value is to be delivered – fresh, hot and high-quality. Any one of these may be sufficient, why choose all three? Knowing why make subsequent decisions about business model, operational strategies and so forth quite clear

- a key differentiator is articulated – 30 minutes or less (and in their advertising they backed this with a guarantee-or-free offer)

- a key operational characteristic is defined – to the customer’s home. If taken literally (which they did), this kept them focused on the home delivery model and away from building sit-down or walk-in or stores, and has clear direction for their location and logistics strategies. 

- even the omission of something can be useful — the vision only mentions pizza. No mention of other products or open-ended placeholders for other foods or items that could be thrown in. It is about pizza, plain and simple.

Not all businesses are as simple as Domino’s. Or is it that not all businesses go to trouble of defining their businesses in such clear and practical ways? I’m sure the answer is in the middle somewhere but I will argue it falls toward the latter.

As always I welcome your feedback and look for a post soon on the first of the questions that must be answered to complete the rest of the Practical Strategy process.

On a plane ride home recently I read a review of a family biography of Henry James, Sr. and his remarkable children. One son, Henry, Jr. was one of the great novelists of all time. Another, William was an intellectual powerhouse and author of the breakthrough work “The Fundamentals of Psychology”. The interesting thing was the odd and hardly idyllic – shall we say “difficult” – familial environment they endured. But it didn’t end there. It wasn’t enough to have an unusual father – one who traipsed around Europe with his family in tow, never putting down roots, subjecting his children to his unique “theories” on education, spirituality, and life and never giving them any chance for normalcy — it was an on-going family saga of unending and dramatic ups and downs which Henry Jr. and William endured throughout their own adult lives.

Why am I writing about these people? Before I say, let me take a tangent to another topic. Recently our adult Sunday school class watched a program about and discussed Randy Pausch’s “Last Lecture” . The story is famous enough that I’ll spare any details of it (although I cannot recommend it enough). What our class discussed, inspired by Randy’s lecture and life, and what we asked ourselves was: “what are the lessons you would like to leave behind for your children when you die?”

The one I offered was “to persevere”. I said the usual trite stuff we heard as kids about “if at first you don’t succeed, try, try again” and “nothing ventured, nothing gained” and that this would teach my kids to have stamina and courage – which have at various times in life served me well. I want my kids to learn that lesson too, I said. But reading about the James family gave me a different perspective and one that seems more insightful – and powerful. The James’ brothers (as well as Randy Pausch) didn’t endure and overcome as much as they used their life journeys instead to draw strength from, and lead them to, greatness. The challenges they faced turned out to be the “roadmap”, revealing the path to greatness. Why? Because they embraced their life and experiences, continually mixing all of them – the difficult and the tragic, with the good and the bland – until something worthy and satisfying emerged.

So, I’m changing my lesson for my children. While persevering is not a bad lesson to learn, it sounds too episodic to me now, like advising them to simply get over the hump, and just admonishing them to “leave your troubles behind you”. Revealing your potential for greatness is a process (like rocks being polished into gems, a beautiful pearl being developed by an oyster as protection against harmful bacteria), and an attitude that adversity is an opportunity to improve and add to your current greatness.

Following the recent election season I heard a pundit comment that, as usual, winning candidates from both sides had mastered the art of successfully positioning themselves – and their opponents – through powerful use of anecdotes. They found anecdotes that resonated with the electorate and used them to either effectively portray themselves positively or their opponents negatively. The power came from repeating these anecdotes in speeches, campaign literature, political advertisements, and those much-hated automated campaign telephone calls such that people began to believe them simply because the repetition gave them an air of being factual.

Now, I looked up the definition of the word anecdote. An-ec-dote \ˈa-nik-ˌdōt\, noun, “short account of a particular incident or event of an interesting or amusing nature, often biographical.”

I wasn’t sure how this could be so powerful – sounds sort of innocuous. Then I looked at synonyms of the word – I often find synonyms to be interesting perspective on word definitions. Here’s what I found:

Story
Tale
Yarn
Fish story
Fairy tale

Ah ha! Now I get it – tell a story that is rooted in some specific truth but with an edge of humor and human interest, repeat it often enough and it becomes accepted fact!

Now, I do NOT want to make my blog into a political one. I use the above to set up a simple point that I think is important in personal and business situations and has nothing necessarily to do with politics. Often in my business career and in my consulting work in the area of data-driven decision making (for strategic planning or in marketing), I have used interviews with an organization’s associates and executives to get a baseline on the current environment from various stakeholders. Without fail, a common thing I hear is “we have lots of data, we are drowning in data, but we make most decisions based on opinion or conventional wisdom”. Probing a little further I find that what happens is one or a small set of facts become favored (sometimes for pure but often for political reasons) and then repeated and re-used until it becomes the rationale for many decisions.

A great quote I just recently found is: ““data is not the plural of anecdote”. I think I’ll use it going forward to help me make the point about breaking away from opinion-based decision making and moving to data-driven decision making. As in politics, we often fall prey to simply repeating – and believing – what we’ve heard before rather than demanding data-supported facts, particularly fresh ones and from multiple sources that clearly support recommendations and decisions.

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!

To my steadfast and loyal readers (yes, there are a few) and those that amble by for whatever reason:

I see I am coming up on my 1000th visitor. In a previous post I commented that some popular blogs get that many each hour. I do not necessarily strive for a similar popularity but popularity can breed a true community and the valuable, candid interactions that come from it. I would say that this is my aim, to have a community of readers who find the content herein valuable enough to add to it with comments, links to other relevant content, or to debate with me and the blog’s community about whatever seems important to get right, to improve or discard if need be.

YOU can help and in doing so, win the aforementioned “fabulous prize”.

The prize: for the 1000th visitor I will give “one day of free consulting” on whatever topic he or she chooses. And I will do the same for the 2000th, then 3000th, and so forth.

No further obligation and I won’t put an expiration date on the offer. I figure if you have taken time to subscribe and participate then I hope to maintain a relationship with you for the long haul.

You can help by doing what you already do, which is subscribe to my blog. You can help even more by passing along the link to my blog and recommend it to your friends (no enemies, please ). More readership increases the sharing amongst the community and will increase the velocity of my awarding the “1000″ prize and with it your chances of winning!

OK, is the prize self-serving? Perhaps. I’m a small business person and I’ve got to earn a living and feed my family and this is (hopefully) an acceptable way to do some promotion along the way to building a better blog community. You can of course tell me if you think otherwise !

Thank you for your support in the meantime watch this space for the announcement of visitor 1000!