Potential: Revealed
Strategic Thinking, Innovative Ideas, Growth Marketing, and Revealing of PotentialArchive for analytics
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.
Happy Customer
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!
Business slowing? Then Accelerate!
It has been a difficult 2008 — almost every business would agree, unfortunately. In 2009, the natural inclination might be to hunker down and weather the economic storm (that is forecasted to rage well into if not all the way through 2009). Recently in working with a client in the business-to-business software market, we decided we’d do the opposite — they’d accelerate their sales efforts with a strategy to gain market share and position when others were pausing and flat-footed.
Our thinking is: regardless of economic climate, the success of any business depends on acquiring, growing, and retaining profitable relationships with customers. Customers (your best ones, especially) have complementary aims. They also want to grow and to maintain their profitability. My client is convinced that in tough times — now more than ever — an aggressive selling stance, tuned by exploiting deep and precise knowledge about such things as which customers are buying and why, which reps and channels are being most successful will push them past their competition.
To pull this off requires insight about your customers and prospects that are in your sales pipeline and managing that pipeline more effectively than you ever have before. In fact, the heart of any sales process is the sales pipeline – where sales opportunities are managed from qualification to closed sale. Sales pipeline performance is essential for breaking out from the pack in 2009, and reaching the level of success you desire your business to achieve.
Many businesses struggle, though, with myriad pipeline management challenges such as determining which accounts should be of highest priority, what actions will best spur the sales process, and whether and how to reapportion pipeline opportunities to maintain a healthy distribution across the sales force. The results of failing to address these challenges include lengthening sales cycles, stalled opportunities, and results versus forecast that bring unpleasant surprises.
The difference then for the most successful sales organizations is identifying and taking the intelligent steps needed to achieve measurable improvements in sales pipeline performance.
Addressing the challenge
In order to address the challenge, I worked with my client to define clear steps to take to enhance sales pipeline performance in 2009:
Define Your Sales Pipeline Process
As a foundation for success, it is critical to understand the distinct stages of the sales pipeline. Each business is different and the investment of time to define a process that specifically matches your business needs is well worth the time. By way of example, the stages might include lead qualification, customer need assessment, opportunity prioritization, customer decision, and opportunity close out. Understanding each stage in enough detail to be able to describe clearly how to advance from one stage to the next is critical. Another key aspect to understand and document is the typical time required to move from one stage to the next — this aids in assessing whether an opportunity is moving along appropriately or is stuck.
On-demand Visibility into Opportunities
Across a given time horizon, sales opportunities will evolve with new opportunities emerging and some current opportunities declining in priority or ceasing to be worth pursuing. On-demand visibility allows rapid and appropriate response to these changes. Visibility that also includes customer and current opportunity profitability, stage within the pipeline process, and the latest activity history all provide insight and illuminate the overall health of the pipeline. A healthy pipeline will have opportunities distributed in a relatively balanced manner across all stages – and an uneven distribution provides cause for addressing the imbalance before it has a negative impact on the sales forecast and ultimately realized revenue.
Create a Process to Monitor Performance
Improved performance can only be achieved and sustained if the on-demand visibility is integrated into the larger context of the sales planning and execution process. A typical process might include sales management setting sales rep revenue targets, reviewing the pipeline periodically for performance and issues, updating forecasts and reviewing results against efficiency and effectiveness metrics while sales reps throughout are qualifying and managing pipeline opportunities and updating data about each opportunity.
Preferably across and within this process management and the sales team will have aligned goals. Achieving this alignment depends upon metrics that go beyond merely high-level revenue targets. Examples of ideal metrics include:
- percentage of sales reps meeting quotas
- number of leads in the pipeline (by rep, type, age, geography, etc.)
- pipeline velocity (expected time for opportunities to move from one
stage to the next)
Providing on-demand access to this information – to sales management as well as sales reps – facilitates the necessary alignment and unambiguous communication throughout the sales cycle.
Combine Analytics and Action
With an appropriate foundation of visibility and on-demand information, a business can not only be more proactive with their planning – it can harness analytics to drive well-timed and appropriate action. For example:
- what-if analyses to test actions that might close pipeline gaps or free stuck opportunities
- mining customer buying behavior to help sales and marketing identify customers with the highest propensity to buy
Through this more advanced use of analytics and insight, the broadest possible set of stakeholders can be engaged – and kept well informed – and doing so can ensure unpleasant surprises are avoided and breakout performance goals are reached in 2009 … and beyond.
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
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).
4 P’s, lots of D, stir and taste repeatedly.
Working recently with a client in the banking industry, they remarked about “growing beyond our ability to manage customer relationships by feel”. What they meant was that they prided themselves on being a “high touch” banker and saw it historically as one of their competitive differentiators but that it had, alone, lost its ability to create this much needed differentiation. The loss was probably imagined though (I offered) … you probably never really had it.
When you are a large commercial organization, and a bank is protypical of a large commercial organization, it is difficult to truly be “high touch”. It merely sounds good (on marketing messages, in PR ,and advertising). To scale the abilty to have your customers perceive that you know them well and catering to them as individual, highly-valued customers (that is, to have them feel as though you are high-touch), a more integrative approach is usually required. Large organizations, with their scale and resources are uniquely able to pull this off and can create differentiation for themselves, but a fair amount of change is required — procedurally, organizationally and culturally. I’ll spare all the details but a simple, high level way to think about it is:
- manage the 4 Ps of marketing as an integrative set. Define your target markets and customers in ways that you can focus on those where you clearly understand customer needs, can deliver a differentiated offering, can reach them effectively through carefully chosen channels and promotion strategies, and because of the former three you can price in a way that delivers sustainably high profit margins.
- in today’s world, you cannot possibly do this without adding plenty of D, for Data. In a subsequent post I’ll talk specifically about what’s required to add the D required for knowing the market and your customers in a way that leads to differentiation and success. Data, turned into “customer insights”, can be the glue that binds the organization together to act in concert from the inside (typically in the headquarters where the data is stored and managed) to the outside (where customer-facing associates in sales, relationship management, and customer service areas touch the customer directly).
- last but not least, managing the 4Ps intergratively and conquering the capability curve to be able to add data as a competitive weapon is not easy. I believe it requires an ”iterative experimental” approach. This is very different than the perfuctory approach of “piloting” a new business process or system. I’ll use a cooking analogy (which is a great metaphor-rich activity): often times the first attempt with a new recipe in an uncharted style may be good but not yet fully satisfying. Yet the experienced, creative and most successful cook will use the experience to adjust (a little less salt? a dash of cayenne?), evolve the recipe and find the successful combination … and then continue to evolve the recipe and themselves, challenging and striving for even higher culinary heights and delights. The delighted look on a dinner guest’s or dining room patron’s face is all they need for validation. A similar mindset is required for the successful company that wants to tackle the integrative maketing and data challenge because they are convinced the results will be a uniquely delighted customer.
