Advanced Analytics

22 03 2010

A major item organizations grapple with is the concept of advanced analytics.  They want it, but have little idea how to use the various tools to make it happen.  Unfortunately too much information often blurs the lines.

For example, I watched a sales presentation on Predictive Analytics where the key outcome showed how to build databases with the tool yet almost completely missed the fact that the real benefit should have been something like “we were able identify two segments to target a marketing program for more effectiveness.  Instead of spending $500k on a generic campaign we were able to identify key attributes that drove increased customer interaction and focus the campaign to only $200k on those segments.”

Why is this? The primary reason is we do not truly understand the tools and how best to use them.  A Swiss army knife is not good for home repair, but is the perfect tool to throw in a hockey bag, or car trunk for occasional use as a widget to get you out of a jam – a screw needs to be tightened, a shoelace needs to be cut, or an apple peeled.  We need to understand which tool to use in the most appropriate situation instead of thinking of various tools as universal.

Business Intelligence, Planning, What-If Scenario Tools, Optimization, Dashboarding, Scorecarding, Cubes, Cluster Analysis, Predictive Analytics are all different tools for vastly separate purposes yet have similar uses.

Advanced Analytical Tools

Here are the core elements of Advanced Analytical tools:

  • Business Intelligence – great for creating an enterprise-wide, data visualization platform.   If you do this right, you should create a single version of the truth for various terms within an organization.  It should enable better reporting consistency standards for the organization.  In the end, it reports what the data says.
    • Scorecard & Dashboards – These are primarily BI tools that have a more organized or structured methodology for presenting ideally the Key Performance Indicators.  These are great tools, but to be most effective, they need a specific purpose that is highly integrated into a management process.
  • Enterprise Scenario Planning – Most enterprise planning exercises are giant what-if scenarios that try to plan out financial outcomes based on a series of drivers (employees, widgets, sales reps, etc.).  We build out plans based on a number of assumptions, like the average sales rep drives $2mil in business, or benefit costs for the year are going to be #of employees * average salary * 2.  We do this primarily to lay out a game plan for the year and we do it as part of an annual or rolling cycle.
  • Tactical or Ad-Hoc What-if Scenario Analysis – Besides the full scale project we do to plan out the company’s cash outlays, we also do a significant amount of smaller, typically tactical “what-if” scenario tests.  This is traditionally done in Microsoft Excel.  We dump a bit of data into excel, make a number of assumptions and try to build out likely scenarios.  For example, “if we were to create a customer loyalty program, what would be the cost and a likely reward.”  We are doing this to test ideas, so yes it might be ideal to bolt those into the Enterprise planning tool, but it typically takes too much overhead.  It is easier to just get something done quickly, then make a go/no go decision.
    • Data Visualization can also be a great help with this – to bolt on a couple of reports to see the data and how different scenarios impact the various facts and dimensions.  This can help us with our conclusions and recommendations.
  • Predictive Analytics – This tool is best used when we have historical data, or representative data set and we want to make a conclusion based on mathematics.   The key is math.  This is not guessing, it is improving the chances of being right with math, or a structured approach to remove risk from decision making.  With a planning tool, we primarily use assumptions to create plans.  We cannot use predictive analytics for all decisions, but for a few specific types of decisions:
    • What transaction details and customer insight can we use to determine credit card fraud?
    • What customer attributes create our buying segments?
    • Which customers are most likely to abandon our offering?
    • What products are most often purchased together?
    • Which taxpayers most likely need to be audited?
  • Optimization Analytics – This is perhaps the most specific advanced analytics tool when looking to solve the specific business question: “With the given parameters of these trade-offs, which mix of resources creates the most effective (or efficient) use of those resources?” This helps make decisions around production locations and product investment.  Like predicative analytics, it is mathematically based (though you may need to make a couple of assumptions as well) in how it determines the answer.

Advanced Analysts

Another reason we lack understanding is analysts.  Our analysts are commonly from the IT team, trained in data structures, or from the finance team, trained in accounting.  Neither is wrong, they just have a default mindset that falls back on using the tool they best know.  This lacks the business/statistical trained person who can both layout the hypothesis and, more importantly, explain the results.

We do not want correlation explained in R-squared values, “63% of the variation of the data is explained by our independent variables.”  While this may make sense to other statisticians and mathematicians, it is lost on the business.   One key value of using a math-based concept is that the explanation should sound more like, “We have found a way to decrease fraud by 3.2%, which should result in a $576K return to the business every quarter” or “We have tested our marketing campaigns and have found three segments that are 25% more likely to purchase based on the campaign, which should result in a payback period of 3 months.”

The right tool with the right skill set is imperative to successfully using advanced analytics.  We also need the discipline to have the right people using the right tools for the right information to drive action.  If you have an algorithm that predicts customer defection, you need to use it and test the results.  It is never going to be perfect, but in most cases, you can bet it will be better than not using it at all.





Predictive Analytics, Business Intelligence, and Strategy Management

9 12 2009

I was having a discussion with one of my clients this week and I thought he did a nice job summing up Predicative Analytics.

So in the World According to Reed (WOTR) – “queries answer questions, analytics creates questions.” My response was “and Strategy Management helps us to focus on which questions to answer.”

Reed Blalock is exactly right, traditional BI is about answering the questions we know. Analytics is really what we create with data mining – we look for nuances, things that might give us new insight into old problems. We use human intellect to explore and test. And yes, there is a little overlap. But what is really happening is that we have a different level of human interaction with the data.

BI is about history, analytics attempts to get us to think, to change, and idealistically to act.

The danger with both of these is that they can be resource intensive. Neither tool, or mindset should be left to their own devices. What is needed is a filter to identify the priority and purpose. This is where strategy management and scorecarding comes into play. We have built out massive informational assets without understanding where, when, and how to use it. We have pushed out enormous reporting structures and said “it’s all there, you can find anything you need” yet we scratch our heads when we see adoptions levels are low.

What we have typically not done all that well is build out that informational asset by how it helps us be more productive along product lines, divisions, sales region, etc. We have treated all dimensionality the same. Why, because it was easy. The BI tools are tremendous in how quickly you can add any and all dimensions.

“But because you can, doesn’t mean you should”

As we built out these data assets, we did not align them to performance themes.  We have gotten better with some key themes, like supply chain management, and human resource management, but what about customer performance?  We might look at sales performance, but that is a completely different lens than customer performance.

How do we determine which assets to start with…what assets do we need to be successful 3-5 years from now, or what are our biggest gaps to close today.  Think about customer value, or employee satisfaction (and that doesn’t mean more HR assets).  Think about your gaps in Strategy.

How often do we discuss…

  • Are our customers buying more or less frequently?
  • What are our best, and better customers doing?
  • What are the costs associated with serving our least profitable customers?
  • Where are our biggest holes in understanding?




Predictive Analytics Gets Closer

17 09 2009

I am always a little shocked by a company’s resistance to using predictive analytics.  My guess is that is a combination of not really understanding the value, fearful that they won’t get it right, or not having the right talent to use it.  It has long been labeled as “white lab coat stuff” and perhaps that is a bit accurate.  But software is making this easier, and MBAs are studying it so this label should be diminishing.

The Value:  Reducing costs, increasing returns, quicker identification of issues – these are all critical wants of every organization.  If we can only chase five opportunities with roughly the same make up, a little predictive analytics should be able to tell you who is more likely to have a higher customer lifecycle value.  If you only can cover 10% of the market with a marketing campaign, predictive analytics can help you determine which 10% is likely to have the greatest yield.

The Fear:  I understand this, but it is a little irrational as all decisions involve some level of risk.  All predictive analytics do is make decisions based on an elevated likelihood of being right.  If I told you I could make you 10% smarter, wouldn’t you listen?

The Talent:  This is perhaps a realistic barrier, but one simply corrected.  Predictive Analytics, while getting easier every day, is still about advanced computations.  Not only do you need to understand how to do them, you need to understand when and where to use them. And more importantly, you need to understand how to transform the information into values an executive team can put into action.

Where do you begin:

  1. Find someone in the organization with a good statistical and business mind (or hire one).  This may not be the technical team – it often takes a little different skill set.  Or find a small team.
  2. Find a business process where there is pretty good data and that will add value at the end of the day – customer attraction, attrition, fraud detection, scrap reduction, etc.
  3. Put a small project in place to try it.
  4. Enter my favorite stats words – Parsimony:  Find the most simple answer.  This is easier to explain and digest of how to put the project into action.  (Why is a word that strange about the simplest answer).  It is easy to end up tweaking a project to death.  Don’t do it on the first pass.  You get lost in data and often find it far more difficult to explain and complete the project.
  5. Try it and accept the results.  The is tremendous learning in failing (and chances are likely you won’t fail if you didn’t bite off that much).

Examples:

  • Let’s say you can identify customers who are likely to abandon you and then work to make sure those customers are treated better.  If your abandonment rate drops by 10%, what is the value to the bottom line?
  • If you can identify customer segments that are less price sensitive, what is the value of a 1% increase in average deal size (note that the entire amount really should drop to the bottom line as well)?
  • What if you can reduce fraud by 5%?

The numbers show that predictive analytics are very real.  It is not about guessing, it is about reducing the risk of guessing.  And if you follow many blogs, all of a sudden there is a lot more information on predictive analytics.  IBM is finally putting together some wood behind the arrow of its SPSS purchase which may also begin to influence more decision makers in the space.

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