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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Pretty Words

15 07 2009

Listening to Sonia Sotomayor retrack her “wise Latina” comments made me think about an old Vince Gill song – Pretty Words.  “They’re just pretty words” seemed about right.  This is often the role of the politician, to say things that make people feel better.  We have limited manner in which to hold them to their words, so we often judge the words based on if we believed what they were saying.  Think of how we now perceive Roger Clemens, Alex “A-Rod” Rodriguez, and the steroid gang.

One of the problems we have as leaders is an overuse of pretty words.  We are often asked questions that can not be answered at that time, thus forcing us to spin a response:

  • Are we having layoffs?
  • Are we selling the company?

While these hurt credibility with the front line, they are necessary to keep some level of sanity and productivity.  Yet, what happens when executive communication seems to be only about spin and pretty words.  If the rank and file feel “pretty words is all he is giving you” then we have a problem with communication and trust.  If these are broken, you can bet productivity is no where near optimal levels.

As executives and leaders we can know, or we can think we know if people are listening.  What I have often seen is that the good ones assume they don’t know and find out – thus reinforcing positive communication.

  • When was the last time you had an outside, independent team assess “trust” in the organization?
  • What would be the value to the organization?
  • What if you hear something you don’t like?




Productivity Management

9 07 2009

Performance typically ebbs and flows along a number of fronts.  In the worst cases it declines across multiple areas when it is not managed consistently.  Most productivity initiatives create curves something like below:

Productivity Management

  • At point A – we have identified a performance issue and have created a plan to improve performance.
  • At point B – we have succeeded and typically move on to solve another issue.
  • At point C – we start to see productivity decrease from lack of management and attention.

Unfortunately, most things don’t have pretty economic curves or requires focused thought to create one.  And if we do not create a performance plan we typically see the inflection point at C happen closer to A.  This happens because we have not put a plan in place and/or when we feel some momentium we abandon management of the initiative to fight the next battle.