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.

Related Links:





Mulligan

23 07 2009

If you were given a corporate mulligan, how would you use it? What would stop you from doing it over?

Or think of it a different way…

How would your competition use your product platform, your assets, your customers to take advantage of you?  What happens if you push all of your unprofitable customers over to your competition?  Let them deal with the headache, the loss of time and money.

One of the most interesting thing about this current economic environment is that there is a new entrepreneurial spirit.  With this brings new technologies, new business models, etc.  Would GM and Ford take a mulligan when Toyota entered the US market?  Take a look at Blockbuster.  Did they see Netflix coming?  If they did how could they have reacted?

Can someone do this to you?





Key Risk Indicator (KRI): Customer Abandonment

1 07 2009

How often do you look at the indicators that a customer is thinking about leaving you?  Do you have a process around this, or do find yourself saying “why did this customer leave us?”

What are the possible indicators for a customer leaving:

  • Time between purchases
  • Decrease in volume
  • Time to Pay bills
  • Increase/decrease in calls to customer servicce
  • Temperature of calls into customer service
  • Longer sales cycles
  • Time between customer visits

Some of these may be difficult to quantify, but are well worth understanding.  I would venture a guess the cost to replace that customer (including lost opportunity value) is less than the effort to put a process into discussing customer abandonment.

If you have stories on how you track customers, or customers you lost and how you should have known, please share them.





Focus on Operational Performance Management

26 05 2009

When was the last time you discussed how your customers were performing?  Do you have a formula to determine their lifetime revenue potential?  And what it costs to serve them?  Does this determine how you segment and market to your customers?  Do your sales people use this value as a tool in the negotiation of price?

Basically how do you manage customer performance?

One of my clients was a credit card processing shop and what we found was that they were spending $4 for every $1 they were collecting from bad debts.  While it was not the whole story, it was evident that we needed to better understand the customer lifecycle.  This client did have specific marketing programs and processes, but they had not been challenged in quite some time and were common industry practices.  

What we find out when we look at commonly held beliefs is that their assumptions are no longer (if they ever were) valid.  We get into a groove of momentum that we find difficult to change our beliefs and behaviors.  We also lack a mechanism and the focus to understand which processes to look at.  One of the most critical to me is around customer performance.  

Ask yourself if you know which customers are driving profits and which are destroying them?  If not, this might be the best place to start thinking about improving insight and process improvement.





Customer Lifecycle Value

1 05 2009

Depending upon on how well your know your business, a great discussion to have somewhat regularily is whether or not the customer lifecycle value is increasing or decreasing.  To achieve this we need to know a few things…

  • How much has the customer purchased from us?
  • How long are they likely to stay with us?
  • What does it cost us to serve them?

None of these are necessarily easy questions to answer, but that does not mean we should not talk about these items. Worst case, you should at least be looking at the average revenue and cost per client and see how those are changing. They are probably pretty good indicators of lifecycle value.  If we look at the trends of our revenues, costs (COGS & SGA), and profits per customer this should certainly indicate if we are doing better or worse.

While most of us do this to some degree, we probably also throw in a great deal many more variables and business rules and end up discussing various concepts. What about once a month or once a quarter getting all the department heads together and discuss progress on only these items.





Align to Customer Value

16 03 2009

On thing to consider in terms of developing KPIs (Key Performance Indicators) is how they are aligned to the customer’s wants.  All to often we ignore this perspective, yet it is perhaps one of the most important factors.  

For example, one of the growing cost saving tools companies use is call automation services.  “For sales, press 1.  For customer service, please hold while we test your patience.”  

Companies do this because they are measuring cost per call, or efficiency.  What the customer really wants is a convenient resolution to their call, or effectiveness.  Clearly these goals are working against each other and in most cases destroys customer loyalty and brand value.  

In the end, we need to balance costs with value, and we need to understand customer and corporate strategy.  Are we focused on customer intimacy as our core business focus, or operational excellence?  Are we measuring the business in a manner that reinforces our business model and customer value creation, or strictly by the bottom line?