Analytics Competency Center

28 09 2009

We spend a lot of time on Business Intelligence, Master Data Management, Data Governance, Standardization, off-shoring, etc., yet I rarely hear organizations spending time and energy on analyzing the data.  We have cubes, we can do all sorts of things with reports and dashboards, yet I still hear people say “I need more information!”

It is impossible that we are short on data!

  • How then are we not getting enough information out to the organization?
  • Is it possible that we are spending all of our time and energy on data preparation and data movement?
  • Are we creating value, or just planning to create value?
  • What about creating a center of excellence around the business user?
  • Or something around the levers of the business?




Performance Management Defined

17 09 2009

Last week I asked Jonathan Becker of Manage by Walking Around blog and Gary Cokins of Closing the Intelligence Gap blog to argue the definition of Performance Management and what it might look like…and in all fairness, I need to also share mine:

Performance Management is composed of three distinct disciplines, Strategy Management, Operational Performance Management, and Financial Performance Management. It is a systematic and standardized management and communication process to proactively enhance performance gaps.

  • Strategy Management – to set direction, foster alignment, and communicate priorities
  • Operational Performance Management – where we execute our goals and objectives by creating customer value along with our core processes.  This is also the most widely defined as each industry handles this somewhat differently, but how we manage it should be integrated with a common process.
  • Financial Performance Management – to provide insight into what resources we have and how best to use through monitoring and reporting upon the budget.

In addition to this we need to use within the same system our enabling support structure.  This includes managing technology, culture, people, etc.  Each element needs to be improved upon based upon strategic need, thus helping to eliminate personal politics and squeaky wheels.  Below is my Performance Management framework.

PM Framework Master

Gary makes a great point that most people create a framework that is intentionally incomplete to enhance their offerings (and I completely agree).   I built the above framework with the goal of a complete framework.  It is not perfect, but I feel provides a strong starting point to assess our process improvement gaps.

In the end, management is just a process, albeit a very important one.  It needs to be enhanced and improved to leverage the most of the management talent.





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:





The Spandex Rule

8 09 2009

“Because you can doesn’t mean you should” (unknown source)

While this is a little entertaining, it actually makes a great deal of business sense.  All too often we do things we should not do – and sometimes we did it just because we could or we wanted to.  Hope is not an effective strategy, and willingness should not be misplaced for must.

What we need to do is understand strategic gaps, and build out solid plans to close them.  We need to create a process to identify performance issues early in the cycle and put initiatives in place to fix them.  We also need to understand the core processes in our organization that create customer value and work tirelessly to improve them.





Survival of Innovation

31 08 2009

In 1988 Pinnacle Brands broke into the baseball card market.  The market had long been dominated by a couple of players (Topps,  Donruss, and Fleer) and the market was doing fairly well.  It catapulted onto the scene by throwing in new features to the market, more colorful cards, full edge bleeds, more information, etc with their Score brand.  Over time they added in brand variations that were targeted at very specific markets:

  • Score:  Lower price point, more kid friendly
  • Select:  Mid price point, geared for the beginning collector
  • Pinnacle:  Higher price point for the more serious collector

If you followed the baseball card market at that time you will remember it as a rather unique time.  It was perfect example for economists.  The value of each card, pack, box was independently valued by third parties.  Card shops popped up in nearly every neighborhood to trade cards, and serious collectors were following the distribution trucks buying entire cases at a time before they even hit the shelves.  The catch was that you could not make all the cards you wanted.  The more you made the less you sold, and vice versa.

One of the main things that happened was the wrong sales mentality.  What made them successful, new  innovation, also hurt them.  They tried to stack the cards to the ceiling and create a consumer good mentality, not realizing the principal that the card would really only sell if they kept product very limited.

Hindsight being perfect (still a good lesson none the less) they should have kept production runs low, elevating the brand and looked for other ways to extend the brand.  As a last change, they started to get into other types of cards.  I think in the beginning they had the brains to come up with demand creation card games like today’s Pokeman genre.

Upper Deck came along in the same year and appears to be the leader in the field today.  Usually someone is going to survive, are you doing everything you can to make sure it is you?





KPI: Overhead per Customer

22 07 2009

If you are trying to measure management improvement, how about looking at Overhead per Customer (or per transaction).  This should be a decent indicator in terms of management and overhead scalability.  If we are doing a better job of managing the business we should see some increased returns in the management function.

  • If the trend is increasing, we should be discussing the scalability of the organization.
  • If the trend is decline, is it for the right reasons?

While you are at it, you might also include cost of sales per transaction.  This one is perhaps a little more debateable in that we don’t want to artificially manage this number.  Reducing the number of sales reps, may drive down the number.  Reducing compensation plans may chase away our better sales reps.





Price of Distraction

21 07 2009

Over the weekend, I was telling the story of Informix (now part of IBM) and the number of databases it tried to market and sell.  At one point in time, Informix marketed the following databases:

  • Standard Engine (SE) /OnLine 5 / IDS 7 / IDS 9 /RedBrick

It then aquired Ardent Software and added two more databases, UniVerse and UniData.  While the company was looking to build a data warehouse focused organization, the database was taking less and less focus.  There were a number of problems the company was facing.

  • There were not enough people at that time who could sell the complex technology well
  • The market was not really ready for the high end product
  • Each change in leadership elevated a different product to the forefront
  • A confused customer base
  • A skillful competitor in Oracle
  • A little SEC troubles

Informix itself is a great case study.  At one point, I simply asked the question “what if we sell OnLine 5 and SE to remove the distraction?”  Both OnLine 5 and SE were great products in their day, unfortunately those days were long past.  Both products still did somewhat well in the VAR space and were highly profitable the late 90’s.  My rationale was that we only made $10 million a year on each and most of that was profit.  We were shooting for the $1 billion plateau in annual sales and a $10 million product was a rounding error.

At the time I was the product manager for all of the legacy products, which accounted for approximately 50-60% of the companies revenues.  I answered enough requests for OnLine 5 and SE to understand that they were a distraction to the sales force.

Going back to the Seth Godin blog on “don’t sell to bar owners” this is a perfect example where the sales force was not equiped effectively enough to sell the product line.  And most importantly, the customer was confused into what they needed to buy.

  • Is your product strategy consistent and in line with customer needs?
  • Can your sales and marketing teams, concisely explain the positioning of each of the products?
  • Does the customer get what they need, or what the sales rep wants to push?
  • Do sales compensation plans align with customer need?

In the end, the lack of performance resulted in the company being aquired by IBM.  All companies reach stall points, make sure your don’t create your own stall points.  And if you do, recognize your actions and work to minimize the distraction and inconsistency.