Survival and Adaptability – Hot Arple Pie

20 09 2009

I noticed an ad today absolutely worth noting…it simply and succinctly said:  “Does your marketing suck?”

At first I was shocked and appalled, but the more I looked at it I found myself compelled to click on the ad.  Who in there right mind would start an ad that way – probably someone willing to try something different.

It also jostled an old memory from one of marketing professors about a similar incident.  As he was driving to a client one day he passed a sign that said “Hot Arple Pie.”  He knew it was an apple pie, and was not really all that interested in apple pie, yet the sign got him thinking enough that he turned around to actually see if it was “apple” or “arple.”  And as you guessed it, 30 miles and one sale later and it was confirmed “Hot Apple Pie.”  And 20 years later, I am still thinking about “Arple Pie”

So back to “Does your marketing suck?”  It was catchy, adversarial, and in the end it moved me from unknowing to slightly informed. Perhaps in this case, the ends certainly justified the means.

Take a look at your marketing material:

  • Does it intrigue?
  • Does it invite action?
  • Is it any different than your competition’s?
  • When was the last time you changed up your marketing campaings, slogans, taglines, etc?
  • Can you afford for your programs to perform at the rate they are performing?
  • Would you consider your company competitive in terms of adaptability?

Interestingly enough, once I clicked on the ad I was taken to the company’s home page where I found a great quote:

“It is not the strongest species that survives,

nor the most intelligent;

but rather the most adaptable to change.”

Charles Darwin





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:





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.





Data Warehouse Design

24 06 2009

One of the main problems with Data Warehouses is that they are designed to answer any question.  The problem is that they usually fail to answer the one someone is asking.  DWs are usually good for referencial information – meaning I can answer questions like “how many customers do we have that have spent over $100,000” or “which customers bought the blue widget.”

There are a number of points of failure that hamper DW projects:

  • They are usually complex and very costly
  • The business changes (regions, product lines, sales heirarchies, etc) in the middle of the process
  • The end use is not well defined
  • Lack of analytical skill and knowledge of data structure in the business users to get the right data
  • The end result is too complex for the users to understand where to go to get the right information
  • No one tells the organization “thou shalt” use the data warehouse – so people get data from all different sources making a common version of the truth difficult to get to
  • There are often no rules of engagement for how to use the environment, or data in general

If organizations only use 6-10% of the data they collect, how do you design the DW for greater adoption?

For starters, understand the common business questions and the potential levers that can be pulled. For example, one of the areas that always surprises me is the lack of information around the success of marketing campaigns. Marketing campaigns and price are really the only levers we can pull in the short term to increase revenues. What we often fall back to is the sales whip – where we put more pressure on the sales team to perform. This is a strategy of hope (which is not a recognized as a successful strategy practice). We apply the pressure without providing much in the terms of support.

Instead let’s say we are ending the 3rd quarter and our numbers are a little behind and the pipeline is not as strong as we would like.  We know we have some time, but the programs have to be very tactical to find low hanging fruit. Instead of reviewing the potential marketing programs or trying something new, we cross our fingers and yell at the sales team. We could cull the DW to find large groups of customers who had not bought specific groups of products and offer incentives for them to buy.  We could identify the groups/verticals of customers with the shortest sales cycle and build a promotion and program for them as well.

Yet why do we not do this…we typically lack the information in a format we can use in a timely manner.

So if we design the data warehouse (or perhaps data marts) around specific business levers we stand a better chance of answering the one question we need. We just might trigger some very interesting questions about our business.