Lessons from the Vegas ecosystem

1 11 2010

There is nothing like Las Vegas. Suits and sweats sitting beside each other sharing risk. Long confusing mazes of machines that clank and spin and take more than they give.

We can point to a number of great brands in this age, yet in many ways Vegas might be the strongest brand of all. Its current tagline “what happens in Vegas stays in Vegas” may also be one of greatest slogans.  It is the perfect pitch that both captures the spirit of the place as well as tap our primal instincts.

Perhaps we can’t create the same type of offering as Vegas, and it may not be our cup of tea, but we should admire it and learn from it for what it has created. What was once a desert, an airport, and a couple casinos is now one of the most interesting consumer ecosystems. Now there are limitless entertainment options at all price points for all audiences.

Above all things, Vegas is all about innovation. They are focused on the customer with unrivaled focus. They test, they listen, and they learn. Vegas is a 24×7 incredibly well lit human lab.

What can we learn:

  • Test, move, learn. Most companies are stuck in ruts.  They do the same things over and over again.  New ideas are forced into ill fitting old marketing programs.  Customers are hit with the same message in various mediums. We fail to hypothesize and test any more.
  • Create and/or leverage communities. Vegas is all about mustering resources around the customer.  Bring more and unique services to your customers so they never have to leave.
  • Fill in gaps. Vegas is always looking at ways to fill in the seams around the business.  How often do you look for ways to not only increase the product offering, but look to enhance the ecosystem around you?  How well do you use the partners whose products depend upon you?
  • Be unique. Where else can you find a castle, a pyramid, a two story lion, and a replica of New York city all on the same street corner.   What is interesting here is that this is where I think the casinos are starting to fail a bit.  Clearly, Vegas is unique but I think the experience is starting to become too similar.  Every casino has a hip dance club, a comedy routine, high end shopping, and now there own Cirque-du-Soleil shows.  Needless to say, the unique stuff is what helps us differentiate ourselves from the pack.  Without it, we start to compete on who is cheaper.  That is a game only a few can win.




Visualization Methods

14 10 2010

I thought this was worth sharing….Periodic Table of Visualization Methods.  This is a nice visualization of the different types of visualization.  It shows some good examples, and some not so good examples of visualization. Make sure you mouse over the different elements.

Rules of visualization designed to create action:

  1. Keep it simple, clear, and concise – with the emphasis on simple.  Don’t use complex charts to explain simple ideas.
  2. Know your audience.  Don’t present glorious details of each step in the analytical process to executives – trust me, they don’t care.
  3. Find a chart style that works well with the data.  Line charts show historical trending, bars charts do a better job of showing relativity.
  4. Don’t use 10 charts when 1 could suffice.
  5. Label well.  Take the time to make sure all of the information is explained.  The last thing you want to happen is for someone to look at it and say “what does it mean?”
  6. Understand there is a difference in analysis and presentation.  If you are trying to convince someone to act, then make sure the data (and you) tell the story.
  7. Start with the big picture, then explain (if necessary) how you got there.  People learn by seeing the picture first, then seeing how the parts go together.
  8. Document your assumptions.
  9. Explain your conclusions, don’t expect your audience to jump to the same answer.
  10. Highlight the relevant points within the data that augment your argument – use a color scheme that calls out the item if you can (red bars vs gray).  Do not be afraid to use the power of a printed report and some hand written notes with arrows to the corresponding areas.
  11. Understand where and why the data does not support your conclusions.  Be prepared to defend against those points, because your audience will likely be looking for ways to contest your conclusions.
  12. Practice what you want to say.  The more proficient you sound the more convincing you will be.




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.





Mass Layoffs Jan 2010

1 03 2010

Sorry I have been a little short on blogs the last few weeks…

The US Department of Labor – Bureau of Statistics released the January Mass Layoff Events data for January.  I have been watching the Mass Layoff events for a while now for a couple of reasons, but primarily as a leading indicator of the economy.  I spoke last year a great deal how the number had exceeded 2000 events for 12 straight months and how this was most likely a sign of a protracted recovery period.   The January number was 1,761 which was roughly the same for the last three months.  While the move under 2,000 was at least a step in the right direction it appears as if we continue at an elevated rate.

Job creation is one of the primary keys to economic recovery and it seems as if we are still shedding above normal levels of jobs.   Continuing at 1,700+ events (which in Jan actually meant 180,000 claimants – or an annualized number of over 2 mil initial claimants.  The point is that I feel the economic climate is still contracting, though perhaps at now slower rates.

From a street level assessment I am starting to hear of more projects starting, consulting firms seems to be a little more optimistic outlook for the year, and less people concerned about their current state.





Analytics Process

23 11 2009

Over the last couple of months I have been writing about a handful of US Economic Indicators.  While I have reviewed these over the last few years of my life, I had not done so on a regular basis.  This inconsistent and let’s call it a casual curiosity lead to never really understanding the implications behind the numbers.  Sure I could talk about them, but I could not leverage them.  While not an expert by any means, I can see a lot more now than I did when I started this blog series.

This is similar to ad-hoc analysis without purpose.  We do something once and create a little hype.  When we don’t have any vehicle to take advantage of the newly found ideas, the idea dies as does the learning.

Think about the process of how you handle ad-hoc analytics within your organization:

  • Do you have the right minds constantly looking for new issues?
  • Or, do you put the right minds on solving issues when they arise?
  • Can you name your best analytical minds?  Are they assigned to thought leadership and problem solving?
  • Do you use your analytical minds to challenge the knowledge levels of others?
  • How do you foster new thinking?

 

Consistency breeds familiarity, and familiarity breeds knowledge





The Death of the Dissenting Opinion

16 11 2009

Typically, the person with the shortest shelf life within an organization (either in terms of politics or employment) is the team member willing to pose the question, “Is this the right thing?”

  • Why do we demand everyone line up and support management philosophy?

I know organizations don’t set this as a mandate, and it is probably more an example of personal politics, but it is amazing how destructive this mentality becomes. Why are we so worried about having someone in our business ask critical questions?

The are obvious examples when we need someone to play the role of the Devil’s advocate.

  • Would tobacco products have been created with such strong addictives?
  • Would Nasa have launched the shuttle Challenger?
  • Had the US intelligence agencies worked together, might we have stopped at least one of the fateful 9/11 planes?
  • Would Enron still be an energy giant today if we listened to employee concerns?

We love good debates, so why not embrace the power of dissenting opinion?  Collect all the feedback and you probably have a stronger argument for moving forward.  In the end, you can still continue an initiative or program.  When we politically assassinate the people with a strong voice, we send a message to agree or be rendered ineffective.  This evolves into a “yes” culture and we risk leading lemmings.





Price of Oil

27 10 2009

One of the biggest impacts to the US economy is the cost of oil.  We are still the leading consumers, though our lead is being taken over by China.  It is no surprise that the price of oil/gas can either fuel US economic growth, or bring it to a crawl.  I remember (somewhat fuzzy) as a kid waiting in line for gas, and I sold my Ford Expedition in fear that gas was going to see $5/gallon last year. While perhaps I sold the car a little prematurely, the basic fundamental truth about the control of the price of oil is well beyond me. And in someways beyond any of us.

OPEC mostly gets away with what it wants in terms of prices, and China is clearly working to leverage its relations with OPEC countries to improve its position.  While this isn’t necessarily bad for the US, we do lose some of our bargaining power.  And as China continues to increase demand, it drives up market prices.

I am going to try to add the Price of Oil to the Baumohl Indicator series on a bi-weekly basis.  My goal is to continue to explore some of the indicators of US Economic Performance and how they impact business cycles.