The emerging role of the analyst…

6 10 2011

A few days ago, I wrote about the analyst function being dead, which spurred conversations about the emergence of a new breed of analysts. Organizations, with all of their investment in data capture, data generation, and business intelligence, still struggle to use data effectively to make decisions. With the explosion of data over the last couple of decades, the analyst moved away from business and morphed into an IT role.  The role became more about writing business requirements and providing reports than understanding data and helping the organization digest meaning from it.

Now the analyst needs to move back to a business role, but with more of a mathematics and statistics background.  They have to be curious about improving the business and have the acumen to do it.  They need to know how to blend traditional data with today’s non-traditional data feeds from blogs, social media, video, etc.  The value of the analyst is back in creating business value through relevance, context, and timeliness.

To achieve this, the emerging role of the analyst requires a new skill set and must:

  • Understand how to derive information out of data and present it in business terms – this is perhaps the most important of all of the new skills.  The analyst must be able to take a tremendous amount of information and coalesce that information into business terms leading to action.
  • Integrate various types of information – as data is coming from all different places and in new forms, it is increasingly important to understand how and when to leverage potentially rich data, and decipher what is irrelevant quickly.
  • Design problems with various concepts – the analyst needs a consultative style in which different models are applied to solve ever more complex issues.
  • Use technology – with Business Intelligence, Planning, and Predictive Analytic style software becoming easier to use, the analyst needs to know not only how to use these tools, but when to use them.
  • Delegate – traditionally the analyst needed to do everything.  Now as technology, data sources, and businesses have become more diverse, the analyst needs to know how to farm out some of the analytics to specific expertise at the right time, guide the project, and integrate the results.

The analyst also needs to transform informational projects into a process where requests for information are appropriately managed. This includes breaking down information into four areas: persistent information or basic reporting of facts on a regular timeframe; performance measures that have higher level KPIs; problem analysis; and data exploration.

Gone are the days where the analyst was a report writer, spending too much time on data acquisition.  They must now know how to enhance data to get more out of it in a timely and fashion and present that back to the business in a manner that drives value creation.





Analytics Blogarama

29 09 2011

You’re invited to join in the Analytics Blogarama!

Theme: The Emerging Role of the Analyst
When: October 6
Where to post: Your own blog
Who’s invited: All bloggers with an interest in analytics
How to get in on the link promotion friendliness: Send an email with title and url of your post to cliff@socialmediatoday.com.

Analytics bloggers Michael Ensley and Meta S. Brown invite you to join us for the very first Analytics Blogarama – one day when we share our individual views on a common theme. Smart Data Collective will be spreading the word and linking to all participants’ posts. All bloggers with an interest in the theme are welcome to participate, so please share this invitation with your blogging pals.

Why participate? Build your readership! Everybody gets a link from the Analytics Blogarama page. Collaboration among participants (exchange ideas, comment on posts, link to one another) is encouraged!

So, on October 6, post your take on The Emerging Role of the Analyst on your blog. Please include a link to the blogarama navigation page, so your readers can find their way to other viewpoints. And send an email with title and url of your post to cliff@socialmediatoday.com, so your post can be listed there, too!

Blogarama navigation page url: http://smartdatacollective.com/40832/analytics-blogarama-october-6-2011





The analyst function is dead

8 09 2011

The role of the operational analyst has moved from the business into both Finance and into IT.  The Finance team typically focuses only upon the financial outcomes of the business and has left the operational side of the business to the IT team.

Here is a conversation a client of mine recently had with their analyst…

ANALYST: ” Here is the report on units sold this year.”

BUSINESS:  “What happened here?”

ANALYST:  “That is a spike in the data.”

BUSINESS:  “Right.  But what happened?”

ANALYST:  “That is what the data is showing.”

Sadly, this is not uncommon in the business world today.  Billions of dollars are spent every year on Business Intelligence software to help us visualize what is happening within the business, yet we are really no better off in terms of insight.

WHY is this happening?

  1. The biggest reason why this is happening is we have changed the role of the analyst.  It used to be a marketing person looking at marketing data, or operations looking at manufacturing information.  We have now moved that role to IT, or IT has promised that that can do it better with their understanding of data structures.
  2. We have wrongly assumed that a picture is worth a thousand words.  In BI terms, a chart is worth a handful of questions. IT can not predict that next series of questions and is then left to prioritize what questions to tackle next.
  3. The pace of business, or at least the pace and variety of business questions (like the data we collect) has risen exponentially and scaled faster than our ability to respond.
  4. IT is over burdened and lacks the political power and will to say “no.”  They are in complete reaction mode and lack the resources to cover the demand.

WHAT can we do to fix this?

  • First off, we need to understand the analytical gap within the organization.  IT can manage the data and needs to partner with the business, but the business needs to own the intelligence.  It is easier to teach the business a little about technology, than teach the IT resources about the business.  The business side needs to find that type of person who understands a little about technology, but has a solid mathematical or statistical mind with a curiosity about improving the business.
  • The organization needs to find a better way to integrate better analysis back into the management process.  We need to give the analysts a frame of reference in which to explore ideas and present results.  Some of this will follow reporting upon weekly/monthly operational outcomes, while most will likely by ad-hoc hypothesis or what-if scenarios about some aspect of the business.
  • The culture has to reward critical thinking.  This is not true in most corporate cultures.  All to often, the analyst is criticized for not “going along” with the current belief.  If the culture does not reward new thinking, then the analysis will quickly fall in line with visualizations that support the status quo.
  • Invest in tools and training beyond just the core cubes and reports of the BI market.  While a good portion of analysis can be done with Microsoft Excel and a data dump, the more we want out of our analysts, the more we need to give them.  We need them to look at market baskets, threshold containment, frequency curves, optimization models, assumption testing, correlations, and many other types of analytical tools.

 





When More is much, much Less

2 08 2011

Recently I was cleaning up my Gmail inbox and it was clear to me that some people treat email like free marketing.  For example, Dick’s Sporting Goods was sending me 3-4 emails a week.  While I shop at Dick’s Sporting Goods and like the brand, it was very clear to me that they really weren’t paying attention.  My lack of response, nor opening of any emails should have been a trigger to them.  More was much, much less.  They were not alone, but one of the worst examples of over-communication.

Thoughts for email marketing:

  • Use the information effectively.  Not only have I asked them to stop emailing me all together, they have hurt their brand standing with me.
  • Test your campaigns.  Because they are free doesn’t mean everyone should get everything.  That’s just laziness.  There are too many tools out there not to be able to do some type of segmentation based upon gender, usage patterns, social, and economic demographics.
  • Learn! This is probably the most important aspect.  If a customer gives you their email address, then treat it like a valuable asset and learn from it.  It is not a resource to be used up.  Offer different things at different times, send emails in different patterns, send different offers and test the response.  And if they don’t respond to anything, pull back and wait.

I know this sounds way too obvious, but here is an example from someone with the size and clout to know better.  Chances are your marketing organization is overusing their free marketing channel and just don’t know it yet.  Go ask them for an analysis of how many emails are being sent out to each customer segment each week.  Ask them how often they clean up their contact list to trim out people who have never responded. And wait for the dreaded, “we don’t want to skip anyone in case this is the campaign that will get their attention.”  Trust me, there is a breaking point.





Obesity in the US

29 04 2011

This is again perhaps a little off topic for me, but it does pose some really interesting strategic points for consideration…

The cigarette of today’s generation is fast food, sodas, and poor eating habits in general.  Obesity in the US is projected to be about 20% of our annual health spending – or roughly $350 billion (USA Today) by 2018.  This means the number will double from 10% of the spending to 20% by 2018.  Food related deaths account for more than half of our causes of death (CDC) and we focus very little attention to it.  And for the first time in decades the US life expectancy is projected to decline by 5 years (National Institute of Health) with this generation.

So from the viewpoint of Strategy, this poses a wild number of potentials.  Depending upon your industry this either opens you to a tremendous opportunity, or a concerning level of risk.

Opportunities:

  • Food industry – being an early mover to healthier versions of your food may attract more customers
  • Education – providing content for school, churches, communities, etc may open more doors for you
  • Healthcare – with increasing costs, providers that can target care to show health gains with children, or keep their clients healthier may see improved demand for their products while at the same time controller their costs.
  • Marketing – Branding your self as a healthy alternative
  • HR – being seen as a healthier employer may improve your retainment and attraction to new employees.  You may also see a reduction in your health care costs over time.

Risks:

  • Fast food – This entire industry may be about to come under ever increasing levels of attack.  The attacks will likely be on menu, ingredients, nutritional labeling, and potentially lawsuits.
  • Sports drinks – As parents become more aware of the level of sugar in these drinks, demand is certainly at risk.  As one of their core segments is children, it is also possible that even the marketing placement will be called into question.
  • Education – As Jamie Oliver’s Food Revolution has clearly pointed out, he is certainly targeting the school system menu.  Once the parents get involved school district lunch menus will likely need to change dramatically.
  • Healthcare – spiraling costs will force most healthcare companies to make very difficult decisions to remain profitable.

Here is Jamie Oliver’s presentation on TED.

Here you can see the growing obesity problem in the us (CDC).





Changing Market Place

7 04 2011

Yesterday in the NYTimes was a story about the speed of the changing U.S. race demographic.  As our demographic changes, so will tastes and demand.  Many companies have sat atop their markets feeling they are invincible, yet with these changes many of the companies will find out much too late that they were not as solid as they once felt.

Have you asked yourself any of the following:

  • What percent of our clients come from the majority?
  • Do we have products that meet demands from all sectors?
  • Are we at risk if the legislature, or governing boards, can their ethnicity over time?
  • Where are our biggest threats in this new market?
  • Where are our greatest advantages?
  • What else can we do to capture more in this changing market?
  • Where might new competitors come after our market?

If you are not strategically discussing questions like these, then you elevate your risk of something happening to undermine your position within your market.

 





Analytics: Frequency Distribution & Bell Curves

8 11 2010

A statistical method we often overlook is the distribution curve.  I think most of the time it is dismissed because people get nervous about using statistics if they are uncomfortable with math.  While there are some advanced concepts around using a frequency curve, it can also be used visually as a simple tool to explain results.

A simple stats lesson….

Normal Bell Curve – roughly 68% of the population is within 1 standard deviation (measure of variation) of the average and 95% is within two standard deviations. Below is an example of IQ scores.  The average score is 100 and 68% of the data is between 85 & 115.

While this visualization doesn’t do a tremendous amount for us, this is what we assume when we think of populations, like customers and employees.  And because of our limited statistical training we make a large number of assumptions based on averages.  We love to look at average revenue: average revenue per employee, average revenue per customer, etc.  This thinking also gets us looking into the outliers (that <5% that sits way out to the left or right of the chart).  How much time do you spend on less than 5% of the business?

OK, so back to thinking of this in terms of running a business….

Let’s map out our revenue per customer.  I would be willing to bet it looks something like the following:

If this is the customer revenue distribution, if we use the average number in our analyzes we can quickly generate a number of wrong assumptions.  First and foremost, our typical customer is larger than reality.  It might lead us to think we are serving mid-sized businesses than more likely smaller market customers.  I am also willing to bet our profitability per customer has a similar curve to it.  In this case we are likely spending money on the wrong customers and aligning our better services to a lower profit generating customer (or more likely a profit destroying customer).

Do we need to use it in everything? Of course not, but it might help everyone once in a while to challenge our overuse of the mathematical average to reassess perspectives of our business.  A great place to start is map out the customer base in terms of revenue (profitability is better, but takes a lot longer to do).  It might just lead you to understand your customer (think customer segmentation) better.

Real life example…I was once part of a research project to understand discounting to one side of the outliers (<1% of the business).  The outcome was to focus on reducing discounting to that <1% of the business.  What I argued was to focus on the larger part of the business, where the same efforts would have resulted in millions more in terms of profits.  It was a clear lesson is where to apply process improvement.





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.





Mass Layoffs Sept 2009

22 10 2009

The Bureau of Labor Statistics today announced the Mass Layoffs from September.  The number of events (more than 50 people laid off) is down a little from August, but we are still seeing much larger levels than normal.  The September number of 2,561 layoff events is roughly 2x the normal average.

This is a slight sign the economy may be bottoming out.  What is still disturbing here is the consistency of the level of mass layoffs.  For the last 12 months (see blue box in chart) we have had over 2,000 events per month compared to a normal level of 1,250.  We are still adding too many people to the unemployed – and a system that is built to run on full employment.  Over the last 20 years we have only seen two other spikes, yet in both of those periods we only saw a brief period of spike.  And again in neither of those cases did we ever reach 2,500 events.  Over the last 12 months we have seen 5 months in excess of 2,500 events.  Not good news on any front.

Quite soon, we need to see a substantial drop in Mass Layoffs to give us a positive  indicator that the economy is turning around.  We see a good number of positive signs, but this one is still a big red flag.  Again, if we look at the post 9/11 trend you can tell this trend tapers back to normal.  Mass Layoff Events Sept 2009





Producer Price Index Sept 09

20 10 2009

This morning the Bureau of Labor Statistics released the September 2009 Producer Price Index report.  The PPI dropped a little this month mostly due to cost of gas declines (0.6% decline).  In August we saw a significant increase at 1.7% raises a little alarm in that the fluctuations are evident.  The fact that most of this is based on energy prices swinging is both a little calming and potential for more signs that oil prices are moving too much.

“Wholesale prices in the U.S. unexpectedly fell in September on lower fuel costs, a sign inflation remains muted and the Federal Reserve has leeway to keep borrowing costs low as the economy recovers.”  Bloomberg

What does this mean to me: we will probably not see much increase in prices over the coming months (keep watching the price of oil/gas).  This is also a sign that while some of the recent indicators have been good, we might see a lull in the recovery process.

As a part of this series, I am also going to add the price of oil.  It was not one of the Baumohl Indicators, but I think that might have been because it comes out of the financial markets.





The Dow (DJIA) hits 10,000

16 10 2009

On Wednesday this week (October 14, 2009) the Dow Jones Industrial Average topped 10,000.  Although it struggled out of the gate this morning, I am curious why we did not take the time to celebrate re-reaching this milestone.  Clearly, this is a sign that the economy is chugging forward again.

If we look back to September 19th, 2008, the DJIA closed at 11,388 and only days away from near free fall.  Over the next couple of days, panic would set in and the markets were paralleled to “The Great Depression.”  The DJIA at 10k represents we have recovered 70% of what we lost since September 19th, 2008.  We still have a way to go, but a little celebration might just be what we need right now.  Not much, we can’t afford the hangover, but perhaps a little toast to reaching 10k again and may we keep doing a little better every day.

Looking back…Between Oct 24th, 2007 and September 19th, 2008, the DJIA shed about 20% of its value (14,093 to 11,388).  It would then lose an additional 34.4% by March 9th 2009 when the market reached its lowest level in 12 years at 6,547.  If we start the clock at Oct 24th, in two years we have recovered about 45% of the losses we incurred during the recession of 2008 and 2009.  Perhaps not as happy a picture, but consistent progress in the right direction.  This still represents ~50% recovery in 6 months, what we lost over two years.  If the trend holds, perhaps we are back to 2007 levels within another 6-8 months, and then can continue to recover on the lost time.

DJIA Recovery

The other item worth noting is the declining variation in the closing gains.  Over the last three months, the variability has diminished to levels not seen during the recession.  This is another great sign indicator of stability and overall economic health.  The market likes gains, but it loves consistency.

DJIA Recovery Variation





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?




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:





Employment Situation Aug 2009

9 09 2009

This is a new series of blogs in which I will call out and blog on a number of economic indicators based upon the musings of Bernard Baumohl in The Secrets of Economic Indicators.  In this series, I will work to provide a visual or two to explain the situation as well as a link to the press release.  The goal will be to post a blog covering the reported data and to build out a series of informational charts based upon the data.

Employment Situation is one of the more important indicators of US Economic health, and perhaps even more so in this economic climate.  It provides us an indication if the economy is expanding, or contracting in terms of jobs and therefor money to be spent.  Here is the press release from 9/4/09 which is August 2009 data.

Key points (from press release):

  • Non-Farm payroll employment declined by 219,000
  • Unemployment increased by 466,000 to 14.9 million
  • Unemployment increased to 9.7% (up .3%)
  • While job losses continued, the losses are not as bad as the months before
Aug 2009 Unemployment Data by gender and race

Aug 2009 Unemployment Data by gender and race

Analysis:  We are still losing jobs in the economy.  Teenagers are at almost 2.5x the national average, and minorites having 2x the increase as the average.

Risk:  While we have some indications that the recession is getting better, it is clear we have some elements that still have some ways to go.  If your business is targeted at teenagers and/or minorities you may want to plan for sales to remain weak until the trend at least turns back to positive growth.








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