Introduction to Closing the ‘People Gap’ in BI

August 28th, 2012 by quantisense

As I was reading through some recent blog postings by Nikki Baird with RSR (People Don’t Change at the Pace of Moore’s Law) and the Moneyball for Retail series by QuantiSense’s own Garrett Sinclair, their insights made me contemplate the big changes I have observed in the retail business intelligence (BI) sector over the past 20 years.

Let me start with some reflection on retail BI’s past 20 years and how far we have progressed since the early days.  In the early 90’s, when we called it “decision support”, very few retailers could even afford a data warehouse.  For example, the first Wal-Mart data warehouse database cost around $20 million (in 1990 dollars!); it had 169 Intel-286 processors with over 600 gigabytes of disk space.

Wal-Mart clearly viewed BI as a competitive weapon, allowing their manufacturers in the early days of RetailLink to see inventory levels at the store and use this technology to help scale Wal-Mart at astronomical rates.

The use of BI quickly spread in the coming decades as the decrease in cost of computing power (Moore’s Law) and the advances in database technology and querying tools have provided an environment where today business intelligence is virtually affordable to every retailer, from the start-up in Brazil, to the multi-national conglomerate.  In addition, retail BI has seen advances in automation and predictive algorithms, giving us a dizzying array of technology at our fingertips.

But has the average retail staff been able to absorb these incredible tools at the same rate of change?  Have the hundreds of millions of dollars invested in retail business intelligence paid off?  Nikki’s chart (below) seems to imply that “people changes” tend to lag “technology changes”, so the answer at times is uncle.

This is something that we at QuantiSense think a lot about and discuss with our clients and prospects on a regular basis.  What are the keys to getting payback on the BI investment?  How does a retailer quickly close the “people gap”?

As you may have guessed, there is no one simple answer for this question. This is a multilayered answer, but it is one that we have studied with our clients who are getting the most from their BI investment.  We have broken the success adoption of BI best practices into three areas:

Each of these areas is important, and you cannot have the success by focusing on one or two of the these.  All three areas must be executed to get the optimal return.

In coming weeks we will look at each of these areas in detail and discuss best practices around getting the most out of your BI investment. We will also share how to deal with changes to the process and organization in a way that drives adoption at rates that start to come much closer to the increases in the underlying technologies.

Andy Winans, President

Posted in Business Intelligence Adoption, Local assortment, Merchandising, Retail Analytics, Retail Business Intelligence

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Moneyball for Retail Part 3

July 26th, 2012 by quantisense

In the movie version of Moneyball, there is a great scene where Brad Pitt, playing the role of Oakland A’s general manager Billy Beane, has bought into the concept of Moneyball and has tasked Peter Brand (played by Jonah Hill) to evaluate three players using the new metrics.

Peter Brand: I wanted you to see these player evaluations that you asked me to do.
Billy Beane: I asked you to do three.
Peter Brand: Yeah.
Billy Beane: To evaluate three players.
Peter Brand: Yeah.
Billy Beane: How many you’d do?
Peter Brand: Forty-seven.
Billy Beane: Okay.
Peter Brand: Actually, fifty-one. I don’t know why I lied just then.

Brad Pitt acts stunned during this exchange. He cannot believe that he is getting 48 additional player evaluations. This is 16x more than what he requested.

Ahhh… the power of software automation!

See, the secret sauce of Moneyball is not simply the creation of new metrics. If Peter Brand had new metrics, but could only evaluate three players a day, the principles of Moneyball would have been interesting but would not have been practical to implement. The Oakland A’s would have never made the playoffs. Billy Beane as general manager would have been fired. No Moneyball book. No movie. What movie would Brad Pitt have had to do to make millions? Oceans 14?

But with the ability for software programs to do the player evaluations automatically, the world of player evaluations for not just baseball, but all of sports, has changed forever.

As we discussed last week, to implement similar principles of Moneyball to the business of retail, the sheer volume of “player evaluations” (thousands of items by store analyzed thousands of times) is exponentially greater than the challenge Peter Brand was faced with in Moneyball. Leading retailers are using Moneyball-like metrics coupled with software automation engines to find the overlooked opportunities in their inventory and avoid unnecessary investments, ultimately netting them millions.

So what have we learned in the last few weeks?

1. Leading retailers are starting to use Moneyball-like metrics in addition to the “standard” KPIs that have been used for nearly a century to run their businesses.

2. Retailers are using these new metrics to support new business strategies and corresponding decisions (like optimizing assortments by individual store), netting them millions to the bottom line.

3. Without software that automates the process, there is no practical way to make the principles of Moneyball a reality in the retail industry.

I hope you have enjoyed the Moneyball for Retail series and encourage you to lead the charge and become the Billy Beane and Peter Brand characters in your respective retail companies.

Garrett Sinclair, Vice President, Marketing

Posted in Local assortment, Merchandising, Retail Analytics, Retail Business Intelligence

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Moneyball for Retail Part 2

July 19th, 2012 by quantisense

In last week’s Part 1 of Moneyball for Retail blog posting, we mentioned that retail executives, just like baseball executives, are using new metrics to assess how their inventory is performing.

In baseball, the Moneyball approach was used effectively to sift through 20,000 players to build a playoff caliber team of 25 players, while making the minimal investment possible. The “optimization” goal in baseball is simple:  maximize wins while minimizing payroll.

Peter Brand: We’ll find value in players that no one else can see. People are overlooked for a variety of biased reasons and perceived flaws. Age, appearance, personality. Bill James and mathematics cut straight through that. Billy, of the 20,000 notable players for us to consider, I believe that there is a championship team of twenty-five people that we can afford, because everyone else in baseball undervalues them.

Does Moneyball work? You be the judge. Last year of the four teams that participated in the league championship series, only one of the four had a payroll in the top 10 teams in all of baseball. Only one, that’s crazy!

If you are a retailer who cannot compete with the likes of Wal-Mart or the other 800 pound gorillas sheerly on size, you should be taking notice! But as we will see, your task is not as easy as a baseball manager.

In retail, executives have a much bigger and more complex challenge. They must create a winning team of thousands of players (inventory items) in a single store, whereas a baseball manager only needs to pick twenty five. Then, to make matters worse, retailers need to modify these teams by having tweaks to their local inventory across hundreds of stores. This is essentially like having to pick hundreds of teams with hundreds of players, a daunting task indeed. So how do you do it? Exactly what kinds of new metrics are retailers using to spot the diamonds in the rough while also avoiding inventory mistakes that only lead to lost sales, markdowns and margin erosion?

Winning retailers are using new metrics to spot and correct late arriving items, avoid out of stocks and better handle misallocated sizes. Others are using metrics to spot imminent stock outs and are calculating the impact on estimated lost sales. Retailers are also using new metrics to track and optimize local assortments by highlighting where assortments are too deep or shallow, too wide or narrow and even have too many “good”, “better” or “best” priced items. Just like in the case of Moneyball, it is the leading edge companies who are taking advantage of the new wave, while those who are only looking at the old metrics of sales, turns, weeks of supply are not keeping pace.

Next week we will discuss a challenge that the Moneyball pioneers did not address, how to automate the principles of Moneyball. Until then, “Play Ball!” and enjoy a few games.

Garrett Sinclair, Vice President, Marketing

Posted in Local assortment, Merchandising, Retail Analytics, Retail Business Intelligence

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Moneyball for Retail

July 11th, 2012 by quantisense

Last night Major League Baseball held its annual mid-summer classic, the All Star Game.  I grew up a huge fan of the National League, so I loved every moment of their blowout victory.

While watching the game, I kept thinking about how the players were supposedly the “best of the best” in their respective positions based upon the vote of baseball fans from all across the globe.  Most fans base their votes based on statistics, looking at commonly used “metrics” like batting average, home runs and runs batted in for both this season as well as the lifetime averages of the players.

But are the players who made this year’s All Star teams really the best?

Bill James, Billy Beane and anyone who has ever read the book or seen the movie Moneyball would likely disagree.

“We are card counters, at the blackjack table, and we are going to turn the odds on the casino.”  - Brad Pitt (playing Billy Beane in the movie Moneyball)

So what does any of this baseball stuff have to do with the business of retail?  The short answer is:  “A lot!”

Leading retailers are starting to use the principles found in Moneyball and are applying it to better run their businesses.  What they are figuring out is that sometimes the most common metrics used in retail, metrics like weeks of supply, sell-through, sales, turns, etc. can be misleading, and even worse, can lead to bad business decisions on buying, planning, allocating, pricing and more.

At its core, the principles of Moneyball were all about finding really good players who were overlooked and or undervalued while at the same time avoiding bad investments in players who were overrated.   The teams who pioneered the principles in Moneyball augmented their player evaluations with brand new metrics, ones that every other team in baseball either knew nothing about or flat out ignored.

In retail, your players are your inventory items.  As a retailer, it is time to start looking at your inventory using the tactics found in Moneyball?  So what are these “new metrics” that retailers are using to better select their respective “All Star” teams of inventory?

More on that next week. For a teaser you can visit here.

Garrett Sinclair, Vice President, Marketing

Posted in Local assortment, Merchandising, Retail Analytics, Retail Business Intelligence

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