In a recent interview on Bloomberg TV’s “Taking Stock with Pimm Fox,” APT CEO Anthony Bruce comments on how consumer-facing companies can measure the return on Facebook advertising dollars.

In 2007, Swedish professor Hans Rosling gave a Ted Talk about global development that changed the way many people look at data. His talks, which have now garnered millions of YouTube hits, provide a key lesson for corporate executives: decisions not supported by data can often be wrong.

Rosling starts his talk by presenting the results of a survey that he gave to top Swedish graduate students. The survey asked students to choose the country in a given pairing with the highest child mortality rate. The pairings were Sri Lanka or Turkey, Poland or South Korea, Malaysia or Russia, Pakistan or Vietnam, and Thailand or South Africa. If you are like the top Swedish students, you would have incorrectly answered Sri Lanka, South Korea, Malaysia, Vietnam, and Thailand.

It is easy to make business decisions based on intuition alone; but as the above survey exemplifies, sometimes intuition fails even the smartest and most successful people. Obviously, there were no repercussions for answering Rosling’s questionnaire incorrectly. But what if you were using this same kind of judgment to make a $5 million advertising decision or a $100 million capital expenditure investment? Rosling explains that the problem many people face is not ignorance, but preconceived ideas.

Top executives at any of the Global 2000 companies have incredible business sense, but when making high-stakes decisions, it is necessary to glean actionable insights from the data. And Rosling does this by weaving hundreds of thousands of data points into incredible stories to show how global development is evolving, debunking many myths about how the world actually works.

Just as Rosling turned thousands of lines of data into a meaningful story, corporate executives are beginning to understand how to turn their data warehouses into business insights by testing key initiatives in a subset of their locations. Sometimes these tests confirm previous intuition, but often executives gain new and profit-generating insights.

To provide just one illustrative example, companies have tested profitable network-wide local advertisement programs and found out that only a small percentage of the local markets were contributing to incremental profits, while the other markets were losing money.

Looking at huge amounts of data in innovative ways, just as Rosling does with his global development work, enables individuals to move past preconceived ideas and extract valuable information from their data that will lead to profitable decisions.

Value Added: Ballston firm APT battles tech giants Microsoft, Google and Facebook for workers — and wins

http://www.washingtonpost.com/business/economy/value-added-ballston-firm-apt-battles-tech-giants-microsoft-google-and-facebook-for-workers–and-wins/2012/04/01/gIQAo0VfpS_story.html

Daily coupon deals may not work for buyers, deals – check it out:

http://www.usatoday.com/money/industries/retail/story/2012-03-15/restaurants-using-daily-deal-sites/53553794/1

Retail and consumer companies are rife with stories about highly fastidious founders.  A colleague recounted how he witnessed the head of a major chain of hotels getting on his hands and knees to personally check if the drip pans under the refrigerators were clean.  Similarly, Sam Walton was famous for flying a prop-plane from store to store, and along the way, touching down wherever he saw a promising empty lot for a new store.

Today, leaders need to look beyond physical sites, store managers, and DCs and apply that same discipline to other aspects of their business.  The foundation for good decision-making is data.

In today’s volatile environment, managers are adjusting their business in near real-time, and most initiatives require the cooperation of multiple groups and departments.  Operating a retail business without consistent, up-to-date, integrated data is challenging and error-prone.  Here are the details to focus on:

• Consistency:  Do finance, marketing, and merchandising have the same measure for sales … and margin?  While working with a major US retailer, we discovered numerous disparate systems for storing sales by item, by store.  Each had been setup for a unique purpose and required maintenance and reconciliation with other systems.  An inordinate amount of time was wasted in sharing across the organization (How do your numbers foot to the system that we use?) and caused a general reluctance for team-members to work across the organization.  There will be resistance to centralize – various dependencies will need to be rewritten – but this is problem only worsens with time.  Better to act now before it ossifies and becomes even more intractable
• Frequency: It’s no longer enough for managers to know today’s performance next week.  Understandably, the delay between the time something sells and when the data is ready for use exists for good reason.  Raw data needs to be audited and scrubbed for fraudulent accounts.  Late-breaking transactions need to be integrated.  Data is often augmented with additional information (e.g. mapping between credit card number and customer ID).  Even so, what managers should push for is making available data accessible immediately.  Sure, the data may have a few holes and analysts will need to be cautious, but generally speaking, the data is mostly right and giving managers the data a week early offsets any downside from the modest inaccuracies.
• Accessibility: Consistent, up-to-date data is of no use if it’s locked in a vault.  The gains from data come primarily in two ways: a) enabling a greater number of managers to make decisions in a data-driven way and b) allowing managers to have a more complete view of the business.  There are certainly some sensitive datasets (e.g. HR) that need to be carefully doled out.  But, in most cases, greater accessibility should be the goal.  This exposes a question about governance — what if users interpret data incorrectly to draw false conclusions — a real concern that warrants deeper discussion in a subsequent post.

Data, of course, is just the first step in building a robust retail organization … one that can nimbly “read the writing on the wall,” continually improve the business, and drive profitable innovation.  In subsequent articles, we’ll discuss the role of analytics and governance.

APT VP Marek Polonski discusses how testing can help grocers enhance their loss prevention efforts in “Caught in the Act,” an article for Grocery Headquarters.

http://www.groceryheadquarters.com/2012/03/caught-in-the-act/

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Do you remember twenty years ago when you were deciding between 20 or 40 MB of storage for your computer? Today, 2.5 quintillion (10^18) bytes of data are created every day, enough to fill your computer’s 250 GB hard drive about 10 trillion times daily! Experts estimate that 90% of all of the world’s data was created in the last two years, and the amount of data in existence will continue to grow at a mind-boggling 40% annual rate. So it’s no wonder that the McKinsey Quarterly is convinced that Big Data will become a new type of corporate asset that, if understood correctly, could lead to a serious competitive advantage.

People often throw around the term “Big Data,” but what exactly is it? Though there is no exact definition, the consensus opinion is that Big Data refers to datasets that are too large to work with in the absence of specialized database management tools.

And where does this data come from? Every time a retailer records a purchase, a customer uses a mobile application to research a product, or a tracker records the movement of an RFID chip as a SKU makes its way from inventory onto the shelf, there are hundreds, if not thousands, of data points recorded.

In the old days, there were two types of companies: those that collected data but didn’t know how to optimize its value, and those who didn’t collect it at all. As data center, inventory management, and transaction-level technologies have all rapidly improved over the last couple of decades, collecting and storing this Big Data is no longer a problem for leading retailers. Today, “how do we monetize it?” has become the million-dollar question. Merely having this data is not in itself useful, but the decisions that come from close analysis have the potential to be. Unfortunately, these decisions are often based on simple reports and correlations that are not scientifically sound. Sophisticated analytics can now not only show correlations, but can more accurately show cause-effect relationships. Companies that most successfully adapt to these new strategies will distinguish themselves from their competitors, leaving other retailers to wonder how they missed the boat.

Testing is the best way to leverage data to make accurate, targeted, and profitable decisions. The McKinsey Quarterly argues that “using controlled experiments, companies can test hypotheses and analyze results to guide investment decisions and operational changes.” Many retailers have already taken their analysis to the next level by testing key initiatives and making confident decisions based on the results. At a major US retailer, for example, there was a disagreement about the effectiveness of a Buy One Get One (BOGO) vs. 30% off price promotion.

Using scientific testing methods, the retailer discovered that BOGO led to higher incremental profits in lower income areas, whereas the 30% off promotion was more profitable in less competitive markets. Based on these results, the store was able to target promotions for specific stores and generate $3.2 million per year in additional profits. These kinds of results are now expected among innovative retailers who have leveraged their data to test important decisions.

As the economic recovery remains somewhere between anemic and lukewarm and raw material prices fluctuate with great volatility, it is safe to say that margin pressure is here for the long haul. In the face of this pressure, retailers who don’t fully realize the extractable value of their data will face grave headwinds as they watch their competition test and learn, and blow right past them.

Imagine this scenario: a shopper with a half-filled basket perusing the aisles of your store stops for a moment to inspect an item. She pauses, pulls out her smartphone, and, using one of many apps available for this purpose, scans the barcode pulling up a price comparison that spans both your nearby brick-and-mortar competitors, as well as online retailers.

Depending on what she saw, she may opt to buy out the stock, or, at the other extreme, walk out of the store, leaving her basket behind. Likely, she will simply not buy that item if the price differential is too high and her desire for immediacy too low.

While some shoppers have always compared prices, with nearly 70 million U.S. consumers armed with smartphones and a plethora apps, never has the barrier to price comparison been so low. As of July 2011, smartphone penetration hit 40% of the mobile market, while RedLaser, a popular barcode scanning app, has been downloaded an estimated 12 million times.

Retailers might ask themselves how this will impact their pricing decisions. Competitive pressures have always been a factor in the pricing decision, but, until the advent of online retail, shoppers were poorly equipped for the task. By bringing the price comparison into the store, smartphones and price checking apps represent a new level of pricing pressure. How should a retailer respond?

A solution may be another app.

Imagine that same shopper, perusing your aisles suddenly stopping to look at her smartphone – not a call or a text – but a push notification about a special promotion. Retailers have already started testing such apps that push offers to smartphone equipped shoppers hoping to drive sales. This past year, Best Buy partnered with ShopKick to test their location-based promotion system, where Best Buy customers with the ShopKick app are sent rewards and offered deals for visiting stores and perusing the aisles.

More recently, retailers have begun developing their own apps that allow shoppers to track shopping lists and scan barcodes to pull up reviews, recommendations, coupons, and more. For instance, Wal-Mart just announced that it has launched a mobile appto take advantage of the mobile trend.

With mobile retail traffic expected to double this holiday season, many retailers are wondering how to ride the mobile wave. But with mobile traffic currently accounting for only 9.6% of online sales as of October 2011, the opportunity may still be limited. While retailers are hopeful that mobile will be a boon, the economics are still unclear. For many retailers, developing your own app may not be an option, and deploying location-based promotion systems to your entire network may not make the hurdle (especially in areas with low mobile penetration). As always, testing out these options in a sub-set of your network if possible can help reduce the risk of such investments.

Consumer Goods Technology covers how American Greetings uses testing to “push innovation and explore new areas” in a recent article.

http://consumergoods.edgl.com/case-studies/American-Greetings-Confirms-Consumer-Appeal76666