Lessons from the Transit of Venus

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YESTERDAY I ATTENDED an event that will never be repeated in your or my lifetime. It was a viewing of the transit of the planet Venus across the face of the sun. That’s something like a solar eclipse by the moon, except much rarer and quite a bit harder to observe since Venus is much farther away.

The kind folks at the Loews Ventana Canyon Resort here in Tucson hosted the afternoon on the hotel patio, and scientists from The Planetary Science Institute, also based here, were our very enthusiastic guides. They set up several specialized solar telescopes for public viewing and presented a series of lectures which explained what was happening and what it meant, astronomically speaking.

The story of the transit of Venus is as much about cultural history as it is about science. For many centuries, natural scientists have been aware of the relative movement of the sun, moon and planets. Venus is the most visible object in the night sky, after the moon itself, but it is not normally visible in the day time. The transit itself happens in pairs, eight years apart; pairs then follow alternately by spans of 121½ years and 105½years. This makes it nearly impossible for a single observer to study.

According to the PSI scientists, it took several centuries for European astronomers, working in concert, to recognize and work out the basic facts of the transit. Once they did get it figured, it yielded important insights about such matters as the distance and size of the sun and whether more distant stars might also have planetary systems.

With the special telescopes it was easy to for us guests to observe the dark dot of Venus as it crept slowly across the solar disk. Several sunspots and solar prominences were a fascinating bonus. The lecturers had tons of anecdotes and insights about what could be learned from observing and measuring the transit.

Since I tend to view our world (and other worlds!) through the peculiar lens of the retail marketer, I was bound to consider what lessons we might derive from the transit of Venus. Several learnings came to mind:

You can see a lot just by looking.* The transit of Venus is hard to view due to the overwhelming brightness of the sun, but as I learned yesterday it’s not that difficult if you have a plan and the right scope. Active observation is key. This made me think about the challenges of in-store sensing and of capturing shopper insights in general. Valuable observations don’t happen by accident; they are a result of carefully planned and executed practices. (*Props to the Yankee sage Yogi Berra.)

Some misses are forever. June 5 marked your last chance to see a transit of Venus. It won’t happen again until 2117. Luckily astronomers recorded this event, so you may watch the video. How many merchandising opportunities and rare marketing insights pass us by just like this? What can we do now to ensure that we don’t miss out on future learnings that may enable us to to be better prepared for the next window of opportunity? In retail merchandising and marketing, it begins with active sensing and collaborative data sharing.

Long cycles are hard to track. Under the most fortunate of circumstances, an individual astronomer gets to see the transit of Venus twice in a lifetime. Many never see it once. Even the lucky ones must count on other recorded observations to grasp its periodicity. With such a slow rhythm, it’s tough to draw reliable conclusions about the nature of the phenomenon. In the product marketing world, we discover that fast-turning consumable products offer some informational advantages as compared with infrequently purchased, higher consideration products, like cars, TVs and appliances. With many fewer data points and behaviors to draw upon, slow-moving consumer goods engender a less granular picture for marketers.

Sometimes you just need a team. Understanding the transit of Venus and its implications has required numerous observations separated by both time and physical distance. The relevant data has been collected by teams of scientists and coordinated among them with a common intent. Consumer insights also accumulate from observations collected across many locations and moments in time. You can’t unlock their potential alone. The implications are too vast, and the effort must be shared and sustained over time to reveal actionable insights and best practices.

The transit of shoppers through retail stores can reveal insights that we can best capture through systematic tracking and observation. When we can get the shoppers themselves engaged in documenting and sharing their actions and preferences – as through mobile devices – even greater wins are possible.

© Copyright 2012 James Tenser

The Optimization Arc – From Black Box to the Cloud

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Joshua Trees

WHEN THE GOOD FOLKS at DemandTec asked me to commence writing a series of short commentary pieces on this blog, I accepted the assignment in large measure because the company’s story is a reflection of the story of merchandising analytics in all its facets.

Like DemandTec, an IBM Company, my history as an analyst in what used to be called the “price optimization” sector dates back more than a decade. In 2002 I was asked to try to make this very powerful new retail science more accessible by explaining its benefits and justification in terms other than technical. Price optimization was a new idea, and its target purchasers were wary of its mysterious mechanisms.

Retailers’ objections about the apparent “black box” nature of base price elasticity permeated the sales cycles of industry pioneers, DemandTec’s included. Prospects worried that using computers to model price elasticity and interaction effects to maximize margins was too manipulative. What kind of push-back would they face if shoppers found out?

It took some effort at first, but we correctly reasoned that since optimization is based on measurements of shopper response, it is inherently shopper-centric in nature. Overall, the process tends to deliver more consistent competitive value to shoppers, while retailers maintain sustainable gross margins. These ideas are familiar now, but they were new territory ten years ago.

At about the same time, other pioneers began applying the principles of optimization to other complex merchandising decision processes, notably to the depth and timing of markdowns, and the terms of in-store promotions. Other folks were advancing assortment and space planning tools from the category management side of the house. Pretty soon, it dawned on the smarter people that that the interconnectivity and interaction effects they observed within each of these areas of discipline also exist across these areas of discipline; and not just within the retail organization, but between it and its trading partners.

A simple example might arise when a lower everyday price for a popular item revs up its turnover rate. The existing number of facings may become insufficient, creating intermittent out-of-stocks. The lost sales may tend to distort apparent demand and delay re-orders, and the problem perpetuates. Fold in other concurrent events within the category, such as new item cut-ins and shelf capacity constraints and the problem grows very knotty indeed.

Fly by Wire

When I was first learning about all this, someone I respect explained to me why the mathematical model behind pricing optimization is related to the intricate “fly-by-wire” flight control systems that keep stealth aircraft from dropping out of the sky. Both critical objectives – keeping thousands of interrelated SKUs properly tuned, and keeping multiple interrelated flight surfaces properly tuned – share several traits:

  • The model is big
  • The model must be dynamic and continuous
  • The model must be highly reliable under duress
  • The model must be continuously updated at a time cycle that is rapid enough to support critical decision-making
  • The model must be appropriately accessible to decision makers

In one respect, those flight control systems may be simpler than retail demand models – there’s only one cockpit in an aircraft. A retail organization, by comparison, may have dozens or hundreds of individual decision makers and planners and trading partners interacting with the merchandising model through various dashboards. Each needs appropriate analytics and decision support according to his or her role.

To the Cloud

As DemandTec developed and acquired its portfolio of software offerings over the past ten years, it placed evident emphasis on connecting users with the data and with each other in practical and beneficial ways. It was an early advocate of the software as a service (SaaS) application business model, which placed the heavy application power in outside computer servers, relieving clients from the burden of maintaining these systems in-house.

Lately the tech industry tends to refer to service-based computing as “the cloud.” In fact DemandTec’s current positioning, “The Collaborative Analytics Cloud,” reflects that. The explosive growth of major social networks has reinforced this concept, as have some of the largest IT companies. IBM, which acquired DemandTec last February, uses the tagline, “Smarter Commerce on Cloud” to describe its core strategic approach.

The company’s DemandTec Connect™ social layer is a recent development in this regard. The platform leverages social-media-like interaction with embedded analytical applications to help shape collaboration across the merchandising ecosystem. Like any social media network, the platform is cloud-based. Its ability to provide role-appropriate access to a variety of optimization analytics is pure DemandTec.

© Copyright 2012 James Tenser
This article was commissioned by DemandTec Inc. which is granted the right of republication. All other rights reserved.

Call It Mobile Yellow

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Scan these tags!

I SPIED THE WHOLE grocery universe in a single bunch of fruit. This transcendental experience occurred just last week in West Des Moines, IA. (Quite rightly.)

It all happened in a bright, spacious HyVee supermarket in an upscale, verdant neighborhood that a decade ago was pretty much a cornfield. I entered the store thinking, “They built this, so I have come.”

But my Field of Dreams reverie dissolved when I stepped up to the produce department. My first view was an abundant display of bananas – the largest selling produce item in most supermarkets.

I pulled out my smartphone and snapped this picture, with the classic lyric by Donovan resonating in my brain like a prophetic soundtrack:

Electrical banana is gonna be a sudden craze
Electrical banana is bound to be the very next phase

You see, what really blew my mind were the two little stickers on this hand of bananas. On the left, a stacked UPC bar code and on the right a QR (quick response) 2D code. Each of these artifacts tells a little story about the impact of digital technology on our business. (Quite rightly)

The UPC bar code is of format known as RSS_14 designed to convey more information than old-style 9-digit codes. When I scan it with a gadget on my smart phone it correctly identifies the product as Dole bananas. Presumably the POS scanners in this HyVee supermarket are just as smart. The advantage: it saves the checker a few keystrokes and accurately enters the PLU (price look up) code for this variety of produce which is sold by weight.

The QR code on the right is used to direct shoppers to a web site called yonanas.com . When I scanned it with the same smart phone app, it led me to a page pitching a simple kitchen appliance that lets consumers make a soft-serve dessert from frozen ripe bananas and other fruit.

The Dole brand is featured prominently on the target Web page. Presumably there’s a deal behind it all. The Yonana appliance is marketed by Healthy Foods LLC, itself a division of Winston Products LLC in Cleveland, Ohio.

Peering at my smartphone screen under the fluorescent lights in that immaculate HyVee store in one of the greenest places in America, I found myself thinking, “It really has come to this: agricultural and digital have converged in America’s heartland.”

Then the digital banana concept set off a minor cascade of linguistic play: Bananas come in hands… Digit is Latin for finger… Smart phones are made with gorilla glass… (Quite rightly)

So in tribute to Donovan, the iconic Sunshine Superman inducted this year into the Rock and Roll Hall of Fame, I’d like to offer this (somewhat less lilting) variation on his theme:

Digital banana is gonna be a sudden craze
Digital banana is bound to be the very next phase
I call it mobile yellow (Quite rightly)

© Copyright 2012 James Tenser

Of Habit and Target

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THE New York Times Magazine made people nervous with its February 19th cover story by author Charles Duhigg. Its chilling headline, “How Companies Learn Your Secrets,” seems to have compelled readership as a matter of personal protection. I make this inference from the number of acquaintances who asked me about it.

[Author’s Note: This column was originally published on March 13, 2012 in the TradeInsight CPG Chatter blog.]

“Creepy” was the adjective repeated most from individuals who read about how Target Stores applied data mining techniques to shopping baskets to infer which shoppers were most likely pregnant, then sent them promotional offers for pre- and post-natal products.  Motherhood is pretty personal business, so I can’t say I disagree with the folks who were offended. What gives them the right?

Focusing on this creepy surveillance was a pretty crafty editorial decision by the editors at NYT Magazine, who used the cover line: “Hey! You’re Having A Baby!” The analytics behind pregnancy detection was actually just one example from Mr. Duhigg’s just-released book “The Power of Habit: Why We Do What We Do in Life and Business.” Having a baby, as it turns out, is one of a handful of predictable moments in life when our consumption habits change big time. For eager marketers that information is, well, mother’s milk.

Even if we look past the intrusiveness of offering coupons for cocoa butter lotion to stretchy young mothers-to-be, Mr. Duhigg’s larger thesis about the enduring nature of habit remains compelling in a different, less sensational way.

It tends to strengthen my own observations of long-lasting retail shopper behaviors, such as trip planning, coupon clipping, list-making and response to promotional cues within the store. In “Shoppers’ Perspective,” research I helped co-author for CPG manufacturer Henkel USA in 2009, we learned that shoppers could be sorted into fairly stable groups based on these enduring habits. It took the pain of the subsequent economic downturn to disrupt the patterns. As a result, coupon redemption statistics turned upward to what we may hypothesize to be a new norm.

The central example of the Duhigg article – Target’s effort to target “new natals” in its promotion marketing – is interesting too, but it offers little, truly new insight about behavioral segmentation analytics. Food, drug and mass retailers have understood the buying traits of new and soon-to-be parents (and other behavioral segments) for decades, without the need for sophisticated data mining tools. These insights are easily inferred from examining the contents of shopping baskets from the store’s point-of-sale transaction records – or better, from frequent shopper data.

It is not necessary to know individual identities, but such knowledge does enable the delivery of more personalized offers and services. These may be welcomed by opt-in frequent shoppers, but can be downright creepy when they seem to be the outcome of cyber-stalking.

The NYT Magazine editors correctly surmised that an article excerpted from Mr. Duhigg’s book could quite possibly be a snooze for readers not already fascinated by human behavior, retail analytics and segmentation and targeting. So they focused – perhaps a bit unfairly – on Target’s stalker-ish behavior toward new moms.

For us retail pros, however, divining the nature of repeat behavior is solid stuff – part of our every day thought work. It reminds us that when we try to influence purchase behavior positively, we also take on the challenge of overcoming pre-existing habit.

© Copyright 2012 James Tenser