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Assortment – A ‘Matrix’ View

May 31, 2022August 14, 2012 by James Tenser

IN FAST-MOVING CONSUMER GOODS, the art and science of merchandising requires an informed balance of interrelated decision processes.

Microeconomics tells us that product sales rate will be related to price, albeit somewhat elastically. Space planning endeavors to allow sufficient quantities of each product to be stocked to meet shopper demand, without tying up excess capital. Assortment planning attempts to fit the most productive and satisfying mix of items into the space available. Inventory management balances the labor costs of replenishing shelves against in-stock levels.

There are other “levers” that figure into the process – from promotions, to new product introductions, to the depth and timing of markdowns, to the influence of competitors and even the weather. Taken collectively, these amount to a matrix of influencers on productivity and rates of sales.

For the retailer this comes down to the simultaneous management of customer engagement, assortment optimization, and pricing and profitability management. Or, as IBM DemandTec director of product management Carol Teng expressed in “A New Generation of Assortment Optimization,” a recent webinar hosted by PlanetRetail: “The right focus; the right product; the right price.”

[Learn more about IBM’s DemandTec solutions at its “Revolutionary Decisions” microsite.]

Teng shared five guiding principals for assortment optimization that are well worth summarizing here:

Put the customer at the center.Make decisions based on actual customer demand, enabled by lowest level of data available. SKU proliferation adds costs for both retailers and manufacturers. Extreme choice does not necessarily drive more sales. Manufacturers face added costs due to forecasting and planning. Shoppers ultimately pay the cost for more unneeded variety.
Don’t rationalize. Optimize. Keep key variety on the shelf, not just a simplified assortment that results from a “rank-and-cut” process. Use analytics to identify the weak-but-unique SKUs that are incremental and therefore important to keep. Identify products that are duplicative in the middle of the assortment curve, freeing up space to add truly incremental items.
Localize. Average assortments yield average results. Store clustering enables assortments that are appropriately tailored to variations in consumer demand. Employ clustering tools that enable the right assortments to be derived based on demand variations across categories, banners and stores.
Leverage available technology. This unlocks greater analytical potential. More sophisticated merchandising decisions are possible because computing power, customer intelligence, item intelligence, and connectivity are all on the increase. Mine available rich data sources: store, category, shopper and operational.
Processs and organization changes are critical. Advisory functions currently in place support Category Management and vendor relations. In the near future, new advisory functions are needed with specific assortment optimization expertise at the cluster and banner level, based on insights about customer behaviors.

“We believe customer assortment truly is the next growth lever in retail,” Teng said. The building blocks for this capability begin with superior data sources, like POS and basket analysis; frequent shopper data on re-purchase and brand switching behavior; and shopper panels that reveal losses to competitors.

Assortment and other merchandising decisions are best made not in isolation, but in an inter-connected environment – a matrix, if you will. Ultimately assortment optimization will depend on an understanding of incremental demand and transferable demand. Practitioners must monitor these continuously as situations evolve. For each and every SKU and store cluster.

© Copyright 2012 James Tenser
(This article was commissioned by IBM, which is granted the right of republication. All other rights reserved.)

It’s All About Conversion

May 31, 2022July 30, 2012 by James Tenser

DOES YOUR MERCHANDISING WORK? Where do shoppers travel and pause within the store? How and when do they view and respond to items on display and in promotional locations? Do you have a mechanism in place to capture and act on this vital information?

Chances are, your e-retailing rivals are way ahead of you when it comes to sensing, capturing and analyzing shopper behaviors, product views and conversions. Like it or not, the informational and analytic norms of the online world are today redefining best practice for brick and mortar retailers.

That reality is evolving fast. With the advent of new video technology solutions that sense and analyze shopper behavior, merchants are gaining the ability to understand what shoppers are doing, in every store, every day. Practical in-store sensing is coming of age. Meaningful conversion analytics can be at your fingertips.

Familiar traffic-counting systems no longer meet analytic and operational needs of the brick and mortar retail industry. Retailers face competition from online stores and from each other, as convenience stores, big box stores, and even apparel stores and supermarkets diversify their merchandise to compete for a larger share of shopper wallets. In much the same way that e-tailers use online analytics to improve their conversion rates, brick and mortar retailers need empirical data to gain actionable in-store insights and make better merchandising decisions.

Commissioned by LightHausVCI and prepared by James Tenser, principal of VSN Strategies, The Conversion Advantage explores why actionable insights begin with capturing key metrics about shopper behavior: by store, by category, and by product. The white paper demonstrates how using Visual Customer Intelligence (VCI) systems delivers these key metrics by capturing data on customer movement, browsing behavior, engagement, and shopper demographics. It shows how these metrics help retailers increase conversion rates, optimize staffing levels, refine marketing plans, and create winning strategies. (Click either graphic to download.)

Lessons from the Transit of Venus

May 31, 2022June 6, 2012 by James Tenser

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.

The Optimization Arc – From Black Box to the Cloud

June 1, 2022May 25, 2012 by James Tenser

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.