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Revolutionizing Category Management By Embracing AI

ABSTRACT

Applied to the important planning process known as category management (CatMan), an emerging class of cross-enterprise AI

capabilities can enhance the output of all functions within the consumer-packaged goods (CPG) managerial ecosystem. That makes an AI platform more than just an analytical tool. Because it enables cross-enterprise management and enhances

competitive differentiation, CPG executives need to understand why and how to focus the power of AI on specific business-aligned use cases.

Written by Gordon Wade, in collaboration with r4 Technologies

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Table of Contents

Executive Summary 3Shopper Behavior and New Market Realities Complicate CPG Management 4Organizational Silos Hinder a Cross-Enterprise Approach in an Age of Rapid Change 5What Is AI? 5What Is Cross-Enterprise Management? 6Cross-Enterprise Capabilities Are Fundamentally Transforming CatMan 6

Applied properly, several AI capabilities can enhance CatMan dramatically 6

Identifying changes in shopper behavior and developing insights rapidly 6

Making merchandising decisions at the most granular levels 7

Improving store-level forecasting 7

How These Capabilities Affect Each Step in the CatMan Process 7Examples of Driving Volume and Profit, Using AI and CatMan Principles 8

Sample use case #1: A factory accident creates a profitability and customer-satisfaction conundrum 8

Sample use case #2: Optimally introducing a new product line 8

Sample use case #3: Optimizing profits in new and limited space 8

Sample use case #4: A retailer wants to improve its “click-and-collect” profitability 8

Sample use case #5: A retailer wants to prioritize investments 9

Sample use case #6: Where to locate new production capacity 9

What to Ask When Evaluating an AI Platform 9Should we license an existing third-party platform or build our own system? 9

Does the AI solution require redesigning of legacy systems and processes? or is it an agnostic cloud-based system that can link to existing systems and leverage in-place processes? 9

What kind of data does the platform accept? 10

Does the third-party AI platform continuously learn? 10

Can the platform address issues across the entire enterprise, including demand and supply streams, or is its scope limited? 10

What is the platform’s process for selecting data relevant to your specific issues? 10

Can the platform communicate its recommendations using your existing communication systems? 10

Has The Licensed Platform Addressed Issues Similar To Yours, And If So, What Were The Results? 10

The Race Is to the Swift 11What to Do Tomorrow 11About the Author, Gordon Wade 11About r4 12

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Executive Summary As digitally empowered shoppers disrupt the consumer-packaged goods (CPG) environment, executives can’t manage by using legacy (and now-outmoded) tools. Fortunately, new, sophisticated artificial intelligence (AI) platforms can empower CPG

executives to manage and master the complex cross-enterprise and market challenges that are eroding share, volume and margins.

An emerging class of cross-enterprise AI capabilities can be applied to the important planning process known as category management (CatMan). Here AI’s remarkable capabilities can enhance every step in the CatMan planning process through its

granularity, speed and capability to uncover new shopper insights. Indeed, AI enables a new way of practicing category management, taking it from the current model (CatMan 2.0) to the next level: CatMan 3.0.

But AI has another major advantage: it improves the execution of the plans at store level by breaking down functional silos with superior communication capabilities across the supply chain—down to the aisle level. An AI-enabled CatMan 3.0 process enhances

the output of all the diverse functions within the CPG managerial ecosystem. AI platforms become not just an analytical tool but a management weapon with significant competitive implications. That’s why CPG executives need to understand why it is so

important to focus the power of AI on specific business-aligned use cases. Then they can intelligently decide how to integrate the capabilities of AI in their business, and especially into CatMan.

Sample use cases provided in this report provide real-world examples of how AI can transform CatMan by leveraging data insights into better category plans spanning demand and supply. The questions to ask provided in this paper will help CPG executives

narrow their focus and cut through the hype when starting to evaluate options for AI capabilities enabling cross-enterprise management and CatMan 3.0.

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Shopper Behavior and New Market Realities Complicate CPG Management CPG management has developed organizational structures, processes and tools to compete effectively in commercial environments. As these environments change, managers must develop and deploy new options to address new competitive and

market realities.

During the 65 years following World War II, the pace of change proceeded at a brisk but manageable rate. Changes were

evolutionary, and responses emerged in a timely enough manner to address them effectively. However, in the last 10 years—and especially in the last five years—the rate of change in shopper behavior and attitudes, communication platforms and retail models

has accelerated significantly.

The rate of change itself has become a source of disruption because managers simply cannot cope with changes coming this

rapidly. The bad news is that the rate of change is accelerating.

And even more data will be created because of the “digital natives” we know as millennials and Generation Z. These younger

consumers, who are completely comfortable with the new shopping tools, don’t shop the way their parents did. They don’t read printed circulars and plan a weekly shopping trip. Instead, digital natives scan screen-based circulars and emails, search the

Internet for all kinds of deals from many different retailers, and want to shop whenever it's convenient for them.

In short, “shopping” has changed in profound ways. The very act of shopping and buying is creating more data than ever, which in

turn is driving the need for and use of more sophisticated analytics.

For example, managers depend on data to make informed decisions. But data springs up quickly. It comes in different types with

varying levels of detail. Managers often can’t readily tell which data is good, bad, meaningful or meaningless—or which data is critical but missing. They often lack the framework and the tools necessary to develop new conclusions and integrate them into

new strategies and tactics. When new retail channels spring up simultaneously, and new supply chain options emerge to accommodate the retail options, the new shopping behavior creates myriad problems and opportunities.

Three examples illustrate how new data reveals new opportunities:

• A new in-store shopper tracking tool revealed that the placement of end-aisle displays in bricks and mortar retailers hadwidely varying effects on volume and profit by category and brand. No retailer had measured this effect or quantified theopportunity for incremental volume until the new data revealed it.

• A retailer, frustrated by flat category volumes, combined various data sources to reveal that its own customers werebuying 30 percent of category volume in other retailers. This huge leakage problem revealed a major volume opportunitythat few retailers even knew existed and almost none had developed plans to capture.

• New online purchase tracking tools uncovered a wealth of data about shoppers’ evolving retail choices being often drivenby price. This opened new opportunities to vary pricing by category, brand and item to attract bargain-hungry shoppers.

These are but three examples of opportunities driven by changing shopper attitudes and revealed through new data sources. But

the same opportunities also represent threats. As newer brands and more nimble startups have taken advantage of lower cost of entry to address changing behaviors, the practical effect of all these reinforcing changes has been share loss by many existing

brands and slow growth by most leading companies. In at least one recent year, nearly all leading brands and retailers lost volume, share or margin; in some cases, it was all three.

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Organizational Silos Hinder a Cross-Enterprise Approach

in an Age of Rapid Change While shopper behavior is changing rapidly, CPG operational structures and processes are mired in the past. For more than 50 years, CPG companies have organized themselves into functional silos on the demand and supply sides of the enterprise. This

model of vertical silos that interconnect and interact primarily in the C-suite served managers well in times of relatively slow change. But in this new era of accelerated change, the silos have become outmoded structures, like the French Maginot Line. Even

within the individual demand and supply silos, the pace of change conflicts sharply with established operational structures and processes, hindering rapid decision making.

Against fast-changing operational targets, demand data can be overwhelming. Sometimes, it’s not specific enough to be of real value. Other times, the data that’s sufficiently granular comes in such high volumes that it’s too difficult to grasp. Managers don’t

know how to piece it together, make sense of it, and most importantly distill insights that align to business needs and goals. Before being passed to a related silo, decisions and processes are delayed as data-derived judgments slowly move up and down a

functional silo. The CPG business moves at a snail’s pace.

The situation is worse on the supply side. Dramatically different neighborhood demographics make store-by-store forecasting a

daunting challenge—especially during promotion periods. Forecasting errors create out-of-stock or overstocked conditions across multiple stores, causing lost sales from out of stocks or increased inventory cost from sub-optimized space usage. E-commerce

and its unique fulfillment and delivery challenges further complicate this situation.

The good news is that properly designed AI platforms function cross-enterprise. These can create rapid data flows that provide

natural-language recommendations on a category-by-category, brand-by-brand and store-by-store basis. AI offers remarkable granular insights at unpresented speed.

What Is AI? Leading IT research firm Gartner defines artificial intelligence (AI) as the application of advanced analysis and logic-based

techniques, including machine learning, to interpret events, support and automate decisions, and take actions. In simper terms for business, AI is a means by which one can apply logic, data analysis and automation to answer a question or solve a problem

without human intervention. For example, in the CPG universe, the business question being asked may involve estimating the optimal profit from a specific category in a specific store based on the shopper buying habits in the neighborhood and the unique

space-to-profit ratio of the category’s items.

AI is the combination of human-derived instructions (formulas, algorithms) driven by data relevant to the question being asked.

The “machine” (the computer) initially does what the humans have told (programmed) it to do—but does so at speeds and pattern-recognition levels well beyond the capabilities (and endurance) of humans. Depending on the complexity of the question

being asked and the data available, AI systems also “learn” by noting previous behaviors, identifying new patterns, comparing past results to new data, and then drawing new conclusions.

In CPG, people create a plan to optimize category performance. A well-developed process, CatMan, is used to complete the multiple calculations required. This task uses known and disparate data from different, often hard-to-connect sources to optimize

assortments, prices, shelf presentation and promotion.

AI offers the opportunity for a step change in plan development and specificity, as well as the deployment and rapid modification

of planned activity based on real-time shopper behavior. This is especially true for the emerging management discipline called cross-enterprise management.

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What Is Cross-Enterprise Management? Cross-enterprise management is an entirely new approach to managing and operating a business. This new discipline is enabled by a new category of enterprise software known as cross-enterprise AI.

Cross enterprise management starts with a full understanding of real-time demand and markets being served. With those insights, executives can configure the business to exactly match supply with demand, and achieve one or more high-level business goals—

such as maximizing revenue, improving margins, or increasing market share. Revenue-impacting business decisions are fully informed, and optimized and orchestrated across the entire business portfolio to deliver maximum yield and return.

Cross-enterprise management also enables executives to measure their businesses in innovative ways—for example, basing product production and placement decisions on entirely new KPIs like time to achieve target yield, instead of old KPIs, like

inventory turns.

Properly orchestrated, cross-enterprise management enables a business to continually sense what customers want, and use that

understanding to “configure” the business to respond quickly and optimally in ways that both provide value (for example, eliminating out-of-stock conditions in stores), and support new KPIs (such as time to target yield) enabled by cross-enterprise

management. These sense-and-respond capabilities also form a feedback loop that continuously and symbiotically improves demand understanding and supply optimization, and enables the business to respond faster.

Cross-Enterprise Capabilities Are Fundamentally Transforming CatMan Although the CPG industry developed category plans for years, these plans were suboptimal because trading partners (retailers

and brand-owning manufacturers) did not collaborate by sharing data in a common process. In the early 1990s, industry leaders realized that sharing data and focusing on shopper needs was a win-win-win scenario. This attitude change set the stage for the

creation of the CatMan process.

Two years ago, a blue-ribbon consortium of 17 thought-leading companies completely updated the discipline for the Category

Management Association. That effort became known as CatMan 2.0.

In this era of accelerating change, the capabilities delivered through the application of AI have transformed the discipline yet

again. This latest iteration is CatMan 3.0.

Applied properly, several AI capabilities can enhance CatMan dramatically

Identifying changes in shopper behavior and developing insights rapidly

AI can analyze and predict changes continuously as they occur. Virtually all retailers and manufacturers collaborate once a year on a category plan and may tweak it every six months. Now consider what could be achieved if that were done every week or every

few days. That's possible because of the speed and efficiency of model-based AI platforms. As new about shopper behavior is applied against the marketplace model being curated by the AI platform, new analytics, insights, predictions and

recommendations—aligned with the CatMan discipline—are delivered. It’s CatMan at the speed of life.

An excellent example of this is the new “transparency” data developed by Label Insight. This data offers an entirely new

vocabulary of higher-level product attributes—such as food-allergy information, corporate social policies and product ingredients aligned to specific dietary regimens—that some shoppers intensely desire. Based on the availability of this data, as shoppers

change or align their behavior, AI can identify entirely new category segments, as well as compute market-share trends for these new segments, and respond to the shopper attitudes and behaviors, at the store and neighborhood level.

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Making merchandising decisions at the most granular levels

AI can recommend changes in assortment, pricing, shelf array or promotion. AI can also recommend marketing effort by shopper

cohort. With appropriate data, an AI platform can recommend digital marketing programs, even suggesting where new products should be placed into distribution more rapidly.

A major issue for most CPG brand owners is where they spend merchandising dollars to generate the highest ROI. AI can address

this issue rapidly and dynamically. Sophisticated AI platforms can communicate with individual direct store delivery (DSD) drivers and suggest differences in assortment dynamically. These same platforms can communicate directly with individual store clerk’s

inventory/placement system to direct attention to a specific issue (such as relocating one product or displaying more of another).

Improving store-level forecasting

Enhanced store-level forecasting has two implications:

• Out of stocks are reduced, which in turn increases shopper satisfaction, and boosts retailer volume and profit

• Promotional forecasting—notoriously difficult at the individual store level—can be improved to eliminate out of stocks,which negatively impact sales volume, as well as overstocks, which impair asset utilization and inventory turns.

This promotion- and display-driven forecasting error is probably the most widespread and costly of all CPG store-level supply-side

problems, contributing to significant financial losses. AI can readily and easily solve the decades-long problem of improved forecasting accuracy.

How These Capabilities Affect Each Step in the CatMan Process The figure below shows the steps in CatMan 2.0.

The aforementioned fundamental characteristics of AI affect every step in the process. For example, when the higher-level

attribute data associated with the shopper’s desire for transparency are fed into an AI marketplace model, every step in the process is enriched. The market-structure step is changed to include the totally new segments “created” by the transparency data.

This in turn affects the assessment, scorecard and strategy steps in CatMan 2.0. The engine in the AI platform rapidly identifies new growth opportunities in assortment, pricing, shelf presentation and promotion, as well as new possibilities in the supply side.

One step in the CatMan 2.0 process, the category scorecard, is significantly enhanced by AI enabling:

• More granular metrics that reflect the buying behavior of specific shopper cohorts

• Metrics based on unstructured, attitudinal data that are difficult or impossible for traditional analytics to accommodate

• Predictive analytics that rapidly reveal how the plan is measuring up to the goals established

The AI platform can then suggest corrective actions to ensure the category will meet its scorecard objectives.

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Examples of Driving Volume and Profit, Using AI and CatMan Principles

Sample use case #1: A factory accident creates a profitability and customer-satisfaction conundrum A baked-goods company manufactured numerous products on similar production lines. A serious fire put several lines out of

commission, dramatically reducing production capacity.

To optimize profitability and customer satisfaction until the fire-damaged lines can be repaired or replaced, the AI platform

needed to answer two critical business questions:

• What items—and how many of them—should be manufactured?

• To what stores should the available products be distributed by the company’s delivery sales force?

Because this single use case involved both a supply-side and a demand-side solution, the platform needed several sets of

complementary data from the supply side (for example, SKU production rates by type and available line) as well as data from the demand side (such as most profitable items by retail location).

A problem of this complexity would require weeks or months to solve without an AI platform. With an AI marketplace model in place, the cross-enterprise AI engine in the AI platform could readily answer these questions, delivering a detailed product-by-

product production plan and a store-by-store distribution plan in days.

Sample use case #2: Optimally introducing a new product l ine A processed food manufacturer decided to launch a new line of dried soups featuring potatoes, cauliflower, broccoli and squash.

The company had limited production capacity as the introduction began. It wanted to know what items to produce, in what quantities, and in which retail outlets to place them to optimize the return on its manufacturing investment. The questions they

wished to answer involved the complex relationships between demand and supply, marketing and manufacturing, and profit and loss. These are the types of complex optimization issues that AI excels at solving.

Sample use case #3: Optimizing profits in new and limited space A major distributor of wines and spirits wanted to optimize its limited category space in 100 new retail locations (in new geographies) that were just acquired by a current customer. The distributor wanted to identify the most desirable products for the

diverse retail locations, as well as control delivery and inventory costs. Not only did the solution need to match demand by product type and brand, but it also had to develop in-store inventory models that took into consideration space availability and demand

patterns by store. Once again, these complex, interconnected, cross-enterprise supply and demand issues are best solved using a model-based cross-enterprise AI platform.

Sample use case #4: A retailer wants to improve its “click-and-collect” profitability A retailer had been offering a new curbside “click-and-collect” service for several months in a test market. Before rolling the

service out to all 10 of its markets, the retailer wanted to explore how it could increase the service’s profitability. Specifically, the company wanted to know:

• Who it should target

• What incentives it should offer them to try the new service

• How to best increase order size

• How to minimize labor costs associated with filling orders

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These queries span demand, shopper-targeting, and promotion issues, as well as fulfillment costs associated with labor—yet

another example of the complex optimization issues that AI excels at solving.

Sample use case #5: A retailer wants to prioritize investments This retailer had identified several large important investments but had no way to prioritize them. Using an AI platform, they were able to quantify the cost-benefit ratios of these diverse investments in store remodel costs by design elements and store location,

in a new e-commerce software platform, in a new private-label line and new-store locations. The cross-enterprise insights derived from the AI platform enabled the retailer to proceed confidently with an aggressive investment program.

Sample use case #6: Where to locate new production capacity A manufacturer had six factories and needed to determine the most profitable plant to install a new high capacity line to serve a growing business. The decision involved projecting:

• Volume growth by geography

• Retail customers

• Raw material acquisition and delivery costs

• Post-production transportation costs to service areas

Even though this was an investment decision, the cross-enterprise complexity inherent in the question lent itself to needing to use a sophisticated AI platform to efficiently and effectively find the right answer.

What to Ask When Evaluating an AI Platform Executives considering the acquisition of AI capabilities for their companies—whether retailers or brand owners—should ask the

following questions about the risks and rewards of the solutions being reviewed.

Should we license an existing third-party platform or build our own system? For all but the largest companies, building your own system is a bad idea. History has shown that it is costly, time consuming and

unlikely to get you a best-in-class capability. Over time, the third-party systems benefit from being able to leverage years of work and experience, having superior functional design, and being able to learn from the needs of their licensees. In short, they gain new

insights and improvement ideas faster because of the breadth of problems their platforms address. For most companies, building your own system is an expensive death march to an unknown destination—and most business leaders will want to avoid that kind

of risk at all costs.

Does the AI solution require redesigning of legacy systems and processes? or is it an agnostic cloud-based system that can l ink to existing systems and leverage in-place processes? Although cloud-based systems can be expensive to use, they can address needs much more rapidly than internally purpose-built

ones because they don’t require an expensive overhaul of existing software systems.

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What kind of data does the platform accept? Does the platform accept only structured data, such as that housed in an Excel spreadsheet? Or can it utilize unstructured data such as a conversation scraped from an Internet blog? How about illustrations or partially complete data from PowerPoint

presentations? The most sophisticated systems can accommodate virtually any data from any source—and use that data in its analysis and recommendations—without requiring the data to be transformed into a different schema or loaded into an

intermediary platform (such as a data warehouse).

Does the third-party AI platform continuously learn? Continuous ingestion of new data will lead to near real-time insights to identify opportunities. An AI platform should include

continuous-learning capabilities—that sense and communicate how to capitalize on opportunities, based on business requirements. Some sophisticated platforms lack this capability, which means additional time and effort will be required to “re-

educate” the system on a regular, periodic basis (which both increases costs and slows the business’ reaction time).

Can the platform address issues across the entire enterprise, including demand and supply streams, or is its scope limited? For example, could it predict product demand and share gain at specific price levels by store—and then recommend how to manage inventory across all locations to maximize a revenue or profit goal?

What is the platform’s process for selecting data relevant to your specific issues? No platform will perform well unless knowledgeable people are also involved. This may take the form of simply identifying external

third-party data ideally suited to facilitate a specific use case or encoding specific internal databases for optimal use by the platform. The more extraction, transformation and loading of data that is required, the higher the cost (initial and ongoing) and the

longer it will take (again, initially and on an ongoing basis) to start achieving real results.

Can the platform communicate its recommendations using your existing communication systems? For example, if route truck drivers need to know how many of item “A” to leave at Store “B,” can the platform send the answer to internal systems that are accessed by your sales teams’ devices of choice? Or do you need to buy new programs or systems—and

spend time and money training drivers on them—to send these instructions?

Has The Licensed Platform Addressed Issues Similar To Yours, And If So, What Were The Results? Look for platforms with proven, real results—for example, increasing profits, across multiple use cases, at least five to 10 times more than the cost of the license.

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The Race Is to the Swift Many huge enterprises—retailers and manufacturers—are spending hundreds of millions of dollars to develop their own AI platforms. Among traditional brick-and-mortar retailers, Kroger is transforming itself and its shoppers’ experience through

aggressive investments in leading-edge omnichannel technology. Walmart and Target also are investing heavily in data and systems across their enterprises.

The good news is that smaller companies can license, at a fraction of the cost, third-party cross-enterprise AI capabilities that will, in many cases, outperform the Goliaths’ internally developed platforms. (And even the Goliaths may find that third-party

capabilities complement their existing efforts.) The question is whether these companies can move fast enough to ensure they can compete effectively in a rapidly developing AI-dominated CPG environment.

What to Do Tomorrow Those charged with addressing complex problems or tasked with evaluating an AI solution should:

• Assemble a small multi-functional team—across demand and supply—that includes representatives from IT and finance,as well as your internal analytics leader

• Identify business problems or opportunities that comprise use cases you need to address

• Ask third-party AI suppliers the questions from the section “What to Ask When Evaluating an AI Platform”

• Create a beta-test use case, and use it to understand how well an AI system can profitably address your issues, and whatimpact it can have on key business metrics (such as revenue and profitability)

• Prepare an evaluation for the C-suite discussing the options and recommending a plan

In addition, you should only be considering AI providers that can— in less than nine months—deliver initial results against your

beta-test use case and develop a longer-term recommendation to management.

About the Author, Gordon Wade During his 40-year consulting career, Gordon Wade has counseled scores of companies in dozens of categories globally. He has developed a panoramic vision and deep insight into the CPG marketing and value

creation ecosystem. Mr. Wade was one of the three original developers of the Category Management discipline. He recently led a 20-company blue-ribbon committee in updating and integrating the CatMan and shopper

marketing processes into CatMan 2.0. That committee included thought leading companies such as P&G, Mondelez, Walmart, Walgreens, McKinsey, Kantar Retail, JDA, IRI, Market Track and Decision Insight.

Mr. Wade’s deep involvement with CatMan has given him an unparalleled understanding of the data, analytics and processes driving modern CPG marketing. He interacts frequently with the thought leaders on the retail, manufacturing and

solution provider community. Mr. Wade has also worked extensively with the Advertising community through the Association of National Advertisers, the world’s largest marketing trade organization. In 2005, they asked him to create the process to make

marketing expenditure more accountable. This effort resulted in two white papers establishing industry standards.

Mr. Wade is an alumnus of P&G’s marketing department and a graduate of Harvard University where he earned a BA, Classics.

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About r4 Founded in 2013, r4 Technologies is the leader in applying AI to cross-enterprise management, delivering better business outcomes as a service. r4 was created by founders of Priceline, leveraging decades of expertise in extracting profit from data.

In 2019, r4 was named a Gartner Cool Vendor in AI Core Technologies. The company is based in Ridgefield, CT, with operations in San Francisco, Austin, Washington, DC, Dublin, London, Munich, Delhi, and Pune.

38C Grove Street / Ridgefield, CT 06879 / +1 203 461 7100 / www.r4.ai

© 2019 r4 Technologies, LLC