certain products go together

At Sentrana we believe that companies can increase sales, even in tough economies, by understanding their own demand environments at the most detailed level possible – in other words, to be able to predict what products to offer to what customers, and to use insights from available sales data to make targeted recommendations around pricing, promotional activities and timing. In foodservice hundreds of thousands of products pass through any given distribution channel every day to hundreds of thousands of restaurants and other operators. To meet this challenge effectively manufacturers and distributors need to contribute their respective insights about products and customers onto a common platform from which to obtain a full picture of demand. Recently this has motivated prominent industry players to collaborate in managing performance across key product categories.

Despite these different goals there is a way for category management to lead both manufacturers and distributors to direct financial benefits, not merely demand shift. Consider the case of tomato sauce we used as an example above. Now, at any point in time a single manufacturer – call it Manufacturer A – has a certain market share for each product it sells. The end customer – the foodservice operator – may be buying Manufacturer A’s brand or it may be buying a competing brand. Over any defined market (e.g. regional sales territory) the incidence of purchase of Manufacturer A’s brand should be equal to this manufacturer’s share of the market.

Let’s focus first on what is happening at the distributor level. The distributor’s goal – call it Distributor A – in this scenario is to create conditions by which an end customer will want to buy a certain product from Distributor A that the customer now buys from somewhere else. That is understandable in the abstract, but in the real world how is Distributor A supposed to know which customer to approach, which product to offer, and the terms at which to make the offer such that it will be attractive to the customer to shift purchase?

The answer to this involves a technical term – association and classification modeling – and a more reader-friendly explanation: certain products go together. The distributor’s sales data may identify 100 customers who have recently purchased prepared pizza crusts, tomato sauce and mozzarella. If the 101st customer recently purchased pizza crusts and mozzarella, it is a reasonable prediction that the customer is purchasing tomato sauce from somewhere else. The models we referred to above spot this opportunity and alert the relevant decision makers. We have homed in on which product to offer to which customer.

We still have a problem, though. We have identified the opportunity at the product level – tomato sauce – but do we know enough about the customer to understand his or her preferences within that product area? From the distributor’s perspective the answer is probably: no. The distributor’s job is to move product, not to be deeply familiar with the qualities and attributes of individual brands and SKUs. So now we must move the focus upstream to the manufacturer, who does possess that deep brand knowledge. Manufacturer A can tell us what product attributes may be most attractive to the customer to whom we are trying to sell the tomato sauce. This helps Distributor A move to a further level of granularity and identify which SKU/s, out of all the possibly hundreds that exist in the tomato sauce classification, may be the most likely to induce the customer to switch from their present distributor. Manufacturer A can even provide supporting sales collateral like recipes and usage suggestions to help Distributor A’s sales representatives close the deal.

Now we come to the real value proposition for the manufacturer. What has transpired in the scenario we described above is that a sale of any tomato sauce by any distributor has become a sale of a specific tomato sauce SKU to a deliberately targeted customer. The sale of “any tomato sauce” may have involved one of Manufacturer A’s brands or it may have involved a competitor’s brand – in aggregate, as noted above, this would be in proportion to Manufacturer A’s market share. For every instance where the customer would otherwise have purchased a competing brand, the sale of a targeted SKU through Distributor A results in incremental sales growth for Manufacturer A. Not demand shift, but real incremental growth.

Managing product categories for optimal performance in foodservice presents three key challenges that category partners need to solve: how to manage data reporting and analysis, conduct effective selling logistics, and close the sale. This post examines these three problems and identifies practicable solutions for manufacturers in collaboration with their distribution partners.

Data Reporting, Management and Analysis

Manufacturers often do not have regular, dependable access to sales data. Transaction information typically resides downstream, so the manufacturer must negotiate with its distribution partners to establish a mechanism for information sharing. Assuming such agreement is reached, the process may give rise to a variety of data problems. Data integrity issues are prominent among these. It is unlikely that the manufacturer will receive specially prepared sales reports – information more probably will come in the form of raw data untreated for accuracy, correctness or clarity. Readers of these reports will find it hard to obtain insights in them from which to take action on a timely basis.

What needs to happen to remedy this problem is a deeper level of collaboration between the manufacturer and distributor where each side is able to contribute the insights it possesses – product attribute knowledge from the manufacturer, customer purchase habits information from the distributor – and share this information via a common data platform. Bringing this information together in a robust data environment can help manufacturers and their partners obtain intelligence from which to make decisions about the right products to bring to targeted customers.

Sales & Marketing Logistics

Once category partners deal with the data management problem and successfully come up with actionable insights, they then need to figure out how to get those insights through the channel. “How do we get the right products onto the store shelves?” is how this exercise typically goes in the retail industry. But in foodservice a different question must be asked: “How do we get the sales representatives in the field to know what products we want to offer to specific customers, and to call up that knowledge in real time when the opportunity presents itself?” That is a different challenge than the one commonly addressed by simple SKU rationalization.

Bear in mind that the typical sales representative or marketing associate (MA) in foodservice has a full plate of selling and administrative duties he or she must perform every day, and not much capacity left over for assimilating and processing new information. Bear in mind as well that this typical MA may need to have on tap individual SKUs from over 200 product categories to supply to the regional customer base as demanded. That is far more information at the product-customer level than the MA can be expected to keep in mind without the benefit of effective selling tools. However, the MA cannot be expected to readily go up a new learning curve each time a manufacturer comes along with a new sales tool to apply to one of those 200 categories. MAs must be spoon-fed with the simplest, least time-consuming methods to get the right recommendations through the pipeline to the right customers. That means relying on what is already familiar to them, rather than overburdening them with new methods and processes.

Closing the Sale

That brings us to the last of the three challenges. Having managed to get the right products to the right customers, there remains the task of convincing the customer to actually make the purchase. Two things can help improve the odds of getting to yes. The first is knowing what combination of price and promotional discounts to offer to encourage the customer to switch from its current provider. The second is being able to back up the offer with relevant, impactful product collateral to drive home the key advantages of the products you are trying to sell.

Now, remembering that the sales representatives lack the capacity to juggle lots of different sales tools, how is it possible to actually mobilize all this information – price and promotional terms and supporting collateral – link it, and bring it to bear at the point of sale?

The Benefits of Working Backwards

The key is to keep it simple, and the best way to do that is to work backwards from the point of sale. It pays to ask how the salesperson can make this sale, armed with the right information, with as much ease and as little extra expended effort as possible. In the course of their work salespeople will tend to make use of certain selling tools on a regular basis. When a salesperson already knows how to use a tool and understands why it delivers performance benefits, a big part of the challenge is solved by leveraging off that tool to deliver category management initiatives.

Working backwards is not intuitive to everyone. Too often, when thinking about the implementation of a new performance system, decision makers create pages and pages of process work flows and front-end requirements and organizational change management specs, without asking themselves how it is going to work, realistically, in practice. A better approach is to envision how, at the point of sale, the salesperson can (a) know the right products to sell to certain customers, (b) be armed with pricing and promotional offers to increase the odds of inducing the customer to purchase from him or her, and (c) have appealing and persuasive collateral at our fingertips to close the deal. What can category management partners do to most effectively accomplish this given the constraints on the salesperson’s time and information capacity? Working backwards can offer a higher likelihood of both partners getting an impactful, measurable return from category management collaboration.

The current trade spend paradigm also does not work in the best interests of distributors. While they do benefit in the short term from the financial impact of the trade dollars they receive from their suppliers, distributors do not obtain insights from current trade spend practices that could help them more effectively grow demand across products and categories. Of more benefit would be product and assortment education from their suppliers, enabling them to identify tangible ways to tap into new sources of sales growth.

Category management, a standard practice in many retail sectors that is now gaining currency in foodservice, can be a way to attain this knowledge, use it to effectively drive growth for both manufacturers and distributors, and ultimately to phase out the unproductive aspects of the current trade spend paradigm.

Category management in foodservice can offer the following value proposition:

•    A collaborative platform between manufacturers and distributors based on shared investment of time and resources, as opposed to the general mistrust that permeates trade spend relations
•    Combining knowledge about products, customers and assortment from the manufacturers with transaction-specific insights about timing, location and volume from the distributors to create a holistic view of demand at the micromarket level of every product and every customer
•    Advanced technology to facilitate in-depth analysis and predictive recommendations around all key demand levers e.g. pricing, promotions, assortment, and purchase timing

From “Pay to Play” to “Category Captain”

The common view today in foodservice is that trade dollars are for all intents and purposes a “pay to play” ante required for manufacturers to get their products through distribution channels into the establishments of restaurants and other foodservice operators. These trade dollars are the biggest line item expense after COGS on manufacturers’ income statements, and thus comprise a commensurately large revenue item for their distribution partners. While the conventional wisdom may be that the current system is too entrenched to change, the fact is that a well-executed collaborative category management program can be far more effective than traditional trade spend in identifying the best uses of promotional budgets and delivering on them. Here the manufacturer is not simply cutting a check for some loosely-defined trade campaign and hoping for the best, but instead is taking a more active role in educating, guiding and supporting the distribution partner to increase sales. The vehicle through which the manufacturer can take on this active role is that of category manager, also known as category captain.

In taking on the role of category captain the manufacturer is essentially investing its own time and resources into helping the distributor achieve stated performance objectives. Distributors face a significant challenge in improving category performance. They lack the in-house knowledge about products, customers, and assortment that can help match the right products and offers with the right customers. Manufacturers can supply this knowledge along with supporting tools, including engaging and informative product collateral, suggested product uses, recipes and so forth to help meet objectives such as: increasing demand among existing customers; identifying new customers; and improving sales turn with higher velocity products.

Scalable Category Management

When scaled widely across multiple product categories over time, this approach can ultimately prove to be a more sustainable form of revenue than traditional trade spend dollars. One important difference is that the results delivered by an integrated, data-driven category management program can be measured against quantitative performance targets. Unlike trade spend, where unsystematic and non-automated processes make any kind of ROI analysis problematic or outright impossible, category management puts hard numbers into the hands of decision makers and executives so that they can evaluate how effective the initiatives have been.

The advantages of category management do not just accrue to the distributors. Here again this approach offers up compelling advantages as compared to traditional trade spend. Manufacturers will gain more from their investment of resources into category management than they do from their funding of trade activities. They will have access to a downstream view of demand, supported by actual daily transaction data that has traditionally proven elusive. They will have a much better sense of the timing, quantity and logistical details of customer transactions – and they too will be able to quantify this value with performance measurement tools.

In the prevailing economic climate foodservice is likely to experience strong headwinds to achieving sustainable growth and profitability. Trade spend dollars – which account for about 18% of every sales dollar manufacturers generate – are a dead weight on an industry sector that cannot afford such waste. Replacing the current paradigm with a more efficient, data-driven collaboration model such as category management offers a potential path for industry players to improve their own profitability and that of their industry partners.

So you have been asked by your most important foodservice distribution partner to be a category captain. What happens next? As captain you are tasked with managing the assigned category for optimal performance. That entails the following:

•    Analyze all products across the category (not just your own brands)
•    Augment the data provided by the distribution partner with your own internally generated insights
•    Provide structured, actionable recommendations based on intelligence obtained from the data

These recommendations relate to product assortment, pricing policies, promotional activities and other important demand levers for driving profitability. At the same time you need to educate your distribution partners, both at corporate headquarters and in the field, about the product characteristics that can help increase demand. This requires an intelligent approach to data analytics.

What insights about products can help drive category sales?

What might a good analytics model for category management look like? Let’s consider the key tasks we identified in the previous paragraph.

Analyze All Products Across Category

Category management is driven by analytics. As category captain you will receive transaction data from your distribution partner to form the basis of your insights and recommendations. The first issue with which you will likely have to deal is the quality and completeness of this information. Bear in mind that foodservice distributors are typically not used to sharing sensitive sales data with their suppliers, and may lack effective internal processes for making it available. Robust data management solutions like Sentrana’s MarketMover™ help collaborative category partners overcome this challenge by providing timely access to clean, customer-level data.

The next order of business is to map out the analytical processes that can best support your distribution partner’s objective to improve category demand. This may be best approached through posing a series of questions. For example:

•    What intelligence can we derive from the data to help identify ways to improve product demand among existing customers?
•    What patterns and associations will provide us insights about products that current customers are not buying from our distribution partner but could be enticed to buy?
•    How can we improve sales turnover by encouraging customers to switch from lower-to higher-velocity SKUs?

Augmenting Data with Internally Generated Insights

In answering those and similar questions one of your most important activities is to augment the data your distribution partner provides with your own unique insights about the products in the category. An important example of this are the product attributes that drive demand among certain customer types. Perhaps you are charged with managing baked goods and you need to figure out what the right use of shelf space will be for muffin products. Your distribution partner’s objective is to increase total muffin sales – for example along the lines of one of those three questions posed in the previous paragraph. As a manufacturer you can provide your own deep knowledge about what features and attributes drive sales among certain customers.

A critical data challenge, therefore, is to have the ability to map specific attributes to specific products. Category managers should be able to access the product database and establish product groupings and categories based on like attributes. Using the above example, for every product you can assign a  quantitative attribute metric. “Butteriness” may be an appropriate attribute for muffins, and you can rank all applicable products along the lines of “very / moderately / not very buttery”. This can facilitate more rational product groupings within the category that better enables you to analyze and evaluate assortment trade-offs, pricing strategies and promotional approaches.

This kind of product administration capability brings up in turn a whole series of issues around how to create standardized attribute definitions for each relevant subcategory and product set. By allowing category mangers to create new product and subcategory groupings, it becomes likely that these categories will not map directly to those of the distributor. A category administration functionality is required that will manage the interface between the distribution taxonomy and the specific product and attribute groupings mapped by category managers at the manufacturer.

Supporting Analytics

As you map out these processes you can get a better sense of what the analytical capabilities may look like. For example, what data exploration functionalities can help you analyze effectively? To orient your own understanding of the structure of subcategories and products to its organization in your distribution partner’s data records you need a mechanism for guided drill-down and drill-up within products, as well as in regard to customers, sales territories and other key information. There should be a filtering mechanism that allows you to view products along a number of descriptors: for example, all the SKUs currently stocked at a particular operating company of your distribution partner, or all products with the item description “frozen”.

Visualization and editing capabilities are also important components of analysis. How do you want to see the information and organize it into compelling formats for your targeted readers? You will probably want to have a variety of formats and the tools to manipulate data into different visual representations to underscore the insights about customers and products you wish to communicate. You will also want to be able to easily access and modify the reports and formats you use most frequently, and to share reporting and editing capabilities with others working on the same projects.

Providing Guidance and Recommendations

Category managers need to consider how best to translate analytical insights into actionable recommendations for their partners. For example, developing strong promotional content around products for the distributor’s sales force can be an important way to execute against category performance targets. A system for uploading, managing and exporting product-related content is thus an important functionality to consider. Another valuable feature could be scenario analysis capabilities to map out alternative approaches to pricing decisions, promotional opportunities and assortment trade-offs. Finally, manufacturers need to consider how to incorporate data from their own market sources: for example sales information at the total market level rather than just the share occupied by their distribution partner.

Collaborative category management can evolve into a long-term relationship that will improve category performance for distributors and improve overall product sales for manufacturers. Over time the scope of a category management program may expand to include enhanced predictive initiatives and a fuller set of demand levers. Building a good foundation with the right data analytics is a good place to start.

Building a robust data management environment is in many ways like building a house. There are three components to building a good house. First of all, there are some fundamental questions you need to ask before doing anything. Why are you building the house in the first place? What are the important goals and benefits you want to enjoy? What other things are you willing to trade off to realize those benefits? Asking and answering those questions will help with the second component: building a model, or architectural blueprint. There are many different ways to build a house (or a data management system). Not all of them will be right for the needs you have in mind. There are efficiencies to designing and building in certain ways – and, as always, there are trade-offs with any given choice. Finally, once you have established a workable model, it’s time to build out the infrastructure. That starts with the plumbing. Nothing else in the house is going to work well without good plumbing which, seamlessly and unobserved, harnesses the flow of water (or data, in our analogy) to efficient uses as and when needed. Then comes the foundation – the platform to support the house according to your model. Think of the plumbing and the foundation as the transmission pipes, the controls to regulate the flow of information, the storage repositories and the other critical supports for your data management platform.

robust systems need good blueprints

Asking the Questions that Matter for You

It’s hard to imagine that someone would build a home without first asking and answering some basic questions about what purpose the home is meant to serve. But all too often enterprise managers think of their data intelligence needs in terms of generic, one-size-fits-all products and solutions. They may be driven by the perceived urgency of getting immediate results, so they do not put the extra time into thinking through all the details that have to be in place in order for a solution to best meet their targeted needs. They build up organizational IT resources but fail to adequately integrate these resources into business decision-making processes so that business goals and technological capabilities are aligned. By not asking the right questions up front, managers increase the likelihood that their IT investment will fail to achieve the specified goals.

The place to start is by asking what kind of business you are. What are the core elements of your business strategy? What kind of information – about customers, products, channels, territories, campaigns, transaction dates and so on – do you need to pull into your organization every day in order to make decisions that help accomplish that strategy? What performance measures do you seek to optimize – for example gross margin, market share or something else? Is there a single overriding imperative for all territories or are there more nuanced considerations for local markets? Who needs access to the data, both within the organization and among trade partners? These broad-based questions can then lead into more detailed questions around specific tactical opportunities that can help support the overall strategic goals.

Modeling Around the Right Questions

One homebuilder wants a large kitchen with lots of counter space for preparing gourmet meals. Another wants to optimize for green initiatives –creating spaces for solar panels and the like. Every homebuilder needs an architectural blueprint and model that defines the best tools, materials and processes to accomplish the job at hand. These decisions should flow naturally from the questions asked and answered in the previous stage.

Data models are designed for different purposes. Some place emphasis on providing a continual stream of information for decision-making in real time. Others focus on high quality analytical tools applied to batch data that are aggregated for a particular time period – for example daily, weekly or monthly. A critical driver of data architecture is the volume of information the system is expected to handle and the types of activities users expect to be performing when they access the databases. Information processing has a cost, which includes a time component and a money component. Thinking back to the homebuilder who wants a state-of-the-art kitchen space, the architectural  blueprint for that kitchen has to think through all the activities our homebuilder-chef is going to perform – how to optimize the process of retrieving ingredients, setting up preparation work stations, pulling out pots and pans and chef’s knives, and cooking on the stove or in the oven. The data architecture similarly has to visualize the user retrieving various bits of data from different locations at different times for different purposes, and figure out the configuration that works best.

Laying Pipes and Building a Platform

The most elegant model in the world will be of little use if the infrastructure is not solid. Living in a house we tend not to think about the plumbing as long as it is working – we wash dishes, water the plants and fill the ice trays without wondering about how the system works. But we will certainly notice when it doesn’t work – when water doesn’t  come out of the faucet or when it floods the basement. Likewise a business user at a computer workstation is probably not going to think about how all that information flowed from somewhere out there to show up at her desk – unless it doesn’t show up or is in an unusable format. Laying the transmission conduits and controls that regulate the flow of data to where, when and how it is needed in the organization is a serious undertaking.

When information comes into the organization from “out there” it is unfortunately a bit more complex in form than the municipal water that enters your home’s plumbing system. It is easily corruptible and subject to misuse if robust protocols and procedures are not in place to keep it clean and usable. In a surprisingly large number of cases data “formats” consist of little more than unprotected Excel spreadsheets with inconsistent definitions and naming conventions for product categories, subsets, SKUs and customer types. That will likely prove to be of little use in facilitating actionable analysis. To be usable data need to be clean and granular – in other words, accurate, in the right format and at the right level of detail for the analysis to be performed.

Organization Matters, Too

Business managers do not always take into account the organizational aspect of data management. The solutions architecture will not be effective if the enterprise is unable to successfully weave it into the fabric of the organizational culture and existing decision making processes. Empirical evidence suggests that rigorous analytical methods play a relatively minor role in the decision making practices of many organizations – especially those at higher strategic levels but also those related to daily tactical opportunities. Ask yourself how different levels of decisions are made in your organization. What role, if any, does data analysis play? How persuasive are data-supported arguments – for example in comparison with rigid top-down planning imperatives, an ad hoc “firefighting” culture or political and personality-centered negotiations? What needs to change in terms of both formal processes and the informal cultural context for actionable solutions architecture to make a lasting impact and lead to more informed decisions?

Like a house, a data management system is best if it can last for a long time and have the flexibility to adapt to changing needs and circumstances. Business strategies will change, as will technology. Far better that a system can adapt to this change without requiring an entire rebuilding effort from scratch. Start with the right questions, and you have a much better chance of building a data management capability that can successfully evolve with your organization.

To be more effective, managers need to break down the organizational barriers, bring their diverse trade activities, information and processes onto a common platform, and mobilize the vast amount of data available from their purchase history records to the task of analyzing opportunities for more precisely targeted trade campaigns with a higher likelihood of success. This can provide the foundation for a holistic approach that helps foodservice enterprises achieve that objective much talked about but not often achieved – turning trade spend into trade investment.

Trade spend needs to target the right products and the right customers

This holistic approach starts at the beginning, with a broad-based trade budget which managers plan across different product platforms, trade vehicles and customer types. How is the budget initially divided up? As a practical matter there is a fundamental problem here: manufacturers often end up sending most of their trade dollars to their largest customers – who are often the ones who need these dollars least – rather than the ones who could potentially grow their business and expand their product sales more aggressively. A better way to spend trade dollars is through disciplined quantitative analysis and economic scenario testing that ultimately reaches a very granular level of detail: what combinations of products and customers are likely to be the most receptive to trade initiatives? The goal is then to build a trade program that can effectively reach these target audiences, to execute campaigns with precisely defined messages and incentives, and to measure the results so as to have a plausible quantitative measure for return on trade investment (ROTI).

The building blocks for this approach are the data that can provide important insights leading to more economically effective allocation of each trade spend dollar. To get to those deep insights requires intelligent and careful preparation work around the systems and processes that store, extract and analyze data. Everyone involved in making trade-related decisions needs a common view of that data, clean and in a standardized format. One of the unfortunate outcomes of the organizational fragmentation of trade spend is that multiple formats and processes exist for a variety of different spend types. Related claims originate from different parties along the supply chain and are often not handled in a streamlined, rational, systematized manner within the organization’s claims processing functions. It is therefore little wonder that the problem of duplicate claims arises frequently across accounts, requiring time-consuming adjudication to resolve. While duplicates will always be an issue, the resolution process can be made considerably less cumbersome with a rules-based approach to adjudicating overlapping claims across accounts, and more broadly governing the rules by which negotiated agreements will interface with each other in a logical hierarchy.

Another very important process to bring into a holistic trade management approach is void/compliance identification. Resolving void/compliance means comparing contractually mandated spend items with actual transaction activity in order to red-flag items not in compliance with approved terms. Decision makers must be able to analyze and resolve this information in proof of performance data formats at the unit contract level (i.e. that of each contract/GPO group member) before moving ahead with targeted campaign activities.

Whether executed on a collaborative basis with trade partners or as a stand-alone exercise, our experience with foodservice clients has shown that trade campaigns based on empirical, data-driven analytical insights can result in success rates of triple or more in comparison to traditional methods. With the insights available from this approach, managers can determine what trade messages, incentives, pricing strategies, bundled offers and sales force effort to apply to specific customers for specific products at specific times. These insights can even help provide visibility into what products your existing customers are not buying from you currently but likely are buying from your competitors. This can give you the wherewithal to design specific trade programs around these products to induce customers to switch their purchases from your competitors’ products to yours.

Foodservice marketing managers are under increasingly intense pressure to do more with less. In the competitive climate of the industry today the old paradigm of trade spend as “pay to play” – an unavoidable cost of doing business – is no longer viable. Managers need the ability to make trade spend decisions based on empirical evidence that indicates where each dollar spent will have the highest likely impact on sales and profitability.  To do this requires a substantial recalibration of existing disparate practices and processes in favor of a holistic, data-driven approach operating off a common platform transparent across organizational silos. The ability to measure return on trade investment (ROTI) will give foodservice manufacturers a new capability to get the most out of their trade programs.

It's a new world for FAFH, but the industry remains stuck in unproductive practices

The foodservice industry, comprised of the food prepared away from home (FAFH) sector of the food & beverage market, accounts for about 46% of all consumer dollars spent on food and beverage products in the US. Over the past twenty years this business has changed considerably as American lifestyle habits, choices and spending propensities have evolved with regard to food and beverage consumption. Yet manufacturers, distributors and operators in the foodservice industry have in many ways been slow to adapt their sales and marketing practices to better serve the evolving preferences of the end consumer. As a result there are considerable inefficiencies up and down the value chain resulting in suboptimal performance for all parties. Trade spend management, campaign marketing and other critical activities suffer from an absence of data-driven input for decision-making, as well as the inability to effectively monitor and evaluate performance. Relations between trade partners are often characterized by mistrust and a lack of willingness to work together for win-win outcomes.

There are ways for companies throughout the foodservice value chain to dramatically improve existing practices and enjoy enhanced market share and profitability performance. However, it is first necessary to pinpoint where existing practices are falling short, and why.

The Trade Spend Albatross

Any discussion about sales & marketing challenges in foodservice usually does not take long to get to the subject of trade spend. According to the MarketIntelligence 2010 Foodservice Trade Survey, over 75% of foodservice manufacturers are dissatisfied with their trade spend programs and regard them as inefficient. Moreover, fully 85% of the respondents said that they do not employ business intelligence or analytical tools to improve the effectiveness of their trade spend initiatives. Foodservice manufacturers spend an estimated 18% of every sales dollar generated on trade spend, and it is typically the second-largest line item on their income statements, after COGS (cost of goods sold). Yet for all the financial resources they consume, trade spend programs clearly are not delivering the results that manufacturers need or expect. Trade spend is seen as more of an institutional necessity – an “ante” or cost to be in the game for manufacturers and an essential component of net margin for distributors – rather than a variable to be optimized for improved financial performance.

An Absence of Data-driven Insights

A big part of the problem with trade spend is the way in which it is often allocated – primarily through zero-sum negotiations with adversarial distributors rather than through data-driven collaboration for mutual benefit with trade partners. Additionally, large amounts of effort are spent “analyzing” mountains of program claims from trade partners – claims that usually come in multiple data formats and accounting conventions, are riddled with errors and consume enormous amounts of manual resources in resolving. Void/compliance and duplicate claims issues are persistent headaches for trade program managers – and all the time spent resolving these types of problems is time that could be better spent identifying opportunities to grow sales through targeted offers of specific products to specific customers.

The need for effective campaign marketing

Because trade spend programs have become so entrenched with little effort employed to identify and improve their efficiency, the data that could help managers identify and execute more precise marketing campaigns currently lie unutilized. Campaign marketing is an area of vital importance for foodservice manufacturers. Over the past twenty years the industry has seen a vast proliferation of products, concepts and venues related to the experience of dining out. In fact, “dining out” itself is an outdated term – as long ago as 1996 McKinsey & Company pegged changes taking place in the foodservice environment by noting that where the food is consumed is less of an issue than where and how it is prepared. These changes have had a direct impact on the demand for specific products and categories in specific venues at specific times – an impact that has flowed upstream from the end consumers themselves to foodservice operators, distributors and manufacturers.

To execute effective campaigns, manufacturers need better insight into the many variables that affect the sales of each of their products – and, moreover, the nuances that affect the same products differently for different customers. The good news is that those insights are available. The data do exist and can give manufacturers a deeper understanding of how to improve the sales of targeted products across precisely-targeted accounts. However, in order to develop this capability and prosper in the new foodservice environment, industry players will need to adapt to the changing paradigm.

Growth prospects for foodservice in the decade ahead are likely to be muted. This new environment will require trade partners to adopt practices that improve their visibility to downstream demand, and when possible find ways to collaborate with each other for win-win outcomes. The common theme among these practices will be the improved use, management and analysis of data for informed decision making.

a unique processing system

Modern business intelligence systems are rising to meet this challenge by encompassing more explanatory variables into their algorithms.  But even so there is still a problem.  These models are still confined to looking backwards, to past events, to formulate guidance about what to do in the present and future.  Sales & marketing decision makers need to complement their insights from historical data with an approach that can work in the constantly changing environment of their markets in real time.  That approach has to draw on the processing capabilities of a system uniquely suited to the ambiguities and constant flux of dynamic markets: the human brain.

Analysis on the run

Humans and machines don’t “think” in the same way.  Consider the following scenario.  A sales representative for a seller of food products has a particular customer with whom she has a track record of selling condiments like ketchup, mustard and soy sauce.  The customer is a fairly frequent buyer and so there is an extensive transaction history going back over a number of years.  The sales rep’s company uses a quantitative analysis and optimization tool to predict likely demand, providing its sales reps with guidance for pricing and other marketing decisions.

The sales rep is doing some prep work ahead of a meeting later this week with the customer, including reviewing the price recommendations the optimization system has provided.  As she glances through the morning paper, the sales rep notices that one of her company’s major competitors has launched an intensive three-week promotional campaign for customers in the local area.  Immediately she knows that her customer also buys from the competitor, and no doubt will be fully aware of the competitor’s promotional campaign.  She leans back in her chair, looks again at the price recommendations in the display screen of her PDA, and takes a deep breath.  What should she do?

Gut instinct versus science

The traditional answer would be: go with your gut instinct.  After all, this is what good salespeople do – call up their years of experience to figure out how to play nuanced competitive angles and make the sale.  It is likely that within seconds of reading that newspaper announcement about her competitor’s promotional campaign, the sales rep had already processed in her mind the  information, evaluated its importance and formulated the outlines of a plan.  As the sales call approaches she will refine her thinking even further, and may in fact defer the final decision about how to make the offer until minutes before the meeting, depending on whatever factors she believes to be in play at that time.  It would seem, then, that the prudent thing to do would be to override the system’s price recommendations.  After all, the computer’s algorithms are not privy to any of the knowledge and insights that are firing up the neurons in the sales rep’s brain.

Overriding the system is not an optimal solution, however.  In opting for human insight, the sales rep is losing the benefit of the scientific insights culled from a highly sophisticated analysis of patterns in past activity.  That analysis is highly granular – it applies to the unique characteristics of this particular customer and the set of products that are featured in the sales call.  Surely there is enough value in these insights to give our sales rep pause before she hits the override button?

Quantitative intuition

Fortunately, she won’t have to make this choice between human instinct and scientific analysis.  One of the most important recent innovations in predictive technology platforms is the means to provide real time input from the field – even in the moments right before a live sales opportunity – into the system.  We call this making the system “market aware”.

Market aware systems can confer distinctive advantages.  The key is how to essentially “code” qualitative input from decision makers into the algorithms that run the scientific analysis.  For example, our sales representative needs to convey to the system that the sale will likely be under more competitive terms than usual.  In other words, she needs to take a qualitative statement along the lines of “this is going to be a tougher sale because of our competitor’s aggressive promotion” and translate it into a digitized language the computer will understand.  There are different ways this can be done – from a relatively simple algorithm numerically ranking competitive intensity to more complex machine learning analytics.  Machine learning is a fast-developing field helping to make commercial technology solutions increasingly adaptable to constantly changing environments.

What happens from a practical standpoint is that our sales rep codes the information into a feedback loop that interacts with the system’s algorithms – effectively “informing” them about something happening in real time that they need to take into account.  The models are then able to recalibrate their analysis and provide new recommendations to the salesperson.  Now she has the benefit of two important – and distinct – types of analysis: the quantitative knowledge of historical demand patterns with this customer and the basket of goods she is trying to sell; and the qualitative judgment around an ambiguous situation that requires brainpower more than computational power.  Combined, these two analytical approaches provide what we call quantitative intuition – a means to combine deep granular insights from your company’s transactional data records with the ability to adapt to dynamic market developments in real time.

Unique challenges require customized solutions

Increasingly, sales & marketing decision makers find themselves in need of highly customized solutions to the problems that are specific to their organizations and their place in the value chain.  Manufacturers of food and beverage products may primarily be concerned with making more productive trade spend decisions, while CPG producers might focus on shoring up brand equity for premium products facing competition from substitute offerings.  Wholesalers may prioritize increasing the value of each transaction basket by inducing customers to purchase products from them that they currently purchase from competitors.  And retailers might want to better understand how their promotional campaigns resonate with target customers and what they can do to earn a better return for each campaign dollar.

These challenges are distinct, yet they share two common attributes.  First of all, whatever problem you are solving depends in turn on a multitude of factors including those you can control – setting prices or allocating advertising dollars for example – and those which you cannot control like competitor actions or seasonal influences.  You need analytical tools that can help give you a holistic understanding of these factors rather than merely isolated fragments.  A holistic understanding comes from the ability to disentangle the many different factors at play and focus on the ones that have the most impact. For example, how do pricing decisions interrelate and influence product campaigns, and how can these decisions be calibrated to optimal effect?

Second, the challenge you face for one set of customers and products is not the same as the challenges you face for other customer-product combinations.  You cannot solve nuanced micromarket problems with solutions that play to aggregate market averages.  Every sales & marketing decision you make should proceed from the individual behavior of every customer in relation to every product.  Perhaps you sell something as thoroughly commoditized as salt or margarine.  There still are likely to be substantial areas of variance between the needs and preferences of any two of your customers in relation to every purchase they make of these or other products.  You will be well-served by analytical tools that help you see and quantify those differences on the problem at hand.

Piecing together the micromarket story

Ten years ago opportunities hidden in the fabric of your micromarkets would most likely have remained undiscovered.  Today we have robust computational technology to help unearth them.  We really can view the most basic units of activity in our markets.  But that by itself is not enough for us to turn insights into actionable guidance for decisions.  The challenge is this: from the information we now have at micromarket level, how do we piece together an understanding of what really drives demand?  How do we distinguish between the truly relevant factors that affect sales outcomes and those that are in effect little more than noise?

Every record in our purchase history has a story to tell, but the story is often incomplete.  Some product-customer combinations have many data points resulting from frequency of activity over close time intervals.  Others – the so-called “long tail” – offer up very little to help us put the story together.  Incidence of customer-product activity in the long tail can be so infrequent that making any truly insightful analysis would seem to present an impossible challenge.  How can we accurately quantify the effects of a price reduction, sales promotion, seasonality trend or something else when there are only two or three data points over the course of an entire year?

This is where the “science” of scientific marketing earns its name.  Problems like data sparsity can be solved with the application of advanced methods such as Hierarchical Bayesian modeling, which “borrows” information from similar transactions to develop demand models with associated probabilities for specific outcomes.  Think of these methods as storytelling aids, helping us to fill out and enrich those incomplete snippets of information found in the transaction records.

Connecting decision points in the organization

Robust computational technology can help us peer into the micromarket and see activity  at the most granular level of every customer and every product.  Advanced science can help overcome problems like data sparsity and identify what is driving customer behavior in the unique circumstances of your own market.  But there is one final challenge to address: how to connect all the different places in the organization where marketing decisions are made and align them for optimal responses to the opportunities which present themselves every day.

Marketing decisions typically are not made in one place, but rather in many places throughout the organization.  Pricing analysts as a rule don’t work within the same formal departmental structures as salespeople, advertising managers don’t share common information channels with trade spend executives, and so forth.  The advantage of understanding the factors that influence customer behavior can only be converted into predictive actionable recommendations if all the decision-making agents relevant to these areas are able to look at the same data and align their decisions for optimal effect.

Now, no piece of decision support technology can make people in different silos talk to each other.  To a large extent the capability to overcome the silo mentality lies within the organization’s own communications processes and related activities.  Most likely, organizations that are serious about this will be investing in some form of enterprise-wide technology like Enterprise Resource Planning (ERP) systems to ensure that the right data are available to anyone who needs it anywhere in the organization.  To provide the most precise guidance for customized marketing decisions, the analytical tools and support systems you use should anticipate the existence of an enterprise-wide platform acting as the system of record, and be able to seamlessly link into it.

Customizing micromarket-level decisions is not easy.  You need a combination of robust computational capabilities to crunch billions of terabytes of data, creative scientific approaches to solving daunting practical challenges like long-tail data sparsity, and organizational alignment to facilitate optimal decision-making from integrated decision support systems.  As tough as the challenge is, the effort has the potential to return a substantial multiple of your investment by directly addressing and providing guidance on those problems that are your own top priorities, in the context of your own unique marketplace.

These are all valid concerns.  The problem is that the decision makers are sitting in different parts of the organization, their objectives are often in  conflict with each other (or at the very least require trade-offs and compromises), and they are not armed with sufficient information to understand the broader impact of each price decision on firmwide performance.

Coordinating all these disparate pricing activities is a daunting challenge, but a critically necessary one.  Pricing is one of the single most important levers an enterprise operates to influence performance.  Various research studies have shown that a 1% increase in the average price of goods and services for a typical Global 1200 company can lift operating profits by more than 8.5%.  With stakes like these, decision makers cannot simply kick the can down the road and hope for the best.  So how do you solve your organization’s coordination challenge and align pricing activities for optimal performance?

Let’s break the coordination challenge into three component parts: where decision makers are sitting, what data they have access to, and how they are making decisions.

organizational silos can impede effective decision making

Most likely they are sitting in organizational silos.  The term “silo” is used to describe discrete islands of activities within the enterprise: each one self-contained and disconnected from the others. The term carries a negative connotation; yet it is in many ways a natural, probably necessary byproduct of the increased complexity of the business landscape.  Over the past twenty years the number of products, customer segments, geographic regions and sales channels enterprises have to deal with has exploded.  The activities that businesses have to perform to effectively serve their markets have multiplied in number and are far more specialized – hence the silo.

The challenge is not how to make the silo go away, but how to overcome the silo mentality.   Here is where we introduce the “what” factor: in addition to where pricing decision makers are sitting in the organization, we need to understand what data they have access to for making decisions.  What we are looking for is a way to ensure that people in different silos have access to the same information – that they have a transparent view into the demand environment from which to make informed, coordinated pricing decisions.

That is easier said than done.  Firmwide enterprise resource planning (ERP) systems can be an important first step by having a system of record and system of execution to provide integrated data visibility throughout the organization.  That by itself is a tough enough challenge – ERP system integration is a notoriously complicated, expensive, resource-consuming process.  But it is still only a partial step to achieving pricing coordination.  It addresses the issue of what data pricing decision makers can access and analyze, but not necessarily how they are using the data:

• How can a salesperson in the field obtain precise guidance about price recommendations for a specific customer-product combination?

• How could that same salesperson get additional insight about opportunities for cross-sell to increase share of basket?

• How can a pricing analyst at corporate headquarters create new pricing rules without a cumbersome internal process requiring support from the IT department?

• How can her manager respond in real time to an exception request from the field?

• Finally, how can all these activities happen seamlessly in a timely manner without being at cross-purposes with each other?

Answering the “how” question requires specialized tools that can perform the activities particular to each silo where pricing decisions are being made, while at the same time being able to connect into the system of record to make available the data necessary for common visibility.  For companies with thousands of customers and products you need the leverage of advanced science and powerful computational technology to make sense of the environment, better understand the behavior of your customers, and provide intelligent, informed recommendations armed with knowledge about demand signals, price rules, cost-to-serve, and the other critical factors that influence price.

This coordination challenge is not easy, even with the right decision support tools.  But remember the stakes – matching the right customer with the right product at the right price can have a profound effect on firmwide performance measures such as market share and profitability.    It’s a challenge you can ill afford not to solve.