Increasing Demand in a Flat-Growth Environment

Economic growth in the US continues to face many daunting challenges. Companies across a wide range of industry sectors are experiencing top-line sales growth that is anemic at best, and in many cases negative. Foodservice is no exception: belt-tightening by households certainly impacts the food away from home sector. In the absence of the natural demand increase provided by a growing economy, what can enterprises do to improve their top-line performance?

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.

Manufacturers and distributors approach the growth challenge in different ways. For distributors the goal is to grow sales in the category across all products and brands; while for manufacturers the key goal is to sell their own brands at the expense of those of their competitors. At first glance it may seem like these goals are at cross-purposes. If a collaborative category management program helps the distributor capture a sale that would otherwise have been made by a different wholesaler, then that distributor generates income it otherwise would not gain. From a manufacturer’s perspective, however, this may amount to little more than channel shift – the same case of tomato sauce, say, being sold by Distributor A rather than Distributor B, and thus not a net gain to the manufacturer’s own income statement.

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.

Not every opportunity will be realized, of course. There will be plenty of occasions when, for whatever reason, the end customer is not convinced to make the switch and continues to buy through the current distributor. In our experience, though, robust predictive technology contributes a significant positive impact with the potential to enjoy success rates well in excess of traditional penetration campaigns. In foodservice, manufacturers and distributors are only just beginning to realize the potential benefits of collaboration and establish platforms to leverage their respective contributions. With the economic landscape continuing to look challenged for the near to intermediate term, the timing could hardly be more fitting for taking this collaboration to the next level.

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Working Back from the Point of Sale

Solving Three Key Challenges to Profitable Category Management

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.

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Managing the Category Beyond SKU Rationalization

SKU proliferation has been a fact of life in foodservice much as it has been in other industries in recent years. Proliferation creates considerable pressure throughout the value chain to make tough decisions about SKU assortment across numerous product categories. In foodservice the problem is not shelf space as it is in retail; rather, it is the limited amount of product information that a sales representative can manage in his or her head in order to match the right products with the right customers on a daily basis in real time. As managing assortment has grown more complex, manufacturers and their downstream partners have looked to SKU rationalization to reduce streamline product offerings and manage inventory costs for improved category performance. While SKU rationalization can address these challenges to some extent, it does not get to the core of the problem. The most effective way to improve category performance is to increase demand for products in that category. In turn, the best way to grow demand is to seamlessly match unique customers with the products whose attributes they most highly value. This requires a holistic category management approach, supported by robust data analytics that can take into account the key levers of demand – assortment, promotions, pricing and purchase timing.

The Importance of Collaboration

In foodservice, manufacturers and distributors are the logical partners for a collaborative category management venture. Manufacturers possess deep insights into the product attributes that drive demand for specific customer types, and have a strong understanding of how to manage assortment. On the other side, distributors have the benefit of daily transaction data at a very granular level – what quantities of products in the category are being sold to what locations with what frequency. Combining these insights – ideally through a single integrated data management system able to process inputs from multiple sources and generate insights and actionable recommendations to the relevant decision makers – can create a coherent, unified picture of demand that provides a basis for specific assortment, pricing and promotional activities to grow sales.

Reducing the Guess Factor

A traditional SKU rationalization program may analyze aggregate transaction histories for all the SKUs in a category and mark for elimination some subset of those that occupy the so-called “long tail” – products with sparse data records due to infrequent activity. A typical goal in this regard may be to eliminate 20-25% of all SKUs in the category. The problem with this approach is that without an appropriately detailed level of analytical insight, managers are left to guessing what the resulting effects will be on sales. Transaction frequency is only one variable in presenting a composite picture of demand. For example a certain product may transact on an infrequent basis only, but it may also be a popular niche product with attributes highly valued by major customers. What will the sales impact be of not having this niche product available when a major customer wants to add it to his or her market basket? How can decision makers recognize and differentiate between niche products and other long tail denizens that really deserve to be eliminated from the active product line?

A holistic category management solution, driven by advanced predictive science, can supply answers to these questions. By integrating product attribute knowledge possessed by the manufacturer with quantity and purchase timing data known by the distributor, the system can make recommendations about when to stock the low-frequency but desirable niche items with a higher likelihood of coincidence with the customer’s purchase decision. Techniques such as Hierarchical Bayesian modeling help overcome the analytical challenges typically presented by sparse data. Rather than losing all or part of the customer’s market basket for the sake of an incremental SKU reduction – in most likelihood a losing proposition – the result is retaining a satisfied customer.

Focus on Growing Demand

This approach to category management program shifts attention away from simple cost reduction through inventory rationalization and focuses instead on the revenue side of the equation – growing demand in the category. There are two critical requirements for this to be successful. First, the data management platform must be sufficiently granular to provide meaningful insights at the level of every customer and every product (for example as in the long tail analysis described above). Second, the platform must seamlessly transform into a practical tool which sales representatives can use in the field. This is a particularly important requirement. Foodservice sales & marketing representatives as a rule have very little time for incremental effort above and beyond their existing selling and administrative responsibilities. They certainly do not have sufficient time to juggle multiple sales tools offered by multiple manufacturers acting in the role of category manager. The ideal tool is one with which the representatives have existing familiarity (to avoid time-consuming learning curves for new processes) and which can seamlessly integrate data from multiple input sources.

Manufacturers and distributors need more than just a rationalization program to optimize performance at the category level. A holistic approach, supported by robust analytics delivering actionable real-time guidance to sales professionals in the field, can improve category performance all along the foodservice value chain.

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Fair Price, Optimal Price

Businesses seek to maximize the value they can obtain from their revenue models.  Price is the key lever decision-makers can operate to influence revenue, and in recent years a growing number of businesses have sought to implement strategies for actively managing the price lever – strategies such as demand management and revenue optimization.  However businesses are also highly sensitive to the perception by individual consumers and the society at large that their prices are fair, in other words that they do not violate widely held individual or societal norms.  Fair pricing matters – it matters to me, and to you, and perhaps ever more so in a climate characterized by economic uncertainty, downward pressure on demand and a perceptible decrease in the citizenry’s trust of public and private institutions.

Fortunately for business decision-makers, fair pricing and optimal pricing are not at odds with each other but can comfortably coexist.  Over the course of the coming weeks my colleagues at Sentrana and I will be approaching the rich topic of fair pricing in a series of exchanges on this blog.

debating the age-old question of fair price

What is a fair price?  This question has perplexed humanity throughout history.  Leading thought output of the ages, from Aristotle’s Nicomachean Ethics to the Summa Theologicae of  Thomas Aquinas, Pierre de Fermat’s probability proofs and Adam Smith’s classsical economics, have all weighed in with considered opinions on the fairness and justness of alternative ways to price economic goods and services, and the debate continues today.  A series of letters exchanged between Blaise Pascal and Pierre de Fermat in 1654 is often regarded as a primal cause of the development of modern probability theory: this exchange was actually an attempt to establish a scientific basis for the notion of fair price.  In his paper “The Unity and Diversity of Probability” Rutgers professor Glenn Shafer shows how these letters created hypothetical games of value that we today can recognize as the application of probability methods to defend a price as ‘fair’ under conditions of uncertainty. Read the rest of this entry »

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In Economic Modeling, Can Hindsight Lead to Foresight?

In thinking more about my last posting here on failed Wall Street quant models and the dimensionality curse I started to wonder whether we could ever be more than the archetypal Monday morning quarterbacks:  commenting brilliantly on all the reasons why X should never have happened, after X has already happened and done its damage.  Can the mistakes of hindsight lead to foresight?  In other words, can we apply foresight to develop “good” economic models that won’t blow up in our faces?

In trying to answer this postulation we must go back to examine the eternal challenge of good modeling: how to create a simplified representation of reality that in ignoring many real-world features still manages to convey an inherently robust facsimile of the real thing.  For example, one of those maps of New England you buy at Exxon gas stations can serve as a good model for getting you from Hartford, CT to Boston, MA even if it ignores most of the streets and alleyways and other real-world detail that exist along the route.  In their book “Complex Adaptive Systems: An Introduction to Computational Models of Social Life” John H. Miller and Scott E. Page observe that the “ability to ignore is a crucial component of scientific progress”, using the image of a parent’s being able to respond to the incessant “why” questions of a three year old child by saying “just because”.  The trick, as the authors point out, is knowing when (and perhaps more importantly when not) to say “just because”.

While I wholeheartedly agree with that assertion I don’t think that it quite gets us to an adequate level of comfort in applying foresight to the creation of good models.  In his fascinating book “The Origin of Wealth” Eric D. Beinhocker points out that economic modeling took what many consider to be a wrong turn back in the latter years of the 19th century when leading thinkers of the day like Leon Walras and William Stanley Jevons borrowed heavily from the referential context of physics to create models for explaining economic activity, including such notable concepts as a mathematically representable state of equilibrium that continue to serve as the conceptual foundations of modern economics textbooks.  As Beinhocker elaborates, the problem with these models was that some of their fundamental assumptions – like the perfect, robot-like rationality of human beings in making economic choices – didn’t seem to simplify reality as much as contradict reality.  Thus we find ourselves in the present ruminating over the precise, mathematically elegant language of physics and the complex, evolutionary language of biology and debating whether a choice of the wrong science by the founding fathers of economics back in the 19th century led to the failure of models to adequately explain much of what is going on in the economy today and in particular the string of boom-bust upheavals that have become part and parcel of the last 20-odd years of economic activity.

I still don’t think we are there yet in getting closure on the foresight question, but we may be getting closer.  To tie in the strands of thought presented by Miller & Page and Beinhocker, when we get to those basic defining assumptions, Read the rest of this entry »

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Forget Your Competitors, The Power to Consistently Lead Your Market Lies In Understanding How Every Customer Values Your Product

Far too often, we have companies seeking our expertise to ascertain their competitors’ competitive strategy vis-à-vis their pricing, as if this will provide the magical insight they need to help them maximize their own revenues. My advice: save the detective work for Colombo and forget about your competitors! Your bottom line profits should not hinge upon a competitive response strategy that reacts to your competitors’ price moves, where you surrender control over your revenue structure and end up locking your firm into a race-to-the-bottom pricing with the rest of the industry. Escaping this destructive cycle lies in focusing relentlessly on your customers rather than your competitors. If you’ve read the news in the last 10 years, you may have realized that your customers are the most informed consumers in the history of the world! They are utilizing every available resource, from various news and industry websites to trade magazines to word-of-mouth gossip to Oprah to… well, even your price helps them determine their perceived value of your product. They are better informed about their purchases than ever before, but I wonder if you are learning as much about them and how they view your products?

Here’s an example to help you understand the magnitude of the problem your organization is facing: you sell thousands of products to tens of thousands of different customers each and every day, which is equivalent to millions (if not billions) of distinct customer-product interactions every day – impossible for even the most experienced sales managers to analyze individually. Now grab a pen and some paper and write this down: every sale is an interaction whose revenue can be uniquely maximized! Most companies fail to detect the subtle changes in their customers’ preferences over time, leaving significant profits on the table. And hence the reason for the detective work we’re often called to do; companies don’t realize they have all of the necessary data to maximize revenues right under their noses.

The solution here is Scientific Micromarket Management, which makes it possible for organizations to assess how each customer values your product and offer exactly that price every day in every market. Sure, we may be talking pennies and nickels here, but if you multiply these adjustments by the millions of potential customer-product combinations, then multiply these daily adjustments over the course of a year, and you will realize the significant amount of impact this will have on your bottom-line. Capitalizing on these billions of tiny demand shifts with a dynamic pricing system more targeted than human intuition enables companies to finally understand why every single customer buys what they buy from you and what they are willing to pay for it every time. This is far more comprehensive than any pricing strategy; this is a complete revenue optimization solution. Your customers are getting smarter about you, I think its time you got smarter about them.

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The Price You Pay for Not Changing Price

The WSJ ran a story on 3/10/09 on the financial success of McDonald’s Corp. throughout the present recession. Since the company is one of only two DJIA members (the other being Wal-Mart Stores, Inc.) to have ended 2008 by posting a gain for the year, it is perhaps only fitting that the Journal devote a few inches to McDonald’s. The only student to pass a difficult exam rightly deserves a gold star. But amidst the discussion of McDonald’s zeal for succession planning, controlled expansion and keeping a lid on costs in the face of the last year’s commodity price swings, one item deserves more attention than it received: McDonald’s is encouraging individual locations to experiment with prices.

Restaurants sit at the crossroads of both cost and demand volatility. Much to their detriment, companies such as McDonald’s often buffer both their customers and their upstream suppliers from feeling the financial impact of this volatility. Now McDonald’s is at least hinting that it wants out of this arrangement, and our experiences working with multi-billion dollar partners in the food distribution industry points to this being a wise move. We have long observed significant daily fluctuations in food prices across all categories. Couple this with the effect that a strong dollar can have on McDonald’s overseas business, and it quickly becomes clear that understanding how much a customer is truly willing to pay for a menu item is of huge value for a company so proud of its billions and zillions served.

The real question is why don’t more restaurants (or any number of businesses for that matter) treat their price as the valuable asset that it is? It is not overly difficult for a restaurant to approximate a schedule of demand and create several different menus with prices tailored to different Cost of Goods Sold (COGS) environments. For a restaurant grossing $500,000 in revenues annually, every 1% increase in sales corresponds to a $5,000 improvement to the top line (subtracting the printing costs later). In our experiences in food distribution, a 1-2% increase in the organization’s top line can translate into a bottom line improvement of over 8% – an observation that we have seen replicated in numerous industries. Projecting forward a few years, I would be willing to bet that the majority of companies with the highest valuations among their industry peer groups will also be the ones that are trying to actively shape demand through their pricing strategies.

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