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 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.

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.

Uncertainty is the 800-pound gorilla in the room when it comes to price-making decisions.  Buyers and sellers operate from positions of considerable uncertainty in approaching transactions with each other: buyers have only partial information about the features of what they are buying such as quality, reliability, service support and the extent to which a given offered price may be reasonable in relation to these features, while sellers have a limited perspective on what demand exists for their products and what combination of levers such as price, assortment and marketing could influence that demand.  Buyers thus face the risk of inequity in their exchange – paying more than the intrinsic worth of the object acquired, while sellers face the risk of their transactions being unprofitable and, if persistently so, driving them out of business.

Having worked for a number of years in the investment industry I offer up a useful model from this corner of the economy for dealing with uncertainty.  In the investment world uncertainty commands a price: investors demand more compensation, in the form of return on investment, for assets that exhibit higher levels of short term volatility.  Participants widely view this as fair: it is not thought ‘unfair’ that an investor in, say, a 5-year U.S. Treasury note earns a dependable return of 5% whereas someone who takes a punt on the shares of a small-cap biotechnology company may potentially earn over 25% in the same time period.  There is more likelihood that the value of the biotech shares will plunge in the wake of unexpected news or that the company will go out of business than there is of the U.S. government failing to honor its legal obligations to bondholders.  A capitalist economy offers the potential for greater rewards to the investor willing to assume greater risk.

what matters is the customers who don't come

How is this concept analogous to the uncertainty faced by businesses that sell in markets for real (i.e. non-financial) goods and services?  I thought about that the other day while dining out at one of my favorite Northern Italian restaurants, located in a trendy urban area chock-full of good eats.  As I looked around the dining room on a late September Tuesday evening it occurred to me that the uncertainty this business experiences on a daily basis is plainly visible: the number of empty seats during peak dining hours.  Restaurant patronage is a notoriously fickle notion to quantify and is subject to considerable fluctuation in real time.  I wondered about the methodology through which this restaurant’s owner translates the uncertainty of empty seats into the revenue model. It seems to me that the real art to the formulation of this model is not based on the tables that have patrons sitting at them, but rather the ones that are empty.  The hard part of revenue calculation is not figuring out what the average occupied table will spend on any given night – it is dealing with the uncertainty of those empty tables.

Now in theory, the owner could simply build an ‘uncertainty factor’ into menu prices as a partial compensation for the prospect of empty tables.  In practice this is unlikely, and the reason why it is unlikely brings us back to the concept of fairness.  Prospective restaurant patrons (including yours truly) are very unlikely to be sympathetic to the notion that they should have to pay a higher price for the verze e luganega because it helps the owner’s revenue model – to us patrons, that is an unfair offloading of the owner’s problem onto us.  We don’t even have to explicitly know the owner’s motivation.  Discerning customers have plenty of access to comparative information – from other restaurants in the area, our social networks, Internet reviews and so forth – to form strong perceptions of the fairness or unfairness of prices at any given spot.  We will wield our verdict of ‘fair or unfair?’ with much self-righteous certitude in making future dining out decisions.

So what is a ‘fair’ way for our poor restauranteur to manage uncertainty toward a more profitable outcome?   Rather than accepting empty tables as a given fact of life the owner can try to figure out intelligent ways to fill them.  Who may be walking by the restaurant in the late afternoon, or working in a nearby office building and considering an after-work dining outing with colleagues?  What combination of factors might entice these and other prospective patrons to choose this establishment over numerous other choices?  Is there a way to figure out attractive deals that would lure certain prospective customers and to surgically target each such customer with a unique offer?  Yes – it is possible through scientific micromarketing techniques that optimize at the granular level of the customer-product interaction.  The next question – if it is possible, is it also fair?

All those centuries of debate on the notion of fairness and justice in economic commerce now come back into this discussion.  Paul Krugman expressed a concern about this in a New York Times op-ed piece titled “What Price Fairness?” all the way back in October 2000, when price optimization methods were in a much, much earlier stage of development.  His remark (related to the notion of dynamic pricing in general) was that while it may be “arguably good for the economy,” dynamic pricing is also “…unfair: some people pay more just because of who they are.”  Sitting in the restaurant, I imagined a hypothetical case where the gnocchi with sweet basil pesto, which I ordered for the menu-listed price of $14.50, was being enjoyed by the gentleman at a nearby table for $11.30 simply because the restaurant’s micromarketing system contacted his iPhone with a targeted discount offer just before he left his office just down the road.

Is that unfair?  I don’t think so.  Who wins and who loses in this scenario?  The gentleman who receives the offer wins – he gets the opportunity to enjoy a dining experience targeted to his personal preferences.  The restauranteur wins by filling a table that would otherwise be empty, reducing uncertainty and improving the nightly profit intake.  I am still enjoying the gnocchi I ordered at full price and am no worse off than I would have been otherwise; having already concluded that $14.50 is a reasonable price for the dish and ordered on that basis.  On a broader social scale the notion of micromarket pricing does not discriminate between the two of us in a way that I would deem unfair.  I have my own set of preferences that may benefit me with a different offer set on a different day.  In fact, were I to be made aware of the circumstances under which the gentleman got his gnocchi for a lower price, I may well be inclined to leave my own contact information with the establishment in anticipation of future benefits.

There is a road ahead before scientific micromarketing becomes a more accepted feature of B2C commerce situations like that of my hypothetical imaginings while dining out (no doubt helped along by the delights of a 2003 Castello di Camigliano Brunello).  And I expect that a vigorous debate about the question of fairness versus optimality will be part and parcel of this journey.  At day’s end, though, I believe the two are fundamentally compatible.

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, when we decide what the model is going to ignore and what it is going to retain, when we say “just because”, that is the point where we have the chance to either build a robust foundation that can withstand the slings and arrows of a continual barrage of economic X-factors or an elegant, commercially viable sandcastle that gets washed away with the next high tide.  If we are going to apply foresight, this is where we have to apply it.

Not that applying foresight is easy, of course, and that is why at Sentrana we are engaged in a continual process of building better models by going back to the basics, asking seemingly simple questions that have complex answers, and trying to ensure that we get it right when we get to that point where it is time to say “just because”.  In our world of Scientific Micromarket Management saying “just because” at the right time can, for example, open up the vista of a complex demand environment to reveal a unique configuration of product mix opportunities at optimal pricing points.  It’s good that we are seeing a heightened level of public discourse about these issues: models may seem to be abstract notions for abstemious minds, but they have real-world economic consequences.  Getting it right has never been more important.

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.

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.