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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4-Cs Series: Connecting to your Demand Signals in Real Time

In introductory college microeconomics classes students are exposed to the concept of price elasticity – that is, the predicted response of a buyer in terms of quantity demanded when a seller raises or lowers the price of a certain good or service.  In the real world, companies in competitive industries are continually trying to extend insights about elasticity and other behavior-response metrics across thousands of customers and products.  The problem with this is that real-world business problems bear very little resemblance to the theoretical examples contained in Microeconomics 101 textbooks.  First of all, customer behavior is very hard to pin down.  Numerous variables affect every translation.  How can we say with a high degree of confidence that a price change was the main cause of a change in demand?  Why not something else – perhaps an especially strong and persuasive effort by the salesperson to make the sale? Or something completely outside our control, like the weather that day?

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. Read the rest of this entry »

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4-Cs Series: Customizing Sales & Marketing Guidance with Micromarket Precision

What marketing decisions are most critical for your organization?  Are these priorities predictable or do they fluctuate with day-to-day changes in your market?  Do you have the right data, tools and support systems at hand to make the best decisions on an ongoing basis?  Is your organization wired to optimally deploy these tools to enable different organizational silos with access to  a common view of your demand environment?

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. Read the rest of this entry »

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Crunch the Numbers that Really Matter (hint:they’re the ones that relate to downstream demand)

A New Approach to Trade Spend for Foodservice Manufacturers

There is no shortage of quantitative analysis in the trade spend practices of foodservice manufacturers.  Unfortunately, very little of this analysis helps give decision-makers insights about the effectiveness of their trade spend programs.  The numbers being crunched do not relate to signals about actual downstream demand, but rather to the formidable mountain of claims from their distributors.  These claims come in all manner of data formats and accounting entries and it typically takes armies of brokers, salespeople and financial staff to figure them out.  After all the cumbersome and error-prone line-by-line calculations to validate claims are said and done, you are no more informed about the profitability or the potential risks associated with any given program.  No wonder there is widespread dissatisfaction with the effectiveness of these programs.  Over 75% of manufacturers in this sector consider their trade spend initiatives to be inefficient, according to the 2010 MarketIntelligence Foodservice Trade Survey. Read the rest of this entry »

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