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 Market Intelligence Foodservice Trade Survey.

Pricing signals matter for getting the most from trade spend activities

Decision-makers at foodservice manufacturers need a new approach: one that creates greater visibility throughout complex information chains; and applies analytical methods in order to better align and optimize trade decisions with pricing and other key marketing levers.  Abundant data exist, as do the analytical methods to gain insights from them.  Better measurement and analysis can lead managers to more profitable decisions for themselves as well as their trade partners.

Low-tech, non-standardized processes generate waste

The hodge-podge of disparate programs scattered around the organization with a variety of process and data formats do not easily lend themselves to effective measurement, performance tracking, or coordination with other key marketing and pricing decisions.  Programs tend to have non-standardized and duplicative contracts, cumbersome claims and dispute resolution procedures, and generally low-tech operational processes.  Manufacturers have little way of knowing whether the dollars they are putting into these programs are having measurable impact at the operator and patron level or whether they are simply staying in the pockets of the distributors.  The complexity of the information chain creates a tremendous amount of waste in the system over time that negatively impacts profitability throughout the chain.

New trends in distributor pricing mean opportunities for manufacturers

Such archaic practices stand in sharp contrast to a sea change taking place in distributor pricing: namely, the growing trend of setting prices according to downstream patron and operator demand rather than based on an arbitrary mark-up on the zero sum negotiated price between manufacturers and distributors.  Scientific pricing, an increasingly prevalent practice in the food services wholesale space, offers predictive demand insights for each potential product and customer combination.  Prices thus contain more information about actual downstream demand, enabling products to be pulled through the channel rather than pushed downstream based on the subjective outcomes of manufacturer-distributor negotiations.  Manufacturers have an opportunity to use the same demand signals that inform scientific pricing to guide a more accurate allocation of their trade funds to drive greater overall volume and profit.

Social networking is now standard operating procedure for many restaurant-goers

Let the Facebook Generation work for you

These demand signals are especially relevant because technology has thoroughly transformed the way that retail operators (such as restaurants and caterers) and their patrons communicate.  Digital social networking is now an established way of life for a rapidly growing group of Americans, the majority of whom fall within the most desirable demographic segments of the consumer market.  Sites like Yelp, Urban Spoon and TripAdvisor ensure that salient details about a given restaurant’s menu, prices, food quality, social environment and numerous other attributes are readily available at the fingertips of smartphone-wielding prospective patrons preparing to decide where to gather and dine for the evening.  Clearly, operators have strong incentives to match demand with available supply.  For manufacturers this means abundant information coming from points downstream that can help inform smart trade promotion and pricing decisions.  Decision-makers can gain insights about demand as it relates to geographic and demographic segments; further refine this understanding as it pertains to product categories; and experiment with alternative what-if scenarios to predict the effect of various trade promotion and pricing decisions on demand.

More about trade spend on Sentrana’s blog

In the coming weeks we will be spending some more time on this blog site looking in detail at different aspects of the trade spend challenge and the opportunities we see for foodservice manufacturers to improve performance.  Forthcoming areas of focus include: collaborative campaigns to create win-win programs with trade partners; trade program design; issues related to program execution; and other topics that can help reveal active ways to turn trade spend into trade investment.


To answer this question, let’s begin briefly by dissecting what IBM has introduced. Imagine that you are receiving a continuous stream of data, such as stock prices on the Nasdaq. These figures must be quickly analyzed so that the proper buy and sell orders can be placed. Suppose that you also need to base your decisions not just on the Nasdaq prices but also the numbers figures coming in from dozens of other exchanges.

No Match for the Information at Hand

Just for fun, let’s say that you also want to include up-to-the-second weather information of 20 cities in your decision process. We can safely say that there are not a great deal of human beings in the world that could look at all of this information in the blink of an eye, make the best possible decision, and then repeat the exact same process a split-second later with new information. Well, this example highlights the best application for stream computing. In the stock price analysis outlined above, we needed to evaluate information coming to us on a continuous basis from a number of different sources, evaluate it, and deploy the result in a desired manner. Let’s say that we wanted analyze the correlation between Motorola’s stock price movements and the price movements of 50 other stocks on 50 different exchanges, as well as the outdoor temperatures of every single world capitol. IBM’s breakthrough allows you to do this and also update your analysis in real-time as new information is received. That’s the good news.

The bad news is that there are real limits to the insight that can be obtained this way. There are really two areas in which stream computing can be applied: link detection and itemset detection. These two are related – link detection tries to identify events that are correlated, and itemset detection tries to identify events that are co-incident (i.e. the prices of GE and Sony stock went up, AND the price of Motorola went up).

Not Quite Forward-Looking

Itemset detection algorithms have progressed to the point where they can be accurate and effective without taxing the computer’s resources too heavily. IBM’s accomplishment lies in deploying this algorithm for real-time itemset detection on unprecedented scale, but ultimately, the business insight that can be extracted from correlation and co-incidence is limited. Frankly, IBM’s press release statement that System S allows users to “…create a forward-looking analysis of data from any source,” is a bit of a fairy tale. Any competent manager will tell you that “forward looking analysis” based only on correlations in historical data is not a recipe for success. With itemset detection (which is closely related to and sometimes confused with association mining), you can now look at more data more quickly than ever before, but you can’t ultimately do much new with it.

Truly forward-looking analysis requires going several steps beyond itemset detection, because making accurate predictions about events that have never happened requires that you do more than analyze past correlations. For example, how could you predict the highest price at which a customer would be willing to buy a product that they have never purchased before using only their previous purchase information? Or how about predicting the effect an advertisement will have in a market that you have never advertised in before? Integrating techniques that are better geared for this analysis such as principal components analysis, collaborative filtering, and others make these types of predictive analysis possible, not to mention significantly more robust than correlative analytics. The downside of integrating of all of these techniques into your analysis is that it becomes extremely expensive computationally. Other related breakthroughs are required to make this feasible in a business setting (see Cheating Your Way Into Business Visibility). To make better informed decisions with an understanding of the likelihood of possible outcomes, managers and policymakers alike need a way to reduce the uncertainty inherent in them. The aforementioned “virgin sale” process is a very micro-example that exemplifies this problem. Sadly, being able to comb through data more quickly by itself does not get us to that higher end-state. IBM can claim a notable achievement in computing, but hailing it as a quantum leap for businesses decision support understates the true challenge of making better business decisions.