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.

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. This can help turn trade spend into trade investment.

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.
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What are the implications of the words “pinpoint” and “optimal” when market behavior is so uncertain?

In other words, is it possible to find a single decision that will maximize the odds of earning a handsome payoff when the outcome of any decision is uncertain?  In a rather extreme example, in the highly uncertain world of gambling, can I make some decisions that are clearly better than others in light of the uncertainty?

Let’s say that all of a sudden I have the urge to gamble, and head for Monaco.  I don my tuxedo, and enter the Monte Carlo casino, where I see 3 different tables offering 3 different games.  I have €1,000 to spend, but the house has imposed the constraint that I must pick a single table and commit myself to that table for the entire evening.  Now, let’s say that at each one of the 3 tables, the wager amount for a single bet is €10 (admittedly, a far-cry from what I should be prepared to spend at Monte Carlo), which allows me to play 100 games (€1000 total wallet size ÷ €10 per game) at each table.  So, how does the evening unfold?

Since I am not a gambler, I will have to fabricate some numbers to convey the point.  Let’s say that Table A offers a 49% chance of winning, and each win produces €18 (for a gain of €8 based on my €10 bet); Table B offers a 10% chance of winning, and each win produces €85; whilst Table C offers a 32% chance of winning and each win produces €25.  Needless to say, everything is all but certain inside Monte Carlo, and as a rational man I should stay out.  But if I do venture in and wish to risk my money, can I pinpoint the table that will optimize my returns?

To answer this question, I must travel through several centuries of mathematical thought, and invoke the laws of probability.  I know that if I flip a coin a sufficient number of times, I can say with great certainty that 50% of the outcome will be heads, and the other 50% of the time will be tails).  Notice, the key here is that I must flip the coin a “sufficient” number of times.  Due to the vagaries of random chance, a small number of throws may not reveal the true nature of the coin – with just 7 coin tosses it is possible that all 7 times I see heads.  But, with 700 throws, it is quite unlikely that I’ll see 700 heads.  Instead, I’ll probably see close to 350 heads and close to 350 tails.  This conclusion is driven by a theorem known as the Central Limit Theorem, which was originally put forward by the French-born mathematician Abraham de Moivre.  It states that if we know the probability of an outcome from some event (like a coin toss), we will see that outcome occur as often as the probability multiplied by the number of times the event occurs.

In our hypothetical Monte Carlo excursion, the gambling event can occur 100 times (given my €1,000 allowance and €10 per gamble – i.e., per event).  This means I can expect the following (as I hear de Moivre’s voice from centuries past):

Table A: 100 Events x €18 Won per Event x 0.49 Chance of Win per Event = €882 Expected Winnings

Table B: 100 Events x €85 Won per Event x 0.10 Chance of Win per Event = €850 Expected Winnings

Table C: 100 Events x €25 Won per Event x 0.32 Chance of Win per Event = €800 Expected Winnings

With this arithmetic, de Moivre guides us to Table A – indeed, once can say that he has pinpointed Table A for not in spite of the uncertainty, but in light of the uncertainty. Now, it’s important for us to recognize that the power of de Moivre’s insights rest on understanding the uncertainty – i.e., on “quantifying the chance” of an outcome.  Let’s say that Table A is a Roulette Wheel.  As the host spins the Wheel and we all place our bets, it is impossible to state exactly whether or not I will win or whether or where the ball will land.  There are too many factors to humanely consider:

1. The bounciness of the ball
2. The initial rate of spin with which the host turns the wheel
3. The amount of lubrication in the bearings that bind the wheel to the axle
4. The amount of humidity in the room which can slow down the wheel
5. The air currents in the room when the Air Conditioning comes on
6. The initial height from which the ball is dropped onto the wheel
7. Etc, etc, etc.

And even after knowing all of these factors, the equation used to predict the balls final resting position is nonlinear and the solution is going to be chaotic, as shown in the figure above.  We simply cannot predict where the ball will land.  But, there is hope!

We can predict the probability of winning, and we can do so using 2 different methods.  First, we can look at the fact that there are 37 pockets on a European Roulette wheel, and the odds that our ball will land in any pocket is therefore 1/37 = 0.027, which means we have almost a 3% chance of winning.  Alternatively, we can take a sample of all the previous people that have played in the prior 6 months and look at what fraction have won.  This gives us what is known as a “Frequentist Probability”, and can be used within the context of de Moivre’s principles to help guide us to the “optimal table” on which to place our bets.  The discovery of these probabilities is one of the fundamental pursuits of the entire discipline of Econometrics, and has become pivotal to achieving Pricing excellence (further exposition on this important topic can be found in the “Modeler’s Mechanics” of our blogs, and within our white papers).  As shown in the figure to the left, this probability discovery process leans heavily on historical market data (and remember, the past does not provide a definitive glimpse into the future, but does provide a very good glimpse into the probabilities of many different futures), with a heavy dose of computing power to produce predictions that have so far proven to dramatically improve the pricing manager’s decisions when compared to using their human instincts alone.

For instance, companies will often either adopt an Every Day Low Pricing (EDLP) or a High-Low Pricing (HLP) strategy.  The former seeks to keep prices low by achieving enormous economies of scale and tight supply-chain operations, whilst the latter seeks to lower prices to almost unprofitable levels a few times per month for select products in the hope that it will consumer attention and fuel the sales of additional products in the store.  A major problem with HLP strategies is that the momentary dip in price can wreak havoc on demand predictions for not only the low-price product, but for other products that are swept up within the consumer frenzy.  In one of Canada’s largest retailer, we found that their ability to predict demand to within +/-15% of actual demand occurred only 32% of the time.  But, with probabalistic methods and the recent advances of scientific pricing, this same retailer was able to predict demand to within +/- 15% of actual demand about 87% of the time!  Note, we are not pinpointing the demand value here, nor are we optimizing which specific products to invent and stock.  Rather, we are pinpointing a price that will maximize demand and revenue by understanding the probability of the market’s needs.

I believe that in order to respect the important points that several respondents to the “At the Mercy of the Market” blog have raised, it is important for us to be careful and augment words such as “pinpoint” and “optimal” with the phrase “expected returns.”  In a world of uncertainty, predicting the expected results using the Laws of Probability rather than the absolute results using a Crystal Ball is indeed the best we can do.  It is extremely important for us, and all other scientists / modelers, to inform the audience that we are in no way aspiring to peddle a crystal ball.