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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thirst-quenching - but is it fairly priced?

Here’s a test of fairness.  Imagine you are lying on the beach on a hot summer day and find yourself craving a cold, satisfying beer.  What price would you be willing to pay to quench your thirst?  Now imagine two alternative scenarios.  In one, the only place within walking distance to buy a beer is the poolside bar of a swanky five-star beachfront hotel.  In the other, there is a rather run-down beachfront grocery store that sells beer.  Imagine further that both the hotel and the grocery store sell the exact same brand and type of beer.  Does your maximum price point change depending on whether you think you are getting the beer from the hotel or the store?  Do you think it is fair for two different establishments to sell the same commodity for a different price?

Those questions were at the heart of a study by a team of behavioral economists and reported in the Journal of Business in 1986 (“Fairness and the Assumptions of Economics” by Daniel Kahneman, Jack Knetsch and Richard Thaler).   Participants (playing the role of the thirsty beachgoer) were told where the beer would come from (ritzy hotel or rundown grocery store) and asked what their maximum permissible price would be.  The results were interesting: respondents who thought their beer was coming from the downmarket store were willing to pay a maximum $1.50 while those who were told the beer would be purchased at the luxury hotel were prepared to shell out $2.65.

What’s so fair about that?  We have to assume that, give or take, the procurement cost to each vendor was roughly the same.  The results of the study seem to indicate a calculus in the minds of the respondents that the beer will inevitably cost more if it comes from the hotel, so they were willing to adjust their own demand curves upwards to meet the perceived point of supply, as opposed to boycotting the transaction opportunity because of a perhaps unfair price differential.  Instinctively that makes sense to me.  Putting myself in the position of the parched beachgoer in the shadow of the ritzy hotel I think I would be more likely to go along with the reality of the $2.65 hotel beer than take a principled stand on the arguable unfairness of a 77% markup.  My experience tells me that it’s simply the way these things work, like it or not.  The results of the Kahneman study say largely the same thing: despite a potentially strong case to be made for the unfairness of the hotel’s pricing scheme, most people willingly go along with its reality and adjust their own internal pricing mechanisms accordingly.

Most of us have been somewhere where we have paid much more for something than we would otherwise – the infamous mini bar and local telephone call surcharges in hotel rooms come to mind.  Ordering a bottle of wine in a restaurant brings about the same experience – I know that a particular 2005 Gigondas retails for $18 at the local wine store but I’ll have to shell out $40 for the same quaff over candlelight and soft music at that romantic little cuisine provençale place down the street.  That $8 bag of peanuts or $40 bottle of wine become reference points – prices we anchor in our brains as reflective of actual experience, and call upon each time we are presented with similar transaction opportunities.  In this process a subtle shift takes place; we are no longer focused on the inherent fairness or not of the underlying state of affairs (high markups in restaurants and hotels) but rather on the fairness of any transaction offered to us in relation to its reference point.  So, going back to the sun-baked beach, if someone offers to go buy a beer for me and tells me the only option is from the hotel bar then my brain calls up the reference point of prior hotel-based transactions and I set my maximum price accordingly.  That $2.65 is an imprecise stab at establishing a benchmark for what the hotel bar should charge for my drink, and as long as it is somewhere in that neighborhood I am okay with the purchase.

salamancan scholars found fairness in the micromarket

Luis Saravia de la Calle, a member of what was known as the Salamancan School of the Late Scholastic period in 15th century Spain, stated that “the just price of a thing is the price which it commonly fetches at the time and place of the deal.”  Interestingly the Salamancans strongly influenced the philosophies of later Austrian School thinkers like Joseph Schumpeter, but also seem to resonate with the more recently emergent tenets of behavioral economics avatars like Kahneman (the 2002 Nobel laureate in economics) and the late Amos Tversky.  In this line of thinking fairness is not some arbitrary notion of a justifiable price based on component costs of production and delivery (like a cost-plus model); if it were, then more people would throw down the gauntlet at the prospect of shelling out 77% more for the same beer just because of where it happens to be sold.  It’s more along the lines of de la Calle’s notion of what prevails at the “time and place of the deal” – which is also what we at Sentrana think of as Pricing 4.0 – the intricate configuration of the needs and propensities of each individual customer at the point of interaction with each individual product.

We may not be able to pinpoint the precise meaning of fairness at all times and all places for all people.  But by better understanding the reference points that anchor buying and selling decisions in the micromarket we have an improved chance of achieving results that are both fair and profitable.

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.