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Win-Win
Trade Planning
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Dale
Hansson
Client Director, ACNielsen
David Singer
Category Development Manager,
Heinz
Today’s environment for more information, faster, at more
discrete levels of geography has resulted in the change with
which we are able to communicate information. What were once
presentations and sales stories based on simple, sometimes outdated
top-line results are now materials filled with detailed analytical
insights for particular regions, retail accounts and consumer
target groups. In part due to technological advances, this has
led to new thinking about what defines a win-win solution for
manufacturers and retailers.
For Heinz U.S. Consumer Products, the intention was to find
the right mixture for trade planning by account. The goal was
to provide sales managers and broker business managers with
hard-hitting data that recommended the right everyday base price
point, the right promoted price point and associated tactic
(what level of feature and display or TPR, how many should be
run, how deep should the price point be driven), all with the
purpose of delivering higher annual dollar and unit sales—and
most importantly, profit—for both the retailer and Heinz.
Recently, Heinz U.S. Consumer Products began evaluating how
Regular Price and Promotional activity affects sales and profits
for manufacturers as well as retailers on one of its key brands.
The work outlined in this article consists of two parts:
Regular price regression model that looks at the influence of
absolute price as well as the price gap to its competitors on
sales and profits;
Promotional model that looks at what promotions are working
and which are generating the most profitable volume.
Additionally, we evaluated marketplace dynamics by integrating
ACNielsen Homescan panel purchase data. This information allowed
us to evaluate shifts in retail channel purchasing, compare
categories, evaluate consumer based opportunities, and begin
to formalize our approach as to where to focus our analytical
efforts.
Then we shifted our focus to store movement information, and
based on our consumer knowledge, we utilized price and promotion
modeling to help drive profitable business across brand portfolio
and retail partner businesses.
Determining how to take action against price and promotion information
is sometimes like playing a five-dimensional chess game. Two
parts make up “regular” price—your absolute
price elasticity and price gap elasticity. The other three are
promotionally driven—the type of promotion, the frequency
and the discount. You can reach a volume target by concentrating
on one of these elements, but not as profitably as when managing
all of the pieces. In our example account, we will use all of
the pieces to come up with the best strategy.
Elasticity Defined
Elasticity describes the effect on volume when you change your
price (up or down). It is explained in increments of 1%. For
instance, -1.88 elasticity means if I change my price 1%, I
will have a 1.88% change in volume. Elasticity is traditionally
expressed as a negative exponent. One way to think about it
is that volume always moves the opposite way of price and it
is similar to compound interest. It is usually shown in a curve
where the higher the price change the greater the sales loss
and the steeper the curve [See chart 1].
For this project, we created account level elasticity by developing
and running detailed models based on our ability to clearly
identify the supply and demand features of individual stores.
This gives us the ability to evaluate activity in stores based
on the types of characteristics they have and the profiles of
consumers that shop in them. This detail allows us to better
identify how price change might impact one account differently
than another—even when they are in the same market, or
even stores within a chain. It allows us to better understand
the potential for one product versus another on a store-by-store
basis. Additionally, this detail also provided the ability to
better measure regular price barriers to see if there is a greater
than average sales loss at particular price points.
We also utilized the same modeling process in evaluating trade
promotion activity and volumetric performance. This is important,
as it provides us enhanced ability to predict volume and profit
as we change different variables at the retail level.
In addition to price change, gap change, promotion type, frequency
and discount, there is a great deal of flexibility that can
be explored. For example, one can incorporate brand- and retailer-specific
information into the model, like specific list and ad prices
or special events within a retailer. Other variables to evaluate
could include increases in advertising or brand growth efforts
of new products from a marketing perspective, or things like
forward buy, special event programming and fixed and variable
program costing schedules.
Buyer Conversion Is a Factor
One other factor we employed in developing our path was to understand
the opportunity for different retailers in terms of their ability
to attract and retain consumers. Measured by “buyer conversion,”
if we can quantify the amount of lost sales to an account, we
now have the basis for a meaningful conversation [See charts
2 and 3].
From a manufacturer perspective, Heinz wanted to gain more utility
for their trade investment and sought to attack forward buy,
high fixed fees and margin creep, with the final goal being
to gain a better return on trade investment by event. At the
end of the day, they were not looking to pull money away from
the retailer, but rather, to re-invest it more effectively to
drive profitable sales.
As complex as modeling sounds sometimes, there are really several
basic measures we used in discussing this with our field sales
organization. Those were annual dollar profit and sales, annual
unit sales, and variable and fixed cost measures. These lead
us to determining an optimal mix. Note that the end users have
been trained and are empowered to take over and own the process
moving forward. Also, when done well, it can be used to simulate
potential activity for decision-making in simple, easy-to-use
formats [See chart 4].
A Real-Life Example
The Category Manager requested greater frequency and deeper
discounts but was initially unwilling to give up fixed fees
or margin. By including him in the session, he had a direct
hand in owning the plan—and agreed to hybrid F&D/TPR
approach that has turned his category around.
Heinz saved close to $1 million in inefficient trade spending
and grew dollar share and profit. The Category Manager grew
his category 9% and was recently promoted to GM.
Chart 5 outlines another example from an area of the country
that likes to run “high low” and has a special affinity
for “buy one, get one” (BOGO) pricing. The broker
and the buyer were quite reluctant to change until they had
an opportunity to participate in a live planning session using
the tool.
Heinz was able to use the information available, and the tools
developed as a result, to convince them to dial back the fixed
fees and compress some on their margin. We were still able to
run BOGO events, grow his category 4% and meet our profit objectives
while saving $750,000 in inefficient spending.
If we understand the approach that the consumer uses when purchasing
the category, we can gain guidance in terms of having the proper
pricing and promotions.
In this way, we can provide the greatest reach and value to
the consumer while optimizing manufacturer and retailer profitability.
We can then measure our performance and re-plan our next steps
with our retailer partners.
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