Trade Promotion Optimization

Trade Promotion Optimization for a food manufacturer

A US based food manufacturer revisits trade strategy

The client is a multi billion-dollar food manufacturer based in the US. The client ran promotions to the tune of $2 Billion globally. The challenge for the client was to identify improvement opportunities from a sea of metrics. Even after identifying opportunities, enforcing these changes was a challenge due to misalignment between internal teams and due to unique constraints at each retailer


  1. Very few promotional variations were tried historically – the same items were being promoted at the same depth during the same events, year over year
  2. Excel based trade promotions management – every brand was measuring promotion effectiveness using their own methods. There was no alignment of methodology on an organizational level
  3. Different functional units were operating in silos and chasing misaligned goals
  4. All functional units did not have complete visibility into all metrics related to their own trade promotions
  5. Category and sales teams were spending significant amount of time in pulling and understanding data and not enough time on identifying opportunities
  6. Central brand teams were unable to enforce changes to promotional strategy due to account level constrains created by retail partners
  7. Individual account level challenges were being handled in an ad hoc manner by sales team who often did not have access to all the data necessary for making these decisions


  1. Single source of truth for all historical promotions was created in our web-based solution. Every team got access to a common set of metrics. All promotions were evaluated using a common methodology
  2. Price and promo elastic models were created: This allowed brand management and sales teams to do scenario analysis for different promotional tactics
  3. Optimization algorithm: This module generated an automated trade marketing plan which adhered to various brand and account level restrictions
  4. Governance forums: Governance forums created to supplement the tool with relevant process to ensure tool adoption as well as adherence to recommendations. Specific dashboards created for higher management to ensure that they can monitor metrics on the tool.
  5. Rollout program: Classroom training sessions were conducted. Weekly tool usage was monitored. Users with low utilization were aided with additional one on one training sessions. The tool has built in tutorial videos which serve as an on-demand training mechanism
  6. Power users: Power users were identified early in the program to ensure category and account level nuances were being addressed by the tool. These users were also early users of the tool who participated in beta testing. After launch these users were able to evangelize the tool among the other users in the organization.


Promoted Product Group: When the performance of a promoted product group was broken down to an item level, we found that one or two “hero” products were carrying the performance of the entire product group. Depending on relative lifts the tool gave three types of recommendations:

  1. Trim the offer to only some items which show lifts and promote them more: Some items in promoted product groups do not see enough unit lifts
  2. Break the group into multiple groups: In cases where the hero item was “cannibalizing” the other items int the group, the group was broken in such a way that consumers did not have to choose between the hero or niche items. In this was margins on niche items were better preserved
  3. Keep the group as it: In certain situations, the gains arising from breaking or trimming the offer were not substantial. These product groups were preserved as is

Optimization flexibility: Implementing an optimization solution takes a lot of time and effort. And constraints may differ across brands and accounts. Constraints may also evolve with time.
Tradesmart comes with a constraint screen which can be configured to each brand and account’s requirements

Shifting focus of account managers: Tradesmart generates a promotional plan. It does so in less than a minute. This means that an account manager can focus on understanding the output and reviewing strategy. The buyer has to spend significantly less time on making sense of metrics and designing a plan.

  1. It is most useful to look at promoted pricing for channels and retailers that tend to run short-term promotions with price cuts.  This means typically NOT Walmart.
  2. If your product gets any Feature or Display activity, make sure to look at your price when you are getting that Quality support and then when you are just getting TPRs.
  3. Promoted pricing at the national level is useful when developing an overall pricing strategy but of limited value to understanding what’s really happening in the marketplace.  You’ll usually look at merchandising and promoted pricing at the retailer level in order to identify truly actionable insights.
  1. The first step in understanding sales is to separate distribution and velocity.
  2. Velocity is always some measure of sales divided by some measure of distribution.
  3. When looking within one market (not across markets), you can use either Sales per Point of Distribution (SPPD) or Sales per Million (Sales per $MM ACV).
  4. When looking across markets, use Sales per Million. Do not use SPPD.
  1. Are there any retailers that are not carrying our #1 item?
  2. Are we meeting the distribution goals we set for our new product?  Which retailers are selling at least one of the new items?
  3. My broker is saying sales are down because we are losing distribution.  How can I tell if this is true?  What is really happening in the stores?

TDPs: Total Distribution Points

TDP Elasticity – 0.6-1; Typical value 0.8


Price Elasticity is typically available – use base price/eq


Average items carried/average items selling

Promoted Product Group

Data Integration

Do the different sources represent the same time period

Do they measure the same market

Do they measure the same product?

Do the metric definitions match?

Does data trend the same way?

Is there any way to triangulate the data?

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