The stages of promotion planning: Why beginner methods fall short of driving incremental sales

In my first blog of this series, How agile promotion planning increases forecast accuracy, reduces ineffective promotions and more, I covered the inefficacy of retail promotions and some of the reasons why this is the case. I also wrote a bit about how agile promotion planning combines AI and machine learning to improve forecasting accuracy, eliminate ineffective promotions, achieve category objectives and more.

In this blog, I want to get into the first two of four stages of our progressive maturity model – and how the elements within help you move toward agile promotion planning by truly understanding the performance of your promotions and leveraging these insights more efficiently.

Agile promotion planning maturity stage 0

Stage 0 is where plans are built based on last year’s promotions and those involved use gut feel and intuition to figure out what worked and what didn’t. At this stage, work is highly manual, often exchanging Excel files where retailers create a base plan and requesting that promotions be submitted by CPG partners. And, after receiving spreadsheets with proposals from CPGs, retailers must go thru the highly manual process of submitting individual submissions for depth, timing funding, profitability and sales — all against last year. Then, the back-and-forth negotiation begins.

A few more notes on this stage:

  • The initial analysis and on-going evaluation are not based on true sale incrementality or lift, just on assumptions and hypotheses on potential improvements on last year.
  • The majority of decisions are based on the past – which items had high sales when advertised before, or on guesses of what might work.
  • Time wastes prevail with manual efforts – when agreed upon, the trading partner submits deal sheets that the retailer must validate against plan.
  • CPGs wield significant influence in decisions, which may benefit them much more than retailers due to the lack of robust planning – and systems to support it.

Shortfalls of this stage

Ultimately, this stage leads to promotions that don’t resonate with customers or produce outcomes aligned with the retailer’s strategies or financial objectives.

All too often, there is:

  • No ability to make informed decisions
  • No visibility to leadership for desk, director, VP, department, or total store performance
  • No standards for consistent forecasting or post analytics
  • Inefficient process that consumes time and detracts from other valuable tasks

Agile promotion planning maturity stage 1

In this stage, the retailer obtains a basic understanding of promotion performance as it occurred by focusing on sales and margin lift – in other words, the sales or margin gain over a baseline of what they would have sold if the item wasn’t on promotion. Using data and basic analytics to enable this approach to incrementality helps and is a significant driver of achieving a better answer to “why did last year’s performance happen?” This helps retailers better estimate sales and margin growth from promotions. Retailers also collaborate better with CPG partners, enabling both a more common understanding of what’s good and bad.

While both often employ tools or systems that provide better data and analysis, these tools don’t always provide one version of the truth for all promotions – sometimes with differences in data hierarchies or lift calculations.

The beginning of the upside

In stage 1, a noticeable benefit is that retailers begin to improve the circular planning process by instituting business rules to slot offers and ads for the valuable front page of the circular.

Based on historical performance, the benefits of reaching this stage are as follows:

  • Reduction of ineffective promotions by 5-10%
  • Revenue Growth of 1-2%
  • Margin relief 0.5-1%

In my last blog in this series, we’ll explore how stage two begins to surface more intelligent promotion planning, with better forecasting – and ultimately, in stage 3, leverages the power of AI and machine learning to drastically reduce inefficiencies in promotion planning.

Related reading:


Speak with an expert

Just provide us with a few details and we’ll be in touch to discuss your needs