Using Science & Mathematics to Find The Perfect Mix Of In-Store Trade Spend Activities

Calculating The Perfect In-Store Trade Spend Mix Using Science

Trade spend is tricky. If you don’t spend enough your sales could slump; and if you overspend, you eat away at your profit margins. The trick is to find the perfect in-store mix of promotions, from the frequency and placement to the overall displays.

When talking to VPs of Sales regarding execution around in-store trade spend we always ask two basic questions:

How would you rate the success of your programs?

What adjustments could you make to maximize your profit, even if you’re not increasing revenue?

We get a variety of responses. But before we get to those, let’s first define trade spending.

Trade spending is a common practice amongst consumer-packaged goods (CPG) and retail companies. Essentially, trade spending is the amount a company spends to increase demand for its products, including coupons, preferential shelf display locations (slotting) and co-advertising, to name a few. So in short the main goal of any trade spend strategy is to increase revenues by utilizing an optimal promotional mix. Traditionally CPG brands run as many programs as possible within their budget, and at the end of the quarter they review and analyze their sales dashboards to see how well the promotions ran.

However, we are now in a world of zero-base budgeting and managers need to stretch every dollar. They need to be able to deconstruct the program, identify how each in-store display impacted the program as a whole, and understand if the incremental sales were worth the additional costs. After all,  those large budgets are starting to shrink with the growth of digital.

Unfortunately without the proper strategy and technology, understanding execution around in-store promotions can seem quite overwhelming. Fortunately, we’ve created a strategic outline on how to best start the process.

First thing is to understand all the subtle differences that must be accounted for so there are no surprises during the analysis phase. To keep it simple for now we broke the variables into two groups

Various Costs

  • Varying Costs Per Promotions: The cost of an end cap is different from the cost of a clip strip, and that of a stand out display.
  • Varying Points Of Interruptions and Locations: Some accounts can have two or three end caps/promotions running, while others might have only one or two. Additionally, location sells and should at least be noted for.
  • Varying Store Opertations and Formats: Stores vary on the number of promotions and how they conduct promotions. For example, HyVee is one mentioned often as store orders only. Also, many retailers are not capturing trade spend efforts online, Gartner reports, which impacts the campaign’s success.

Various Returns

  • Nothings Directly Proportional: Incremental gains aren’t directly proportional to the number of promotions. One display might yield a gain of 15% in sales while adding another might only contribute an additional 7% and a third might only add an additional 2%. Suppliers need to strategize where to invest.
  •  Sales Velocity Fluctuates Across Banners: Suppliers need to account for a weighted ACV in their calculations.

It’s like an epic algebra problem and it changes all the time with variables like  numerous brands across multiple categories, and myriad trade spend campaigns in different forms running in stores that have different customer bases (even in the same chain or city.) Plus each of the stores has its own policies, and other campaigns going on.

But don’t give in to the temptation to over-simplify it because you’ll cost yourself money—potentially a lot of money. The future is in incremental, strategic, data-driven decisions that respond to a fluid market. Now, we’ll outline exactly how to execute that.

The Science Behind Incremental ROI

So where to start? Assuming you have a retail execution field application that’s state of the art let’s first start by taking a page out of science, and approach this the same way we would a complex experiment. Keep things simple, form your hypotheses, keep your constants consistent, and remember to compare apples to apples.

  • Identify your controlled and experimental stores
  • Group stores by their importance 1 – 3 (weighted ACV distribution)
  • Select several key markets
  • Select items/categories you want to track
  • Select reps
  • Utilize field app that will allow you to track and verify the promotions
  • Establish timetable that will allow you to compare data across accounts
  • Execute for a quarter, begin analyzing.
  • Make assumptions then RETEST

What to Expect

You will now be able to identify what combination of in-store promotions types, stores, and products gives you the biggest lifts and which just drains your budget. From here you can make adjustments based on that data and run the test again. The market changes too fast to coast on any static data set. If you make testing part of the way you function, you’ll increase profits by finding the best type of promotions for your team to run as well as…

  • Finding the best number of promotions for your team to run
  • Finding the ideal promo mix for items across categories
  • Saving money on meager incremental gains

Obviously, incremental gains have to be measured more often and more carefully and tweaked more consistently to make sure they’re worth the investment. Once you track and understand dive more into the data to see what’s going on, the goal is to build it up to it so you’re not trying to boil the ocean. In a year’s time, you’ll have the data to push profits even further and to keep pushing them year over year.

The following two tabs change content below.
Bruce is the founder and CEO of RW3 Technologies. Having spent more than 35 years in the consumer goods and grocer space Bruce has experienced the industry disruption first hand and understands how artificial intelligence, POS data, and mobile technology can transform a good organization into a great one.

Comments are closed.