What are the challenges comparing pro-forma and post-deal P&Ls?

The best business practice for TPM is to include pro-forma P&Ls in your promotion planning workflow.  Including these important metrics in the trade promotion life-cycle provides all the stakeholders important feedback as to what the we think the future promotion will do.

After the promotion is completed and all the discounts, allowances and rebates are paid, short-pays and deductions are resolved, trade promotion solutions can show us what the completed promotion has actually done.

Before we identify some of the challenges comparing these two different views of the promotion, let’s review what’s different:


Measure / Attribute Pro-forma Deal P&L Post-Promotion P&L
Volume: Sell-through Estimated Actual POS, syndicated data, or distributor data.
Volume: Sell-In Estimated For direct ship accounts, we know actual shipments.

 

Lump-Sum, also called fixed-fee Estimated Actual payments, deduction resolutions, and settlements
Allowances and Discounts Locked in per agreement or contract Actual payments, deduction resolutions, and settlements
Percent redemption Estimated Can be calculated from actual data if the sales or movement data is available.
Retail execution Anticipated Can be documented if observed or recorded
Promotion dates Locked for deals based on purchases, could vary for deals based on sell-through If promotion dates were flexible, actual performance dates must be observed or obtained and documented
Total Profit  (manufacturer perspective) Estimated, using anticipated COGS Actual cost-of-goods-sold
Incremental Profit Estimated baseline Estimated baseline

As the table above shows, there are many data measures used for both pro-forma and post-deal analysis.  So what are some of the challenges?

The dates could change:    Your estimated promotional window might be longer or shorter than the actual event.    Comparing total spending and volume for a planned 3 week event versus a 1 week actual event can be misleading.   Be sure to include measures that normalize the data to a common measurement.  i.e., average sale per week, average percent lift, etc.

Retail execution could be different:  When retail execution differs from what you expect, use this information to look for ‘root causes’ to help explain why actual promotion results are different from your original estimates.

COGS could be different:  Your estimated vs. actual profitability could vary just based on different cost-of-goods.  If your brands in are a category where COGS don’t vary much, this isn’t an issue.  However, for seasonal products with large swings in COGS, make sure one of your metrics highlights significant variances in COGS between what was estimated vs. actual.  As with other measures, you can’t look at total COGS, because a number of other variables could make this misleading.  Look at COGS on a percent of list price.    You may not be able to control your COGS, but you also don’t want to blame poor event profitability on another attribute when COGS is the root-cause.  i.e., your promotion actually performed as expected, but your original COGS forecast was wrong, so the pro-forma P&L over estimated profitability.

Baselines could be different:  Your incremental metrics could vary because you used different baselines for your estimates vs. those used to analyze actual trade promotion results.   Of all the metrics used for trade promotion analysis, this is one of the most challenging.  We’ll have more blogs on this topic.   One approach I’ve seen is to apply the same baseline to both pre and post analysis.  In other words, the baseline you used to evaluate the future deal, is replaced with the new-and-improved baseline when you have actual shipments.  This provides an apple-to-apple comparison of the pre and post promotion P&Ls.  This approach doesn’t , however, show the original pro-forma P&L used to review and approve the promotional event.

Don’t just compare the total trade spend for the event:    If you sell more volume than originally estimated, that’s a good thing.     Compare estimated to actual spending as a percent of list price.  Just looking at total cost can be misleading.  Lump-sum is a fixed cost for promotional events, so keep in mind that promotions look much better when actual volume exceeds the original estimate.   This effect happens because the lump-sum is allocated across more volume, reducing it's cost-per-unit.

As you can see in this blog, there are many challenges with comparing the pre-promotion P&L to the post-promotion Profit-and-Loss statements.   Use these insights to adjust and adapt your processes when evaluating the effectiveness and efficiency of your trade promotions.

If you use NetSuite as your ERP, we invite you to take a look at iTPM, a native SuiteApp that manages trade promotion spending inside NetSuite.

 

Alex Ring

President

CG Squared, Inc.

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