Why is "Scoring each event against a baseline the previous events lowered" a common mistake when using the Reference Price Erosion Projector?

Symptom: Every event shows a clear positive ROI in the report written after it runs. Meanwhile the yearly brand-health review shows that sales in the weeks with no deal running are slipping a few percent a year. Nobody puts the two reports side by side. Fix: **Track non-promoted weekly volume as a top-line KPI, beside event ROI.** Each event is measured against the baseline the promotions before it left behind, so as the baseline sinks every event looks a little better. Each week, report the average volume across the most recent 13 weeks, counting only the weeks with no deal running (a rolling average, because it moves forward a week at a time). Plot it against the same figure 12 months earlier, and treat a line more than 5 percent below where it was a year ago as the alarm. The promo ROI calculator on this site scores the event; this tool shows what the whole calendar is doing to the baseline underneath it.

Why is "Treating depth as free for the baseline, or frequency as the only thing that teaches the shopper" a common mistake when using the Reference Price Erosion Projector?

Symptom: A new promotion plan trims the discount year after year but keeps the same number of deal weeks, or cuts events but makes each remaining one deeper. The baseline keeps falling, because the share of weeks on deal times the depth has barely changed. Fix: **Both enter the model, multiplied together.** The shopper learns from how often the product is on deal and how deep the deal is, taken together (share of weeks times depth). Start from the defaults and try each cut on its own. First take the discount from 25 to 18 percent off, which makes it 28 percent smaller. Then, instead, take the events from 12 to 9, which means 25 percent fewer deal weeks. The two cuts slow the decline by nearly the same amount: the baseline reads 91.4 and 91.1 at month 18. Start with frequency anyway, for two reasons that sit outside the formula. Only fewer weeks takes the calendar under the 30 percent ceiling. And only fewer weeks gives the shopper more weeks in which the shelf price is the price they see, which is what rebuilds the price they expect to pay. Then take the depth cut as well, because it also cuts what each event costs.

Why is "Expecting the baseline to recover within a quarter" a common mistake when using the Reference Price Erosion Projector?

Symptom: In the first quarter, the trade marketing team announces a calendar with fewer deals. In the second quarter, it is asked for a brand-health update showing the recovery. In the third quarter, the change is reversed because it did not work. Fix: **Give it 18 to 24 months, and keep the two recoveries apart.** In the lesson's example, a canned soup line was taken off deal. Once the deals stopped, its full-price sales took 12 weeks to climb back to within 4 percent of their level before the pause, and 16 weeks to settle 8 percent above that level. That is the short recovery. The long one is rebuilding a baseline that too much promotion has worn down, back toward where it stood before the over-promotion began. In the lesson's cases it takes 18 to 24 months of steady full-price selling to win back most of what can be won back. Schedule the review no earlier than 12 months in. Sales usually dip first when the deals stop, so get that dip approved before you start.

Why is "Treating the 30 percent line as a level where promotion does no harm, or as a threshold someone has measured" a common mistake when using the Reference Price Erosion Projector?

Symptom: The calendar is pushed to exactly 30 percent of weeks and recorded as fixed. Two years later the baseline is down several points and nobody can say why. Fix: **There is no step in the model and none in the evidence.** At 20 percent of weeks and 25 percent off, the lesson's model still gives up about 7 percent of the baseline over two years, and at the ceiling itself about 10 percent. Thirty percent is this course's own planning rule (a house ceiling). It was chosen because only about the top tenth of real products are promoted more often than that, so the rule holds back only the most heavily promoted products. The median product is on deal about 15 percent of weeks, one in twenty runs above 37 percent, and a 40 percent ceiling would bind on almost nothing but colas. Set your own from your own data. Work out your own k from at least eight quarters of your weekly sales in the weeks with no deal running. Then place your own line at the point where one more week on deal stops paying for itself. That is where the full-price sales that extra week costs you, worked out with your own k, are worth more than the profit it brings in. One thing the evidence does support about which brands need a lower line: a promotion does the most damage to how shoppers see a brand when it departs from what that brand usually does. So a brand that rarely promotes takes more image damage per event, and its ceiling belongs lower for that reason.

Why is "Taking the category sensitivity (k) of 0.06 as a measured number for your category" a common mistake when using the Reference Price Erosion Projector?

Symptom: This tool's dollar figure goes into the review as if it were a forecast of the brand's sales. Someone asks where the 0.06 came from, and all anyone can say is that it was the setting the tool started on. Fix: **k is the one input you have to estimate yourself, from your own data.** The tool's three ready-made settings (0.04, 0.06 and 0.08 a month) are examples the lesson chose to show how the model behaves, and they do not come from any published table. Take your own non-promoted weekly volume, what you sold in weeks with no deal running, and set it month by month against how much discounting you ran: the share of weeks on deal times the average depth. Use eight quarters of history or more. Then work out what share of the baseline you have lost each month, and divide it by the share of weeks on deal times the depth. That figure is your k. Premium-leaning categories are likely to sit lower, because their shoppers judge the product on quality as much as on price. Commodity-leaning ones are likely to sit higher, because price is the main thing their shoppers go on. Both are starting guesses to test against your own history. Quote the tool's dollar figure with k named beside it.

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Reference Price Erosion Projector

An interactive projector of reference-price erosion, the slow drop in the price shoppers consider normal when a brand promotes often. It takes a brand's promotional calendar: how deep the discounts go, how many events a year, and how many weeks each one lasts. From that it forecasts the brand's full-price baseline, meaning the volume it sells in weeks with no deal running, for the next 12, 18 or 24 months. It shows the result as an index where 100 is today's non-promoted weekly volume, and runs two calendars side by side. Then it turns the difference between the two forecasts into the full-price revenue you keep each year by promoting less, ready for the next promotion plan review.

Updated 11 October 2026Extracted from the Trade Promotion Optimization module, lesson 3: Baseline vs Incremental Volume

How this tool works

An interactive projector of reference‑price erosion, the slow drop in the price shoppers consider normal when a brand promotes often. It takes a brand's promotional calendar: how deep the discounts go, how often they run and how long each one lasts. From that it forecasts the full‑price baseline, the volume sold in weeks with no deal running, for the next 12 to 24 months. It runs two calendars side by side and shows each one as an index, where 100 is today's non‑promoted weekly volume. Built for trade marketing directors and revenue growth management (RGM) leads, it lets you set the category sensitivity to Low, Medium or High. That is how sharply your shoppers react to repeated discounts, and the lesson calls the number behind it k. It then shows the full‑price revenue you keep each year by promoting less, so you have that figure before the next joint business plan (JBP) review.

How deep your deals go and how often you run them both teach shoppers to expect a lower price, and the model multiplies the two together. The model is the lesson's: next month's full‑price baseline = this month's x (1 - k x share of weeks on deal x average depth). At the settings the tool opens with, cutting from 12 promotional events a year to 8 leaves the full‑price baseline 3.8 points higher at month 18, on the index where 100 is today's level. For a brand selling 100,000 units a week, that is about $587K a year of full‑price revenue. Since the two are multiplied, a shallower deal and a shorter calendar are worth exactly the same when you cut them by the same proportion. Take a fifth off the depth or a fifth off the weeks and the baseline lands in the identical place. So the model gives no reason to cut depth before weeks or weeks before depth, and the order the lesson recommends rests on reasons outside the model. If you fund deals at 30% off or deeper, the lesson's line for a deep deal, cut the discount you fund back to about 20% first, because a deeper cut raises the break‑even faster than it buys new sales. After that, or if your deals already run shallower, fund fewer deal weeks, because full‑price weeks are what rebuild the price shoppers expect. The retailer decides which promotions it runs and at what price, so treat this order as the funding plan you open with.
The 30% line is an upper limit this course chose for planning: keep a product on deal for no more than 30% of the year's weeks. The lower chart shows the line for reference only, and the model adds no sudden extra loss when a calendar goes above 30% of weeks. A calendar under the line still erodes, only more slowly: 20% of weeks at 25% off gives up about 7% of the baseline over two years. Thirty sits near the top tenth of how often real products are actually promoted (the median product is on deal about 15% of weeks, and only one in twenty runs above 37%), which is why it binds where a ceiling should. No study has measured a threshold there.
k measures how sharply your category's shoppers react to repeated discounts, and you estimate it for your own category from your own sales in the weeks with no deal running. The tool offers three settings for k: Low at 0.04 a month, Medium at 0.06 and High at 0.08. The lesson chose them as examples to show how the model behaves, and they do not come from any published table. So whenever you quote the tool's dollar figure for a lighter calendar, say which k value you used to get it. The reference price, the price shoppers have come to expect to pay, moves slowly in both directions: down while you promote heavily, and back up once you cut back. Give a lighter calendar, one with fewer or shallower deals, 18 to 24 months before you judge the recovery. Sales usually dip first when the deals stop, so get that dip approved before you start.

Purpose: Estimate how much of a brand's full‑price volume, what it sells with no deal running, its promotional calendar will wear away over the next 12, 18 or 24 months.
How to use: Pick the category sensitivity and the horizon in steps 1 and 2, then set today's calendar in step 3 and the one you are considering in step 4.
What to watch: The gap between the two curves at the horizon, and the revenue it protects. On the lower chart, the loss rises with no jump at 30 percent of weeks.

Full-price baseline volume: 100,000 units / week
Average list price: $3.00/ unit
Revenue if every week sold at full price: $15.6M/ year
Erosion model, per month: k x share x depth
Category sensitivity k (Medium): 0.06/ month, illustrative
House planning ceiling: 30% of weeks (a marker, not in the model)
Default Current scenario: 12 events / 2 weeks / 25% off (24 weeks, 46%)
Default Reduced scenario: 8 events / 2 weeks / 25% off (16 weeks, 31%)

Controls

Set your numbers and choices here

Step 1. Set how sensitive your category is

k, the category sensitivity, sets how fast each deal week wears down your full-price baseline.

Category sensitivity (k)

Category sensitivity 0.06 a month. Mainstream grocery, where price matters but is not the whole story: biscuits, cereals, packaged snacks. The worked example in the lesson uses this value.

These three values are only examples. k belongs to your own category.

Step 2. Choose how far ahead to look

The charts run this many months ahead, and every result is read at the last month.

Projection horizon

Each month's loss comes off whatever the month before left, so the longer the horizon, the further apart the Current and Reduced scenarios end up.

Step 3. Set the Current scenario

The calendar you run today: how deep the deals go, how many events a year, and how long each one lasts.

Promo depth

Average percent off the shelf price across the calendar's events.

Events per year

events

Number of separate promotional events across the 52 weeks.

Duration per event

weeks

Average length of one event, in weeks.

24 of 52 weeks on deal (46%)

monthly erosion = k x share x depth = 0.06 x 0.46 x 0.25 = 0.69% a month

Step 4. Set the Reduced scenario

The calendar you are thinking of moving to, on the same three numbers. The results set it beside the Current scenario.

Promo depth

Average percent off the shelf price across the calendar's events.

Events per year

events

Number of separate promotional events across the 52 weeks.

Duration per event

weeks

Average length of one event, in weeks.

16 of 52 weeks on deal (31%)

monthly erosion = k x share x depth = 0.06 x 0.31 x 0.25 = 0.46% a month

Results

Updates as you move the controls

What is the Reduced calendar worth a year, against the Current one?

Full-price revenue and baseline at month 18

$587KFull-price revenue protectedA year, at list price: Current risk minus Reduced risk

3.8 pointsGap at month 18Reduced baseline minus Current baseline, in index points

88.2Current: baseline at month 1811.8% of full-price volume lost

92.0Reduced: baseline at month 188.0% of full-price volume lost

$1.83MCurrent: full-price revenue at riskA year, at the $3 list price

$1.25MReduced: full-price revenue at riskA year, at the $3 list price

The model is the one the lesson teaches. It is an illustration, not a forecast.

How often is each calendar on deal, against the 30% house ceiling?

Share of the year's 52 weeks on deal

46%Current: weeks on deal16 points above the 30% house ceiling.

31%Reduced: weeks on deal1 point above the 30% house ceiling.

How fast does each calendar wear the full-price baseline down?

Full-price baseline over 18 months

100 = today's non-promoted weekly volume

Current scenario: the calendar you run today, projected forward month by month.

Reduced scenario: the calendar you are thinking of moving to, on the same model.

Is there a share of weeks on deal where the damage stops?

Baseline lost by month 18, by share of weeks on deal

At the Current depth (25% off) and k = 0.06 a month

The line starts at zero and climbs steadily, with no sudden jump at any share of weeks. The 30 percent line is a planning ceiling this course chose.

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Guided walkthrough

Explore the tool, from setup to common mistakes

Five short sections explain the scenario, what each control does, how to read the output, and the mistakes to avoid. Open whichever helps. The tool above works without them.

5.1Scenario setup

The starting SKU, market, and assumptions the model makes.

You run trade marketing or revenue growth management on a biscuits brand portfolio at a top‑three retailer. The brand sells 100,000 units a week at full price, at an average list price of $3 a unit, so it would take $15.6M a year if every week sold at full price. The calendar you run today has 12 promotional events a year, each 2 weeks long at 25 percent off, which puts the brand on deal for 24 of the 52 weeks, 46 percent of the year. Your category director has two questions: is the full‑price baseline slowly wearing away under that calendar, and what would cutting back to 8 events a year be worth over the next 18 months?

Your objective

Project the full‑price baseline 18 months ahead under the calendar you run today, put the 8‑event calendar on the same chart, and read how much full‑price revenue a year the lighter calendar protects. The chart is an illustration built on the lesson's model rather than a forecast of your brand, and the decision it helps you argue for in the next review is real.

Key assumptions

Brand baseline: 100,000 units a week of non‑promoted volume at a $3 average list price. That comes to $15.6M a year if every week sold at full price. The dollar figures in the results value every baseline unit the brand loses (a unit it would otherwise have sold at full price) at that list price, for all 52 weeks. So they count revenue, with no costs taken off, and they show the most the loss could be worth rather than a forecast of it.
The model is the lesson's, applied month by month: next month's baseline = this month's baseline x (1 - k x share of weeks on deal x average depth). Share of weeks on deal is the number of promotional events times the weeks each one lasts, divided by 52. Depth is the average percent off. And k is the category's sensitivity, a monthly number for how sharply its shoppers react to being discounted again and again. Because depth sits inside the formula, a deeper deal erodes the baseline (wears it down) faster, even when the number of deal weeks stays the same. The model has no threshold, no level of promotion below which the damage stops, and no background decline that happens on its own. With zero deal weeks, the baseline loses nothing.
k is only an example: the lesson picked its three settings (Low 0.04, Medium 0.06 and High 0.08 a month) to show how the model behaves. No published table gives k by category. You estimate your own k from your own sales history in the weeks with no deal running. It is the one input on this page you should not leave at the value the tool starts with.
The 30 percent line is drawn on the lower chart for reference, and the formula never uses it. It is the most this course plans to keep a product on deal, counted as a share of the year's weeks. That makes it a house convention, the course's own rule, and it sits where it does because only about the top tenth of real products are promoted more often than that. No published study finds a threshold at 30, 40 or any other share of weeks where the damage suddenly starts. The studies that follow brands over many years find it building up gradually instead, with every deal week adding a little.
Defaults: these are the settings the tool opens with. The Current calendar is the one you run today. It has 12 promotional events of 2 weeks each at 25 percent off, which is 24 weeks on deal, 46 percent of the year. The Reduced calendar has 8 events of 2 weeks at 25 percent off (16 weeks, 31 percent). That is the same cut the biscuit brand in the lesson's Baseline Erosion example made when it went from 24 promo weeks back to 16. Category sensitivity starts at Medium, which sets k to 0.06 a month. The charts start by looking 18 months ahead, and the page calls that distance the horizon. Switch it to 12 or 24 months to see how the gap between the two calendars grows over time.

5.2Controls & toggles

Every input the calculator exposes, its range, and what it changes.

Control	Range	Default	What it changes
Current scenario sliders (depth, events, duration)	Depth 10 to 40% off, 4 to 24 events a year, 1 to 4 weeks per event	25% off, 12 events, 2 weeks	Sets the calendar you run today. Multiply the number of events by the weeks each one lasts to get your weeks on deal. Under the sliders, the box at the foot of the step shows that as a share of the year's 52 weeks, then works out the monthly erosion, the share of the baseline lost each month, with your own numbers filled in (k x share x depth). At the defaults, 12 events of 2 weeks make 24 weeks on deal, 46 percent of the year. That is 16 percentage points above the 30 percent house ceiling, the most this course plans to keep a product on deal.
Reduced scenario sliders (depth, events, duration)	Depth 10 to 40% off, 4 to 24 events a year, 1 to 4 weeks per event	25% off, 8 events, 2 weeks	Sets the calendar you are thinking of moving to. At the defaults, 8 events of 2 weeks make 16 weeks on deal, 31 percent of the year, one percentage point above the house ceiling. On the full‑price baseline chart, each curve tracks one calendar's full‑price volume month by month. The higher the Reduced curve sits above the Current one, the more volume the Reduced calendar keeps. The lower chart shows how much baseline is lost at each share of weeks on deal, and marks where each of your two calendars sits on that line.
Category sensitivity (Low / Medium / High)	0.04 / 0.06 / 0.08 a month	Medium (0.06 a month)	Sets k, the category sensitivity: a monthly number for how sharply your category's shoppers react to seeing the product discounted again and again. The erosion formula multiplies it by your share of weeks on deal and your depth. The higher k is, the faster each deal week wears the baseline down. Premium‑leaning categories, where shoppers judge the product on quality as much as on price (specialty coffee, premium chocolate), are likely to sit lower. Commodity‑leaning ones, where price is the main thing shoppers go on (household cleaning, bottled water), are likely to sit higher. All three values are starting guesses to test against your own history.
Projection horizon	12, 18 or 24 months	18 months	Sets how many months the charts run, which the page calls the horizon. The loss compounds: each month takes its share off whatever baseline the month before left, so the total keeps adding up. That is why the two calendars drift further apart the more months you project. Both curves start at 100, which stands for today's full‑price volume, and at the defaults the Reduced curve sits 2.6 points above the Current one at 12 months, 3.8 at 18 and just under 5 at 24. 18 months is the default for two reasons. In the lesson's cases, winning back a worn‑down baseline took 18 to 24 months. And the next joint business plan (JBP) review, the yearly meeting where you and the retailer agree the plan, looks roughly that far ahead.
Reset to defaults	Single button	12 events / 8 events / Medium / 18 months	Brings back the two default calendars, Medium sensitivity and the 18‑month horizon. Slider changes are not saved when the page reloads, so reset is for moving between experiments in one sitting.

5.3Step-by-step exploration

8-step guided exploration of the scenario.

Read the starting position
Open the tool on its defaults. The Current scenario reads 46 percent of weeks on deal and the Reduced scenario 31 percent. The box at the foot of each calendar's step prints its monthly erosion with the numbers filled in: 0.06 x 0.46 x 0.25 = 0.69 percent of the baseline a month for the Current calendar, and 0.46 percent for the Reduced one. On the full‑price baseline chart at month 18, the Current curve sits at 88.2 and the Reduced curve at 92.0.
Expected outcome: Under the Current calendar the results show 11.8 percent of full‑price volume lost, which puts about $1.83M a year of full‑price revenue at risk: sales the brand stands to lose each year, valued at list price. Under the Reduced calendar the same two figures are 8.0 percent and about $1.25M. At month 18 the Reduced calendar sits 3.8 points higher than the Current one. Those 3.8 points are worth about $587K a year in full‑price revenue. Both calendars are still wearing the baseline down. The Reduced one sits one percentage point above the 30 percent line. Even a calendar below that line would keep losing baseline, only more slowly: its curve would still slope down, just less steeply, and would not become flat.
Reproduce the lesson's worked example
In the Current scenario, set 13 promotional events of 2 weeks each at 25 percent off. That puts the product on deal for 26 weeks, half the year. Leave category sensitivity at Medium. Set the horizon to 24 months, so the chart covers two years. Then move your pointer over the Current calendar's curve at months 6, 12, 18 and 24 to read its values.
Expected outcome: The curve reads 95.6, 91.4, 87.3 and 83.5. Those are the canned soup example's numbers in the lesson's Reference Price Decay card (a shopper's reference price is the price they have come to expect to pay). They match because this tool and that card use the same formula with the same category sensitivity, k. The monthly factor is 1 - 0.06 x 0.50 x 0.25 = 0.9925, and 0.9925 to the 24th power is 83.5, a loss of 16.5 percent, about a sixth of the baseline in two years.
Lengthen the horizon and watch the gap grow
Press Reset to defaults, then switch the horizon from 18 to 24 months. The line for the Current calendar keeps falling, to 84.6, and the line for the Reduced calendar falls to 89.5.
Expected outcome: The gap widens from 3.8 points to just under 5 (4.85), and the yearly full‑price revenue protected rises from about $587K to about $756K, because the loss compounds month after month. The Current calendar's 15.4 percent loss at 24 months sits at the bottom edge of the 15 to 25 percent band the lesson quotes. That band is a calculation from the model rather than something measured in real markets: it is what the formula produces for a brand on deal in half its weeks or more, over 18 to 24 months.
Cut the discount on the Current scenario and keep the same events
Reset to defaults. Move the Current scenario's Promo depth from 25 percent to 18 percent off (a deal 28 percent shallower), and leave it at 12 events of 2 weeks each. The monthly erosion line in the box under step 3's sliders falls from 0.69 percent to 0.50 percent, and on the full‑price baseline chart the Current curve rises from 88.2 to 91.4 at month 18.
Expected outcome: Depth is part of the formula. Each time shoppers see a shallower deal, it teaches them to expect a smaller discount, so the baseline wears down more slowly. The shallower deal also costs you less every time it runs, so you get both benefits at once. What the smaller discount does not do is bring the calendar under the 30 percent ceiling: shoppers still meet a deal price 24 weeks a year, and the Current calendar's weeks on deal figure in the results still reads 46 percent.
Now cut frequency by a similar proportion
Reset to defaults, which puts the Current scenario's Promo depth back at 25 percent. Then move its Events per year slider from 12 to 9. That is 25 percent fewer deal weeks. The share of weeks on deal falls from 46 percent to 35 percent, and at month 18 the Current curve ends up at 91.1.
Expected outcome: That is nearly the same result as the smaller discount in step 4 (91.1 against 91.4). Share of weeks and depth are multiplied together in the formula, so the model gives the same answer whichever one you cut, as long as you cut it by the same proportion. The reason to cut frequency, how often you promote, first comes from outside the formula. Only fewer weeks brings the calendar under the 30 percent ceiling. And only fewer weeks gives shoppers more weeks in which the full shelf price is the price they see, and those weeks are what rebuild the price they expect to pay. A 15 percent discount every other week still teaches the shopper to wait.
Switch the category sensitivity and read the same calendar three ways
Reset to defaults. Switch Category sensitivity from Medium to High. That sets k, the category sensitivity in the model, to 0.08 a month, so each deal week wears the baseline down faster. Read where the Current curve sits at month 18. Then switch to Low, which sets k to 0.04 a month, and read it again.
Expected outcome: The same 46 percent calendar lands at 84.6 on High (15.4 percent lost), 88.2 on Medium (11.8 percent) and 92.0 on Low (8.0 percent). One calendar gives three different answers, and that is why the control is there. k belongs to your own category. The tool's three ready‑made settings are only starting guesses, so estimate your own k from your sales in the weeks with no deal running. Categories where shoppers weigh quality as much as price (premium‑leaning ones) are likely to have a lower k. Categories where shoppers mostly go on price (commodity‑leaning ones) are likely to have a higher k. Neither is a known value until you have measured it.
Look for the safe zone, and fail to find it
Press Reset to defaults, which puts every setting back where the tool started, and switch the horizon to 24 months. Drag the Reduced scenario down to 4 events of 1 week at 25 percent off, so 4 weeks on deal, 8 percent of the year. On the lower chart, watch the dot for the Reduced calendar slide down the line toward zero.
Expected outcome: The Reduced curve reads 97.3 at month 24, a loss of 2.7 percent: smaller, though still there. The line on the lower chart shows how much baseline is lost at each share of weeks on deal. It starts at zero and rises steadily, with no sudden jump at 30 percent. The lesson's own chart makes the same point: 20 percent of weeks on deal at 25 percent off still gives up about 7 percent of the baseline over two years, and the ceiling itself about 10 percent. The 30 percent line is a house convention, the course's own planning rule, placed so that only about the top tenth of real products are promoted more often. Set your own from your own data. Work out your own k from at least eight quarters of your weekly sales in the weeks with no deal running. Then place your own line at the point where one more week on deal stops paying for itself. That is where the full‑price sales that extra week costs you, worked out with your own k, are worth more than the profit it brings in.
Walk it into the JBP review
Reset to defaults. Note three things: the two shares of weeks on deal (46 and 31 percent), where the two curves sit at month 18 (88.2 and 92.0), and the Full‑price revenue protected figure. That figure, about $587K, is the full‑price revenue a year that the Reduced calendar keeps and the Current calendar loses. Write the assumptions down next to them. Category sensitivity (k) is 0.06 a month, an example value rather than a measured one. The horizon is 18 months, and the brand would take $15.6M a year if every week sold at full price. The 30 percent ceiling is the course's own planning rule.
Expected outcome: You walk into the review with the lesson's model run on your own settings, and with the assumptions written next to the result, so nobody mistakes it for a forecast. The usual objection from the trade team, the people who deal with the retailer, is that a competitor will take the deal weeks you give up. The answer is the chart, plus two moves the lesson gives you. First, hold your list price and the shelf price you recommend where they are while you cut back, because raising them in the same quarter makes the damage you are repairing worse. Second, move part of the money you save into support that is not a price cut: a display, a slot in the retailer's flyer, or a bigger pack for the same price. Those raise sales without teaching the shopper to expect a lower price.

5.4Reading the output

Every KPI, the formula behind it, and how to interpret a positive or negative value.

KPI	Formula	How to read it
Share of weeks on deal (for each scenario)	events x weeks per event / 52 x 100, counted up to 52 weeks	Where you sit against real calendars. In one very large study of US retail prices, the median product was on deal about 15 percent of weeks and only one product in twenty ran above 37 percent, with colas the famous exception at 50 to 58 percent of weeks in the 1990s. Keep the unit straight when you read market figures. UK shoppers spent 28 to 33 percent of their grocery money on promoted items in 2025, down from about 40 percent in 2012. That is a share of spend: the part of shoppers' money that went on deals. A share of weeks measures something different: the part of the year a product spends on deal. The lesson assumes, for planning, that a week on deal sells about three times as much as a normal week, the median in that same US study. On that assumption, a product's share of spend on deals comes out at about double its share of weeks on deal. So the UK's 28 to 33 percent of spend in 2025 corresponds to roughly 15 percent of weeks for an average product line, about where the median product sits. The course's 30 percent ceiling is counted in the same unit as the median product's 15 percent: the share of the year's weeks a product spends on deal.
Monthly erosion rate (for each scenario)	k (category sensitivity) x share of weeks on deal x average depth (percent off)	The number the whole projection compounds from. At the lesson's settings, half the weeks on deal at 25 percent off with a category sensitivity (k) of 0.06 a month, that is 0.06 x 0.50 x 0.25 = 0.75 percent of the baseline a month. That is small enough to miss in any one month, and it compounds to about 16 percent over 24 months. At the defaults, the Current calendar (46 percent of weeks on deal) loses 0.69 percent of its baseline a month, and the Reduced calendar (31 percent) loses 0.46. The box at the foot of each calendar's step prints this line with your own numbers in it.
Baseline index at the end of the horizon (for each scenario)	100 x (1 - monthly erosion rate) ^ months	Read it as the share of today's full‑price volume still there. 0.9925 to the 18th power is 87.3 and to the 24th is 83.5, the lesson's worked example. The 15 to 25 percent loss the lesson quotes is what this same model gives for a brand on deal in half its weeks or more, over 18 to 24 months. It is a calculation from the model rather than a finding measured in real markets, and your own category will lose baseline faster or slower than the tool's three ready‑made settings suggest.
Full-price revenue at risk, per year (for each scenario)	(100 - baseline index) / 100 x 100,000 units x 52 weeks x $3	Revenue rather than margin. At the defaults, the Current calendar has lost 11.8 percent of its baseline by month 18. That is about 11,750 units a week, and at $3 a unit for 52 weeks it comes to about $1.83M a year. Every lost unit is valued at list price for every week of the year, which makes the figure an upper bound: in the weeks the brand is on deal, those units would have sold at the deal price.
Full-price revenue protected, per year	full‑price revenue at risk (Current calendar) - full‑price revenue at risk (Reduced calendar)	The number for the review. At the defaults, $1.83M minus $1.25M leaves about $587K a year when you project to month 18, and about $756K when you project to month 24. It scales in proportion to the brand's baseline, so a brand at 200,000 units a week doubles it. Quote it as what the lesson's model says at your settings, with k named beside it, rather than as a forecast.

Use the tool for direction and order of magnitude. Put in your own baseline volume, your own calendar and a k fitted from your own non‑promoted volume history, then read which way the numbers point and roughly how far. The precise figure belongs to a marketing‑mix model that also sees what competitors did, what the retailer asked for and which shoppers moved, none of which this tool has.

Two readings matter more than any single number. Depth and frequency both teach the shopper, so a calendar that only trims depth has still done something for the baseline. And a calendar under the 30 percent line is still wearing the baseline down, only more slowly. The 30 percent line is the course's own planning rule for when cutting back becomes worth the effort. To set your own, work out your own k first. Then place your line at the point where one more week on deal stops paying for itself. That is where the full‑price sales that extra week costs you are worth more than the profit it brings in.

5.55 common mistakes to avoid

Diagnostic patterns that catch the most common misuse of this calculator.

Mistake 1Scoring each event against a baseline the previous events lowered
Symptom: Every event shows a clear positive ROI in the report written after it runs. Meanwhile the yearly brand‑health review shows that sales in the weeks with no deal running are slipping a few percent a year. Nobody puts the two reports side by side.
Fix: Track non‑promoted weekly volume as a top‑line KPI, beside event ROI. Each event is measured against the baseline the promotions before it left behind, so as the baseline sinks every event looks a little better. Each week, report the average volume across the most recent 13 weeks, counting only the weeks with no deal running (a rolling average, because it moves forward a week at a time). Plot it against the same figure 12 months earlier, and treat a line more than 5 percent below where it was a year ago as the alarm. The promo ROI calculator on this site scores the event; this tool shows what the whole calendar is doing to the baseline underneath it.
Mistake 2Treating depth as free for the baseline, or frequency as the only thing that teaches the shopper
Symptom: A new promotion plan trims the discount year after year but keeps the same number of deal weeks, or cuts events but makes each remaining one deeper. The baseline keeps falling, because the share of weeks on deal times the depth has barely changed.
Fix: Both enter the model, multiplied together. The shopper learns from how often the product is on deal and how deep the deal is, taken together (share of weeks times depth). Start from the defaults and try each cut on its own. First take the discount from 25 to 18 percent off, which makes it 28 percent smaller. Then, instead, take the events from 12 to 9, which means 25 percent fewer deal weeks. The two cuts slow the decline by nearly the same amount: the baseline reads 91.4 and 91.1 at month 18. Start with frequency anyway, for two reasons that sit outside the formula. Only fewer weeks takes the calendar under the 30 percent ceiling. And only fewer weeks gives the shopper more weeks in which the shelf price is the price they see, which is what rebuilds the price they expect to pay. Then take the depth cut as well, because it also cuts what each event costs.
Mistake 3Expecting the baseline to recover within a quarter
Symptom: In the first quarter, the trade marketing team announces a calendar with fewer deals. In the second quarter, it is asked for a brand‑health update showing the recovery. In the third quarter, the change is reversed because it did not work.
Fix: Give it 18 to 24 months, and keep the two recoveries apart. In the lesson's example, a canned soup line was taken off deal. Once the deals stopped, its full‑price sales took 12 weeks to climb back to within 4 percent of their level before the pause, and 16 weeks to settle 8 percent above that level. That is the short recovery. The long one is rebuilding a baseline that too much promotion has worn down, back toward where it stood before the over‑promotion began. In the lesson's cases it takes 18 to 24 months of steady full‑price selling to win back most of what can be won back. Schedule the review no earlier than 12 months in. Sales usually dip first when the deals stop, so get that dip approved before you start.
Mistake 4Treating the 30 percent line as a level where promotion does no harm, or as a threshold someone has measured
Symptom: The calendar is pushed to exactly 30 percent of weeks and recorded as fixed. Two years later the baseline is down several points and nobody can say why.
Fix: There is no step in the model and none in the evidence. At 20 percent of weeks and 25 percent off, the lesson's model still gives up about 7 percent of the baseline over two years, and at the ceiling itself about 10 percent. Thirty percent is this course's own planning rule (a house ceiling). It was chosen because only about the top tenth of real products are promoted more often than that, so the rule holds back only the most heavily promoted products. The median product is on deal about 15 percent of weeks, one in twenty runs above 37 percent, and a 40 percent ceiling would bind on almost nothing but colas. Set your own from your own data. Work out your own k from at least eight quarters of your weekly sales in the weeks with no deal running. Then place your own line at the point where one more week on deal stops paying for itself. That is where the full‑price sales that extra week costs you, worked out with your own k, are worth more than the profit it brings in. One thing the evidence does support about which brands need a lower line: a promotion does the most damage to how shoppers see a brand when it departs from what that brand usually does. So a brand that rarely promotes takes more image damage per event, and its ceiling belongs lower for that reason.
Mistake 5Taking the category sensitivity (k) of 0.06 as a measured number for your category
Symptom: This tool's dollar figure goes into the review as if it were a forecast of the brand's sales. Someone asks where the 0.06 came from, and all anyone can say is that it was the setting the tool started on.
Fix: k is the one input you have to estimate yourself, from your own data. The tool's three ready‑made settings (0.04, 0.06 and 0.08 a month) are examples the lesson chose to show how the model behaves, and they do not come from any published table. Take your own non‑promoted weekly volume, what you sold in weeks with no deal running, and set it month by month against how much discounting you ran: the share of weeks on deal times the average depth. Use eight quarters of history or more. Then work out what share of the baseline you have lost each month, and divide it by the share of weeks on deal times the depth. That figure is your k. Premium‑leaning categories are likely to sit lower, because their shoppers judge the product on quality as much as on price. Commodity‑leaning ones are likely to sit higher, because price is the main thing their shoppers go on. Both are starting guesses to test against your own history. Quote the tool's dollar figure with k named beside it.

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