Promotional Event Post-Analysis Tool

? How To Use This Tool

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This is a post-event analysis tool — it uses your actual results after a promotional event has already run. It works at the individual salon level, one salon at a time. It tells you what the event cost financially and what price point would have produced a better outcome. Use those findings when planning future promotional pricing.

DATA ENTRY — TWO OPTIONS

OPTION A — UPLOAD SPOTR

Export your SPOTR Salon Pick List as a CSV file from your reporting portal and upload it. The tool reads all available weeks automatically.

Select your baseline date range (pre-sale weeks), your sale period (single week or a From/Through range), and enter the advertised sale price. Click Load Data into Fields and all fields populate automatically.

OPTION B — MANUAL ENTRY

Pull two reports from your weekly performance reporting portal and enter the numbers manually.

Report 1 — Baseline: The weeks before the promotion ran.
Report 2 — Sale Week: The week the promotion ran.

WHERE TO FIND EACH NUMBER (MANUAL ENTRY)

CC Last Year / CC This Year → Customer count last year / this year
Avg Invoice → Average invoice per customer (includes services + retail product sales)
Payroll % → Payroll as a percentage of total sales
Revenue Change % → Total sales % change vs. prior year
New Cust Return % → New customer return rate (reflects customers from 15 weeks prior)
Repeat Cust Return % → Repeat customer return rate
Sale Price Point → The advertised promotional price — not from the report
Sale Avg Invoice → Avg invoice from the sale week report (actual collected, incl. retail)

HOW TO RUN THE ANALYSIS

1. Enter your salon name or number at the top
2. Choose Upload SPOTR or Manual Entry — either method populates the same fields
3. Review the Assumptions section — adjust visits/year and the coupon-loyalty factor if needed
4. Click ◆ Generate Analysis
5. If required fields are missing, a red alert lists each one by name — fix them and click again

ASSUMPTIONS — WHAT THEY MEAN

Avg visits per year → How many times a retained customer visits annually. Slider range is 6–9×/yr (6 = ~57-day cycle, 9 = ~40-day cycle). Default is 6 for a conservative estimate.
Coupon-loyalty factor → The estimated % of promotional new customers who rotate across multiple salons and will not return at full price. Default 25%. Increases this number makes the analysis more conservative and more realistic. Insight contributed by franchisee operator Greg Thomas.
Price elasticity → How many customers you lose per $1 the price is raised above the promotional price. Default 3%. Higher for price-sensitive markets, lower for loyal locations.

READING YOUR RESULTS

Green banner — Optimal Price Point → The price that would have produced the best outcome. Use this when planning your next event.
Net Position at Optimal → Dollar value of the best possible outcome at that price.
Optimal Price for Next Event → How much better optimal would have been vs. what you ran.
KPI Cards → Revenue sacrifice, payroll spike, retention value, breakeven count, new customers, shortfall.
Price Sensitivity Table → Every $1 price point from sale price to normal avg invoice. ★ = optimal.
Executive Summary → Plain-language analysis at the bottom of the results panel.
Red = financial loss | Green = positive outcome | Gold = near breakeven

Salon

Salon Identification

Salon Name or Numberappears on printed output

Data Entry Method

Step 01

Baseline Performance

Weekly averages for one salon before the promotion ran. This is your normal business baseline for that location.

Baseline Weekscount

CC Last Year
avg/wk

CC This Year
avg/wk

Avg Invoice (baseline)services + retail per customer

Payroll %
baseline

Revenue Chg
vs prior yr

New Cust Return %
after 1st visit

Repeat Cust Return %
each subsequent visit

Step 02

Actual Sale Week Results

Your actual results for the same salon during the week the promotion ran.

CC Last Year

CC This Year

Sale Price Point
advertised service price

Sale Avg Invoice
actual collected (incl. retail)

Sale Payroll %

Revenue Chg
vs prior yr

Step 03

Assumptions & Elasticity

Set demand sensitivity for the price optimization model.

Price Elasticity — customer drop per $1 above sale price 3.0%

Low (inelastic) ←————————→ High (price sensitive)

Avg visits per year
Customer frequency — 6×/yr (~57-day cycle) to 9×/yr (~40-day cycle)

6×/yr

Coupon-loyalty factor
Est. % of new customers who rotate across salons and won't return at full price

25%

What is this? Research by franchisee operators suggests approximately 25–33% of coupon-using customers split their visits across multiple salons — getting 3 cuts at Salon A, 3 at Salon B, 3 at Salon C. These customers are loyal to the coupon, not your salon, and are unlikely to return at full price after a promotional event.

Effect: This slider reduces the retention calculation by the selected percentage, producing a more realistic net position estimate. At 0% the model uses the full optimistic retention assumption. At 25% (default) it reflects a conservative real-world adjustment. Insight contributed by Greg Thomas, franchisee operator.

What is this? Elasticity estimates how many customers you lose for every $1 you raise the promotional price above the sale price. At the default of 3%, if you ran the sale at $9.99 and raised it to $10.99, you would expect roughly 3% fewer customers to show up. Raise it to $11.99 and you lose roughly 6%, and so on.

How to set it: If your market is very price-sensitive and customers shop around, slide it higher (5–6%). If your customers are loyal and location is convenient, slide it lower (1–2%). The default of 3% is a reasonable middle estimate for a mature haircut market. When in doubt, leave it at 3% and see where the optimal price lands — then run it again at 5% to see how sensitive the answer is.

Fields update live · Button forces full recalculation

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Enter your data then click Generate Analysis

Fill in the baseline and sale week fields on the left, then click ◆ Generate Analysis to run the full financial impact and price optimization.

Required: Avg Invoice, Payroll %, NCR, RCR (baseline) · CC Last Year, CC This Year, Sale Price Point, Sale Avg Invoice, Sale Payroll % (sale week)

Post-Event Promotional Analysis

Salon-level post-event analysis · Optimal price recommendation for future events

Optimal Price Point

—

avg invoice —

Net Position at Optimal

—

vs. normal week + retention value

vs. Running at Current Price

—

additional net value

Revenue Sacrificed

—

vs. normal week at baseline invoice

Payroll Point Spike

—

above baseline payroll %

Retention Value Per New Customer

—

NCR × invoice × RCR decay series

New Customers Needed to Break Even

—

at RCR-adjusted retention value rate

New Customers Generated

—

sale week × new customer %

Retention Shortfall

—

under breakeven (excl. ad costs)

Baseline vs. Sale Week

Metric	Baseline Avg	Sale Week	Δ Change
Customer Count Growth	—	—	—
Revenue Change vs Prior Year	—	—	—
Average Invoice	—	—	—
Payroll Percentage	—	—	—

Retention Math — Step by Step

Total sale week customers

—

New customers generated

—

First return (NCR rate)

—

New custs needed for BE

—

Price Optimization — Sensitivity Analysis

CALCULATING

Steps in $1 increments from sale price point to baseline avg invoice. Retention value uses actual visit frequency with NCR/RCR decay. ■ = best net position ■ = near breakeven ■ = negative return

Price Point	Avg Invoice	Est. Customers	Est. Revenue	vs Normal Wk	Breakeven Cust.	New Customers	Net Position
Enter data to generate price table

PRICE POINT = advertised price. AVG INVOICE = estimated actual collected (price point × invoice multiplier from your entered data). RETENTION VALUE = new customers × NCR × avg_invoice × RCR geometric decay (6 visits/yr). NET POSITION = revenue delta vs normal week + retention value. Excludes advertising costs and bypass customer adjustment — true breakeven is higher than shown.

Analysis Summary

—

Enter your data to generate the executive summary.

Methodology Notes & Definitions

New Customer Return Rate

The NCR shown in your performance report reflects customers from 15 weeks prior (105 days), not the current week. Sale-week NCR will not be calculable for another 15 weeks after the event. This tool uses your baseline NCR as the retention proxy — the optimistic assumption. Actual sale-week retention is almost certainly lower because price-motivated customers return at below-average rates.

Bypass Customers

Customers added via the Bypass button during high-volume sale events have no profile created and cannot be tracked for return per GC metric definitions. They are included in customer count but contribute nothing to retention. New customer count and retention value are overstated to the degree bypass usage increases during the sale.

"New Customer" Definition

GC flags as "new" any customer absent for more than 365 days. Sale-week new customers include lapsed customers who timed their return around the discount. These are price-conditioned returnees, not genuine new acquisitions — they are likely to repeat this behavior at the next sale rather than returning at full price on a normal cycle.

What This Model Does Not Include

Advertising and promotional costs associated with running the event are not included in the revenue sacrifice calculation. The true breakeven threshold is higher than shown. All three factors above (NCR timing, bypass customers, lapsed-customer inflation) cause this model to overstate the case for the promotional event. The actual financial result is worse than the tool shows.

THREE IMPORTANT NOTATIONS — POST-EVENT ANALYSIS: (1) BYPASS CUSTOMERS — Customers added via the Bypass button during high-volume events have no profile created and cannot be tracked for return. They are included in your customer count but contribute nothing to the NCR or retention calculation. Actual new customer count is lower than shown during sale events. (2) NEW CUSTOMER DEFINITION — GC flags as "new" any customer absent for more than 365 days. Sale-week new customers include lapsed customers timing their return around the discount price. These are not true new acquisitions — they are price-conditioned returnees who will likely time future visits around the next sale. (3) NCR/RCR MEASUREMENT WINDOW — GC calculates these metrics over a 105-day window, which it also uses to trigger coupon sends. This model applies them to actual visit frequency (~6 per year). Advertising costs excluded — true breakeven is higher than shown.

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