pricing analysis with sample data: complete walkthrough
most solopreneurs price by feeling. they pick a number that feels reasonable, run with it for a year, and never test whether they are leaving money on the table or scaring off buyers. when revenue stalls, they discount. when discounting works once, they discount again. eventually they cannot remember why they picked the original price, and the customer base only buys at sale.
a real pricing analysis takes one afternoon. you pull conversion data at different price points (either from past changes or a planned experiment), calculate the elasticity, and find the price that maximizes revenue per visitor. that single calculation usually surfaces a 15-30% revenue lift hiding in the current pricing. without running it, you are leaving that on the table.
this tutorial walks the entire process with a realistic 12-row sample dataset of pricing experiments. by the end, you will have a working elasticity calculation, a revenue-optimization curve, and a clear answer about where to set your price next. no Profitwell, no expensive pricing tool, just spreadsheets and 25 minutes.
the sample dataset
below is the dataset we will use. paste into Google Sheets, save as pricing-analysis-2026, and follow along.
| test_id | start_date | end_date | price_point | visitors | conversions | revenue | conversion_rate | revenue_per_visitor |
|---|---|---|---|---|---|---|---|---|
| T001 | 2025-08-01 | 2025-08-14 | 19 | 4200 | 168 | 3192 | 4.0% | 0.76 |
| T002 | 2025-08-15 | 2025-08-28 | 29 | 4100 | 144 | 4176 | 3.5% | 1.02 |
| T003 | 2025-08-29 | 2025-09-11 | 39 | 4350 | 130 | 5070 | 3.0% | 1.17 |
| T004 | 2025-09-12 | 2025-09-25 | 49 | 4400 | 110 | 5390 | 2.5% | 1.23 |
| T005 | 2025-09-26 | 2025-10-09 | 59 | 4300 | 86 | 5074 | 2.0% | 1.18 |
| T006 | 2025-10-10 | 2025-10-23 | 69 | 4200 | 65 | 4485 | 1.5% | 1.07 |
| T007 | 2025-10-24 | 2025-11-06 | 79 | 4100 | 49 | 3871 | 1.2% | 0.94 |
| T008 | 2025-11-07 | 2025-11-20 | 89 | 4250 | 38 | 3382 | 0.9% | 0.80 |
| T009 | 2025-11-21 | 2025-12-04 | 99 | 4150 | 29 | 2871 | 0.7% | 0.69 |
| T010 | 2025-12-05 | 2025-12-18 | 49 | 4400 | 115 | 5635 | 2.6% | 1.28 |
| T011 | 2025-12-19 | 2026-01-01 | 49 | 4500 | 112 | 5488 | 2.5% | 1.22 |
| T012 | 2026-01-02 | 2026-01-15 | 49 | 4350 | 109 | 5341 | 2.5% | 1.23 |
a pricing analysis is the calculation of revenue, conversion rate, and revenue per visitor across multiple tested price points to find the price that maximizes total revenue. the standard solopreneur build uses 8-12 weeks of data at varying prices, plots conversion rate against price (the demand curve), and calculates revenue per visitor at each. the optimal price is rarely the highest price; it is the price where the conversion rate has not yet collapsed. the analysis takes 25 minutes in Sheets and consistently surfaces a 15-30% revenue per visitor improvement that can be deployed within a single week.
step 1: structure and clean the data
paste data into A1:J13. validate that conversion rates and revenue per visitor are calculated correctly.
check conversion rate
formula in column I:
=F2/E2
format as percent. expected for T001: 168/4200 = 4.0%.
check revenue per visitor
formula in column J:
=G2/E2
expected for T001: 3192/4200 = $0.76.
drag down. these are your two key dependent variables.
common mistake: most solopreneurs only look at total revenue when comparing price points. that hides the fact that a high-price test may have run during a peak-traffic week. always normalize by visitors using revenue_per_visitor.
step 2: plot the demand curve
select columns D (price_point) and I (conversion_rate) → Insert → Chart → scatter plot.
expected output: as price rises, conversion drops. classic downward-sloping demand curve.
| price_point | conversion_rate |
|---|---|
| $19 | 4.0% |
| $29 | 3.5% |
| $39 | 3.0% |
| $49 | 2.5% |
| $59 | 2.0% |
| $69 | 1.5% |
| $79 | 1.2% |
| $89 | 0.9% |
| $99 | 0.7% |
the curve is approximately linear from $19 to $69, then bends downward more steeply above $69. this is typical: there is a “ceiling price” above which conversion collapses faster than incremental revenue can compensate.
step 3: plot the revenue curve
select columns D and J → Insert → Chart → scatter plot.
| price_point | revenue_per_visitor |
|---|---|
| $19 | $0.76 |
| $29 | $1.02 |
| $39 | $1.17 |
| $49 | $1.23 |
| $59 | $1.18 |
| $69 | $1.07 |
| $79 | $0.94 |
| $89 | $0.80 |
| $99 | $0.69 |
the curve is hump-shaped. revenue per visitor peaks at $49, falls slowly toward $59, falls steeply above $69.
the optimal price is $49. that is the price that extracts the most revenue per visitor.
step 4: calculate price elasticity
elasticity measures how much demand changes when price changes.
elasticity = % change in conversion / % change in price
between $39 and $49:
– price change: ($49-$39)/$39 = +25.6%
– conversion change: (2.5%-3.0%)/3.0% = -16.7%
– elasticity: -16.7% / 25.6% = -0.65
between $49 and $69:
– price change: ($69-$49)/$49 = +40.8%
– conversion change: (1.5%-2.5%)/2.5% = -40.0%
– elasticity: -40.0% / 40.8% = -0.98
between $69 and $99:
– price change: ($99-$69)/$69 = +43.5%
– conversion change: (0.7%-1.5%)/1.5% = -53.3%
– elasticity: -53.3% / 43.5% = -1.23
elasticity interpretation:
– |elasticity| < 1: inelastic (raising price increases revenue)
– |elasticity| ≈ 1: unit elastic (revenue is constant)
– |elasticity| > 1: elastic (raising price decreases revenue)
below $49 you are inelastic and leaving money on the table. above $69 you are elastic and bleeding revenue. between $49 and $69 you are at unit elasticity, the revenue maximum.
step 5: validate the holdback test
T010, T011, T012 are three separate two-week tests at $49. they all converge on roughly 2.5% conversion and $1.23 revenue per visitor. that consistency is your evidence the $49 result is real, not a one-time anomaly.
| test_id | conversion | rev_per_visitor |
|---|---|---|
| T004 | 2.5% | 1.23 |
| T010 | 2.6% | 1.28 |
| T011 | 2.5% | 1.22 |
| T012 | 2.5% | 1.23 |
| avg | 2.5% | 1.24 |
| stdev | 0.05% | 0.026 |
stdev of 5% on conversion across four tests is excellent. the result is real.
step 6: calculate revenue impact at scale
if your monthly traffic is 18,000 visitors:
| price | conversion | rev_per_visitor | monthly_revenue |
|---|---|---|---|
| $19 | 4.0% | $0.76 | $13,680 |
| $29 | 3.5% | $1.02 | $18,360 |
| $39 | 3.0% | $1.17 | $21,060 |
| $49 | 2.5% | $1.23 | $22,140 |
| $59 | 2.0% | $1.18 | $21,240 |
| $69 | 1.5% | $1.07 | $19,260 |
moving from $29 to $49 generates an extra $3,780/month or $45,360/year in revenue at the same traffic level.
if you came in expecting a $29 price was right, the answer is no, you are leaving 21% of your revenue on the table.
step 7: layer LTV considerations
the analysis above is single-purchase revenue. for SaaS, layer LTV.
if higher-priced customers churn faster, the LTV gap may flip the answer.
| price | conversion | first_payment | LTV (12mo) | rev_per_visitor (LTV) |
|---|---|---|---|---|
| $19 | 4.0% | $19 | $190 | $7.60 |
| $29 | 3.5% | $29 | $290 | $10.15 |
| $39 | 3.0% | $39 | $390 | $11.70 |
| $49 | 2.5% | $49 | $490 | $12.25 |
| $59 | 2.0% | $59 | $590 | $11.80 |
assuming churn does not change with price (a strong assumption), $49 still wins. but if churn at $59 is meaningfully higher (say 12-month retention drops from 10 months to 8 months), the answer might shift.
run a separate cohort analysis on customers acquired at each price point after 6 months. our customer churn analysis tutorial covers the cohort math.
comparing pricing analysis methods
| method | data needed | accuracy | best for |
|---|---|---|---|
| willingness-to-pay survey | 100+ survey responses | low (overstates) | pre-launch direction |
| van Westendorp survey | 200+ survey responses | medium | range estimation |
| AB price testing | 1000+ visitors per cell | high | digital products |
| holdout regions | 6+ weeks per region | high | physical/local products |
| price ladder (this method) | 8-12 weeks history | high | digital with traffic |
solopreneurs should use the price ladder method for digital products and AB testing once traffic exceeds 5000/month per variant.
our customer lifetime value calculation tutorial connects pricing to LTV economics, and our ROAS analysis tutorial covers the spend side that pricing decisions ultimately feed. our SaaS metrics founders must track guide covers the broader monetization framework.
frequently asked questions
how long should each price test run?
minimum 14 days per cell to wash out day-of-week effects. 28 days if your business has weekly seasonality.
how many price points should I test?
minimum 5, ideally 7-9. fewer and you cannot see the curve shape. more than 12 and you will run out of patience before the data is meaningful.
what about psychological pricing ($49 vs $50)?
real effects exist but they are smaller than most marketing blogs claim. focus on the price level first. once you have settled on $49 vs $59, run a small AB test on $49 vs $48 vs $47 to find the local optimum.
can I run this on existing pricing without testing?
if you have changed prices over time, yes. use historical data. the limitation is confounding (the market moved, your product changed, etc.). best is fresh experimentation.
what if I have segments with different elasticity?
run the analysis per segment. consumers may have higher elasticity than businesses. find the optimal price per segment and either price-discriminate (B2C and B2B variants) or pick the segment-weighted optimum.
conclusion: run the test this quarter
pricing is the highest-leverage decision a solopreneur makes and the one most often based on guesswork. raising your price 10% with the same conversion rate adds 10% revenue. if you are inelastic at your current price, you can probably raise 20-30% without significantly impacting conversion.
start this quarter. either pull historical data from past pricing changes (preferred if available) or run an 8-week ladder test starting next Monday. paste into the schema above, calculate elasticity, plot the revenue curve, find the peak. the answer is almost always 20-50% above your current price for early-stage solopreneurs.
for related work, our customer churn analysis tutorial tells you whether higher prices increase churn (the only thing that would invalidate this analysis), and our marketing funnel analysis tutorial covers the conversion math at each stage. test, learn, raise.