How to Safely Test Price Increases for Smoking Cessation Courses or Consultations on Sales and Profit

The first time I seriously conducted a "price increase test" was not because I wanted to earn more, but because my order volume was rising while my time was collapsing. In March 2024, I was selling "21-Day Smoking Cessation Companion Consultation" through WeChat private traffic in Hangzhou: a single in-depth assessment at 198 RMB and a four-week follow-up package at 899 RMB. For four consecutive weeks, I received about 18–22 consultation orders per week. The profit statement looked good, but my calendar was filled with voice replies at 9 PM every night. My assistant calculated: if we raised the consultation to 268 and the package to 1099, as long as we didn't lose more than about 20% of sales, total gross profit would actually improve. I could also reduce the weekly number of clients from 22 to about 15, ensuring delivery quality.

The problem is: smoking cessation services are not selling cups. When prices change, clients will ask "Were you overcharging before?" or "Is this price increase a trick?" Safe testing is not about sneaky price hikes, but using controllable range + rollback design + profit-centered reading methods, first seeing if the market can accept it before deciding to go full-scale.

The conventional approach to price experiments in the industry supports this: show different prices to different groups, track purchase numbers, revenue, and subsequent behavior; and looking only at conversion rates is not enough — you must also look at revenue and profit. Small samples can easily deceive, so you need observation windows, stop-loss lines, and qualitative feedback, rather than declaring "price increase successful" after just three days of data.

1. My Position: Protect Trust First, Then Talk About Price Increase Magnitude

I oppose three common practices:

Existing clients take screenshots, compare in groups, customer service can't align their stories — the trust cost is far higher than the extra 70 RMB collected.

198 RMB with 10 sales = 1980, 268 RMB with 8 sales = 2144 — sales dropped, but revenue increased. If delivery time is fixed, per-person service cost remains almost unchanged, so 268 is often more cost-effective. There is a repeatedly validated principle in pricing tests: prioritize looking at revenue (and your gross profit), not just conversion.

"Do you think 1099 is expensive?" is completely different from actually paying with WeChat balance. Surveys can set ranges (like Van Westendorp, Gabor-Granger methods), but the final verdict must come from real orders.

The safety boundaries I support are simple:

2. How to Design a Small-Scale Test: "Being Able to Complete It" Matters More Than "Scientific Perfection"

Strict statistical significance is hard to achieve in private traffic with only a few dozen daily leads. I accept "directional evidence + stop-loss" and don't fantasize about finding eternal truth in one test.

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2.1 Write a Hypothesis First, Then Start Testing

Bad hypothesis: "Let's see what happens if we raise prices."

Usable hypothesis (my March 2024 version was roughly like this):

New client consultation price from 198→268 (+35%), with unchanged script and delivery, new client conversion rate drops no more than 25% within 14 days, and total consultation gross profit during the period ≥ same period at old price; if confirmed, then test package 899→1099.

The numbers can be adjusted, but you must have two baselines: "how much drop is acceptable" and "whether money increased."

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2.2 Two Splitting Methods I've Actually Used

Method A: Split by Entry Point (suitable for forms/mini-programs)

Rotate or randomly distribute through Moments, video channel profiles, and community pins — each user always sees only one price (customer service notes "A/B," payment link bound to the corresponding amount).

Method B: Time-Window Switching (suitable for pure WeChat transactions)

Keep ad spend, response speed, and host/consultant selection as consistent as possible. The downside is that seasonal and content fluctuations can interfere, so I prefer Method A; I only use B when leads are too few.

For sample size, my personal minimum is: at least 40 valid consultation intentions per group (added WeChat and asked about price or process), or at least 15 purchase attempts per group. Any fewer, and the numbers are just stories, not decisions. Public resources also remind: samples that are too small and durations too short are easily led by noise.

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2.3 How to Choose Price Steps

For smoking cessation consultations, I recommend moving 15%–35% at a time, not doubling at once.

3. Data Observation: One Table Is Enough, Don't Be Superstitious About Dashboards

I use Feishu multidimensional table, 10 minutes a day, with the following fields (you can copy this):

Only look at four result indicators weekly:

Articles on pricing practices like Stripe emphasize: conversion, ARPU, retention/churn, and customer acquisition cost should all be examined together. For smoking cessation consulting, customer acquisition cost is the cost of your ad spend or content time allocated to each lead; if lead quality improves after a price increase and ineffective small talk decreases, the effective part of CAC actually drops.

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Three Rules I Insist On When Reading Data

Rule 1: Compare money first, then rates.

Example (close to my March actual measurement scale, rounded):

Conversion rate dropped, but gross margin was thicker — because 3 fewer low-price orders freed up time. If your hourly rate is estimated more accurately, the table becomes even more striking.

Rule 2: Don't make decisions before the window closes.

I run at least 14 days, covering two full weekends (smoking cessation intentions are denser on weekends). If group B performs particularly poorly in the first 3 days, first check whether the payment link was sent wrong or the script omitted the freebies, rather than immediately concluding "prices can't go up."

Rule 3: Qualitative feedback carries weight.

If multiple people in Group B say "Are you guys specifically targeting people trying to quit smoking?" or existing clients come into the group to confront, even if revenue is slightly higher, first fix the value expression before talking about full-scale price increases. Data won't tell you how much that sentence hurts the brand.

4. A Complete Round I Did (Including Mistakes)

Background

March 11–24, 2024, Hangzhou, WeChat private traffic + a video channel homepage link to a form. 2 consultants, unified script. Product: single assessment consultation.

Actions

Potholes encountered

Consultants habitually sent the old payment code, and some in Group B paid 198. That night, reconciliation found 2 discrepancies; we could only make up the difference or treat it as integrity. After that, we stipulated: note the price tag on each lead, read the amount aloud before collecting payment.

Someone directly asked "What spots are limited?" This promotional tone conflicted with the seriousness of smoking cessation. On day 5, we changed it to: "To ensure weekly follow-up quality, I only open 15 new assessment slots per week, and the price includes complete assessment delivery." Questions noticeably decreased — price increase language must match the category's tone.

A client from Group A posted the 198 chat record in the experience group. We publicly stated the rule: "During the test period, different entry points have different prices for new clients; payments already made will be honored at the paid price, and no price difference adjustments will be made." There was some debate, but it was easier to handle than sneaky price changes. Transparent rules are a safety valve.

Results (consultation only, 14 days)

My judgment at the time: can fully switch new clients to 268, leave the package unchanged for now, and test 899→1099 separately in the next round.

In April, I tested the package: conversion rate dropped from about 18% to about 12%, but after the ticket price went up, weekly revenue stayed flat, serviceable client count decreased, and negative delivery reviews dropped from 2 per week to 0–1. For me, profit and reputation improving simultaneously matters more than "volume first."

5. When Sales Drop, How to Decide Whether to Push the Price Increase

Use a simple decision tree (I posted it at my desk):

Regarding "whether people trying to quit smoking are particularly cheap": I've seen people who smoke two packs a day pay 1099 without hesitation, and people who smoke three cigarettes a day agonize over a 50 RMB add-on. What matters more is certainty — "How many days until I see results?" "What if I fail?" When raising prices, adding clear service boundaries and exit rules often works better than reducing the price by 30 RMB.

You can also first do a willingness-to-pay assessment (e.g., step-by-step asking at which price they'd still pay), to set testing limits, but the launch must still be based on real transactions. Pricing experience on course platforms is similar: prices are refined through market feedback, not decided by a single brainstorm.

6. Safety Checklist (Go Through 10 Minutes Before Testing)

7. Final Thoughts

Price increase testing in smoking cessation courses and consultations is essentially asking: Does the market recognize you selling your time at a higher price, and do you deserve this higher price?

Safety is not about not raising prices, but about: controllable splitting, promises not shrink, indicators looking at profit, rollback when things go wrong, and rules dare to be transparent.

My current default strategy is still: new client prices can be tested, existing client relationships are not used for experiments; change only one number at a time; conversion rate can give a little, but trust cannot. If you have very few leads, honestly do "biweekly comparison + stop-loss," don't pretend you're running an A/B test with millions of UVs. The signal of money will be weak in small samples, but service time, price rejection quotes, and refund reasons — these signals will speak from the first week. Listening to them is far more useful than listening to a phrase like "dare to raise prices."

Old vs New Price Comparison