Free tool

Subscription pricing advisor

Find your optimal membership price by balancing content depth, audience signals, and growth goals — with trade-offs at every price point.

Your value signals

Content & delivery

What you offer
3

1 = general cooking, 5 = highly specialized (e.g. autoimmune protocol).

Your audience

25K
5%

Comments, saves, and shares relative to followers — niche creators often see 3–8%.

Business goals

Balanced

Left = maximize member count. Right = maximize revenue per member.

17%

Annual billing typically reduces churn — common discount is 15–20%.

Your pricing range

Based on your value signals: $11 – $27 / month

Grow fast

$11

per month

Est. MRR
$2,332
Est. members
212
Churn bracket
6–9%
Rev. / follower
$0
Revenue potential Medium
Growth speed High
Member commitment Low
Quality signal Low
Churn risk Medium

Price alone does not drive churn — value and engagement matter more (Subbly 2024).

Market position

Entry tier for food creators ($7–$14)

Sweet spot

$18

per month

Est. MRR
$3,114
Est. members
173
Churn bracket
6–9%
Rev. / follower
$0
Revenue potential High
Growth speed Medium
Member commitment Medium
Quality signal Medium
Churn risk Medium

Price alone does not drive churn — value and engagement matter more (Subbly 2024).

Market position

Core food & wellness range ($15–$24)

Premium value

$27

per month

Est. MRR
$3,456
Est. members
128
Churn bracket
6–9%
Rev. / follower
$0
Revenue potential High
Growth speed Medium
Member commitment High
Quality signal High
Churn risk Medium

Price alone does not drive churn — value and engagement matter more (Subbly 2024).

Market position

Premium niche specialist ($25–$39)

Ready to launch at your price?

Member Kitchens handles subscriptions, content delivery, and your branded app — so you can focus on the value members pay for.

Revenue calculator

Research & sources

Benchmarks and insights on this page are grounded in published subscription and creator economy research.

Academic foundation: Tversky & Kahneman (1974) — anchoring bias in judgment under uncertainty.