--- title: "Log member, class, and campaign results so AI plans from your gym's history" description: "Keep one gym context file with membership tiers, attendance, retention, and campaign results so AI drafts and plans from your real numbers." canonical: "https://scalewithsearch.com/articles/ai-for-gym-owners" date: "2026-01-28" modified: "2026-09-25" --- ## Site navigation - [Scale With Search](https://scalewithsearch.com/) - Real estate - Real estate - [Real estate](https://scalewithsearch.com/for/real-estate) - Work - Start here - [Send your brief](https://scalewithsearch.com/work#send-your-brief) - [Prepare your six-question brief](https://scalewithsearch.com/work#prepare-your-six-question-brief) - Build - [Site build, content library with SEO, signal desk](https://scalewithsearch.com/work) - For your business - Trades and home services - [Auto body and collision shops](https://scalewithsearch.com/for/auto-body-and-collision-shops) - [Foundation and home repair contractors](https://scalewithsearch.com/for/foundation-and-home-repair) - 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You ask an AI assistant for a retention email to members who have not visited in 30 days. The draft is generic. You edit it into the gym's voice and send it. The next week, you need a similar email for 60-day inactive members. The assistant does not remember the first email, the voice, or the result. The same gap shows up across the business. You track which lead magnets convert, which seasonal promotions worked, and which classes keep members. The assistant sees none of it unless you paste it in again. It cannot compare this month's retention to last quarter's. It does not know the member lifecycle stages or how the list is segmented. A gym context file fixes the repeated setup. The file holds the operating facts and the recorded results. The assistant reads it before it drafts or plans. ## What the context file holds Start with one file. Split it later when a section grows past a page. | Section | What it holds | |---|---| | Membership | Tiers, what each includes, renewal terms, freeze and cancel rules | | Member patterns | Renewal rate by tier, attendance by day and time, the usual drop-off point, which classes keep members longest | | Classes | Schedule template, which time slots fill first, which instructors keep members, seasonal shifts in demand, results of new class formats | | Campaigns | Each campaign's goal, offer, channel, subject lines, ad images and headlines, targeting, and result | | Retention | Onboarding sequence, intervention points that prevented cancellations, perks that drove referrals, and each experiment with its result | | Voice | How the gym talks to members, words to use and avoid, sign-off | | Decisions | What you changed, when, and why | Record numbers as monthly totals, not as member-level records. The assistant needs "cancellations in March" to plan. It does not need a member's name, health history, or payment details. ## Inspect the file Here is a short synthetic example. The values are placeholders, not benchmarks. ```markdown # Gym context updated:: 2026.09.25 owner:: gym owner ## Membership tiers - Basic: open gym. - Plus: open gym and all classes. - Class pack: 10 classes, expires in 90 days. ## Monthly totals (synthetic values) | month | active | new | cancels | avg visits per member | |---------|--------|-----|---------|-----------------------| | 2026-06 | 410 | 38 | 22 | 6.1 | | 2026-07 | 426 | 41 | 25 | 5.4 | ## Member patterns - Most cancellations follow a gap in visits between week 3 and week 5. - 6 a.m. strength and Saturday spin fill first. ## Campaign log - 2026-01 New Year offer: waived joining fee, email and social. Result: 52 trial signups, 19 converted. Subject line with a question beat the plain one. ## Retention experiments - 2026-05: coach text at day 21 without a visit. Result: fewer cancels at day 30 than April. ## Voice - Short, direct, encouraging. No guilt about missed workouts. ``` Update the monthly table at month end from your membership software export. When the assistant plans from the file, it plans from the numbers you recorded. ## Load the file each session Claude Code reads a file named `CLAUDE.md` from the working folder when a session starts ([Claude Code memory documentation](https://code.claude.com/docs/en/memory), checked 2026.09.25). Put the gym context in that file, or keep `CLAUDE.md` short and list where the context file lives. Obsidian is a convenient editor for the file, because it stores notes as plain Markdown on your disk. The guide to [plain text as the durable layer for AI memory](/articles/plain-text-ai-memory) explains why that format outlasts any one assistant. Claude Code needs a paid Claude plan or an API account. The file stays on your computer, but the text Claude Code reads goes to Anthropic for processing. Retention and training terms depend on your plan. ## Draft retention messages from recorded patterns Ask for a re-engagement sequence for members who stopped after three weeks. With the file, the assistant knows the usual drop-off point. It knows which classes those members took and which messages you tested before. The draft builds on the last result instead of starting over. When the numbers move, ask again. Suppose retention drops at the 90-day mark. The assistant can suggest an intervention from your past successes and the member feedback you logged. It can point to the incentives that worked before. Each new experiment goes back into the file with its result. The next suggestion then rests on your gym's behavior, not on industry averages. The broader case for this pattern is in [stop re-explaining your business to AI](/articles/stop-re-explaining-your-business-to-ai). ## Plan campaigns from last season Before a New Year promotion, ask the assistant what worked last January. With the campaign log, it knows which offers converted and which messages fell flat. It suggests variations from those results, not from generic gym advice. Paid ads follow the same rule. Record which images, headlines, and targeting worked for each run. The next campaign starts from that record instead of from zero. Ask for comparisons that the file can support. "Compare spring signups to last spring" works when both months are in the table. If a month is missing, the assistant must say so and not estimate it. ## Build class schedules from attendance The class section records which slots fill fastest, which instructors keep members, and how demand shifts by season. Ask the assistant for a spring schedule. It uses the winter attendance, member preferences, and the class types that drive renewals. When you test a new class format, log the dates, the attendance, and your decision to keep or drop it. The next time you plan programming changes, that result is in front of the assistant. ## Keep member data out of the file A context file for planning needs totals and patterns. It does not need personal records. Keep these out of the file: - member names with attendance or payment history; - health information, injury notes, or intake questionnaires; - payment card or bank details; - anything a member told a coach in confidence. When a draft must go to one member, fill in the name in your email or messaging system, not in the context file. The guide on [what client data belongs in AI agent memory](/articles/client-data-in-ai-agent-memory) sets out a sorting test you can apply to member records. ## Approve every message before it goes out The assistant drafts. It does not send. You review each retention email, promotion, and ad before it reaches a member or the public. Check three things in each draft: the offer matches a current tier or promotion, the dates are right, and the message fits the voice section. If an offer or date is wrong, correct the file, then redraft. The pattern for this approval step is in [who approves what an AI agent sends](/articles/who-approves-what-an-ai-agent-sends). ## Test the file before the first campaign Run three checks in a fresh session. 1. Ask "What was our cancellation count in July?" Pass: the answer is the number in the table. Fail: the assistant estimates, or gives a number that is not in the file. 2. Ask "What was our retention in 2025?" when the table starts in 2026. Pass: the assistant says the file has no 2025 data. Fail: it invents a figure. 3. Ask for a re-engagement email. Pass: the draft follows the voice section and refers to a class the file names. Fail: it reads like a generic gym newsletter. A fail on the second check is the one to fix first. Add a rule to `CLAUDE.md`: if a month or metric is not in the file, say so and stop. ## Set up the file in about an hour 1. Create the file in the folder where you work with Claude Code. 2. Add membership tiers, the class schedule, and your marketing channels. 3. Add the last three months of totals from your membership software. 4. Add the last campaign you ran and its result. 5. Add one retention experiment, even a small one. 6. Ask the assistant for one retention email and check it against the file. After that, add to the file as you go. Log each campaign result, each experiment, and each change to classes or tiers. 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