Write a falsifiable acquisition hypothesis
For referral software, test whether satisfied customers inviting appropriate peers experience a costly enough problem when the team cannot tell which referrals produced an eligible new customer to consider a bounded change. The decision owner hypothesis is a growth owner designing a customer referral program, and a possible trigger is that a small manual referral experiment has produced enough activity to strain reconciliation.
The current alternative, coupon codes reconciled manually in a spreadsheet, is the comparison point. Your experiment should discover whether the proposed workflow earns adoption despite switching effort. It should not treat a compliment about the product as proof of demand.
Run a bounded experiment
An example planning window is one week, but the buyer's availability and implementation needs determine the actual timing. Agree on a manageable number of conversations with the team running the test. That number is a planning choice, not an expected conversion rate.
| Stage | Action | Record |
|---|---|---|
| Prepare | Define the scope needed to trace a test invitation through an eligible event and an ineligible event without issuing real rewards. | Inputs, access, owner, time boundary, and stop conditions. |
| Research | Use "referral program" "manual tracking" and inspect the original source. | Source, date, observed problem, and unresolved questions. |
| Discover | What verified event makes a referral eligible, and what invalidates it? | The actual workflow and cost of the current workaround. |
| Check feasibility | Who resolves duplicate attribution, refunds, or a disputed reward? | Confirmed requirements and unsupported needs. |
| Evaluate | Check whether a qualifying test referral is attributed once while the ineligible case is excluded. | A verifiable event, a blocked attempt, or an explicit refusal. |
Set decision rules before reviewing results
A refusal, failed pilot, or invalid hypothesis can be a useful result. Preserve it. Do not replace inconvenient outcomes with a different success definition after the experiment finishes.
- Continue the pilot only when its critical dependency is resolved: reward rules and abuse controls need explicit definitions before automating payouts.
- Stop pursuing a poor-fit account when it belongs to teams expecting a referral tool to create product satisfaction or guarantee viral growth.
- If the problem is not recognized in specific examples, revise the audience hypothesis.
- If the problem is recognized but implementation blocks the pilot, revise scope or capability assumptions.
- If the pilot runs, verify the value event: a qualifying test referral is attributed once while the ineligible case is excluded.
- If value occurs but the buyer will not continue, investigate the recurring need and the proposed commercial terms.
Keep a learning record another person can review
Use the illustrative signal “Our customers already introduce friends, but we lose track of which introductions qualify for the reward. How do we reconcile this?” to practice the distinction between evidence and inference before reviewing real records. By contrast, A founder wants rewards to compensate for a product nobody uses yet. cannot establish the same buying intent.
Record what you planned, what actually happened, the source for each observation, and the decision you made. Keep invitations separate from replies and paid outcomes separate from expressed interest. DemandBolt's project and research workflows can hold the context while you decide the next experiment; the software does not guarantee that an experiment succeeds.
About these playbooks and examples
Prepared by DemandBolt with AI assistance as practical planning resources for people building and selling software. The category scenarios and message examples are illustrative, not quotations from real prospects or claims of customer results. Adapt the hypotheses to your product, verify original sources, and review capabilities before making an offer.
The workflow follows our first-customer guide and buying-signal review method. Our PyInvesting and Rae records distinguish actual internal work from targets. Published .