# Partner marketing for experimentation software > Design a partner offer that helps the right customers evaluate and adopt experimentation software. A practical procedure with a worked scenario, category-specific checks and an editable worksheet. Source: https://saas-marketing.net/industries/experimentation/partner-marketing/ Topic: SaaS Demand Generation Type: field-guide Published: 2026-09-17 Last updated: 2026-09-17 Publisher: SaaS Marketing (saas-marketing.net) License: CC BY 4.0. Quote or republish with attribution and a link to https://saas-marketing.net/industries/experimentation/partner-marketing/ ## Short answer A partner can introduce a relevant audience, implement a workflow, provide complementary technology or support an existing customer relationship. Decide which contribution is needed for a product team with enough eligible exposure for a defined test. ## Key takeaways - Choose the partner's contribution. - Map the shared customer situation. - Build one joint proof asset. - Do not let partner collateral imply that significance does not establish practical value or remove design bias is solved without a verified customer-specific review. --- This field guide uses a product team with enough eligible exposure for a defined test as its working context. The buying conversation involves the growth experimentation lead, while the data scientist needs to assign treatments and analyze outcomes under a defensible design. Adapt the scope when those roles, dependencies or operating conditions differ. ## Choose the partner's contribution A partner can introduce a relevant audience, implement a workflow, provide complementary technology or support an existing customer relationship. Decide which contribution is needed for a product team with enough eligible exposure for a defined test. Do not describe every referral source as an implementation partner. The customer should understand who is responsible for the product, the advice and the delivery. A partnership is useful when it reduces a real buying or adoption obstacle. ## Map the shared customer situation The trigger teams cannot distinguish product effects from ordinary variation can create a joint conversation if both organizations have a legitimate role in resolving it. Explain how the partner helps the customer assign treatments and analyze outcomes under a defensible design and where the product's responsibility begins. An integration with feature delivery and analytical data sources may support the offer, but a logo exchange is not evidence that a working integration exists. Verify the supported scope before using it in campaign copy. ## Build one joint proof asset Use sample-ratio checks, a fixed decision rule and a reproducible result as a candidate workshop or evaluation exercise. Assign the preparation, demonstration and follow-up tasks explicitly. Each organization should review claims made about its own responsibilities. Use synthetic or permissioned material, and do not imply a customer endorsement merely because a customer attended an event. A useful asset should remain understandable to someone who did not hear the sales presentation. ## Agree on handoff and conflict rules Define what happens when the same account is already known to both parties, when a request concerns support, or when the prospect declines contact. Record ownership and the permitted information to share. A joint campaign should not result in duplicate or surprising outreach. Keep commercial referral terms separate from the public educational content so the recommendation does not masquerade as an independent review. ## Enable the partner to qualify honestly Teach the partner to recognize the objection "A dashboard will encourage premature conclusions" and the conditions under which the product is unsuitable. Provide current requirements and a route to technical help. Overpromising can create an expensive implementation problem for everyone involved. The partner should know what evidence is required before claiming that a customer can run a test allocation check and verify the primary outcome event. ## Measure contribution and customer outcomes Track the partner's actual role in sourcing, influencing or implementing the account without adding the same revenue several times. Review whether referred customers match the intended segment and whether teams make decisions using prespecified metrics and valid allocation. A partner that produces fewer but better-prepared evaluations may be more useful than one that supplies a large unqualified list. Keep support burden and customer experience beside the commercial totals. ## Category-specific review Experiment results depend on assignment, exposure and outcome definitions. A user assigned to a treatment may never experience it, and exclusions can affect the comparison. Ask which analysis population the team intends to use and what stopping method the design supports. Use a synthetic allocation check and verify the outcome event before interpreting a treatment difference. Inspect missing data and unexpected group sizes. A statistical dashboard does not repair a biased assignment or establish that a small effect is worth implementing. ## Worked situation A partner brings a relevant audience while the product team provides sample-ratio checks, a fixed decision rule and a reproducible result. Three attendees request a joint evaluation; two are already active opportunities. Record the partner's contribution without treating all three as newly sourced accounts. Agree who follows up, what information can be shared and who answers questions about feature delivery and analytical data sources. A successful partnership creates a clearer path for the customer. It does not require either party to claim ownership of every commercial outcome. ## Working worksheet | Working item | Category-specific starting point | Question to resolve | | --- | --- | --- | | Shared audience | a product team with enough eligible exposure for a defined test | Why does this partner reach them? | | Joint customer task | assign treatments and analyze outcomes under a defensible design | Which responsibility belongs to each party? | | Verified connection | feature delivery and analytical data sources | What scope has actually been tested? | | Joint proof | sample-ratio checks, a fixed decision rule and a reproducible result | Who owns each part of the exercise? | | Qualification boundary | A dashboard will encourage premature conclusions | When should the partner decline a referral? | Add your evidence, owner and next action to each row. Read the [worksheet instructions](/resources/#using-worksheets) before completing the file. ## Run the review with the people who do the work Bring the data scientist into the review of sample-ratio checks, a fixed decision rule and a reproducible result. Ask them to identify the input they would actually have, the exception they expect to encounter and the person who receives the output. Then ask the growth experimentation lead which unresolved issue could change the decision. Keep the two answers separate until the team understands whether the obstacle is workflow fit, implementation readiness or commercial priority. Record any dependency on feature delivery and analytical data sources beside the affected worksheet row. A dependency should have an owner and an observable completion condition. If it changes the scope of the offer, revise the public description before the next campaign. This prevents a useful planning exercise from turning into a promise the delivery team cannot meet. ## When to change the plan Do not let partner collateral imply that significance does not establish practical value or remove design bias is solved without a verified customer-specific review. If new evidence changes the audience, required workflow or acceptance conditions, update the brief and explain why. Compare later results against the version of the plan that was actually used. ## Continue with the next decision Use the [product launch plan guide](/industries/experimentation/product-launch/) when that is the next unresolved task, or return to the [experimentation software marketing overview](/industries/experimentation/) to choose a different route. The [saas demand generation hub](/saas-demand-generation/) provides the broader method. ## Reference and scope The [primary category reference](https://docs.statsig.com/) is a starting point for checking product terminology and current capabilities. This page provides an original planning framework. It does not imply a vendor endorsement, firsthand product test, original market survey or guaranteed commercial result. ## Frequently asked questions ### Where should partner marketing for experimentation software start? Design a partner offer that helps the right customers evaluate and adopt experimentation software. Confirm the customer situation and the evidence needed for the next decision before selecting a channel, format or tool. ### What category-specific concern should the team investigate? The concern "A dashboard will encourage premature conclusions" needs an observable test or a clear limitation. Also account for the dependency on feature delivery and analytical data sources; do not assume it is already resolved. ### What does the worksheet include? It contains the working items and category-specific starting points shown on this page. Add your own evidence, owner, status and next review decision. The examples are constructed, not reported results or industry benchmarks. ### How does this connect to customer value? The customer needs to assign treatments and analyze outcomes under a defensible design. A meaningful first checkpoint is to run a test allocation check and verify the primary outcome event; the ongoing condition is that teams make decisions using prespecified metrics and valid allocation. Choose the stage appropriate to this piece of work rather than combining all three into one metric.