Campaign experiment contamination planner

An MVP, or minimum viable product, is the smallest version that your users can actually work with. It is not a cheap version of the full solution. It is a test, built to answer the questions that decide whether the rest is worth building.

1. Pick the riskiest assumption. Here: will marketing analytics teams use it to solve "overlapping campaigns make tests hard to interpret"?
2. Build only what tests it. One team, one use case, a few core modules. People do the rest by hand for now.
3. Run a paid pilot. Agree the acceptance criteria, input limits and reviewer responsibilities before starting.
4. Measure, then decide. Track unresolved contamination risks; reviewer correction minutes; buyer acceptance and repeat purchase. Then expand, change course or stop, with evidence instead of opinions.

MVP scope for this solution. Costed pilot: One organization, one defined input format and one representative pilot batch using approved experiment designs and campaign calendars. Professional judgment, physical inspections, live external actions and production certification remain outside this prototype. Start with one buyer organization and a bounded set of representative inputs. Implement the first two modules: map shared audiences; flag overlapping treatments. Support the third task through an assisted review queue: draft isolation options. Handle the remaining required functions manually until validated. Include input upload, source references, user correction, a reviewer approval step and export of analyst-reviewed experiment plan. Authentication, account isolation, deletion controls and basic operational logging are included. Specialized production certification, live write integrations and broader rollout are not included unless explicitly stated.

After the MVP. After paying customers repeatedly accept analyst-reviewed experiment plan, automate link proposed outputs to original source records; capture reviewer corrections and approval; export a versioned analyst-reviewed experiment plan. Add one tested read integration, reusable customer configuration and scheduled repeat delivery. Increase supported formats or teams only when evaluation cases and reviewer capacity cover the new scope. One organization, one defined input format and one representative pilot batch using approved experiment designs and campaign calendars. Professional judgment, physical inspections, live external actions and production certification remain outside this prototype.

What the build depends on. A defensible calculation model, explicit units, constraint validation and representative boundary tests. Advanced forecasting or optimization needs adequate historical data. Obtain representative authorized inputs, an agreed review rubric and a buyer-side owner. Specific scope: One organization, one defined input format and one representative pilot batch using approved experiment designs and campaign calendars. Professional judgment, physical inspections, live external actions and production certification remain outside this prototype.

Pricing to test

Test USD 750-3,000 for a scoped planning setup and review, then USD 200-900 monthly for refreshes within agreed complexity. Data integration and optimization are separately scoped. All ranges are hypotheses. For this buyer, package the first sale around prepare a sample analyst-reviewed experiment plan from a small authorized set of approved experiment designs and campaign calendars and the defined analyst-reviewed experiment plan. Record actual review effort before offering a recurring allowance. The commercial pilot fee is distinct from the platform development budget.

Message to test

Identify interpretation risks before testing. Demonstrate the result with prepare a sample analyst-reviewed experiment plan from a small authorized set of approved experiment designs and campaign calendars for marketing analytics teams. Use a concrete before-and-after example without promising unmeasured savings.

Where to find buyers

Marketing operations communities and specialist agencies

Lead magnet

Prepare a sample analyst-reviewed experiment plan from a small authorized set of approved experiment designs and campaign calendars

The first 30 days

1. Week 1: interview five prospective buyers from marketing analytics teams and inspect how they handle overlapping campaigns make tests hard to interpret.
2. Week 2: prepare prepare a sample analyst-reviewed experiment plan from a small authorized set of approved experiment designs and campaign calendars using authorized or synthetic material.
3. Week 3: share the demonstration through marketing operations communities and specialist agencies and seek one bounded paid pilot.
4. Week 4: measure unresolved contamination risks; reviewer correction minutes; buyer acceptance and repeat purchase, review delivery effort and ask for a repeat purchase. This is a validation schedule, not a promise that the full product can be built in thirty days.

Paid pilot

Agree the acceptance criteria, input limits and reviewer responsibilities before starting. Run prepare a sample analyst-reviewed experiment plan from a small authorized set of approved experiment designs and campaign calendars and deliver analyst-reviewed experiment plan. Compare unresolved contamination risks; reviewer correction minutes; buyer acceptance and repeat purchase with the buyer's current process on comparable cases; include corrections, missed issues and reviewer time. Seek payment and repeat use. Stop or revise the scope if data access, accuracy or unit economics fail.

Success metrics

Unresolved contamination risks; reviewer correction minutes; buyer acceptance and repeat purchase

Retention and expansion

Build repeat use around analyst-reviewed experiment plan. Save approved configurations and review decisions with permission, revisit unresolved exceptions and show progress on unresolved contamination risks; reviewer correction minutes; buyer acceptance and repeat purchase. Offer a recurring volume allowance after repeat demand; expand to adjacent tasks only when the buyer asks and delivery quality remains acceptable.

Why clients would pick it

A validated domain model, customer-approved constraints and forecast or decision history that improves practical planning. For this concept, accumulate permissioned examples and reviewer corrections around identify interpretation risks before testing. The durable asset is reliable task-specific execution and trusted customer configuration, not access to a general-purpose AI model.

Alternatives and positioning

Spreadsheets, planners, specialist forecasting tools and existing scheduling or configuration software. Position this concept around identify interpretation risks before testing. Compare it against the customer's current process on the same representative task. This is proposed differentiation; no exhaustive competitor study or uniqueness claim has been established.

Main delivery costs

Data preparation, domain modeling, validation, scenario computation, reviewer support and ongoing assumption maintenance. Initial validation additionally budgets for representative sample preparation, interviews with marketing analytics teams, and buyer-side review of analyst-reviewed experiment plan. Track model usage, storage, reviewer minutes, exception handling and customer support per accepted deliverable.