On this page
Compare AI application distribution channels by the useful users they help reach, the effort they consume and the uncertainty in attribution. Referral visits alone do not establish incremental growth, and a channel with fewer clicks can still bring more appropriate users.
Evidence type: research framework and engineering analysis. No customer study, channel experiment or traffic measurement was conducted for this article. Numerical examples are synthetic. The framework complements the first-100-users guide; it addresses measurement design rather than repeating acquisition tactics.
Define the question and target population
Start with a narrow question: does a particular listing or developer tutorial help the intended audience complete the product's first useful task? Define the audience through the problem they have, not merely their country or device.
Specify the unit of analysis. A person, account, workspace and session are different units. A developer tool used by several colleagues may have multiple visits but one adoption decision. Choose a unit that matches the product and document where identity is incomplete.
Define activation before collecting results. For a document assistant, it could be an authorized user obtaining and reviewing a cited answer. For a workflow, it could be a successful import and verified test run. Account creation alone may not demonstrate value.
Describe the channel intervention
A channel is not a fixed treatment. A marketplace listing changes with title, screenshots, category, destination and review state. A tutorial changes with topic, audience and call to action. Record the actual artifact and version exposed during the study.
Separate three questions: who was exposed, who visited and who completed the useful task. You may observe only some of those stages. If impression data is unavailable, do not infer a click-through rate from visits alone.
| Stage | Operational definition | Main uncertainty |
|---|---|---|
| Exposure | Eligible audience shown the artifact | Often unavailable outside your own surfaces |
| Qualified visit | Visit meeting a predeclared relevance rule | Bots, duplicate visits and missing identifiers |
| Activation | First useful task completed under a stable rule | Instrumentation coverage and task variation |
| Continued value | Relevant repeat outcome within a defined window | Cohort maturity and cross-device identity |
| Conversion | A clearly defined paid or developer action | Billing lag, team decisions and assisted paths |
Keep unavailable stages explicit. A partial funnel is valid evidence if its limitations are visible.
Attribution is not causality
A referral parameter can describe the observed path, subject to missing or stripped identifiers. It cannot by itself prove that the user would not have arrived otherwise. A person may read documentation, visit a marketplace and later return directly.
Use a first-touch or last-touch rule only as a reporting convention, with a documented window. Preserve assisted paths when the privacy design permits them. Do not assign all value to the last click and then call that a causal effect.
Randomized assignment can support stronger causal inference when the intervention and audience permit it. Some external channels cannot be randomized cleanly; in that case use a careful observational design and state the remaining confounders. NIST's experimental-design guide provides background on objectives, variables, randomization and blocking.
Predeclare the comparison
Write the hypothesis, primary outcome, assignment method, observation window and stopping rule before launch. Record product changes that could alter activation. Avoid choosing whichever metric looks favorable after the fact.
If comparing listing variants on a surface you control, keep eligibility and assignment stable. If comparing independent channels, acknowledge that their audiences may differ. A high-intent documentation visitor and a broad social visitor are not automatically exchangeable populations.
For small samples, report counts and uncertainty rather than an authoritative ranking. An experiment's required size depends on the baseline, effect of interest, variance and design. This framework does not invent a universal number of visitors or days.
A synthetic funnel comparison
Assume channel A brings 200 qualified accounts and 40 activate. Channel B brings 80 and 24 activate. Activation rates are 20% and 30%, while activated counts are 40 and 24. These calculations describe assumed observations, not real Theaimart performance.
If A takes 20 hours of work and B takes four, the assumed effort per activation is 0.5 hours and about 0.167 hours respectively. That does not make B universally superior: audience overlap, later retention, capacity and causality remain unresolved.
Report both volume and efficiency. Do not extrapolate a small successful channel linearly to unlimited scale, or compare one mature cohort with another whose users have not yet had time to activate.
Build privacy-aware instrumentation
Use a small event vocabulary tied to actual product transitions. Record artifact version, channel convention, task class and a privacy-reviewed account or cohort identifier where appropriate. Avoid sending prompts, private documents or sensitive generated output to marketing analytics.
Document consent and retention requirements for the actual implementation. A hashed identifier is not automatically anonymous, particularly when it can be linked to an account. Restrict access and choose an aggregation level that avoids exposing individuals.
Validate event ordering with controlled test accounts. Duplicate callbacks, blocked scripts and interrupted sessions can distort the funnel. Keep known missingness visible rather than manufacturing complete attribution.
Include the operational cost of a channel
Count preparation, approval, maintenance, support and moderation effort. A listing that attracts users with incompatible devices can generate support cost instead of useful adoption. A tutorial that teaches the wrong current API can create reputational harm even if traffic rises.
Maintain current delivery requirements and pricing. The publishing guide explains the distinction between a listing and hosting. A listing visit is not proof that a user successfully installed an app, launched a hosted product or imported a workflow.
For Theaimart, verify the applicable submission type in listing best practices. This framework does not claim that the platform supplies every analytics field described here; instrument your own permitted product boundary and record what is unavailable.
Interpret geography carefully
The United States, India and United Kingdom can be useful reporting segments when sample size and product relevance justify them. Do not treat country as a substitute for user problem, language, device capability or distribution eligibility.
Compare compatible cohorts and state currency, time-zone and billing differences where relevant. Avoid geography-specific claims from a handful of visits. A regional landing page needs a real regional need, not names inserted for search coverage.
Publish what the evidence supports
Keep the study protocol, artifact versions, event definitions, analysis rule, exclusions and limitations together. Distinguish observed paths, causal estimates and hypotheses. Report unfavorable outcomes and missing data alongside successful cases.
The next practical step is to choose one activation event and one channel intervention, validate the instrumentation and predeclare a bounded study. Until actual observations exist, use this framework to design the work rather than presenting invented distribution findings.