Alternatives keep winning.
Is it capabilities, price, outcome framing or evidence? Find the reason instead of rewriting copy without a hypothesis.
AI agent offer experimentsOffers AI agents
Offers AI agents
choose.
Find out what makes your product the better choice.
We design and test competing offers for your SaaS, API or developer tool. Customer research, Jobs-to-be-Done and pricing inform the offers. Controlled agent experiments show what gets chosen, what delivers and where the economics hold.
For product and growth teams selling through marketplaces, endpoints and agent-assisted buying journeys. Led by Jake McMahon.
The decision environment
The job to be doneGet the outcome.
Stay within budget.
Stay within budget.
Outcome + Total cost + Constraints
The offer fits the job.Now verify it delivers.
Agent offer experiments.
A focused, four-week sprint.
A clear buying problem. Three offer variants. Evidence to decide what to change.
Offer Experiment Sprint
Learn how an agent evaluates your offer against alternatives—and whether the chosen offer delivers a valuable outcome at a viable margin.
- One priority job and channel, three offer variants
- Controlled buyer-agent evaluation with documented limits
- Execution checks, human buying-friction review and delivery economics
- Results readout and an implementation-ready recommendation
$7,500from · USD
4 weeks after agreed scope and required access.
50% upfront, 50% at handoff.
Starting price for the stated scope; production rollout and extra usage are scoped separately. An accepted diagnostic or equivalent evidence is needed before testing.
Not sure what to test yet?
The Agent Choice Diagnostic maps buying friction, economics and the strongest hypotheses. From $1,500 USD · five business days after required access.
Your offer is visible.
But is it worth choosing?
Start with a real commercial decision, not a vague ambition to “win at AI.”
You’re entering a new channel.
A marketplace or agent-accessible endpoint changes what buyers compare. Test the offer before committing your launch budget.
Your pricing hides the value.
The unit looks cheap, but the job feels risky or expensive. Find a structure buyers can evaluate and your business can deliver profitably.
Selection isn’t becoming success.
An agent chooses your product but fails to use it. Separate an offer problem from an access, execution or product-experience problem.
What would make your offer the better choice?
Bring your product, the job you want to win and the alternatives buyers consider. We’ll turn the problem into a testable question.
Start with the job.
Build the offer around it.
Connect customer needs, product capabilities and commercial terms. Then test the connection.
Jobs-to-be-Done
What progress is the customer hiring this product to make?
Define the situation, desired outcome and constraints. An agent acting for that customer needs to recognise the same job—not just a feature list.
Outcome & value-based pricing
What is the outcome worth, and what does it cost to deliver?
Examine pricing units, packaging, limits and delivery risk. Outcome-based billing fits where the result is measurable and enforceable; it is not a default for every product.
Explore pricing & packaging ↗Offer construction
Why choose this offer over a credible alternative?
Bring the promise, capabilities, price, proof and terms together. Make the trade-offs clear for people and agents—without promising something the product cannot do.
Experiment design & statistics
What evidence would change our decision?
Define the comparison and evaluation criteria before testing. Track selection, successful execution and economics separately. Report uncertainty, not just the most flattering result.
Customer insight in.
Testable offers out.
Research tells us what might matter. Agent experiments test whether those ideas influence the choice under the conditions we examine.
Understand the customer
Customer interviews, Jobs-to-be-Done research, reviews, sales objections and pain-point mining. Find the progress buyers want, the alternatives they use and the trade-offs they make.
Output: Jobs, buying triggers and offer hypotheses.
Check the commercial reality
Available usage, funnel, cohort, survey and delivery-cost data. Quantify friction and identify the economic constraints a viable offer must respect.
Output: A baseline, economic limits and success criteria.
Observe the agent’s decision
Purpose-built sandboxes and repeatable procedures. Compare variants against credible alternatives, vary buying contexts and check whether the selected endpoint works.
Output: Selection, execution and uncertainty—not just a recommendation.
Market mining and sentiment analysis
We examine reviews, discussions and customer feedback for recurring pain points, desired outcomes and buying language. Sentiment analysis helps organise attitudes; qualitative interpretation explains the context. Neither proves agent preference. They supply hypotheses to test.
Sources and access are agreed in scope. New interview programmes, extensive data collection and additional instrumentation are not automatically included in the base sprint.
Four weeks.
One supported decision.
The delivery clock starts once scope and required access are agreed. Each stage creates an input for the next.
- Week 1
Define the buying problem.
Review customer evidence, the job, alternatives, current offer and economics. Agree the baseline and what a useful result looks like.
- Week 2
Build the offer variants.
Construct three variants. Document pricing, outcomes, proof and constraints. Set the criteria and controlled test procedure.
- Week 3
Run the agent experiments.
Compare offers in bounded buying scenarios. Check robustness and endpoint execution. Keep failures and exclusions in the record.
- Week 4
Decide what to ship next.
Analyse results and explain uncertainty. Deliver recommendations and the next live-validation or implementation steps.
Not just a report.
A decision your team can use.
Every deliverable connects research to a commercial or product decision.
Buyer job & decision map
The outcome, buying constraints, alternatives and reasons an offer might win.
Know which problem your team is solving—and for whom.
Three testable offer variants
Outcome framing, packaging, pricing mechanics and evidence, built around one priority job.
Compare commercial choices before committing to a bigger launch.
Documented experiment & results
Test design, baseline, run evidence, execution checks and an honest account of uncertainty.
Give stakeholders reasoning they can inspect, not just a slide to trust.
Economics & implementation roadmap
Completed-job costs, margin implications, recommended changes and the next validation steps.
Align product, growth and sales around a practical next move.
Preference and margin improvement are goals to evaluate, not guaranteed outcomes. A well-supported “don’t ship this” can save an expensive mistake.
Choice
≠ delivery
≠ delivery
Controlled experiment programme
A selected offer still has to work.
Our buyer-agent tests paired offer selection with endpoint execution. Some choices won; some executions failed. Both changed what we built next.
That is why the work goes beyond persuasive descriptions: an offer must be chosen, deliver its promise and make commercial sense.
Read the experiment and its limits ↗Test before you commit the roadmap.
Find out whether your next change needs better positioning, different pricing or a product fix. Start with the decision you need to make.
Research, pricing and product expertise.
Applied to your buying problem.
I’m Jake McMahon. I bring behavioral science, statistics and analytics together with hands-on product, Jobs-to-be-Done and pricing work to help SaaS teams make better growth decisions.
I’ve built the sandbox environments and testing procedures used in this work. You work directly with me—from the research question to the recommendation.
Before we start.
Know what you are buying—and where the boundaries are.
Do we need to buy the diagnostic first?
Not always. The sprint needs a clear job, channel, baseline and enough evidence to design meaningful variants. If you have equivalent evidence, we can assess it. Otherwise the diagnostic establishes the starting point.
What if agents aren’t buying our product yet?
We distinguish current behavior from a future-channel hypothesis. The diagnostic can assess readiness and human buying friction. We will not present simulated demand as real customer demand.
Will this guarantee more revenue or better margins?
No. You receive the agreed research, tests and recommendation. Marketplace share, realised revenue and customer conversion require live validation.
What is outside the base sprint?
Production rollout, a wider redesign, extensive instrumentation, new interview programmes and extra API usage are separately scoped. One engagement is in delivery at a time.
After the offer.
Build the growth system around it.
Additional services, scoped separately. Help the right buyers find the offer—and help people and agents reach more value after choosing it.
SEO, AEO & GEO built around a clear offer.
Turn supported customer jobs and offer insights into use-case pages, comparisons, pricing explanations, marketplace listings and useful content. Connect search and AI-answer visibility to your wider go-to-market journey.
- Channel and discovery audits.
- Offer-led positioning and content plans.
- Structured, crawlable product facts and documentation.
- Visibility measurement, with revenue measured separately.
Onboarding, activation & expansion for people and agents.
Design the experience after the choice: for customers using AI agents and for agents using your product directly. Make the first outcome easier and continued use worth paying for.
- Human onboarding and agent setup journeys.
- First-value flows, documentation and execution feedback.
- Repeat-use, upgrade and expansion experiences.
- Product instrumentation and success measures.
48
live pieces across four anonymised B2B engagements
Externally measured discovery work
Show up where buyers research.
Content, search rankings and assistant citations measured from saved external evidence. Visibility is reported as visibility, not booked revenue.
See the measurement ↗Start with the experiment. Build from the evidence.
Tell us what you sell, who needs it and where the buying decision happens. We’ll identify the right starting point—not sell you every service at once.