AI Conversion Experiment Portfolio
Run multiple AI conversion tests and let outsized winners pay for failed trials
- Difficulty
- Advanced
- Time to result
- ~weeks to results
- Steps
- 6
- Confidence
- 96%
The AI Conversion Experiment Portfolio applies generative and predictive AI to specific moments where prospects decide whether to continue, engage, or buy. Teams first map conversion points across landing pages, product pages, email, sales outreach, content discovery, and live chat. They then create focused AI treatments, such as personalized videos, generated email copy, content recommendations, or an informed chatbot, and test each treatment against the current control. The method assumes that several trials will fail, so multiple experiments run as a managed portfolio. This is economically attractive when successful tests deliver gains large enough to offset wasted effort elsewhere. Instrumentation is essential: it allows teams to measure actual lift, scale winners, retire failures, and substitute stronger models later without rebuilding the entire customer experience.
Origin
Extracted from Marketing Against The Grain, where the hosts described HubSpot's portfolio of AI conversion experiments and the unusually large gains produced by successful tests.
Core principles
- 01Apply AI at conversion points rather than using it without a measurable objective
- 02Test generated experiences against a control
- 03Expect some experiments to fail
- 04Use a portfolio because successful tests can produce outsized gains
- 05Instrument implementations so improved models can be substituted later
How to run it
- 1
Map conversion points
Identify moments in the customer journey where a visitor, lead, or prospect chooses whether to proceed. Prioritize points with enough volume and business value to support a meaningful test.
Pro tip Start with an existing bottleneck that has a reliable conversion baseline.
Watch out Do not begin with an AI feature that lacks a defined behavioral outcome.
- 2
Choose a focused AI treatment
Select one intervention, such as generated copy, personalized video, recommendations, or an informed chatbot. Keep the treatment narrow enough to attribute any result.
Pro tip Use AI where personalization or content-production costs previously made the experience impractical.
Watch out Changing several unrelated variables makes the result difficult to interpret.
- 3
Preserve the control
Run the AI treatment against the existing experience. Define the primary conversion metric and any quality or customer-experience guardrails before launch.
Pro tip Record model, prompt, audience, and content versions for reproducibility.
Watch out A before-and-after comparison can confuse AI impact with seasonality or traffic changes.
- 4
Run a portfolio of tests
Operate several prioritized experiments rather than betting the program on one use case. Accept that some tests will consume time without producing a lift.
Pro tip Balance quick tests with a smaller number of technically ambitious opportunities.
Watch out Do not scale an exciting treatment before the data distinguishes it from noise.
- 5
Scale asymmetric winners
Expand treatments that deliver credible conversion gains while retaining quality. Redirect resources away from failed or marginal experiments.
Pro tip Estimate the absolute revenue or pipeline impact, not only the percentage lift.
Watch out Large conversion gains are harmful if they reduce lead quality, trust, or retention.
- 6
Upgrade and retest
Design the implementation so the underlying AI model can be replaced. Retest established treatments when model capabilities improve.
Pro tip Separate business logic, prompts, and model access so upgrades require minimal engineering.
Watch out Never assume a new model automatically preserves previous quality and safety.
In the wild
HubSpot tested an AI-generated email against its control. The hosts reported that the treatment converted 94% higher, nearly doubling the control and demonstrating why a portfolio could tolerate unsuccessful experiments.
→ One outsized winner justified continued investment across a broader set of AI conversion tests.
A business uses HeyGen to create different videos for landing pages, products, or visitor groups. Each version addresses the needs of a defined audience and is tested against the page's existing experience.
→ The business can determine whether previously expensive video personalization produces measurable conversion lift.
A small company uploads accurate material about its products, services, and policies into an AI chatbot. The chatbot provides conversational assistance throughout the customer journey while the company measures qualified conversations and completed purchases.
→ A one-person business gains continuous customer coverage without staffing live chat around the clock.
Common mistakes
Using AI without a conversion target
An impressive generated experience is not automatically a valuable one. Tie each treatment to a measurable customer action.
Expecting every experiment to win
The portfolio works because gains from successful tests can outweigh failed trials. Punishing every failure discourages the experimentation needed to find asymmetric returns.
Optimizing conversion alone
A treatment can raise clicks while harming trust, lead quality, or retention. Track guardrail metrics alongside the headline conversion rate.
Is it for you?
Best for
It is best for businesses with meaningful traffic or outreach volume and clearly measurable conversion events.
Not ideal for
It is not ideal for teams without sufficient traffic, reliable analytics, or a stable control experience against which to test results.
From the transcript
“we have about 12 experiments in flight on AI CR”
“some of these experiments fail and you you waste some time but because the ones that work work so well you can afford to have…”
“using AI to recommend content on your website to drive conversion rate”
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