Tactic 6: ROI Estimation & Business Impact Forecast
When to use:
Use AI for ROI analysis before pitching or implementing a test change, and right
after you have results to summarize impact. It’s especially valuable when you
need to justify experimentation resources to finance or leadership – tying
experiments to revenue, cost savings, or customer lifetime value. In enterprise
settings, this helps speed up decision-making on whether to roll out a tested
change (by showing the payoff) or whether a particular personalization
initiative is worth the effort.
Why it works:
AI is great at synthesizing data points into a coherent story. It can do back-of-the-
envelope calculations and, more importantly, articulate the business significance.
Many enterprise leaders prioritize AI projects that deliver measurable value – by
having the AI explicitly connect an experiment to value, you ensure your testing
program speaks the language of the business. This tactic also uncovers factors
affecting ROI. For example, the AI might note, “If the lift only applies to new users, the
overall revenue impact will be lower,” or “Ensure no increase in cost per acquisition,
so net ROI remains high.” These pointers are gold when assessing an experiment’s
true impact. Plus, AI’s ability to quickly iterate scenarios (e.g. “What if the lift is half of
expected?”) allows you to do sensitivity analysis with minimal effort.
GenAI Quick-Win Playbook for Experimentation
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