Tactic 4
Personalization Performance Analysis and Insights
For example:
“Segment A had a 5% conversion (up from 4%); Segment B
had 3% (no change). Analyze why personalization might have
worked better for A and suggest improvements for B.”
The AI might analyze differences in segment characteristics
What it is:
(perhaps Segment A’s content was more aligned to their
Similar to experiment analysis, this tactic involves
interests, while Segment B’s wasn’t as compelling) and
using AI to analyze the performance of personalized
suggest ideas (e.g.,“Try a different incentive for Segment B, as
experiences and extract insights. Whether it’s the
they seem more price-sensitive,” if it knows that from data).
click-through rate of personalized recommendations,
If you have a lot of unstructured data (like survey responses:
conversion by segment, or customer feedback, AI can
“I liked that the site knew my name” / “the recommendations
quickly summarize how well your personalization
weren’t relevant”), you can ask the AI to summarize common
efforts are working and why. It can also identify
themes or sentiment.
patterns or segments that respond differently,
helping you refine your approach.
“Here are 100 customer feedback snippets on our
personalized homepage. Summarize the key positive and
How to use it:
negative sentiments.”
After launching personalized content (like a tailored
The AI will respond with something like:
homepage or segmented campaign), collect the key
“Users appreciated the personalized product picks,
performance metrics – e.g., engagement rate per
especially when they were recent views (positive). Some
segment, conversion uplift, retention changes, or even
found recommendations off-base if they had unique
qualitative feedback (reviews, customer comments).
tastes (negative).”
Feed these to the AI and ask it to evaluate and explain.
GenAI Quick-Win Playbook for Personalization
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