Close the learning loop after every campaign — what worked, what didn't, and what the team would do differently.
What was the primary goal of this campaign, and how would you rate how well we achieved it?
Name the goal specifically — reach, MQLs, pipeline, brand awareness — and give it an honest grade.
Which channel or tactic performed better than expected — and what do you think drove that?
Better than expected relative to your hypothesis, not just relative to other channels.
Which element underperformed — and what's your hypothesis about why?
Be specific: a creative, an audience segment, a channel, a timing decision. The hypothesis is as important as the result.
What did this campaign teach us about our audience that we didn't know before?
This could be a message that landed or didn't, a segment that responded differently, or a behavior you didn't anticipate.
What would you do differently if you ran this campaign again from scratch?
Assume the same goal and similar budget — what specifically would you change?
Was there anything about how we ran this campaign internally — resourcing, timing, process, alignment — that we should change next time?
Not just what we did in-market, but how we worked. Where did things slow down or break down internally?
After each completed session, Mayetik generates a structured AI summary. Here's an example of the output format — the actual content reflects each respondent's answers.
The respondent's answers highlighted three recurring themes — each supported by specific examples drawn from their experience. The summary captures what was said, not what was expected.
Two responses stood out as unusually detailed and pointed to an area worth following up on. The full text of each answer is preserved below the summary.
Based on the patterns in this session, the AI identified three concrete actions for the project team to consider. These are drawn directly from the respondent's own suggestions.
Generated from 6 questions · ~3 min session