Find the top 5 user pain points this quarter
"Users are complaining" gets you nowhere in a board meeting. The agent counts how many people each problem hit and how many of them never came back.
Medium · 30 min · once a quarter
What you get
a breakdown in the reply · once a quarter
Top pains, Q2: 3,140 tickets 1. Slow delivery to the regions: 412 people did not return within 90 days: 118 (29%) 2. Card payment failure: 187 people did not return: 71 (38%) 3. Sizing confusion: 260 people effect not computed: 190 tickets could not be linked to a buyer.
A sample on made-up data; your numbers will be your own.
Who it fits
- Quarterly planning where you must pick the costliest problem, not the most frequent one.
- A conversation with management where lost-customer numbers land and adjectives do not.
When it won't work
Tickets cannot be linked to a person's later purchases, so you get frequency and no money effect.
How the agent does it
Set quarter and rules
Say what counts as a lost customer and over what window, or the agent invents its own rule. Name the attribute that links a ticket to a person.
Run the analysis
The agent groups recurring problems, counts how many people each one reached and looks separately at what those people did next.
Split counted from guessed
Ask for two lists: pains where the effect is computed from data, and pains where it is only asserted, with the sample size under every number.
What you set
Telegram, Ozon, Wildberries. By hand: the quarter boundaries and your own definitions of churn and of a repeat purchase.
What you'll need
Starter prompt
Copy the prompt or open it straight in a chat with the agent.
Go through the Telegram tickets and the Ozon and Wildberries reviews for [quarter] and identify the five main user pain points. Rank them not by mention frequency but by cost: how many users are affected and what happened to them afterwards [left / did not buy again / reduced their basket]. State the effect on churn and repeat purchases only where you computed it from data you actually saw: attach which rows the figure was built from and how many people are in the sample. Where linking a ticket to subsequent behavior did not work out, write "effect not measured" and leave the pain point in the list by frequency, instead of grading it with words like "probably has an effect". Output five cards (the wording of the pain point, how many users are affected, [3] quotes, what happened to them afterwards) and, as a separate list, the hypotheses that the data does not confirm.
Open in chatCheck the top pain points. List: - pain points where the churn effect is computed and pain points where it is merely asserted, as two lists; - the sample size behind every effect figure and how many people could not be linked to purchases; - pain points that made the top thanks to one noisy channel; - complaints you merged into a single pain point, and by which signal; - cases where the number of users grew along with the complaints and the shares did not change; - which conclusions are verifiable against the data and which are your hypothesis; do not mix hypotheses into the top.
A second prompt: the agent uses it to review its own work and show what's left for you.