True Causes
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On another site, as a live map:
As a picture: https://truecauses.org/d/b3bc98a/map.svg
As data: every judged pair in this room (CSV). Each claim has its own address, /c/b3bc98a/<card>, listed under the map.
Credit when you publish: Causal map: True Causes, https://truecauses.org/d/b3bc98a
THE QUESTION

Why do complaints keep rising while the product improves?

Propose what to do, then judge whether each action would move each root.

Sealed 23 days ago: this room no longer knows who sat in it; only its steward is still named. What it concluded stays.
30 at the table (29 simulated) · 3 actions proposed against 5 root causes. Add one, then judge whether each action moves each cause.
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PROPOSED ACTION
Let agents refund up to a set amount without asking
Nothing said yet.
Some claims influence each other in a circle
↺ A customer who complains twice gets the same f → Most complaints are about waiting, not about t
A circle can be a real finding — a cycle that feeds itself — or a link whose direction was never really settled. The map treats the group as one node, so nothing inside it is a root on its own. If it is the second, the pair is worth putting back to the room.
Score the roots first
why
The method's own practice, before any action is proposed: each root cause is scored 1–5 on how feasible it is to address, how much impact addressing it would have, and how likely it is to resolve by itself with no intervention. Priority goes to roots with high impact, high feasibility and low likelihood of fixing themselves — priority = impact + feasibility − likelihood. Scores are averaged across the group and shown with how many scored.
1 = low, 5 = high. Your own scores; the group's averages beside them.
root causefeasibleimpactfixes itselfpriority
Angry customers are moved to the back of the queue by the priority rules––––
Agents who take the time to fix a problem properly miss their numbers––––
Refund policy is different on the website, in the app and in the script––––
Nobody from product reads the ticket tags––––
Managers see the dashboard, agents see the queue, nobody sees the customer––––
Would each action move each root cause?
Down the side: the actions people proposed. Across the top: the root causes the map found. Every square is one vote on one question — would this action significantly reduce that cause? One side has to reach 75% of the votes cast to settle it; short of that the square stays split. Each square shows how its votes fell — yes / worse / no — and a square is settled when one of those reaches three quarters of them.
why
Answered at 75% of votes cast, like every other question here. Split means people voted and neither side reached that bar — it is a result, not a queue, and it stays split until enough people change their minds. A square nobody settles stays undecided; it never turns into a no. Judging each pair on its own is what stops a popular action being credited with fixing things it does not touch.
ACTIONROOT CAUSES · does the action on the left reduce it?
Angry customers are moved to the back of the queue by the priority rulesAgents who take the time to fix a problem properly miss their numbersRefund policy is different on the website, in the app and in the scriptNobody from product reads the ticket tagsManagers see the dashboard, agents see the queue, nobody sees the customer
Measure agents on problems solved at first contact, not tickets closed per hour discuss✓ yes, it reduces this
24 yes / 0 worse / 5 no
not decided yet
not decided yet
✓ yes, it reduces this
27 yes / 0 worse / 2 no
✓ yes, it reduces this
25 yes / 0 worse / 4 no
Let agents refund up to a set amount without asking in focus ↑ · close ✕✗ no, it does not
4 yes / 0 worse / 25 no
not decided yet
not decided yet
✗ no, it does not
5 yes / 0 worse / 24 no
✗ no, it does not
5 yes / 0 worse / 24 no
A weekly hour where product reads the top ten tags with support in the room discuss✗ no, it does not
4 yes / 0 worse / 25 no
not decided yet
not decided yet
✗ no, it does not
5 yes / 0 worse / 24 no
✓ yes, it reduces this
25 yes / 0 worse / 4 no
Highest leverage so far
Measure agents on problems solved at first contact, not tickets closed per hour reduces 3 of 5 root causes
A weekly hour where product reads the top ten tags with support in the room reduces 1 of 5 root causes
THE ACTIONS ON THE MAP
Showing every action the room judged: it acts directly on 3 of the 5 root causes. It does not act directly on 2.
Read from the squares the room settled at Actions, nothing else. A root in green is reduced by at least one of the shown actions; in grey, none of them acts on it directly; a red outline means one of them makes it worse.
Angry customers are moved to the back of the queue by the priority rules → Agents who take the time to fix a problem properly miss their numbersA customer who complains twice gets the same first reply both times → Most complaints are about waiting, not about the answerA customer who complains twice gets the same first reply both times → Complaints are counted only when someone uses the word complaintMost complaints are about waiting, not about the answer → A customer who complains twice gets the same first reply both timesMost complaints are about waiting, not about the answer → Complaints are counted only when someone uses the word complaintAgents who take the time to fix a problem properly miss their numbers → Complaints about one feature spike after every releaseAgents who take the time to fix a problem properly miss their numbers → Refund policy is different on the website, in the app and in the scriptManagers see the dashboard, agents see the queue, nobody sees the customer → Survey requests go out before the problem is solvedNobody from product reads the ticket tags → The best agents leave within a yearNobody from product reads the ticket tags → Survey requests go out before the problem is solvedRefund policy is different on the website, in the app and in the script → A customer who complains twice gets the same first reply both timesRefund policy is different on the website, in the app and in the script → Agents cannot issue a refund without a manager, and the manager is in meetingsSurvey requests go out before the problem is solved → The same question is answered a hundred times because the help page is wrongSurvey requests go out before the problem is solved → Templates make every reply sound like nobody read the messageAngry customers are moved to the back of the queue by the priority rules ① Measure agents on problems solved at first contact, not tickets closed per hourAngry customers are movedto the back of the queue bythe priority rules①what the shown actions do to this root causeREDUCED BY 1 ACTIONManagers see the dashboard, agents see the queue, nobody sees the customer ① Measure agents on problems solved at first contact, not tickets closed per hour ③ A weekly hour where product reads the top ten tags with support in the roomManagers see the dashboard,agents see the queue,nobody sees the customer①③what the shown actions do to this root causeREDUCED BY 2 ACTIONSNobody from product reads the ticket tags ① Measure agents on problems solved at first contact, not tickets closed per hourNobody from product readsthe ticket tags①what the shown actions do to this root causeREDUCED BY 1 ACTIONThe best agents leave within a yearThe best agents leavewithin a yearSurvey requests go out before the problem is solvedSurvey requests go outbefore the problem issolvedAgents who take the time to fix a problem properly miss their numbersAgents who take the time tofix a problem properly misstheir numberswhat the shown actions do to this root causeNO ACTION ACTS ON ITDIRECTLYTemplates make every reply sound like nobody read the messageTemplates make every replysound like nobody read themessageThe same question is answered a hundred times because the help page is wrongThe same question isanswered a hundred timesbecause the help page iswrongComplaints about one feature spike after every releaseComplaints about onefeature spike after everyreleaseRefund policy is different on the website, in the app and in the scriptRefund policy is differenton the website, in the appand in the scriptwhat the shown actions do to this root causeNO ACTION ACTS ON ITDIRECTLYMost complaints are about waiting, not about the answerMost complaints are aboutwaiting, not about theanswerpart of a feedback loop⟳A customer who complains twice gets the same first reply both timesA customer who complainstwice gets the same firstreply both timespart of a feedback loop⟳Agents cannot issue a refund without a manager, and the manager is in meetingsAgents cannot issue arefund without a manager,and the manager is inmeetingsComplaints are counted only when someone uses the word complaintComplaints are counted onlywhen someone uses the wordcomplaint
Each number on the map marks a root cause that action reduces.
① Measure agents on problems solved at first contact, not tickets closed per hour
② Let agents refund up to a set amount without asking
③ A weekly hour where product reads the top ten tags with support in the room
FROM ACTIONS TO A PLAN
The grid judged each action on its own. A plan is a set of them the room would carry out together.
Members put plans together from the actions that reduce a root cause, and the room compares the plans two at a time, at three quarters of the votes cast, as it judged everything else. Each plan shows which root causes it acts on directly, which it does not, and which it makes worse.
The steward can open a plan comparison.
What we expect to follow
Predictions about the actions: what each should change, how to measure it, and when to check. On the date, someone who was in the room writes what actually happened beside it.
why
A map says which actions move which causes. It does not say whether anyone did them, or whether anything changed. An entry here is a prediction with a date and something to measure, written after the map settled. When the date comes, what actually happened is written beside it and the prediction stays as it was: a wrong one is the useful kind. Nobody voted on any of this and it changes nothing the room found.
due 23 Jan 2027
actionMeasure agents on problems solved at first contact, not tickets closed per hour
we expectfewer people have to come back a second and third time
measured by% of contacts resolved at first contact
guessesthe room: 59 (median of 15, spread 45 to 72)