What the output looks like
One sample row, walked end to end
A ranked list only helps if a rep can act on a row without asking what it means. Here is one row from a sample run, broken into the parts a rep actually reads.
The sample row
Names and figures illustrative. Ranked by possible loss.
Reading it in the field
Step 1
Signal
Orders fell from 6 to 3. The last order was 21 days ago against a usual gap of 9 days. Both facts come from the order file alone; no extra integration is needed to see them.
Step 2
Reason
The row states the change in plain language rather than a score, so the rep can see why this dealer is above the others before picking up the phone.
Step 3
What to ask
The action column tells the rep what to check first. For a fall in order frequency against the account’s own rhythm, that is a call to confirm whether demand, stock, or a service issue is behind it.
Step 4
Outcome field
What the rep finds gets recorded against the account. That feedback is what improves the next run, and it is the difference between a list that is used once and one that stays credible.
How the ranking is tiered
Every dealer lands in one of three action tiers, so the list reads as a work queue rather than a wall of names:
- Call now — a clear break from the account’s normal pattern, worth contacting this week.
- Win-back — the account has already slowed sharply or stopped, so the conversation is a recovery attempt.
- Watch — the pattern looks like accounts that churn, but there is no confirmed change in orders or spend yet.
What this page is not claiming
The sample is illustrative of the output shape, not a measured result. AutoModel identifies a loyalty-risk pattern from order behaviour; the rep confirms the cause on the call, and it does not prove a switch to a named competitor.
See it on your own dealers
The sample shows the shape. One free run on your own order file shows whether the signal is useful in your territory.
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