What We Do
The whole customer,on one screen.
Purchase history sits in one system, support history in another, and health scores in a third. Nobody can answer how an account is really doing without opening all three and guessing at the gaps. We put the full picture in one place, including the part most companies never assemble: which parent company an account rolls up to.
The problem
Somebody asks how an accountis doing. Three teams answer.
Sales knows what they bought. Support knows how much trouble they have been. Success has a health score built from something. Finance knows what they are worth. Nobody has all four in front of them at the same time, so the answer depends on who you asked.
In an enterprise business there is a fifth problem underneath those. Half the accounts are subsidiaries of each other, and the system of record does not say so. A rep is looking at a mid-sized customer without knowing it rolls up to one of the largest relationships the company has.
We assemble the whole thing at the account level, including the family tree, and put it on one page.
What lands on the page
Everything a rep would otherwise ask four people for.
Who they really are
Parent and child relationships resolved, so an account shows its corporate family and you can roll the whole family up. Region, segment, tier and the named person who owns the relationship.
How healthy they are
Health scored by measure rather than as one opaque number, with the trend over the last several months and the change since last quarter. Satisfaction survey results kept on their own scales instead of blended into something that means neither.
What they cost to serve
Support cases, returns, and professional services hours bought against hours used. The accounts that look profitable until you count what it takes to keep them.
Whether they are leaving
Cohort retention by order sequence rather than calendar month, so businesses with different billing cadences are comparable. Expiring cards, lapsed visits and falling engagement, delivered as a list somebody can work today.
Where the data runs out
A blank beats a numberyou find out was wrong.
On one build, roughly half the accounts had a named success manager in the source system and half did not. The tempting move is to fill the gap with a placeholder. We render nothing instead, because a placeholder gets read as fact by the third person who sees it.
Same reasoning elsewhere on that page. Health scores are suppressed for accounts already marked churned, so a dead account does not show up looking like a disengaged live one. Time-to-close is calculated only over cases that actually closed. A dataset that is complete on volume but not on revenue says so on its face.
Leadership stops trusting a system the first time it is confidently wrong. Most of this work is making sure that day does not come.
Case study
Membership revenue leaking through invisible cracks. We made it visible before it was gone.
For a membership-based wellness operator, the worst kind of churn is the kind no one sees coming. Cards expire. They get cancelled, reissued with new numbers, frozen for a trip. By the time a payment fails the revenue is already gone, and so is the best window to re-engage the member. Most operators find out when the billing platform logs a failed charge, and by then the conversation is reactive.
We pulled card status straight from the booking platform into the warehouse, then surfaced a daily list of every member with an expiring or at-risk card, sortable by location, expiry window and membership tier. Staff could reach out before the card ever failed. What had been invisible became a daily task with an action attached to it.
The infrastructure already held the data. It just was not going anywhere useful. Getting it into a form the team could work was the only thing standing between them and the revenue they were losing.
Outcome
Membership revenue that had been slipping through gaps the billing platform never surfaced became predictable and recoverable. Operations moved from reacting to failed payments to preventing them.
What changed
What it changed.
546 accounts, one family tree
Sales could not tell which parent company an account rolled up to, so one global relationship looked like a dozen small ones. Every account shows its family, its health and its owner now, and account planning starts from what the whole relationship is worth.
The true conversion rate
They believed roughly half their customers were converting to subscription. A large share had bought both in the same order, so counting the bundles separately showed which acquisition path pays back.
Churn visible before it happened
Expiring cards, lapsed visits and falling engagement arrive as a daily list sorted by location and renewal date. The save conversation happens before the payment fails instead of after it.
What else
This one rarely shows up alone.
Can you see a whole customer today?
Tell us where the pieces live and we will show you what one page looks like.