Know which customers pay back, which pay more, and which are about to leave.
Not as an average across the base, but per channel, per cohort, per customer. You get the figure, the reasoning behind it, and a plain no on the questions your data cannot answer yet.
You are not short of numbers. You are short of a decision.
A cost per acquisition figure. An ARPU snapshot. A deck from the data science team that describes the base accurately and stops short of saying what to do about it. And a marketing team holding next quarter’s budget with no better reason to move it than where it sat last quarter.
We answer the same questions forward, on your own base. When a cohort crosses into profit, and which channels get there first. Which customers are ready to be sold more. Which are about to leave, which of those are worth the save, and which are cheaper to let go. Take whichever is most urgent, or take all three.
Illustrative example. Not a client figure.
The questions your reporting does not answer
Commercial questions, not analytical ones. They get asked in budget reviews and answered with an educated guess, because the numbers that would settle them live in systems nobody has joined.
- Which acquisition channels generate profitable customers?
- When does a customer reach break-even?
- Which campaigns deserve more investment, and which should stop?
- Which customers are ready for a second product or a higher tier?
- Which part of the base is worth an offer, and which is worth leaving alone?
- Which segments are we over-contacting for no return?
- Why are customers leaving?
- Where are we leaking recurring revenue?
- How healthy is the subscription base underneath the growth?
One customer base. Three decisions. One set of numbers.
One joined view of your customers, not three products bolted together. Take one of them, or all three: the joining work underneath is done once and serves whichever you add next.
Know what a customer costs, what they return, and when
What you paid to acquire a customer, what they return, and the day they cross into profit. One arithmetic rather than three reports, broken out by channel, campaign and cohort.
See how it worksKnow which customers are ready to buy more
Subscribers pressing against the limits of their plan show it in billing data long before anyone asks them. We rank the base by readiness and name what gives each one away.
See how it worksKeep the customers who are actually worth keeping
Not every cancellation deserves a save offer. We rank why customers leave, which of those causes you can actually move, and which cohorts carry enough value to justify the spend.
See how it worksUnderneath all three, KeelShift Flybridge holds one definition of a customer, a campaign and a conversion steady across systems that each count them their own way. It is the reason the three answers agree.
Explore all three solutionsA finding that only holds where it was built is not a finding.
Most analysis is scored on the data it was built from, which is the one place it is guaranteed to look good. Nothing here reaches you until it has been tested somewhere it has never been.
Held back
Customers and whole periods are set aside before the work starts, and nothing we build gets to see them. They are fixed at the beginning rather than chosen at the end, which is the only version of this that means anything.
Tested against them
Every finding is checked against that held-out data. What survives reaches you. What does not is reported as not having survived, rather than quietly left out of the deck.
Gaps stated, not closed
Where two of your systems disagree, you get the size of the disagreement. Where the evidence is thin, that sits next to the finding in writing instead of waiting for you to find it.
If it earns it, we agree what comes next. If not, you keep the report.
The question is not whether you get a dashboard. It is what is standing behind it.
They visualise what each system already holds, on that system's own definition of a customer, a conversion and a month. Quick to read, and silent on the question the budget actually turns on: which spend produced customers worth having.
We answer that question directly, show the reasoning it rests on, and say plainly where your data will not carry the conclusion. How the answer reaches you is your call, a report or a dashboard, on our side or inside your own estate. The judgement behind it is the part that is ours.
Everyone has AI now. Not everyone knows where this data lies.
Mobile VAS, carrier billing and digital content from the inside, not from a case study. We have owned the churn and lifetime-value numbers inside a major operator’s value-added services business, chased revenue the aggregator report and the settlement file disagreed about, and defended a payback figure to a commercial director who had every reason to doubt it.
Anyone can point AI at a dataset now, ourselves included, and it makes us considerably faster. It will not tell you that a failed charge is not a cancellation, or that the base moved last March because the operator changed something. That is the part that decides whether the number is worth acting on.
About Us- Senior practitioners who have run acquisition, churn and lifetime-value analysis inside telecom, VAS and iGaming operators.
- Every figure traces back to the customer behaviour behind it, so your team can check the reasoning rather than take it on faith.
- Your data, your terms. Hashed identifiers by default, processed in the EU, deleted when the work is done. On-premise available.
Start with the Revenue Diagnostic
Fixed scope, ten business days. Send an extract and you will know which of these questions your data can already answer, which ones it cannot yet, and what the answers are worth in budget. What comes after that is agreed with you: how often, and in what form.
