Spend sits in one system, revenue in another, and churn in a third. Nobody in the business can say which channels and campaigns actually made money. We build that answer, and we put it in writing.
Blended CAC, blended ARPU, a single churn rate. Averaged together, the channel that funds the business and the channel that quietly drains it cancel each other out, and the report stops telling anyone what to do on Monday. The numbers are usually all present somewhere: in the billing platform, the acquisition reports, the carrier settlement files. They are just never joined on the same definition of a customer.
Illustrative example. Not a client figure.
See what we answerThese are commercial questions, not analytical ones. They get asked in board meetings and budget reviews, and they usually get answered with an educated guess because the underlying numbers live in systems that were never joined.
These are commercial decisions, not analytical ones, and they are not three separate products bolted together. They run on one joined view of your customers, built once from your acquisition, billing and behavioural data, and held together by Flybridge so the definitions underneath them stop moving.
What you paid to acquire a customer, what they generate month after month, and the month they cross into profit. One arithmetic rather than three reports, because a channel is only cheap relative to what its customers turn out to be worth. Broken out by channel, campaign and cohort.
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.
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 the behaviour behind each ranking, so offers go to the customers worth making them to.
The aggregator, the ad platform and the carrier settlement file each count a conversion their own way, and taxonomies get rebuilt every time someone new takes over. Flybridge is the mapping layer across all of it: one definition of a customer, a campaign and a conversion, held steady so this quarter can be compared with last. It is the reason the three answers above agree with each other.
Revenue forecasts are cheap to produce and almost never scored afterwards. So the first thing we hand over is not a dashboard. It is a number we can be held to.
We commit the forecast to a sealed, timestamped file before you act on any of it. It cannot be altered afterwards, by you or by us.
We state the accuracy we expect, in writing, before a single result comes in.
Thirty days later we open the seal against what actually happened. Live. No retrofitting, no quiet edits.
If it earns it, standing monthly reporting starts. If not, you keep the report.
Illustrative example. Actual accuracy is declared and verified against your own data.
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 reconcile those systems onto one definition, answer the commercial question directly, and say plainly when your data will not carry the conclusion. You get a decision with its reasoning attached, not another tile to interpret.
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 UsFixed 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. The sealed forecast is the step after that.