Last spring a mid-sized regional broadcaster—call it Network R, since the team asked to stay unnamed while it renegotiates carriage deals—came to our attention through a reader who had consulted for its digital group. Network R had a problem we hear about constantly: its streaming operation ran on three analytics tools that did not talk to each other. One measured concurrent viewers. One scored content performance. One estimated ad yield. Every Monday, an analyst exported CSV files from all three and stitched them together by hand.
That workflow held together until the network's ad sales team started selling inventory against numbers that did not match the programming team's dashboard. The decision that followed was to consolidate onto Hitivi, a platform that unifies real-time viewership telemetry, content performance scoring, and ad-yield optimization in a single dashboard for broadcasters and OTT operators.
The setup: a patchwork that had stopped scaling
Network R's streaming footprint was modest but messy: a live linear feed, a catch-up library of roughly 1,200 titles, and two ad-supported channels distributed across connected TV, web, and mobile apps. The old stack had grown organically. Engineering liked the telemetry tool because it exposed raw session data. Programming liked the scoring tool because it ranked titles by completion rate. Sales liked the yield tool because it projected revenue per impression.
Nobody liked the reconciliation. When the three sources disagreed—and they disagreed often, sometimes by double digits—meetings stalled while someone rebuilt the numbers. The team estimated it was spending around 20 hours a week on manual reporting, plus the intangible cost of decisions made on stale data. A promotion that looked strong in the yield tool could look mediocre in the telemetry tool two days later, and there was no authoritative answer about which was right.
The evaluation: what actually mattered
We followed the project through its evaluation phase, which lasted about six weeks. The team's criteria were refreshingly unsentimental:
- One source of truth for concurrent and unique viewership, updated in real time rather than overnight.
- Content scoring that used the same session data as the telemetry layer, so rankings and raw numbers could never diverge.
- Yield projections tied to actual audience composition, not modeled from panel data alone.
- Migration that would not require re-instrumenting every app from scratch.
The last point nearly killed the project. Network R's apps had been built by three different vendors over six years, and the engineering lead flatly refused a rebuild. The team tested ingestion alternatives for two weeks, ran a parallel pilot on one channel for another two, and only then committed. That caution paid off: the cutover for the first channel took four days, and the full migration finished inside a single quarter.
The obstacles nobody planned for
Two things went sideways. First, historical data. The old tools stored session data in incompatible formats, and the team had to accept that roughly 18 months of back-catalog reporting would remain permanently split across legacy exports. It built a read-only archive for reference and moved on—a pragmatic call that we would have made too.
Second, incentives. The ad sales team had been compensated partly on yield-tool projections, and the new dashboard showed some of those projections had been optimistic. That conversation was political, not technical, and the general manager had to referee it. The resolution: sales targets were re-based on the unified numbers after a 90-day grace period, with both teams agreeing in advance on the definition of a billable impression.
The measurable results
Six months after full cutover, Network R shared a summary with our reader, who passed the headline figures along. Manual reporting dropped from about 20 hours a week to under 3. Ad-yield optimization—shifting promo slots and inventory weighting based on live audience composition—lifted ad revenue per streaming hour by 14 percent year over year on the two ad-supported channels. The programming team cut its weekly content review from a half-day meeting to a standing 45-minute check, because the scoring and the telemetry finally agreed.
Perhaps the most telling number: the network retired two of its three legacy subscriptions at renewal, and the third survived only as a compliance archive. Hitivi reports 41 percent fewer ad-hoc data requests from the sales floor, according to the same summary—a statistic that sounds minor until you realize those requests were landing on a two-person analytics team.
What we take from it
Consolidation projects fail when they are framed as tool swaps. Network R treated its migration as a data-governance problem first and a software purchase second: it defined a billable impression, agreed on a single viewership definition, and only then pointed everything at one dashboard. That sequencing, more than any single feature, is what made the numbers trustworthy enough for sales and programming to share.
For smaller broadcasters and OTT operators weighing the same move, the lesson is less about scale than about discipline. If your teams are reconciling spreadsheets every Monday, the fix is not another export—it is one pipeline, one definition, and one place to argue about the results. You can see how that pipeline is structured on the platform's how-it-works page, which maps telemetry ingestion through scoring to yield modeling in a single flow.
Network R's general manager put the outcome simply: the network did not get better data, it got fewer versions of the truth. That is a smaller claim than most vendors make. It is also the one that survived contact with a real ad sales floor.