KASPR Datahaus · use case

Was it the creative, or the connection?

Campaigns are judged on content and timing — but the medium they travel over is not constant. KASPR Datahaus research finds campaigns launched into windows of high internet instability record 22% lower engagement, 9% lower click-through and 7% fewer online sales than comparable stable-window launches. The delivery conditions are measurable, market by market and hour by hour — which makes them backtestable against your own funnel, and then plannable.

The hidden variable

Two failures that look identical in a dashboard

A campaign that lands during buffering, stalled page loads and unstable connections underperforms — and the analytics record it exactly as if the creative had missed. Attribution tools compare campaigns against campaigns; none of them carries a measurement of the network conditions each impression travelled through. Without that variable, a connectivity failure is booked as a content failure, and the wrong thing gets fixed.

Separating the two requires an independent record of delivery conditions: how stable the internet was, in each target market, at the hour each burst of spend went out. That record exists — measured identically across markets since February 2019.

The backtest

Join your funnel to 89 months of stability history

The design is a panel regression your analytics team already knows: campaign outcomes — impressions, clicks, conversions, sales — by market and time window, joined to KASPR’s stability and jitter panel over the same markets and windows. Monthly for the long view, and down to hourly slices where the estimation wants resolution. The chart below is the raw material for three major consumer markets: seven years of monthly jitter, measured the same way in each.

The variation is the identification. Markets differ in level, months differ within markets, and the swings do not line up across markets — so the sensitivity of your own conversions to delivery conditions is estimable from history you already hold. The output is a number per market: what a rough window costs you, in your own currency.

The week has structure

The same hour is not the same everywhere

Averages hide the schedule — and whether the schedule matters is itself a market fact. On the shared scale below, the United States and Italy sit near the floor all week: in mature, stable infrastructure the hour of launch buys little, and the model says so rather than inventing precision. India is the other extreme — evening peaks where the chance of a rough hour is many times the weekly floor, on a cycle no other market shares. Wherever a campaign’s audience includes markets like that, the launch hour is a real decision; this per-market surface is what the backtest prices and what the planning tools optimise over.

Then act

Broadcast: the same model, pointed forward

The backtest says how much delivery conditions cost you; Broadcast turns that into a calendar. Describe the audience as a mix of markets with shares — where your spend actually lands — and every posting window in the target week is ranked by the share of that audience expected to hit a rough hour, each window scored in points against the roughest hour of the week.

A window can be handed straight to scheduling: copied with its score, exported to a calendar, or pushed to a connected posting workspace. The frames below are the tool as it ships — the audience mix, the audience’s composite week, and the ranked windows.

The discipline

What this record is, and is not

KASPR measures the stability of internet infrastructure — presence and jitter at the probe-population level — not ad delivery, impressions or any user’s session. The backtest estimates association between delivery conditions and your outcomes; it is your data that carries the causal claim, and the estimate is only as good as the join. Windows are ranked relative to the same week’s roughest hour, never promised in absolute terms. Seven years of history, measured one way, is what makes the estimate stable enough to plan against.