A specialist B2B media publisher with no dedicated data function was running campaign reporting manually across five disconnected platforms. Inflexion Analytics deployed a unified, GCP-native analytics layer — consolidating data from ad server, programmatic, email, CRM and finance systems — into a single managed reporting and intelligence service.
The ad operations team spent 1–2 days per week building performance reports in Excel per advertiser, with delivery averaging 5–10 days post-campaign.
Campaign data lived across Google Ad Manager, GA4, three SSP dashboards, an email ESP, CRM and finance — with no automated reconciliation.
Format and timing varied across advertisers, with no structured feedback loop tying performance back to the sales team's renewal conversations.
Platform and technology costs weren't mapped against attributed revenue by channel — making true margin and vendor rationalisation decisions impossible.
Achieved within two quarters of deploying automated, consistent campaign reporting.
Automated same-day dispatch replaced manual Excel builds.
Advertisers commit to next campaigns faster with structured reports.
Report sent to advertisers. Includes automated branded reports, cross-channel KPI normalisation, vertical benchmarks, and AI narrative commentary per campaign.
Pillar 02 (Internal)Internal P&L view showing net margin by channel, Tech-stack ROI, advertiser concentration risk monitoring, and automated Spend Watchdog alerts.
Pillar 03 (Agentic)Always-on intelligence monitoring CTR and fill-rate anomalies. Auto-drafts weekly summaries and provides rolling revenue forecasts with confidence intervals.
Averaged 5–10 days post-campaign end, limiting actionability.
Looker-generated reports dispatched weekly with no manual build required.
Sales had no consistent data during advertiser renewal conversations.
Plain-English campaign commentary grounded in BigQuery data.
Platform fees weren't mapped against generated revenue.
Net margin by channel with a cost-efficiency table updated daily.
We map the publisher's full data landscape, documenting data availability, quality issues and reconciliation gaps before any build begins.
Data connections and the BigQuery warehouse are established. Dashboards follow in weeks 3–4. AI agents are configured, tested, and handed over by week 8.
Post-launch, Inflexion manages the full data pipeline. Regular reviews identify new data sources to integrate and additional agents to configure.
The advertiser-facing layer of the platform. Every campaign is reported in the publisher's own brand template — same look, same cadence, same KPI definitions — generated automatically from the unified BigQuery model. The advertiser experience is consistent; the production effort is zero.
Every advertiser receives a weekly report in the publisher's own brand template — generated directly from BigQuery and dispatched without manual intervention. Impression delivery, viewability, CTR, conversion events and revenue contribution are broken out by placement and creative. What previously took 1–2 days per advertiser in Excel now ships same-day, end of week, every week.
Different ad systems measure the same metric in subtly different ways — a "view" in GAM is not a "view" in an SSP, and an email "click" is not a programmatic "click". We unify these into a single normalised KPI layer. The result: when an advertiser compares newsletter performance against a display campaign, they're comparing like for like, not vendor-by-vendor approximations.
Raw performance numbers tell an advertiser nothing on their own. Every metric in the report is benchmarked against an anonymised, sector-specific peer set — so the advertiser sees not just their viewability, but how it compares to the typical campaign in their vertical. This converts reporting from "here's what happened" into "here's how you did versus the market".
Each report includes a plain-English summary auto-drafted by Inflexion's narrative agent, grounded in the same BigQuery dataset the numbers come from. The agent explains what moved, why it likely moved, and what to consider for the next campaign. A human reviewer signs off before dispatch — no raw model output is ever sent to an advertiser.
The internal P&L view of the publisher's business. Spend Analytics reconciles every line of platform cost against the revenue it attributable supports — turning the technology stack from a cost centre into a managed margin line.
The Spend Analytics dashboard reconciles platform costs, ESP fees, ad-serving costs and allocated headcount against attributed revenue. Margin is calculated at the channel, placement and advertiser level, refreshed daily. For the first time, leadership can answer "which advertiser is actually profitable?" without a week of finance work.
Each line item in the technology stack — ad server, SSPs, ESP, brand-safety vendor, analytics — is mapped to the revenue it enables. Vendors that fail the cost-vs-contribution test surface as candidates for renegotiation or removal. Most engagements identify two or three vendors whose annual fee exceeds their attributable contribution within the first quarter.
We monitor the Herfindahl–Hirschman index (HHI) across direct advertiser revenue every day. When any single advertiser exceeds 25% of direct revenue, or the top three exceed 55%, a Slack alert is sent to the commercial lead with the underlying breakdown. They no longer do silently.
The Spend Watchdog agent compares live spend against the approved monthly budget for each vendor and channel. Variance beyond a configurable threshold triggers an alert before the month closes — not after. Mid-month corrections are now possible; previously, overruns were only visible in the next finance close.
Always-on intelligence sitting on top of the unified data model. A small fleet of specialised agents watches the data continuously and surfaces what matters — so the team intervenes when it counts, not when it's reviewed.
The Anomaly Detection agent watches CTR, viewability and fill rate continuously and flags statistically significant deviations from the trailing 14-day baseline. Alerts route to Slack with the affected campaigns, the underlying delta, and a one-paragraph explanation. Ad-ops sees the problem on Tuesday morning, not in the post-campaign report two weeks later.
Every Monday morning a portfolio-level narrative summary is drafted: what moved, why it likely moved, which advertisers need attention this week. The commercial team reviews and edits before it becomes the basis for the Monday sales meeting. The Monday meeting starts with insight, not with someone building a slide.
The Revenue Forecast agent produces a rolling 90-day forecast with 80% and 95% confidence intervals, derived from CRM booking signals, historical seasonality and 18 months of revenue data. The forecast refreshes daily as new bookings land. Quarterly reforecasts replace January-once targets — and the board sees a real range.
Every agent is built, configured, monitored and improved by Inflexion. No prompt-engineering, model-tuning or LLM-ops capability is required from the publisher's team — the AI layer is delivered as a fully managed service, in the same way the underlying data pipeline is. The team consumes outcomes. We own the engineering.