How We Helped a National Insurance Carrier's CMO Stop Getting Surprised

A national insurance carrier with $340M in annual marketing spend and 12 media agency partners engaged us after their VP of Marketing Operations identified the root cause of a recurring CMO frustration: leadership was routinely walking into Monday meetings with incomplete competitive intelligence and inconsistent visibility into their own campaign performance. Not because the team wasn't working — they were — but because manually aggregating intelligence from 12 partners, 8 internal platforms, and a competitive landscape that moved daily was structurally impossible. We built the system that made it possible.

Two Gaps We Were Asked to Close

The first gap was internal performance visibility. Campaign data lived in 8 different platforms: Google Ads, Meta, programmatic DSPs, connected TV, linear TV measurement, direct mail tracking, SEO analytics, and the internal policy quoting funnel. Each platform had its own interface, export format, and attribution logic. Getting a unified view required either expensive enterprise MarTech platforms with imperfect integrations or manual analyst work that took days to produce a report that was already outdated.

The second gap was competitive intelligence. The team tracked competitor activity manually — analysts monitoring competitor websites, ad libraries, and media coverage and compiling weekly summaries. It was slow, incomplete, and dependent on what analysts happened to notice. Significant competitor moves were sometimes discovered days after they happened. In insurance, a day late to a competitor pricing change or product launch is a strategic problem.

At $340M in spend, being a week late to a competitive signal isn't a monitoring problem. It's a strategic problem. Your response timeline starts when you notice — not when they act.

What We Built

Unified performance reporting: We built a weekly automation that pulls performance data from all 8 platform APIs, normalizes attribution using a consistent last-touch model (with a separate blended model view), and populates a unified dashboard showing spend, impressions, quotes generated, policies bound, and cost-per-policy by channel. The CMO's Monday briefing document includes the channel summary, week-over-week trends, and a Claude-generated narrative identifying the top three performance drivers and top three areas of concern. Delivered by 7am Monday.

Competitive monitoring: A daily automation monitors competitors' digital footprints: ad creative changes in Meta Ad Library and Google Ads transparency tools, website pricing and product page changes, press releases, and social media activity. When material changes are detected — new products, creative pivots, pricing adjustments — the system generates a structured alert and sends it to the CMO and relevant brand managers within hours. The 48-hour-to-a-week competitive awareness lag dropped to same-day.

Agency invoice reconciliation: With 12 media agency partners submitting invoices against complex media plans, reconciliation had been a quarterly accounting exercise that routinely found discrepancies after the fact. We built a reconciliation automation that checks each agency invoice against the approved media plan on receipt, flags any line where billed impressions, placements, or costs deviate from plan by more than 5%, and routes discrepancies to the relevant agency contact with a structured query. Invoice disputes now resolve in days, not months.

What Changed at Monday Meetings

Six months after launch, the CMO described the change simply: "I know what happened last week before the meeting starts. I know what competitors did. I know where our spend performed and where it didn't. I have a perspective before I walk in the door, rather than forming one during the first 30 minutes of the meeting."

The marketing ops team recovered approximately 60 analyst-hours per week from manual reporting and competitive monitoring. That capacity went to strategic analysis: a marketing mix modeling project, audience segmentation refinement, and a comprehensive attribution study that identified a $22M reallocation opportunity within the existing budget.

The Architecture Behind It

The three automation layers we built — unified performance reporting, competitive monitoring, and invoice reconciliation — are the foundational intelligence infrastructure that every enterprise marketing operation should have but most don't, because building it manually is impractical and enterprise MarTech platforms are expensive and slow. The AI approach is faster and more flexible: platform APIs for data ingestion, automation for aggregation and normalization, language models for narrative synthesis and competitive signal detection. The build timeline was eight weeks. The output is a marketing intelligence function that actually runs in real time.