DataWorks
B2B Marketing Data, Attribution & Analytics
Without visibility, you're flying blind. Without correlation, you're guessing.
Most marketing teams are rich in dashboards and poor in answers. DataWorks covers attribution, measurement infrastructure, and the intelligence layer that makes every other marketing investment legible.
Without visibility you are flying blind; without correlation you are guessing — this section is about replacing both with evidence.
Reddit is taking your category searches: the one move that closes the gap
Reddit is outranking vendors on 957K monthly B2B searches; use a KD 21–60 “SERP wedge” experiment to win category intent and measure lift.
Marketing to mid-market RevOps leaders: the “forwardable” asset play
A practical 2026 play to reach mid-market RevOps leaders: build one “forwardable” asset for internal alignment, then distribute beyond LinkedIn.
Pain signals beat personas: a GTM experiment for 2026
A practical 2026 GTM experiment to re-rank ICP accounts by pain signals, measure qualified pipeline lift with a holdout, and avoid stalled deals.
Stop trusting AI dashboards: three silent ways numbers go wrong
A practical 2026 playbook to cut AI analytics hallucinations by forcing source-backed retrieval, governed metric definitions, and escalation guardrails.
Tech SEO audits in 2026: measure crawl speed, not rankings
A practical 2026 tech SEO audit focused on AI crawlability, server-rendered HTML, and “technical accessibility” so your brand shows up in AI answers.
Gemini dashboards in Google Ads: the real win is faster decisions
Google Ads’ Gemini Dashboards speed up reporting; this playbook shows how to turn prompt-driven insights into weekly experiments with metrics and guardrails.
Identity without oversight is a measurement bug, not a privacy debate
A practical playbook to test identity oversight with a holdout experiment, proving incremental pipeline lift while catching silent breakage and fraud risk.
Ahrefs tested schema for AI citations. Nothing moved.
Ahrefs found adding JSON-LD schema didn’t meaningfully lift AI citations, so treat schema as hygiene and test fan-out query coverage instead.
AEO prompt tracking: the missing measurement between AI visibility and pipeline
A practical 2026 playbook for AEO prompt tracking: build a buyer prompt library, log multi-engine answers, and tie citations to pipeline signals.