// CATEGORY
All Digital Marketing
Campaign engines wired to real event data, so reach is measurable — not anecdotal.
ExploreMarketing Analytics & Attribution
Measurement wired to real event data — tracking plans, first-party pipelines, and attribution that can show when a channel is not working.
Most event marketing reporting is a stack of screenshots. The ads platform claims the registration. The email tool claims the same registration. The agency deck claims both. Add the numbers up and you have more registrations than people who walked through the door.
We fix this at the data layer. ZebIQ builds the registration, check-in, app and streaming systems for live events, so we can join a click to a form submission, that submission to an RFID scan at the entrance, and that scan to session minutes watched or a deal in your CRM. One warehouse, one resolved identity per person, one set of numbers everyone argues from. Spend goes in as INR from the source platforms. What comes out is cost per registration, cost per attending delegate, and cost per qualified opportunity, with the drop-off between each stage visible.
We are direct about the limits. No attribution model is truth. Last-touch, multi-touch and media-mix models are all approximations with different biases, and anyone selling perfect attribution is selling something. So we label every model as a model, run holdout or geo tests where a budget is large enough to support one, and build dashboards that can go red. A metric that cannot show failure is not a metric. It is decoration.
What's included
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Tracking Plan Before Tags
Every event, property and identifier is specified in a written plan and signed off by marketing and finance before a single tag ships. Tags implemented without a plan are how you end up with four different definitions of registration.
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Server-Side Tagging and GA4
GA4 configured against your actual funnel, with a server-side container so conversions survive ad blockers, browser tracking restrictions and app-to-web handoffs. Payloads are filtered on your server, so you control exactly what leaves your infrastructure.
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Consent Under DPDP and GDPR
Notice, purpose limitation, withdrawal and consent state are captured once and carried through to every downstream tool, designed for India's DPDP Act and GDPR together. Non-consented traffic is reported as modelled aggregates, never quietly tracked anyway.
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First-Party Identity Graph
A single resolved record per person spanning ad click, registration form, ticket, badge scan, app login and stream session. Because we own those systems, that join is a foreign key rather than a fuzzy guess on a lowercased email address.
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Attribution Models You Can Audit
Last-touch, position-based and time-decay views sit side by side, with the disagreement between them displayed rather than hidden behind a single confident figure. Where spend is large enough, we run holdout or geo tests so at least one number is causal instead of correlational.
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Dashboards With Failure States
Every chart carries a target, a threshold and a named owner, so a failing channel is as visible as a working one. Green is a finding, not a default state.
How it works
A clear, numbered path from kickoff to live operation — so you always know what happens next.
Measurement Audit and Decision List
We inventory the existing tags, containers, UTM conventions and reports, then write down the decisions you actually need to make: budget shifts, channel cuts, sponsor renewals, venue sizing. Anything that does not serve a decision does not get measured.
Tracking Plan and Schema Design
Event schema, naming conventions, identifier strategy and consent rules go into one document. The definitions of a registration, an attendee and a qualified lead are agreed with the people who will be held to those numbers, before anything is built.
Pipelines and Marketing Warehouse
Server-side tagging goes live alongside scheduled loads from ad platforms, CRM, email, registration, ticketing and streaming into a single warehouse with spend normalised to INR. Each model carries tests for row counts, duplicates and identity collisions, and a failed load alerts us before it reaches a dashboard.
Reporting and Attribution Layer
We build the views decision-makers open: campaign pacing during the ramp-up, live registration and check-in on show days, sponsor and pipeline reporting afterwards. Each view states its model, its date grain and the threshold at which it turns red.
Post-Event Reconciliation and Model Review
After the event we reconcile modelled numbers against the ground truth we hold — badge scans, ticket revenue, stream concurrency — and publish the gap. Where the model was wrong we say by how much and correct it before the next cycle rather than restating the forecast.
Where it shines
Ticketed Conference Across Multiple Cities
Paid social, search, email and partner promotion all claim the same signups, and the organiser cannot tell which channel produced people who actually arrived. Joining check-in scans back to source produces cost per attending delegate by channel, which usually ranks channels differently from cost per registration.
Sponsor and Exhibitor Reporting
Sponsors ask what their money bought and receive a photo album. Instead they get booth dwell time from RFID, session attendance, lead scans and stream watch time reported against the commitments in their contract, in INR, including the deliverables that fell short.
Enterprise Event Driving Sales Pipeline
Marketing spends against an event; sales closes deals months later and no one connects the two. Badge scans and app activity are joined to CRM opportunities so influenced pipeline is reported with the model and lookback window stated on the report itself.
Related services
Frequently asked
No, and neither can anyone else. Attribution models allocate credit using rules or statistics; they do not observe cause. What we can do is show several models side by side, tell you where they disagree and by how much, and run holdout or geo tests on channels large enough to test — that last one is the closest thing to a real answer available.
Because each platform is marking its own homework, and their claimed conversions overlap. They also stop at the form: a platform can tell you someone registered, not whether that person turned up, watched the stream or entered your pipeline. We hold the check-in and streaming data, so the warehouse is where those records finally meet in one row per person.
No. It means consent has to be informed and specific, withdrawal has to work, and you need to be able to show what you collected and why. We build the notice and consent capture, propagate consent state to every downstream tool, honour withdrawal across systems including the warehouse, and keep non-consented traffic in modelled aggregates rather than identified reporting. For events with European attendees or sponsors, GDPR applies at the same time, so we design to whichever rule is stricter for that person.
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Where to go next.
Up to the category, across to complementary services, and into the engine-room notes.
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