Aakash iACST
Conversion analytics
28%
lower cost per conversion, down nearly twice as fast as cost per lead.
Marketing decisions, made on real data
Analytics, attribution, conversion and funnel tracking, server-side measurement, ad-ops QA, BI dashboards, journey analysis, and cohort & revenue analytics — so every decision is accountable.
Institut Français
Funnel tracking
31.7%
of PhD Tour landing-page views became a completed registration.
Apple Switchers
Brand-lift measurement
116.7M
qualified impressions delivered, at 6.4 average frequency.
The GMAT Exam
Ad ops · Campaign QA
69–86%
below the approved benchmark on India’s core display engine, every month.
OJO Life
Cohort & revenue
65%
of quarterly revenue from repeat customers, not first-time buyers.
Data & Intelligence services
Choose what you want measured.
Each service opens into a dedicated page with its own branches and delivery model — clear for visitors and for search: parent pillar, child service, then branch-level detail.
Marketing Analytics & Insights
Marketing analytics, insight systems, KPI frameworks, channel diagnostics, and reporting views that turn campaign activity into decisions teams can use.
Attribution Modeling
Attribution logic that connects touchpoints to pipeline, revenue, and decision quality without pretending one model can explain every outcome.
Conversion & Funnel Tracking
Conversion events, funnel stages, lead tracking, forms, e-commerce actions, and QA so teams know where demand is moving — or breaking.
Server-Side Tracking
Server-side measurement for cleaner event delivery, better platform signal, stronger consent handling, and more durable first-party tracking.
Ad Operations & Campaign QA
Campaign taxonomy, trafficking checks, naming rules, tag QA, launch checklists, and pacing controls that reduce avoidable media and reporting errors.
BI Dashboard Development & Reporting
BI dashboards, automated reporting, revenue views, data models, and leadership reporting that make marketing performance easier to inspect and act on.
Customer Journey & Behavior Analysis
Journey, behaviour, segment, path, and drop-off analysis that shows how people actually move through the website, funnel, product, and sales path.
Cohort & Revenue Analytics
Cohort, retention, LTV, CAC payback, revenue segmentation, and growth forecasting views that connect marketing performance to business quality.
Our approach
One source of truth, not guesswork.
Most work combines more than one service: tracking that feeds attribution, dashboards that surface cohorts, or server-side measurement that protects signal.
Audit
Review the data, tracking, and reporting you have — and the decisions it's failing to support.
Instrument
Implement clean events, server-side tracking, consent, and QA so the data is trustworthy.
Model
Build attribution, cohorts, and revenue models that connect activity to business quality.
Report
Ship dashboards and reporting leadership can actually inspect and act on.
Data & Intelligence FAQ
Questions buyers askbefore they measure
Straight answers on tracking, attribution, dashboards, and the data decisions that make marketing accountable.
What does MarkAge360's Data & Intelligence practice actually cover?
It's the measurement layer behind your marketing — analytics & insight systems, attribution modeling, conversion & funnel tracking, server-side tracking, ad-operations & campaign QA, BI dashboards & reporting, customer journey & behavior analysis, and cohort & revenue analytics. Each service has its own dedicated page, and most engagements combine a few: clean tracking feeds attribution, dashboards surface cohorts, and server-side measurement protects the signal underneath. We start from the decisions you can't currently make and instrument backwards from there.
How is a data & analytics engagement priced?
We scope by the work, not by a fixed package. A focused build — say, fixing conversion tracking or shipping one BI dashboard — is usually a defined project fee, while ongoing instrumentation, QA, and reporting run as a monthly retainer. After a short audit we give you a written proposal with the service mix, deliverables, and price before any work starts, so there are no surprises.
How long before we can trust the numbers?
A tracking audit and the first round of fixes typically land in two to four weeks, which is usually enough to make day-to-day reporting trustworthy. Attribution models, cohort views, and full BI dashboards build on top of that clean data, so they follow over the next several weeks once events are flowing correctly. We'd rather get the foundation right than rush a dashboard that's reporting on broken data.
How is this different from just using Google Analytics out of the box?
Default GA4 tells you what happened on the site; it rarely tells you which marketing decision to make. We design the event model, connect conversions back to pipeline and revenue, add attribution and cohort logic the platform doesn't ship with, and surface it in dashboards leadership can actually act on. The off-the-shelf tool is one input — we build the measurement system around it.
Which platforms and tools do you work in?
We work across GA4, Google Tag Manager (web and server-side), the major ad platforms' conversion APIs, BigQuery and other warehouses, and BI tools like Looker Studio and Power BI. We meet your stack where it is rather than forcing a rip-and-replace, and we keep the tracking, modeling, and reporting layers documented so your team isn't locked out of its own data.
How do you handle privacy, consent, and tracking that keeps breaking?
Consent comes first: we wire measurement to respect consent state, and we lean on server-side tracking and first-party data to keep collection durable and compliant as third-party cookies and browser restrictions tighten. To stop the constant breakage, we add QA, naming and taxonomy rules, and launch checklists so tags and campaigns are validated before they go live — not discovered broken weeks later in a report.
