Know what to measure before you decide how
Measurement strategy, attribution, tracking architecture, and data strategy built before implementation, not after.
Analytics consulting is deciding what to measure, how attribution should work, what the tracking and data foundation needs to look like, and who owns reviewing it, before any implementation begins, rather than configuring a tool first and asking what it should answer afterward.
Tracking implementation without a measurement strategy behind it produces plenty of data and very few decisions - dashboards get built, nobody agreed in advance what they were supposed to answer, and they quietly stop getting checked.
We decide what to measure before deciding how, mapping business questions to specific KPIs and choosing an attribution model that's defensible for your actual sales cycle rather than whatever a platform defaults to. The recalibration matters as much as the initial setup - a measurement plan built for last year's funnel doesn't necessarily fit this year's.
Problems we solve in Analytics Consulting
Tools configured before questions are asked
Tracking gets built first, and what to measure only gets asked once it's half-configured.
Dashboards nobody checks
Charts render and events fire, but none of it was ever tied to a decision anyone actually needed to make.
A model treated as gospel with no record of why
Platform defaults get trusted simply because they require no decision, not because they fit the business.
Tracking added ad hoc for years
A tag added here, an event renamed there, until nobody fully understands what is actually firing anymore.
Data quality is nobody's job
Recurring issues get noticed but never actually traced to an owner.
No named owner for any given report
Once more than one person touches reporting, an undocumented ownership model turns into duplicated or contradictory numbers.
Everything covered in Analytics Consulting
Measurement Strategy
Business questions mapped to specific KPIs before any tracking or dashboard work begins.
Analytics Strategy
The practice built around the tooling: who owns which reports, how often they get reviewed, and how the team's data literacy improves.
Attribution Strategy
Choosing and defending the attribution model that actually fits your sales cycle and channel mix.
Tracking Architecture
A documented data layer, naming convention, and tool integration plan that new tracking can be added to without breaking what exists.
Data Strategy
A plan for collection, storage, governance, and access, so every dashboard built on top of it can actually be trusted.
What actually changes
| Before | After |
|---|---|
| A tool gets configured first, and what to measure gets asked afterward | Business questions get mapped to specific KPIs before any implementation starts |
| Dashboards get built once, then quietly stop being checked | Every report has a named owner and a review cadence matched to how fast it moves |
| Whatever attribution model the platform ships with by default | A model chosen and documented to fit the actual funnel and channel mix |
| Tracking accumulates as one-off decisions made in isolation over years | New tracking follows a documented data layer and naming convention |
| Data quality is nobody's explicit responsibility | Data quality has a named owner, separate from who simply has tool access |
What we report on
Decision-to-metric coverage and dashboard usage
How many real decisions have a specific metric mapped to them, and whether reports actually get opened.
Report ownership coverage and review cadence adherence
Whether every report has a named owner and gets reviewed on schedule.
Divergence from last-click and stakeholder confidence
How much the chosen attribution model actually changes reported results, and whether stakeholders can explain what it shows.
Tracking incident frequency and data quality ownership
Silent breaks after a new integration, and how many core data sets have a named quality owner.
Tools we work with
Every Analytics Consulting service, broken down
Decide What to Measure Before You Decide How to Track It
Measurement strategy that defines the metrics that actually matter before any tracking implementation begins.
Analytics StrategyBuild an Analytics Practice, Not Just Dashboards
We define tooling, ownership, and reporting cadence, so reports actually get reviewed, not just built.
Attribution StrategyCredit assigned in a way the business can actually trust
Attribution strategy consulting: choosing and defending an attribution model that matches your actual sales cycle and channel mix.
Tracking ArchitectureBuild a Tracking Foundation That Actually Lasts
Tracking architecture covering data layer structure, tool integration, and naming governance.
Data StrategyGet a Plan For The Data, Not Just The Dashboard
We cover collection, storage, governance, and access, so every dashboard built on top actually holds up.
Industries we work with
Built for teams like yoursPairs well with Analytics Consulting
Clean data, before anything else
GA4, GTM, and BigQuery implementations that make every downstream decision trustworthy.
A stack audited before anything new gets bought
Marketing technology stack audits, CRM strategy, and platform selection based on actual requirements.
A roadmap sequenced by dependency, not enthusiasm
Digital transformation, roadmaps, and technology strategy built around dependencies and real resourcing.
Why choose EASI7
We fix tracking before we touch results
Every engagement starts with the same audit we'd run on our own accounts, so the numbers you're optimizing against are actually true.
One accountable team, not siloed vendors
The same team that does this also owns the rest of your stack - no handoff, no blame-shifting between vendors.
Built for how AI search actually works
Everything we build is structured to be extractable by AI answer engines, not just ranked in a list of blue links.
Reporting tied to your own dashboard
Every number we report ties back to GA4/GTM events you can see directly - not a screenshot from a tool you can't independently verify.
How to engage us for this
Project-based
A defined outcome with a start and end date - an audit, a migration, a campaign build, a tracking overhaul. Fixed scope, fixed price, agreed upfront.
Ongoing retainer
Continuous management and optimization once the initial build is live - campaigns, SEO, reporting, and iteration run every month under one accountable team.
Advisory
Strategy and oversight without full delivery - we review what's already running, unblock decisions, and point an in-house or existing team in the right direction.
Common questions
Implementation is the technical work of getting a tool tracking correctly. Analytics consulting is the layer above it - deciding what should be measured, which attribution model is defensible, what the tracking architecture needs to look like, and how the underlying data is governed. Skipping that layer usually means the implementation is technically sound but answering questions nobody actually asked.
Just one, in most cases. These five areas inform each other, but engagements almost always start with whichever gap is actually causing pain - often measurement strategy or tracking architecture - rather than a bundled package. We'll tell you honestly if a different one of the five is the actual bottleneck before recommending work in the area you asked about.
It depends heavily on scope - a measurement strategy pass can run two to four weeks, while a full tracking architecture rebuild or data strategy engagement spanning multiple systems can take six to eight weeks or more. We scope against the specific problem, not a fixed package length.
Both, depending on what's agreed at the start. Some engagements end with a documented strategy and plan that your team or another vendor executes. Others include the implementation work itself. We're explicit about which one a given engagement is before it starts, not after.
No - a sound measurement and tracking foundation removes the most common causes of unreliable or unused reporting, but whether reports actually get acted on also depends on internal habits and ownership we don't control. What we can guarantee is that the foundation itself - what's measured, how it's tracked, who owns it - is built correctly and documented, not left implicit.
Not on a fixed calendar - we set specific triggers as part of each engagement, such as a material change in channel mix, sales cycle length, or business model, since a plan built for last year's funnel doesn't necessarily fit this year's.
Lots of dashboards, not many decisions?
That's usually a measurement strategy gap, not a tooling one.
Ready to get started?
We usually reply within 24 hours.
Analytics Consulting, in detail
Scope, area by area
| Area | What we deliver |
|---|---|
| Measurement Plan | Business questions mapped to a KPI hierarchy, with a documented owner and data source per metric |
| Analytics Practice | Report ownership, review cadence, and a staged maturity roadmap |
| Attribution Model | A model recommendation matched to your funnel and channel mix, with the reasoning documented |
| Tracking & Data Foundation | A data layer, naming convention, and governance model that new tracking and reporting builds on top of |
How this compares
| Strategy Before Implementation | Tooling First |
|---|---|
| Every KPI, model, and tracking decision traces back to a specific business question | A tool gets configured first, and what it should answer gets asked afterward |
| Reports have a named owner and get reviewed on a cadence matched to the metric | Dashboards get built once, then quietly stop being checked |
| Attribution and tracking choices are documented and defensible when questioned | Platform defaults get trusted because they require no decision |
None of this guarantees more accurate or useful reporting on its own, it removes the most common causes of reporting nobody trusts or acts on.
What we measure this against
- Decision-to-metric coverage and whether reports actually get reviewed on their set cadence
- Divergence between the chosen attribution model and last-click on the same data
- Tracking incident frequency and data quality ownership coverage across core data sets
Where this applies
- A team has plenty of dashboards but cannot say which decision any of them actually inform
- A stakeholder questions why a specific channel looks unproductive and nobody can explain what the current attribution model shows
- Tracking has been added piecemeal by different people and vendors over several years
- Data is scattered across a CRM, ad platforms, and a website with no consolidated view or quality owner