ANALYTICS & TRACKING
We design and implement tracking, analytics, and reporting infrastructure across web, product, and marketing. Built on clean data, measured against decisions made, and structured so insight compounds instead of resetting every quarter.
Building analytics infrastructure for brands and enterprises
Layers in production: collection, warehousing, modeling, reporting
Analysts and data engineers in house
Tracking implementations shipped across ecommerce, SaaS, and local business
WHAT WE DO
We take analytics end to end: a full data audit, a prioritized implementation plan, then execution across event tracking, warehousing, and reporting. Google Analytics and Tag Manager when speed to insight matters. Server-side tracking and a data warehouse when accuracy and ownership matter. Custom dashboards and attribution modeling when the constraint is trust in the number. Every engagement ships against validated data, defined metrics, and a reporting cadence complete enough for stakeholders to make decisions on it.
WHERE THIS FITS
If you are not sure which one you need, that is a five-minute conversation, not a proposal.
Data infrastructure across collection, warehousing, and reporting: the layer every other channel depends on to know what's working. Foundational, not optional.
Organic visibility across search: technical health, on-page optimization, content, and authority. Compounding, not rented.
Paid search, paid social, and programmatic media for immediate, measurable pipeline. Bought, not earned.
KEY CHALLENGES
Duplicate events, missing conversions, and untested pixel changes mean every report starts with a caveat instead of a decision.
Web analytics, ad platforms, and the CRM all tell a different story because nothing is unified in one place.
Relying entirely on ad platforms and vendor dashboards means losing visibility the moment a cookie or API changes.
Manual exports and static spreadsheets mean the dashboard is always a step behind the decision it's meant to inform.
WHY ASHALL & CO.
Every tracking implementation is tested against real user flows before it ships, not validated after a stakeholder spots a wrong number.
Server-side tracking and a data warehouse under your control, so a platform change or cookie update doesn't wipe out your visibility.
Ecommerce, SaaS, local, or enterprise. We write the tracking and tooling plan with trade-offs and tell you when the simpler setup is the right one.
Historical data mapped and preserved so a platform switch or a GA4 migration doesn't cost you your year-over-year comparisons.
Dashboards built for the people who actually make decisions, not just the analysts who build them.
The analysts who design the tracking plan stay on through implementation, QA, and reporting, so nothing gets lost in a handover.
WHAT WE BUILD
GA4, server-side tagging, and event tracking built and QA'd against your actual conversion paths, not a generic template.
Google Tag Manager and a structured data layer so every tool downstream reads the same clean events.
First-party data pipelines into a warehouse you own, so platform changes and cookie deprecation don't erase your history.
Multi-touch attribution built to show which channels actually drive revenue, not just last-click credit.
Dashboards built for the decisions your team actually makes, pulling from the sources that matter to you.
Funnel and product-usage tracking built for stores and SaaS products that live or die by conversion data.
Full audit and mapped migration plan so a GA4, CRM, or CDP switch doesn't cost you historical comparisons.
Consent management and privacy-first tracking architecture built to stay compliant as regulations shift.
CORE LAYERS
We do not push one tool and call it a strategy. The mix is written down with trade-offs before you commit, and we will tell you when the simpler setup is the right one.
Event tracking, tagging, and data layers that capture clean, consistent data at the source.
First-party pipelines into a warehouse you control, independent of any single platform.
Turning raw events into metrics and attribution models that actually explain what drove a result.
Dashboards and reporting that make the data usable for the people who actually decide.
Already tracking something and don't trust the numbers? A data audit is a scoped engagement with a clear roadmap, not a rebuild you fund twice.
Scope a tracking auditANALYTICS PROCESS
Full audit of existing tracking, tools, and reporting, measured against the decisions your team actually needs to make.
Output: tracking audit, gap listMetric definitions, tracking plan, and tooling decisions mapped to the reports that actually get used.
Output: tracking plan, metric glossary, tooling roadmapEvent tracking, tagging, and data layers implemented and QA'd against real user flows before anything ships.
Output: validated tracking, QA reportFirst-party data piped into a warehouse you own, independent of any single ad or analytics platform.
Output: data warehouse, pipeline docsRaw events turned into metrics and an attribution model that matches your sales cycle.
Output: attribution model, metric definitionsDashboards built around real decisions, with documentation so your team can maintain and extend them.
Output: live dashboards, documentationDATA GOVERNANCE
An analytics stack nobody trusts is worse than not having one. We treat data quality, access, documentation, and reporting as one workstream with four outputs.
Tracking validated the way a build is tested, not eyeballed after launch.
Infrastructure built in accounts you own, not a vendor's demo environment.
Metric definitions and tracking specs written down so 'active user' means the same thing to everyone.
Dashboards and reports built for stakeholders, not just analysts, so answers are a glance, not a request.
PROCESS
Two weeks on your existing tracking, tools, and reports. We benchmark data quality and reporting gaps, then rank fixes by impact and effort.
Metric definitions, tracking plan, and tooling decisions. Signed off before a single tag changes.
Tracking, pipelines, and dashboards built and QA'd in sprints, sequenced by what unblocks decisions fastest.
Data quality and dashboard usage tracked after launch, with an iteration backlog and monthly reporting on what's working.
ENGAGEMENT MODELS
A short engagement that benchmarks your tracking, tools, and reporting, and returns a costed, sequenced implementation plan.
Best when you need evidence before you fund a program.Full audit and mapped migration plan for a GA4, CDP, or warehouse switch, with historical data preserved.
Best for a platform migration or replatform.A standing pod for tracking maintenance, new implementation, dashboarding, and reporting as your stack evolves.
Best when analytics is infrastructure you keep investing in.TECH STACK
FAQS
Scope drives the number, so we quote after the audit rather than publishing a figure that fits nobody. A single tracking implementation sits at the low end of a fixed-scope engagement. A full data warehouse and attribution build sits at the high end. Either way you get a fixed scope and a fixed price before any work starts.
The audit answers that. GA4 and Tag Manager cover most reporting needs on their own. A warehouse becomes worth it when you need to join web data with sales, product, or support data, or when you can’t afford to lose history to a platform change. We write the recommendation down with the trade-offs.
Not if it’s done properly. We audit what exists first, test new tracking in parallel with the old before cutting over, and validate every number against a source of truth. For migrations, historical data is mapped and preserved before anything is switched off.
A single tracking implementation can be validated within two to three weeks. A full warehouse and attribution model takes longer to build trust in, typically four to eight weeks including a QA period. We don’t call it done until the numbers reconcile against a source of truth.
Tracking is built consent-first, with server-side and first-party approaches that reduce reliance on third-party cookies. As regulations or platform policies shift, the architecture is built to adapt rather than break.
Yes, and the same team does both. Tracking plan and implementation, then dashboards built around the decisions your team actually makes, not a generic template pulled from a tool’s demo data.
That is the point of the documentation work. We hand off a tracking plan, metric glossary, and dashboard documentation so your team can extend and maintain it without depending on us for every change.
A single tracking implementation typically runs two to four weeks. A full data warehouse and attribution build runs six to ten weeks. The audit is the first two weeks either way, and we validate before calling anything live.
Data quality and dashboard usage are monitored, and the iteration backlog is driven by what that data says. Retainer clients get ongoing tracking maintenance, new implementation, and monthly reporting from the same pod that ran the audit, so context does not reset every quarter.
Send us access to your analytics setup. Within five business days you get a recorded audit covering tracking accuracy, tooling gaps, data quality issues, and the three fixes we would prioritize first. No cost, no pitch deck.
your first project with Ashall & Co.
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