Growth Shop Co.

Analytics & Reporting

Reporting your team can trust, explain, and act on

Dashboards, KPI logic, tracking cleanup, attribution views, cohort analysis, and executive reports built around the questions that slow teams down every week.

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Practical experience building cleaner, faster, and measurable marketing systems

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Reporting Workbooks

Custom built around the KPIs, channels, and decisions that matter to your team. Tell us what you need to track, where the data lives, and how leadership wants to read it.

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Common reporting problems Growth Shop fixes

Analytics work usually starts when the report is taking too long, the numbers do not line up, or leadership needs clearer answers. The fix is a tighter reporting system that connects definitions, source data, dashboard design, automation, and documentation.

Problem

The same KPI changes from report to report

Lifecycle fix

Define the metric, source, filters, attribution rules, exclusions, and owner so the team can reuse the number without rebuilding the logic every week.

Problem

Dashboards exist, but no one trusts them

Lifecycle fix

Audit source data, calculations, date handling, channel groupings, events, and CRM or ecommerce joins before redesigning the dashboard view.

Problem

Reporting still depends on manual spreadsheet work

Lifecycle fix

Replace repeated copy/paste steps with cleaner exports, formulas, queries, refresh rules, templates, and documented update steps.

Problem

Leadership needs a clearer story

Lifecycle fix

Separate executive summary, diagnostic detail, open questions, and recommended follow-up so performance readouts support decisions instead of debate.

Stop letting unclear numbers slow down decisions

Most reporting issues are not caused by a missing dashboard. They come from unclear KPI definitions, messy source data, inconsistent filters, weak tracking, and manual reporting processes that change from week to week. Growth Shop helps turn that into a cleaner reporting setup: what matters, where it comes from, how it is calculated, how often it updates, and how the team should read it. The outcome is not more charts. It is a reliable view of performance that makes it easier to see what changed, where to focus, and what needs follow-up.

Trusted KPI definitions

Core metrics, formulas, filters, source rules, and channel groupings are documented so the same question produces the same answer.

Cleaner weekly reporting

Recurring dashboards, workbooks, and readouts are designed around the decisions teams make every week, not around every chart a tool can produce.

Data issues surfaced early

Tracking gaps, naming inconsistencies, missing fields, and source-data problems are called out before they quietly distort performance conversations.

Build the reporting layer from question to readout

Growth Shop approaches analytics and reporting as an operating system: clarify the business question, inspect the source data, standardize the logic, build the view, automate what repeats, and document how the team should maintain it.

Define

Identify the recurring questions, KPI definitions, segments, dates, attribution assumptions, and decision cadence the report needs to support.

Audit

Review the data sources, tracking inputs, naming conventions, CRM fields, ecommerce events, exports, and manual steps that feed the report.

Build

Create dashboards, workbooks, executive reports, cohort views, funnel tables, and diagnostic tabs that make the next action easier to see.

Maintain

Document update steps, refresh logic, data notes, known caveats, and ownership so the report remains usable after handoff.

Reporting systems built around the questions the team needs answered

The work can start with a broken dashboard, a messy spreadsheet, a missing KPI view, a reporting request from leadership, or a tracking problem that keeps making the numbers harder to trust. Each build starts by tightening the logic behind the report before designing the view. The dashboard should not hide messy definitions behind nicer charts.

KPI definitions and source logic

Standardize the metrics, formulas, filters, channel groupings, attribution windows, and source rules behind the report.

Dashboard and reporting workbook builds

Create weekly performance views, executive summaries, channel reports, funnel tables, cohort snapshots, and diagnostic tabs.

Reporting automation

Reduce manual copy/paste work with scheduled pulls, formulas, queries, exports, refresh rules, and repeatable reporting templates.

Data capture and tracking cleanup

Audit UTMs, forms, CRM fields, events, ad platform naming, ecommerce data, and lifecycle data before they create bad reporting.

Attribution, funnel, and lead journey views

Show how performance moves across channels, campaigns, stages, sources, cohorts, and customer or lead paths.

Analysis readouts and documentation

Summarize what changed, what likely caused it, what to monitor, and how the reporting system should be maintained.

Common analytics and reporting projects

These can be scoped as a focused build, a cleanup sprint, or recurring reporting support depending on what is broken and how often the team needs the view.

  • KPI reporting dashboard

    A weekly view of performance by channel, campaign, source, lifecycle stage, revenue outcome, or ecommerce result, with definitions tight enough for repeated use.

  • Dashboard rebuild and design cleanup

    A cleaner layout that separates executive summary, diagnostic detail, trends, and follow-up questions instead of forcing every number into one screen.

  • Executive reporting pack

    A concise readout for leadership with core metrics, movement, context, risks, open questions, and the decisions that need attention.

  • Reporting automation

    Replace recurring manual reporting with pulls, formulas, queries, exports, refresh rules, and templates that make the report easier to update.

  • Attribution and channel reporting cleanup

    Practical source, channel, and stage logic that explains performance without pretending attribution is perfect.

  • Cohort reporting and analysis

    Views that show how leads, customers, subscribers, orders, or accounts behave over time by segment, source, campaign, product, or signup period.

  • Lead journey mapping

    Map the path from first touch, form, campaign, or source through CRM stages, sales outcomes, customer behavior, retention, or repeat purchase.

  • Tracking and data capture audit

    Find broken UTMs, missing fields, inconsistent naming, event gaps, form issues, CRM sync problems, and source data issues before they distort reports.

What the client gets

The output should be something the team can keep using after the first readout. Depending on scope, the work can leave behind:

Working dashboard, reporting workbook, or executive report format
A usable reporting asset organized around executive summary, trend context, diagnostic detail, and the follow-up questions the team needs to answer.
KPI definitions, formulas, filters, channel groupings, and source logic
Documented metric rules that explain exactly what each number includes, excludes, and depends on across tools and reporting views.
Data dictionary or reporting notes that explain where the numbers come from
Source notes, field definitions, caveats, and ownership details that make the report easier to troubleshoot and maintain.
Tracking recommendations, naming rules, field requirements, or implementation specs
A prioritized list of fixes for UTMs, events, forms, CRM fields, campaign names, ecommerce data, and other capture points that affect reporting quality.
Automation rules, refresh steps, query notes, export setup, or scheduled reporting process
Repeatable update logic that reduces manual reporting time and clarifies which steps happen on each weekly, monthly, or campaign reporting cycle.
Analysis summary with what changed, what matters, and what should happen next
A concise readout that highlights movement, likely drivers, risks, open questions, and recommended next actions.
Documentation for how the report should be used, updated, and maintained
Handoff notes that cover update steps, metric caveats, owner responsibilities, QA checks, and when the reporting logic should be revisited.

Ways to use analytics and reporting support

The work can be a focused build, a cleanup sprint, or recurring analytics support depending on how often the team needs the reporting view and how much source logic needs repair.

  • Focused dashboard or workbook build

    Create a new reporting view for weekly performance, channel diagnostics, executive updates, cohort analysis, funnel movement, or ecommerce results.

  • Reporting cleanup sprint

    Tighten KPI logic, source rules, naming conventions, tracking inputs, dashboard layout, or manual spreadsheet workflows that are creating confusion.

  • Recurring analytics support

    Maintain weekly or monthly readouts, update dashboards, investigate data questions, document changes, and keep reporting aligned with active campaigns and business priorities.

Bring this in when reporting is slowing the team down

Analytics support is most valuable when the reporting problem is already costing time, creating confusion, or making performance harder to explain.

  • The same KPI changes depending on who pulls the report.
  • Weekly reporting still requires too much manual spreadsheet work.
  • Paid media, GA4, CRM, ecommerce, email, and sales data do not line up cleanly.
  • Leadership needs a clearer view of what changed and why.
  • Dashboards exist, but people do not trust them or use them consistently.
  • Attribution is taking over the conversation instead of helping the team make decisions.
  • New campaigns, funnels, forms, lifecycle programs, or ecommerce initiatives need cleaner tracking before launch.
  • Cohort, retention, lead journey, or funnel questions keep coming up, but there is no dependable view.

Work inside the stack you already have

Tool choice should follow the reporting question. Growth Shop can work inside the systems already in place, improve the logic around them, and add missing pieces only when they make reporting cleaner.

Common sources
Ad platforms, GA4, Shopify, CRM data, lifecycle tools, form tools, SQL tables, spreadsheets, exports, and sales or revenue data.
Common build environments
Google Sheets, Looker Studio, SQL, CRM reports, BI dashboards, ecommerce reports, and lightweight reporting workbooks.
Process
Define the reporting questions, audit the source data, clean the KPI logic, build the view, automate the recurring parts, and document how to use it.

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Bring the messy reporting problem in

Bring the dashboard no one trusts, the spreadsheet that takes too long, the attribution argument, or the executive report that needs cleaner answers. Growth Shop can help tighten the metrics, fix the data capture gaps, build the reporting view, and make the next readout easier to use.

Talk through your reporting View engagement options