Understand the offer, audience, current performance and operating constraints.
Analytics · Strategy to delivery
Data Analytics that moves customers and metrics
A premium, conversion-focused data analytics service designed to connect fragmented information into a clear view of performance, opportunity and next actions.
✓ Tailored strategy✓ Senior oversight✓ Clear reporting
▥MarkezoData Analytics
Looker Studio
Power BI
Google Sheets
BigQuery
Growth signal0%
Always improving
Why this service matters
Connect fragmented information into a clear view of performance, opportunity and next actions.
Built for teams with data spread across advertising, CRM, ecommerce and website platforms.
We connect strategy, delivery and measurement so every activity has a clear commercial job and every review leads to a useful next decision.
Identify the changes most likely to improve customer and business outcomes.
Deliver approved work through controlled, measurable improvement cycles.
Expand what works while protecting quality, clarity and profitability.
Common challenges
Where growth usually gets stuck.
We turn broad frustrations into specific, prioritised problems that can be measured and improved.
▥
Different Platforms Reporting Different Numbers
We trace the visible symptom back through audience intent, experience, tracking and commercial impact before recommending the fix.
▥
Manual Spreadsheets That Are Slow And Fragile
The issue is assessed against evidence, effort and downstream customer behaviour so the team can act with confidence.
▥
Teams Optimising Separate Channels Without A Shared Commercial View
The issue is assessed against evidence, effort and downstream customer behaviour so the team can act with confidence.
▥
Important Trends Discovered Too Late
The issue is assessed against evidence, effort and downstream customer behaviour so the team can act with confidence.
Our solution
A connected system, not a disconnected checklist.
Every engagement is shaped around the commercial objective, the customer journey and the team responsible for maintaining the work.
Discuss your goals ↗Data-source Audit
Planned and delivered in the sequence most likely to improve revenue contribution without adding unnecessary complexity.
KPI And Reporting Framework
Planned and delivered in the sequence most likely to improve customer acquisition cost without adding unnecessary complexity.
Dashboard Architecture
Planned and delivered in the sequence most likely to improve lifetime value without adding unnecessary complexity.
Data Cleansing And Reconciliation
Planned and delivered in the sequence most likely to improve funnel conversion without adding unnecessary complexity.
Cohort And Funnel Analysis
Planned and delivered in the sequence most likely to improve revenue contribution without adding unnecessary complexity.
Forecasting Support
Planned and delivered in the sequence most likely to improve customer acquisition cost without adding unnecessary complexity.
Monthly Insight And Action Reviews
Planned and delivered in the sequence most likely to improve lifetime value without adding unnecessary complexity.
How we work
A transparent process from first question to continuous improvement.
Discover
Data-source Audit
Establish the baseline, priorities and evidence needed for a focused plan.
Diagnose
KPI And Reporting Framework
Complete the work, capture learning and agree the next highest-value action.
Design
Dashboard Architecture
Complete the work, capture learning and agree the next highest-value action.
Deliver
Data Cleansing And Reconciliation
Complete the work, capture learning and agree the next highest-value action.
Optimise
Cohort And Funnel Analysis
Complete the work, capture learning and agree the next highest-value action.
Report
Forecasting Support
Complete the work, capture learning and agree the next highest-value action.
Improve
Monthly Insight And Action Reviews
Complete the work, capture learning and agree the next highest-value action.
Results and value
Measured by business movement, not busywork.
Success measures are agreed before delivery begins, interpreted in context and reviewed against quality as well as volume.
▥Priority metric
Revenue Contribution
0%A clearer commercial baseline and a practical definition of success.
▥Priority metric
Customer Acquisition Cost
0%More focused execution around the audiences, actions and channels that matter.
▥Priority metric
Lifetime Value
0%Better visibility of performance, quality, cost and the next useful decision.
▥Priority metric
Funnel Conversion
0%An improvement system your team can understand, maintain and scale.
Platforms and capabilities
Built around the tools your team already uses.
Looker StudioPower BIGoogle SheetsBigQueryGA4CRM exports
Industries we support
Commercial thinking adapted to your market.
Ecommerce↗Finance↗Education↗SaaS↗Healthcare Marketing↗Professional Services↗
Detailed service guideExplore the complete Data Analytics approach+
Service overview
Data Analytics built around how customers actually decide
Marketing data usually arrives at different grains and speeds: campaign spend may update hourly, CRM stages change after human follow-up and revenue can be adjusted by refunds or cancellations. A useful analytics model reconciles those realities instead of presenting a visually polished but internally inconsistent dashboard.
We organise information around decisions. Executives may need contribution and forecast confidence, channel owners need controllable drivers, and sales teams need lead quality and progression. Each view is connected to the same definitions so teams can investigate a result without debating which spreadsheet is correct.
Analysis becomes valuable when it changes action. Cohorts, funnels and variance reviews are translated into a small number of commercial questions, with owners and review dates, so insight moves beyond observation into budget, product and customer-experience decisions.
Problems we solve
Where data analytics performance commonly breaks down
Each issue is investigated in the context of the offer, audience, technology and team responsible for the next action.
Different Platforms Reporting Different Numbers
This usually hides a mismatch between intent, message and the action being measured. We isolate where the mismatch begins and show how it affects revenue contribution before recommending a fix.
Manual Spreadsheets That Are Slow and Fragile
The visible symptom can lead teams to spend more without understanding the cause. We compare audience behaviour, platform settings and the customer journey to determine which change is most likely to improve customer acquisition cost.
Teams Optimising Separate Channels Without a Shared Commercial View
When this continues, reports become harder to trust and useful learning is lost. Markezo establishes a baseline, identifies controllable drivers and assigns an owner to the correction linked with lifetime value.
Important Trends Discovered Too Late
This problem often crosses more than one channel or team. We follow the experience from first exposure to final outcome, then rank changes according to commercial impact, evidence and effort, with funnel conversion as a guardrail.
Our solution
A service model designed for connect fragmented information into a clear view of performance, opportunity and next actions
Markezo combines data-source audit, KPI and reporting framework, dashboard architecture with data cleansing and reconciliation, cohort and funnel analysis. For Data Analytics, the sequence is chosen after discovery, so foundational problems are corrected before additional budget, content or automation increases complexity.
The remaining work—forecasting support, monthly insight and action reviews—creates the feedback loop. Decisions are connected to revenue contribution, customer acquisition cost, lifetime value, with quality checks and responsibilities visible to the people who will maintain the system.
Key benefits
Commercial clarity alongside better execution
Benefits from Data Analytics are assessed through meaningful customer and business behaviour rather than activity for its own sake.
- Revenue Contribution: Revenue Contribution is reviewed in context, not as an isolated dashboard number. We connect movement in this measure with data-source audit and the quality of the audience or customer action behind it.
- Customer Acquisition Cost: For customer acquisition cost, the reporting view separates volume from value and highlights meaningful changes over time. This helps the team decide whether to protect, investigate or scale KPI and reporting framework.
- Lifetime Value: We agree a practical definition for lifetime value, identify the data source and record known limitations. The measure then becomes useful for judging dashboard architecture without pretending attribution is perfect.
- Funnel Conversion: Changes in funnel conversion are discussed alongside cost, quality and downstream behaviour. That balance prevents local optimisation of data cleansing and reconciliation from weakening the wider commercial result.
- Forecast Accuracy: A baseline and review cadence are created for forecast accuracy. The team can see what changed after cohort and funnel analysis, how confident the interpretation is and which question should be tested next.
Features
What the Data Analytics engagement can include
The Data Analytics scope is modular. A Data Analytics engagement can begin with a focused correction or combine the capabilities below into an ongoing programme.
Data-source Audit
This review traces different platforms reporting different numbers back to its source, checks configuration and evidence, and produces a ranked correction list. The output establishes a dependable baseline for revenue contribution before new activity is added.
Kpi and Reporting Framework
We organise KPI and reporting framework into clear roles, priorities and ownership. This structure prevents manual spreadsheets that are slow and fragile, while making future changes easier to compare against customer acquisition cost.
Dashboard Architecture
We organise dashboard architecture into clear roles, priorities and ownership. This structure prevents teams optimising separate channels without a shared commercial view, while making future changes easier to compare against lifetime value.
Data Cleansing and Reconciliation
We analyse the current approach, rebuild the highest-value opportunity and measure how data cleansing and reconciliation should operate. The recommendation is tied directly to funnel conversion and the commercial effect of important trends discovered too late.
Cohort and Funnel Analysis
This review traces different platforms reporting different numbers back to its source, checks configuration and evidence, and produces a ranked correction list. The output establishes a dependable baseline for forecast accuracy before new activity is added.
Forecasting Support
We audit the current approach, design the highest-value opportunity and govern how forecasting support should operate. The recommendation is tied directly to revenue contribution and the commercial effect of manual spreadsheets that are slow and fragile.
Monthly Insight and Action Reviews
This review traces teams optimising separate channels without a shared commercial view back to its source, checks configuration and evidence, and produces a ranked correction list. The output establishes a dependable baseline for customer acquisition cost before new activity is added.
Our process
A practical path from source reconciliation to insight cadence
Source reconciliation
We establish the commercial question, audience and evidence needed for data-source audit.
KPI design
The current journey and setup are examined so KPI and reporting framework addresses causes rather than symptoms.
Model and dashboard
Approved decisions are translated into a clear plan with ownership, dependencies and a definition for lifetime value.
Stakeholder rollout
Work is implemented, quality-checked and released in a controlled sequence that protects existing performance.
Insight cadence
Results are reviewed through forecast accuracy; learning is recorded and the next priority is selected from evidence.
Why choose Markezo
Specialist execution without disconnected agency silos
Analytics requires precision and restraint. Markezo documents definitions, tests implementations and explains uncertainty, avoiding dashboards that imply more certainty than the underlying data can support.
Your team receives a visible Data Analytics work plan, approval points, access documentation and a plain-English explanation of performance. Strategy remains close to implementation, which allows evidence from Looker Studio and Power BI to influence the next decision quickly.
For Data Analytics, we do not promise a fixed result before understanding the starting point. We commit to clear reasoning, careful delivery and an improvement cycle built around revenue contribution, customer acquisition cost, lifetime value.
Industries we serve
Adapted to different purchase journeys and constraints
Data Analytics changes with consideration time, regulation, product economics, local relevance and the amount of trust required. The strategy is shaped accordingly for the sectors below.
- Ecommerce
- Finance
- Education
- Saas
- Healthcare Marketing
- Professional Services
Tools and technologies
A maintainable technology stack for data analytics
The Data Analytics toolset is selected for fit, data quality and internal maintainability rather than to make the stack look unnecessarily complex.
Looker Studio
Looker Studio is used for configuration and controlled delivery within the Data Analytics workflow. Access, naming and ownership for Data Analytics are documented so the platform remains understandable to the internal team.
Power BI
Power BI is used to validate customer behaviour and conversion signals within the Data Analytics workflow. Access, naming and ownership for Data Analytics are documented so the platform remains understandable to the internal team.
Google Sheets
Google Sheets is used to organise creative, content or operational collaboration within the Data Analytics workflow. Access, naming and ownership for Data Analytics are documented so the platform remains understandable to the internal team.
BigQuery
BigQuery is used to reconcile performance and communicate decisions within the Data Analytics workflow. Access, naming and ownership for Data Analytics are documented so the platform remains understandable to the internal team.
GA4
GA4 is used for quality assurance, diagnostics and ongoing monitoring within the Data Analytics workflow. Access, naming and ownership for Data Analytics are documented so the platform remains understandable to the internal team.
CRM exports
CRM exports is used where deeper segmentation or workflow integration is required within the Data Analytics workflow. Access, naming and ownership for Data Analytics are documented so the platform remains understandable to the internal team.
Results and outcomes
Measures that help the business choose its next move
- We agree a practical definition for revenue contribution, identify the data source and record known limitations. The measure then becomes useful for judging dashboard architecture without pretending attribution is perfect.
- Changes in customer acquisition cost are discussed alongside cost, quality and downstream behaviour. That balance prevents local optimisation of data cleansing and reconciliation from weakening the wider commercial result.
- A baseline and review cadence are created for lifetime value. The team can see what changed after cohort and funnel analysis, how confident the interpretation is and which question should be tested next.
- Funnel Conversion is reviewed in context, not as an isolated dashboard number. We connect movement in this measure with forecasting support and the quality of the audience or customer action behind it.
- For forecast accuracy, the reporting view separates volume from value and highlights meaningful changes over time. This helps the team decide whether to protect, investigate or scale monthly insight and action reviews.
What you receive
Documented work your team can use after delivery
The Data Analytics deliverables are confirmed in the proposal and organised so another authorised team member can understand the system without reverse-engineering it.
- Data-source AuditThis review traces teams optimising separate channels without a shared commercial view back to its source, checks configuration and evidence, and produces a ranked correction list. The output establishes a dependable baseline for lifetime value before new activity is added.
- Kpi and Reporting FrameworkWe organise KPI and reporting framework into clear roles, priorities and ownership. This structure prevents important trends discovered too late, while making future changes easier to compare against funnel conversion.
- Dashboard ArchitectureWe organise dashboard architecture into clear roles, priorities and ownership. This structure prevents different platforms reporting different numbers, while making future changes easier to compare against forecast accuracy.
- Data Cleansing and ReconciliationWe audit the current approach, design the highest-value opportunity and govern how data cleansing and reconciliation should operate. The recommendation is tied directly to revenue contribution and the commercial effect of manual spreadsheets that are slow and fragile.
- Cohort and Funnel AnalysisThis review traces teams optimising separate channels without a shared commercial view back to its source, checks configuration and evidence, and produces a ranked correction list. The output establishes a dependable baseline for customer acquisition cost before new activity is added.
- Forecasting SupportWe map the current approach, prioritise the highest-value opportunity and connect how forecasting support should operate. The recommendation is tied directly to lifetime value and the commercial effect of important trends discovered too late.
Selected work
Creative execution built around a clear commercial job.
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Data Analytics questions, answered clearly.
Focused answers for teams considering data analytics support from Markezo.
How quickly can data analytics start producing results?+
Timing depends on the starting point, competition, sales cycle and implementation scope. We separate early indicators from mature outcomes and agree the first review window before work begins. Some improvements can be visible within weeks, while stronger commercial patterns often need several learning cycles.
Can Markezo work with our existing data analytics setup?+
Yes. We do not replace a system simply because it already exists. We first assess what is reliable, what should be corrected and what can be retained. This protects historical learning and avoids unnecessary disruption.
How will performance be reported?+
Reporting focuses on revenue contribution, customer acquisition cost, lifetime value, funnel conversion and forecast accuracy and the decisions those measures support. You receive a clear explanation of progress, risks, tests and next actions rather than a dashboard without context.
What does your team need from us?+
The most useful inputs are access to relevant platforms, clarity on commercial priorities, examples of strong customers, timely feedback and one accountable decision-maker. We organise the request list so the project does not become difficult to manage.
Is data analytics suitable for a smaller business?+
It can be, provided the scope is focused. We often begin with one audience, one offer and one measurable objective, then expand after the foundation is working. This keeps the activity proportionate and protects budget.
How do you avoid generic agency work?+
We use your customer language, offer economics, internal knowledge and performance evidence. The strategy is then built around the specific barriers and opportunities in your market, rather than copied from a standard channel checklist.
Start a focused conversation
Ready to improve your data analytics?
Tell us where you are now, what you want to achieve and what has already been tried. We will recommend the clearest next step.
