MarketingIntelligence.io

MarketingIntelligence.io provides data-driven marketing measurement and analytics services. The organisation specialises in marketing measurement, media planning support and marketing analytics solutions. Regions of operation can be inferred from client references to Czech organisations, indicating activity in the Czech market and Central European clients.

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Services and Capabilities

• Marketing Mix Modelling (MMM): development and deployment of statistical and machine learning models to measure channel-level ROI, estimate saturation effects and support budget allocation across channels.
• Unified marketing measurement: combining MMM, controlled experiments and attribution techniques to produce consolidated measurement frameworks that address both strategic and tactical questions.
• Attribution and incrementality testing: design, execution and evaluation of experiments and incrementality tests to assess causal impact of media and marketing activities.
• Customer analytics and CRM modelling: customer lifetime value (CLV) prediction and segmentation to inform performance marketing and retention strategies.
• Analytics implementation and automation: productionising models and analytics pipelines, with regularly updated outputs (weekly or daily) and dashboards for ongoing decision support.
• Privacy-first tracking approaches: solutions that operate without user-level identifiers to reduce dependency on cookies and mitigate tracking/consent limitations.

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Industry Focus

• Online marketplaces and consumer services: work includes CRM modelling, CLV prediction and performance marketing analytics to support customer acquisition and retention.
• Digital media and advertising platforms: measurement of advertising ROI across online and offline channels, attribution and media planning support.
• Retail and e-commerce: marketing measurement and MMM to evaluate channel contributions to sales and to optimise media spend.

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Technology and Delivery Approach

• Use of statistical methods and machine learning to build MMM and CLV models; AI/ML techniques are applied to enhance model performance where indicated.
• Integration of multiple measurement methods (MMM, experiments, attribution) into unified measurement frameworks and delivery of consolidated analytics outputs.
• Production deployment of models and automation of analytics pipelines to provide frequent updates and continuous insights, enabling scenario planning and forecasting for media budgets.
• Emphasis on privacy-aware designs: modelling approaches that do not rely on user-level tracking data, reducing exposure to cookie and consent constraints.
• Delivery often includes dashboards and reporting tools for stakeholders; analytics tooling referenced suggests use of visualization platforms in implementation and reporting.