KPMG Consulting Launches Digital Marketing Advancement Support Service Using Causal Analysis Tools
KPMG Consulting has launched a new service to help companies optimize their digital marketing strategies. This service utilizes AI and data science-driven causal analysis tools to visualize the effects and causal structures of marketing initiatives, enabling data-driven decision-making and improved ROI, ROAS, and LTV.
📋 Article Processing Timeline
- 📰 Published: April 24, 2026 at 20:00
- 🔍 Collected: April 24, 2026 at 11:31
- 🤖 AI Analyzed: April 24, 2026 at 15:10 (3h 38m after Collected)
KPMG Consulting Co., Ltd. (Headquarters: Chiyoda-ku, Tokyo; Representative Directors: Jo Seki, Atsushi Taguchi, Masahiko Chino; hereinafter, KPMG Consulting) has launched a digital marketing advancement support service that utilizes AI and data science-driven causal analysis tools to analyze companies' marketing data, visualize the effects and causal structures of marketing measures, and optimize them.
In recent years, many companies have introduced marketing automation (MA) tools, and the vast amount of collected customer touchpoint data is being utilized in various marketing initiatives. However, there are cases where the causal relationship between measures and results cannot be grasped due to a focus on superficial data correlation analysis, or where it is impossible to verify which elements were effective due to the complexity and siloization of customer behavior and data. In such cases, unnecessary measures continue to be implemented, making it difficult to optimize marketing activity costs.
In this context, KPMG Consulting, in collaboration with "Advisory Lighthouse," a professional organization responsible for data and technology Center of Excellence within KPMG Japan, has developed an analysis tool that integrates Causal Discovery and Causal Inference. This tool extracts the causal structure of measures from marketing data held by companies and provides one-stop support for quantitative evaluation of measure effectiveness. By combining advanced data analysis technology with companies' marketing data, KPMG Consulting aims to realize a transformation to data-driven digital marketing and optimize marketing measures.
**[Support Content of This Service]**
By utilizing causal analysis tools for marketing data (customer data, measure data, etc.) held directly by companies and through MA tools, the service provides the following value:
**Discover causal structures and mechanisms, identify factors of customer behavior and decision-making**
AI automatically estimates statistical causal structures and mechanisms from data, and data-driven identifies factors of customer behavior and decision-making. For example, it visualizes the causal structure and mechanism between measures and effects in a data-driven manner, such as whether opening an email newsletter distributed to customers affects their purchasing behavior, whether customer attributes influence purchases, or how customer follow-up contributes to service churn rates, leading to the estimation of the effects brought about by measures. It explores factors based on customer behavior from data that tend to be overlooked in manual data analysis by personnel or analysis relying on past experience.
**Support for formulating optimal measure scenarios**
Based on causal structure data, it proposes measures expected to have the highest effect. It can be utilized for scenario design within MA, as well as for formulating communication plans and marketing scenarios including MA.
**Improve Marketing Return on Investment (ROI) by reducing ineffective measures and optimizing budget allocation**
Data identifies measures with low return on investment, and by reducing them, the freed-up budget is reallocated to effective measures, thereby optimizing marketing costs and supporting the improvement of ROAS (Return On Advertising Spend) and LTV (Life Time Value).
**[Features of This Tool]**
This analysis tool integrates Causal Discovery and Causal Inference, extracting causal structures from marketing data held by companies, analyzing them, and quantitatively evaluating the effectiveness of marketing measures.
The technical features are as follows:
* Execute multiple causal discovery algorithms in parallel to extract highly reliable causal relationship candidates through ensemble.
* Quantify edge strength (coefficients/partial correlations) and the degree of agreement among multiple algorithms.
* Estimate Average Treatment Effect (ATE) as the effect of measures through causal inference based on the estimated causal graph.
* Construct causal hypotheses aligned with expert knowledge and business logic through grouping multiple variables and specifying allowable patterns and exceptions between groups.
**[Click here for service details]**
Digital Marketing Advancement Support Using Causal Analysis Tools
**About KPMG Consulting**
KPMG Consulting is a comprehensive consulting firm that supports the transformation of companies and organizations by combining industry knowledge across five areas: strategy, business transformation, technology/digital, risk consulting, and business innovation. Consultants with rich experience and skills form the 10 professional organizations that make up KPMG Japan.
In recent years, many companies have introduced marketing automation (MA) tools, and the vast amount of collected customer touchpoint data is being utilized in various marketing initiatives. However, there are cases where the causal relationship between measures and results cannot be grasped due to a focus on superficial data correlation analysis, or where it is impossible to verify which elements were effective due to the complexity and siloization of customer behavior and data. In such cases, unnecessary measures continue to be implemented, making it difficult to optimize marketing activity costs.
In this context, KPMG Consulting, in collaboration with "Advisory Lighthouse," a professional organization responsible for data and technology Center of Excellence within KPMG Japan, has developed an analysis tool that integrates Causal Discovery and Causal Inference. This tool extracts the causal structure of measures from marketing data held by companies and provides one-stop support for quantitative evaluation of measure effectiveness. By combining advanced data analysis technology with companies' marketing data, KPMG Consulting aims to realize a transformation to data-driven digital marketing and optimize marketing measures.
**[Support Content of This Service]**
By utilizing causal analysis tools for marketing data (customer data, measure data, etc.) held directly by companies and through MA tools, the service provides the following value:
**Discover causal structures and mechanisms, identify factors of customer behavior and decision-making**
AI automatically estimates statistical causal structures and mechanisms from data, and data-driven identifies factors of customer behavior and decision-making. For example, it visualizes the causal structure and mechanism between measures and effects in a data-driven manner, such as whether opening an email newsletter distributed to customers affects their purchasing behavior, whether customer attributes influence purchases, or how customer follow-up contributes to service churn rates, leading to the estimation of the effects brought about by measures. It explores factors based on customer behavior from data that tend to be overlooked in manual data analysis by personnel or analysis relying on past experience.
**Support for formulating optimal measure scenarios**
Based on causal structure data, it proposes measures expected to have the highest effect. It can be utilized for scenario design within MA, as well as for formulating communication plans and marketing scenarios including MA.
**Improve Marketing Return on Investment (ROI) by reducing ineffective measures and optimizing budget allocation**
Data identifies measures with low return on investment, and by reducing them, the freed-up budget is reallocated to effective measures, thereby optimizing marketing costs and supporting the improvement of ROAS (Return On Advertising Spend) and LTV (Life Time Value).
**[Features of This Tool]**
This analysis tool integrates Causal Discovery and Causal Inference, extracting causal structures from marketing data held by companies, analyzing them, and quantitatively evaluating the effectiveness of marketing measures.
The technical features are as follows:
* Execute multiple causal discovery algorithms in parallel to extract highly reliable causal relationship candidates through ensemble.
* Quantify edge strength (coefficients/partial correlations) and the degree of agreement among multiple algorithms.
* Estimate Average Treatment Effect (ATE) as the effect of measures through causal inference based on the estimated causal graph.
* Construct causal hypotheses aligned with expert knowledge and business logic through grouping multiple variables and specifying allowable patterns and exceptions between groups.
**[Click here for service details]**
Digital Marketing Advancement Support Using Causal Analysis Tools
**About KPMG Consulting**
KPMG Consulting is a comprehensive consulting firm that supports the transformation of companies and organizations by combining industry knowledge across five areas: strategy, business transformation, technology/digital, risk consulting, and business innovation. Consultants with rich experience and skills form the 10 professional organizations that make up KPMG Japan.