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Digital Marketing

Sports Club is Using Causal AI to Identify High-Value Customers and Personalize Marketing Strategies

Challenges in Sports Club Digital Marketing Strategies:

Customer Profiling: Which specific customer group will most likely buy which specific merchandise?

Customer Journey: How the user experience of digital marketing campaign effect the conversion or sales?

Pricing & Promotion: What effect will different promotion strategies, i.e. discount, reward, etc., have on sales?

Marketing Campaign Effect: How can the effect/return of marketing spending can be accurately evaluated?

Causal AI-Powered Sports Club Digital Marketing Strategies Optimization:

Customer Journey: Learning from historical information to map out customer journey.

Marketing Campaign Effect: How user experience through digital marketing campaigns causally influence the decision-making of first-time purchase of season tickets.

Personalization: Effect of individual or combination of marketing communications influence customer purchase decision of season tickets.

Sports Club Digital Marketing Strategies Case:

A Sports Club with 500,000+ members, seeks decision-making support through its CRM data to improve business goals:

Identification of Influential Drivers: Ticket Price, Discount, Loyalty Program, Parking, Food & Beverages, Merchandises, etc.

Identification of Purchasing Decisions Factors

Marketing Strategies to Improve Conversion Rates: Social Media, Phone Call, Email, etc.

Sports Club Digital Marketing Strategies Case Problems Solved:

Causality has been learned from historical information of how customers are engaged through a variety of datasets of which a customer journey dataset is specifically extracted.

Causal analysis has been run to evaluate in a quantitative manner how user experience through digital marketing campaigns causally influence the decision-making of first-time purchase of season tickets.

The effect of individual or combined of marketing communications, such as email or phone call, or any combination, can be quantitatively measured by the causal influence they have on the purchase decision of season tickets.

Why members choose to buy different types of tickets?

How member purchase decision varies by cities/regions?

Identification of highly convertible members

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Commerical Building

Hospital is Using Causal AI for Predictive Maintenance and Improve Energy Efficiency

Challenges in Hospital Building Facility Management:

Aging Equipment and Infrastructure: Aging HVAC systems, electrical systems, and boilers, etc. are particularly prone to failures.

Budget Constraints and Cost Management: Pressure to reduce costs while maintaining service quality and to reduce unplanned repairs.

Energy Efficiency and Sustainability Goals: Balancing sustainability initiatives (e.g., reducing carbon footprints) while reducing rising energy costs.

Technology Integration and Data Management: Siloed data and outdated systems hinder real-time decision-making and workflow automation.

Special Function Rooms: Relocating patients in critical function rooms due to equipment failures will disastrous.

Causal AI-Powered Hospital Building Facility Management:

Predictive Maintenance: Model equipment performance using historical data.

Root Cause Analysis: Identify and address the root causes of faults.

Decision-Making Support: Provide actionable insights for facility management teams.

Energy Efficiency: Energy management to reduce energy costs.

Hospital Building Facility Management Case:

HEPA

Ventilation system, i.e. fan belts, bearings, etc.

Positive pressure control loops

Pumps

Specific faults with chillers, boilers, etc.

Identify and define faults and consequences of the hospital

Hospital Building Facility Management Case Results:

Energy Efficiency: Improved by 4.5%.

Proactive Maintenance: Detect and address issues before they escalate.

Data-Driven Decisions: Make informed maintenance decisions based on granular data analysis.

Effective management of commercial building equipment requires a proactive, technology-driven approach combining preventive maintenance, energy optimization, and stakeholder collaboration.

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Sports Performance Analysis

University is Using Causal AI for Baseball Pitcher Performance Analysis and Improvement Recommendations

Challenges in Hospital Building Facility Management:

Aging Equipment and Infrastructure: Aging HVAC systems, electrical systems, and boilers, etc. are particularly prone to failures.

Budget Constraints and Cost Management: Pressure to reduce costs while maintaining service quality and to reduce unplanned repairs.

Energy Efficiency and Sustainability Goals: Balancing sustainability initiatives (e.g., reducing carbon footprints) while reducing rising energy costs.

Technology Integration and Data Management: Siloed data and outdated systems hinder real-time decision-making and workflow automation.

Special Function Rooms: Relocating patients in critical function rooms due to equipment failures will disastrous.

Challenges in Baseball Pitcher Performance Analysis:

Offensive Analytics: On-Base Percentage (OBP), Slugging Percentage (SLG), and Batting Average on Balls in Play (BABIP).

Pitching Analytics: K/BB (Strikeout-to-Walk Ratio), ERA (Earned Run Average), Spin Rate, WHIP (Walks Plus Hits per Inning Pitched) and more.

Causal AI-Powered Baseball Pitcher Performance Analysis:

Based on historical data and player technique analytics, make improvement recommendations on each KPIs:Technique

Goal

Method

Identify and define faults and consequences of the hospital

Baseball Pitcher Performance Analysis Case:

Identify each KPI deficiencies and consequences of the pitcher:

On-Base Percentage (OBP)

ERA (Earned Run Average)

WHIP (Walks Plus Hits per Inning Pitched)

K/BB (Strikeout-to-Walk Ratio)

Baseball Pitcher Performance Analysis Case Results:

Identify each KPI deficiencies and consequences of the pitcher:

On-Base Percentage (OBP) Improvement Recommendations: Increase SpinRate, Increase ExitSpeed, Improve InducedVertBreak, Optimize HorzApprAngle and VertApprAngle.

ERA (Earned Run Average) Improvement Recommendations: Increase SpinRate, Increase RelSpeed, Increase ExitSpeed, Optimize InducedVertBreak and VertBreak, Reduce HorzApprAngle and Optimize Extension.

WHIP (Walks Plus Hits per Inning Pitched) Improvement Recommendations: Increase RelSpeed, Increase SpinRate, Optimize InducedVertBreak and Finetune VertApprAngle and HorzApprAngle

K/BB (Strikeout-to-Walk Ratio) Improvement Recommendations: Increase ZoneSpeed, Enhance HorzBreak and VertBreak, Optimize SpinRate, Refine Release Angles and Improve Extension

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