Certified Specialist Programme in Digital Customer Support Analytics
-- viewing nowThe Digital Customer Support Analytics programme is designed for professionals seeking to enhance their skills in data-driven decision making. Developed for customer support teams, this programme focuses on the application of analytics tools and techniques to drive business outcomes.
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About this course
Through a combination of theoretical foundations and practical applications, learners will gain expertise in data analysis, insight generation, and actionable recommendations.
By the end of the programme, learners will be equipped to measure customer support performance, identify areas for improvement, and drive business growth through data-informed strategies.
Explore the Certified Specialist Programme in Digital Customer Support Analytics today and discover how data-driven decision making can transform your customer support operations.
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Course details
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Data Visualization for Digital Customer Support Analytics: This unit focuses on using data visualization tools to effectively communicate insights and trends in digital customer support data, enabling support teams to make data-driven decisions. •
Predictive Analytics for Customer Support: This unit explores the application of predictive analytics techniques to forecast customer support needs, identify potential issues, and optimize support processes for improved customer experience. •
Text Analytics for Sentiment Analysis: This unit delves into the use of text analytics techniques to analyze customer feedback, sentiment, and emotions, providing valuable insights for support teams to improve customer satisfaction. •
A/B Testing and Experimentation in Digital Customer Support: This unit covers the principles and best practices of A/B testing and experimentation in digital customer support, enabling support teams to measure the impact of changes on customer behavior and support outcomes. •
Customer Journey Mapping for Digital Support: This unit focuses on creating customer journey maps to visualize the customer's experience across multiple touchpoints, enabling support teams to identify pain points and opportunities for improvement. •
Social Media Analytics for Customer Support: This unit explores the use of social media analytics tools to track customer sentiment, engagement, and support requests on social media platforms, providing valuable insights for support teams. •
Machine Learning for Customer Support Automation: This unit covers the application of machine learning techniques to automate routine customer support tasks, such as answering frequently asked questions and routing customer inquiries. •
Data-Driven Decision Making in Digital Customer Support: This unit emphasizes the importance of data-driven decision making in digital customer support, providing support teams with the skills to interpret data insights and make informed decisions. •
Customer Experience Metrics and KPIs for Digital Support: This unit focuses on defining and tracking key performance indicators (KPIs) and metrics to measure customer experience in digital support, enabling support teams to optimize their performance. •
Advanced Analytics for Customer Support: This unit covers advanced analytics techniques, such as regression analysis and clustering, to gain deeper insights into customer behavior and support outcomes, enabling support teams to make data-driven decisions.
Data Visualization for Digital Customer Support Analytics: This unit focuses on using data visualization tools to effectively communicate insights and trends in digital customer support data, enabling support teams to make data-driven decisions. •
Predictive Analytics for Customer Support: This unit explores the application of predictive analytics techniques to forecast customer support needs, identify potential issues, and optimize support processes for improved customer experience. •
Text Analytics for Sentiment Analysis: This unit delves into the use of text analytics techniques to analyze customer feedback, sentiment, and emotions, providing valuable insights for support teams to improve customer satisfaction. •
A/B Testing and Experimentation in Digital Customer Support: This unit covers the principles and best practices of A/B testing and experimentation in digital customer support, enabling support teams to measure the impact of changes on customer behavior and support outcomes. •
Customer Journey Mapping for Digital Support: This unit focuses on creating customer journey maps to visualize the customer's experience across multiple touchpoints, enabling support teams to identify pain points and opportunities for improvement. •
Social Media Analytics for Customer Support: This unit explores the use of social media analytics tools to track customer sentiment, engagement, and support requests on social media platforms, providing valuable insights for support teams. •
Machine Learning for Customer Support Automation: This unit covers the application of machine learning techniques to automate routine customer support tasks, such as answering frequently asked questions and routing customer inquiries. •
Data-Driven Decision Making in Digital Customer Support: This unit emphasizes the importance of data-driven decision making in digital customer support, providing support teams with the skills to interpret data insights and make informed decisions. •
Customer Experience Metrics and KPIs for Digital Support: This unit focuses on defining and tracking key performance indicators (KPIs) and metrics to measure customer experience in digital support, enabling support teams to optimize their performance. •
Advanced Analytics for Customer Support: This unit covers advanced analytics techniques, such as regression analysis and clustering, to gain deeper insights into customer behavior and support outcomes, enabling support teams to make data-driven decisions.
Career path
Digital Analytics Specialist
A Digital Analytics Specialist is responsible for analyzing customer data to improve customer support operations. They use data analytics tools to identify trends, create reports, and provide insights to stakeholders.
Business Intelligence Developer
A Business Intelligence Developer designs and implements data visualization tools to help organizations make data-driven decisions. They work with stakeholders to understand business needs and develop solutions to meet those needs.
Data Scientist - Customer Support
A Data Scientist - Customer Support uses advanced statistical and machine learning techniques to analyze customer data and improve customer support operations. They develop predictive models to forecast customer behavior and identify areas for improvement.
Quantitative Analyst - Customer Support
A Quantitative Analyst - Customer Support uses mathematical and statistical techniques to analyze customer data and improve customer support operations. They develop models to forecast customer behavior and identify areas for improvement.
Data Analyst - Customer Support
A Data Analyst - Customer Support analyzes customer data to identify trends and patterns. They create reports and provide insights to stakeholders to help improve customer support operations.
Entry requirements
- Basic understanding of the subject matter
- Proficiency in English language
- Computer and internet access
- Basic computer skills
- Dedication to complete the course
No prior formal qualifications required. Course designed for accessibility.
Course status
This course provides practical knowledge and skills for professional development. It is:
- Not accredited by a recognized body
- Not regulated by an authorized institution
- Complementary to formal qualifications
You'll receive a certificate of completion upon successfully finishing the course.
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CERTIFIED SPECIALIST PROGRAMME IN DIGITAL CUSTOMER SUPPORT ANALYTICS
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Learner Name
who has completed a programme at
London School of Planning and Management (LSPM)
Awarded on
05 May 2025
Blockchain Id: s-1-a-2-m-3-p-4-l-5-e
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