Modern marketing strategies are increasingly shaped by the ability to decode consumer behaviour in real time. This course provides a comprehensive guide to using artificial intelligence (AI) for behavioural analytics—transforming raw data into powerful marketing insights.
Participants will learn how AI models interpret online behaviour, segment audiences, personalise marketing efforts, and predict future actions. By integrating machine learning, natural language processing, and data visualisation, this course prepares professionals to harness behavioural data for precise targeting, customer journey optimisation, and higher ROI.
By the end of the course, participants will be able to:
- Understand the fundamentals of behavioural analytics and the role of AI.
- Apply machine learning to analyse customer interactions across platforms.
- Use AI to build behavioural profiles and dynamic segmentation.
- Optimise marketing campaigns through predictive insights.
- Address data privacy and ethical concerns in behavioural tracking.
This course is ideal for:
- Digital marketing professionals and strategists.
- Data analysts and marketing intelligence specialists.
- CRM managers and campaign managers.
- E-commerce professionals aiming to personalise customer journeys.
- AI engineers and developers working on marketing platforms.
- Business owners and decision-makers seeking to improve marketing outcomes.
The course blends expert-led sessions, hands-on labs, case studies, and AI tool demonstrations. Real-life marketing datasets will be used for experimentation, supported by group discussions, performance feedback, and optional follow-up mentoring.
Day 5 of each course is reserved for a Q&A session, which may occur off-site. For 10-day courses, this also applies to day 10
Section 1: Introduction to Behavioural Analytics and AI
- What is behavioural analytics in a digital context?
- Key behavioural indicators: clicks, dwell time, purchases, bounce rate, etc.
- Introduction to AI-powered analytics: models, tools, and opportunities.
- Benefits of behavioural analytics for campaign performance and brand loyalty.
- Case studies: Netflix, Amazon, and Spotify’s use of behavioural AI.
Section 2: Customer Data Collection and Behavioural Tracking
- Sources of behavioural data: websites, mobile apps, CRM, social media.
- Tools for real-time tracking and event-based data logging.
- Integrating customer touchpoints into unified behavioural datasets.
- Ensuring ethical tracking and compliance with data regulations (GDPR, CCPA).
- Workshop: Creating a customer behaviour tracking map.
Section 3: Behavioural Segmentation Using Machine Learning
- Clustering and classification techniques for audience segmentation.
- Algorithms: k-means, decision trees, random forests, etc.
- Segmenting users by behaviour, intent, and life cycle stage.
- Creating personas using AI-driven behavioural analysis.
- Hands-on activity: Building segmentation models using sample marketing data.
Section 4: Predictive Behavioural Modelling and Personalisation
- Using AI to predict customer churn, conversion, and lifetime value.
- Personalising content, offers, and recommendations based on behaviour.
- Email marketing automation driven by behavioural insights.
- A/B testing and multivariate analysis using AI.
- Case study: Behavioural modelling in ecommerce retention strategy.
Section 5: Sentiment Analysis and NLP for Consumer Understanding
- Introduction to NLP in marketing.
- Analysing customer feedback, reviews, and social media for sentiment.
- Classifying emotions and intent using machine learning.
- Monitoring brand perception and adjusting campaigns in real time.
- Live demo: Applying sentiment analysis tools to campaign data.
Section 6: Data Visualisation and Actionable Reporting
- Transforming behavioural data into marketing insights.
- Tools for interactive dashboards (e.g., Power BI, Tableau).
- Real-time KPI tracking for campaign performance.
- Creating storytelling dashboards for stakeholders.
- Group exercise: Building a behavioural analytics dashboard.
Section 7: Strategy, Ethics, and Future Trends
- Designing an AI-powered behavioural analytics strategy.
- Budgeting, KPIs, and stakeholder alignment.
- Addressing privacy, data governance, and ethical AI in marketing.
- Emerging technologies: generative AI, reinforcement learning, and behavioural prediction.
- Final project: Developing a behavioural analytics plan for your organisation.
Upon successful completion of this training course, delegates will be awarded a Holistique Training Certificate of Completion. For those who attend and complete the online training course, a Holistique Training e-Certificate will be provided.
Holistique Training Certificates are accredited by the British Accreditation Council (BAC) and The CPD Certification Service (CPD), and are certified under ISO 9001, ISO 21001, and ISO 29993 standards.
CPD credits for this course are granted by our Certificates and will be reflected on the Holistique Training Certificate of Completion. In accordance with the standards of The CPD Certification Service, one CPD credit is awarded per hour of course attendance. A maximum of 50 CPD credits can be claimed for any single course we currently offer.
- Course Code PI2 - 131
- Course Format Classroom, Online,
- Duration 5 days