AI Personalisation and Data-Driven Strategies in Sales
Explore how AI personalisation and data-driven marketing impact sales effectiveness, consumer behavior, and e-commerce profitability models.
The Impact of AI-Driven Personalisation and Data-Driven Marketing on Sales Effectiveness
Presented by: Muhammad Farhan
University: Vilnius University Business School
Introduction
Why do platforms like Amazon recommend products that perfectly match our needs?
AI and data-driven marketing are transforming modern sales.
Decisions are now based on data analytics, not intuition.
Personalisation drives engagement, conversion rates, and profitability.
AI-driven personalisation improves individual consumer buying behaviour, while data-driven marketing enables platforms to select more profitable sales models.
Combining both approaches leads to higher sales effectiveness and long-term business success.
THESIS STATEMENT
Article 1: Overview
Yin et al. (2025) - The Impact of AI-Personalized Recommendations on Clicking Intentions
Study Purpose:
To examine how AI-personalised recommendations influence consumer buying behaviour.
To analyze clicking intention.
To evaluate e-commerce sales performance.
Article 1: Methodology
Research Design: Mixed-method approach
Interviews: 30 consumers (Grounded Theory)
Survey: 347 consumers
Questionnaires: 1,097 respondents
Theoretical Basis: S-O-R (Stimulus-Organism-Response) theory
Article 1: Key Findings
AI recommendations significantly increase Relevance, Inspiration, and Insightful experience.
These factors drive immersive shopping experiences and technology acceptance.
Privacy concerns reduce effectiveness, while high-quality information rebuilds trust.
Article 2: Overview
Liu et al. (2020) - The Impacts of Market Size and Data-Driven Marketing on Sales Mode Selection
To analyze how data-driven marketing affects sales model choice (agency vs. reselling) and platform profitability.
Article 2: Methodology
Quantitative Analysis of 100+ E-commerce Platforms
Uses regression analysis to model profitability.
Compares Agency Selling Model (Platform acts as middleman).
Compares Reselling Model (Platform buys and sells inventory).
Article 2: Key Findings
Preference for Reselling
Data-driven platforms earn higher profit margins and have better pricing control through reselling.
Agency Limitations
Agency selling limits profitability despite lower risk.
Data significantly improves demand forecasting and sales strategy accuracy.
Synthesis & Comparison
Article 1 (Yin et al.)
Focus: Consumers
Mechanism: AI Personalisation
Outcome: Engagement & Experience
Article 2 (Liu et al.)
Focus: Platforms
Mechanism: Data-Driven Strategy
Outcome: Profitability & Margins
Key Message: Data is the foundation of modern sales success.
Practical Application
What This Means for Sales
AI personalisation increases customer engagement.
Data-driven insights improve decision-making.
Sales effectiveness peaks when Customer Experience and Business Strategy are perfectly aligned.
Case Study Examples
Amazon
Uses AI recommendations to increase conversion rates and enhance customer loyalty.
eBay/Platforms
Uses data-driven marketing to shift toward profitable reselling models.
Actionable Recommendations
Sales teams and managers should:
Implement AI-based recommendation systems.
Use customer data ethically and transparently.
Balance personalisation with privacy protection.
Choose sales models based on data insights.
Continuously analyse customer behaviour.
Conclusion
AI personalisation improves consumer buying behaviour.
Data-driven marketing enhances strategic sales decisions.
Combining both leads to higher sales performance, greater customer satisfaction, and long-term profitability.
Thank You for Listening
Questions?
- ai-personalisation
- data-driven-marketing
- sales-strategy
- e-commerce
- consumer-behaviour
- marketing-analytics