Module 3 — AI-Powered Customer Experience

Personalisation engines use AI to deliver individually tailored experiences at scale. They analyse user behaviour, preferences, and context to determine what content, products, or actions to present to each user.

Two primary approaches: Collaborative filtering recommends items based on similar users' behaviour ("people who bought this also bought..."). Content-based filtering recommends items similar to what the user has previously engaged with (based on item attributes).

Modern systems use hybrid approaches combining both, often enhanced with deep learning. Netflix's recommendation algorithm considers viewing history, ratings, time of day, device type, and millions of other signals to personalise each user's homepage.

The business impact is substantial: Amazon attributes 35% of its revenue to recommendations. Spotify's Discover Weekly playlist drives significant engagement. Personalisation increases conversion rates by 10-30% across industries.

Key success factors: data quality (garbage in, garbage out), real-time processing (relevance decays quickly), diversity (avoid filter bubbles — don't just show more of the same), and transparency (users should understand why they're seeing certain recommendations).

AI is transforming every aspect of marketing:

Targeting: AI analyses customer data to identify ideal customer profiles and predict which prospects are most likely to convert. Lookalike modelling finds new audiences similar to your best customers.

Attribution: Multi-touch attribution models use AI to determine which marketing touchpoints actually drive conversions, replacing simplistic "last click" attribution.

Content generation: LLMs generate ad copy, email variations, social media posts, and blog content. A/B testing at scale becomes possible when AI generates hundreds of content variations.

Sentiment analysis uses NLP to automatically determine the emotional tone of text — positive, negative, or neutral. Applications include monitoring social media mentions, analysing customer reviews, tracking brand perception over time, and processing survey responses at scale.

Advanced sentiment analysis goes beyond simple polarity to detect specific emotions (frustration, delight, confusion), identify topics driving sentiment (price, quality, service), and track sentiment trends over time. This enables proactive response: if negative sentiment spikes around a specific product feature, the company can address it before it escalates.