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Unit 3 of 4 · MBA Sem 4

Unit 3: AI in marketing

Digital Analytics, AI and Marketing Automation notes · PTU syllabus (MBA 974-26)

3 min read7 topics10 exam questions
On this page
  1. Unit summary
  2. AI applications in marketing
  3. Machine learning and predictive analytics
  4. Recommendation systems and personalisation engines
  5. Sentiment analysis
  6. Big data applications in marketing
  7. Data visualisation and dashboard tools
  8. Ethical and privacy concerns in AI-based marketing
  9. Key terms
  10. Quick revision
  11. Important questions

Unit summary

AI lets marketers predict, personalise and understand customers at scale — but raises ethical questions. This unit covers AI applications in marketing, machine learning and predictive analytics, recommendation systems and personalisation engines, sentiment analysis, big data applications, data visualisation and dashboard tools, and ethical and privacy concerns in AI-based marketing.

After this unit you can

  • Explain AI applications in marketing
  • Explain machine learning, predictive analytics and recommendation systems
  • Apply sentiment analysis and big data
  • Use dashboards and address ethical and privacy concerns

PTU syllabus topics

  • AI applications in marketing
  • machine learning and predictive analytics
  • recommendation systems and personalization engines
  • sentiment analysis
  • big data applications
  • data visualization and dashboard tools
  • ethical and privacy concerns in AI-based marketing
ClassificationAI in marketing
AI applications

  • Recommendation engines

    Personalised products

  • Predictive analytics

    Churn, lifetime value

  • Sentiment analysis

    Read customer mood

  • Chatbots

    24x7 conversations

  • Generative AI

    Copy, images, variants

1

Topic 1

AI applications in marketing

ClassificationAI in digital marketing
AI applications

  • Personalisation

    Recommendations, dynamic content

  • Predictive analytics

    Churn, purchase propensity, lead scoring

  • Programmatic advertising

    Real-time bidding and targeting

  • Generative AI

    Ad copy, images, video, product descriptions

  • Search and voice

    Semantic search, voice assistants

  • Pricing

    Dynamic pricing

  • Analytics

    Sentiment analysis, customer insights

  • Cautions: bias, transparency, accuracy of generated content, copyright, disclosure of AI use.
2

Topic 2

Machine learning and predictive analytics

  • Supervised learning: predict outcomes (conversion, churn, CLV) from labelled history.
  • Unsupervised learning: discover segments and patterns (clustering).
  • Reinforcement learning: optimise decisions through feedback (bidding, recommendations).
  • Process: define the target, prepare features, train and validate, deploy, monitor drift.
3

Topic 3

Recommendation systems and personalisation engines

ComparisonRecommendation approaches
How it works
Example

Collaborative filtering

Recommends what similar users liked

"Customers who bought this also bought"

Content-based filtering

Recommends items similar to those a user liked

Similar songs or articles

Hybrid

Combines both with context

Netflix, Amazon, Spotify

  • Personalisation engines: decide content, offers and layout for each user in real time using profiles and models.
4

Topic 4

Sentiment analysis

  • Sentiment analysis: natural language processing classifies text (reviews, posts, tickets) as positive, negative or neutral and identifies emotions and topics.
  • Uses: brand health tracking, product feedback, crisis detection, customer service prioritisation.
  • Challenges: sarcasm, code-mixed languages (Hinglish), context.
5

Topic 5

Big data applications in marketing

  • Clickstream and app data for journey analysis; location data for geo-targeting; social data for trends; transaction data for basket analysis and pricing; IoT data for usage-based offers.
6

Topic 6

Data visualisation and dashboard tools

  • Tools: Looker Studio (Google), Power BI, Tableau, platform dashboards, CRM reports.
  • Design: objective-linked KPIs, trends vs targets, channel and campaign breakdowns, filters, clear visuals.
7

Topic 7

Ethical and privacy concerns in AI-based marketing

  • Concerns: consent and transparency of data use, profiling and manipulation, discrimination in targeting or pricing, filter bubbles, deepfakes and synthetic content, data security.
  • Safeguards: DPDP Act compliance, privacy by design, explainable models, bias audits, human oversight, disclosure of AI use, opt-outs.

Key terms

Collaborative filtering
Recommendations based on similar users
Personalisation engine
System tailoring content to each user
Sentiment analysis
Classifying opinions in text
Model drift
Decline in model accuracy as data changes
Filter bubble
Exposure limited to content matching past behaviour

Quick revision

  • AI applications across the marketing funnel.
  • Supervised, unsupervised, reinforcement learning; ML process.
  • Collaborative, content-based, hybrid recommenders.
  • Sentiment analysis uses and challenges; big data sources.
  • Dashboards; ethical concerns and safeguards.

Important exam questions

Practice questions written to the PTU exam pattern for this unit's syllabus: short answers (Section A style) and long answers (Sections B and C style).

Short-answer questions

  1. Q1.State four AI applications in marketing.
  2. Q2.Distinguish collaborative and content-based filtering.
  3. Q3.What is sentiment analysis?
  4. Q4.Give two big data sources for marketing.
  5. Q5.Name two dashboard tools.
  6. Q6.State three ethical concerns in AI-based marketing.

Long-answer questions

  1. Q1.Explain AI applications in marketing.
  2. Q2.Explain machine learning, predictive analytics and recommendation systems.
  3. Q3.Discuss sentiment analysis and big data applications in marketing.
  4. Q4.Discuss ethical and privacy concerns in AI-based marketing.

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