MBA(A) 216 Marketing Analytics Notes explain all 4 units of this GGSIPU MBA (Analytics) Semester 4 marketing elective in a 71-page printed book, with 25 worked illustrations, 38 figures, 3 case studies and 65 end-of-unit exam questions, following the Scheme and Syllabus 2022-24 onwards.
Written by Prateek Talwar, Founder, Unnati Educations · Reviewed by Ritik Rawat, B.Tech, Senior Full Stack Developer · Checked against syllMBAAnalytics180225.pdf · Updated 26 October 2026 · Editorial policy
Scheme: MBA (Analytics) Scheme and Syllabus 2022-24 onwards | Applies to: batches on this scheme (Semesters 3 and 4 approved 19.07.2023) | Verified against: syllMBAAnalytics180225.pdf, ipu.ac.in
MBA(A) 216 Marketing Analytics at a glance: GGSIPU MBA (Analytics) Sem 4 credits, units and exam pattern
| Code and title | MBA(A) 216 Marketing Analytics (notes print "MBA (A)-216") |
|---|---|
| Course and semester | MBA (Analytics), Semester 4 |
| Category and credits | Marketing stream elective (Elective III or IV, Skill Enhancement) · L 3, no T/P · 3 credits |
| Units and hours | 4 units, 42 hours (10, 12, 10, 10) |
| Internal assessment | Out of 25: Class Test-I 15 + individual presentation, viva-voce or group discussion 10 |
| End-term marks and pattern | Not printed in the official PDF |
| Applies to | Scheme and Syllabus 2022-24 onwards |
| Paired lab | None |
Unit II gets the most time, 12 of the 42 hours, for cluster analysis, multidimensional scaling and marketing mix modelling. Examinations run under GGSIPU Ordinance 11, as the scheme states.
MBA(A) 216 sits in the Marketing elective group with the other GGSIPU MBA (Analytics) Semester 4 papers.
MBA(A) 216 Marketing Analytics Notes unit-wise: 25 worked illustrations, 38 figures and 65 exam questions across 4 units
These GGSIPU MBA (Analytics) Sem 4 Marketing Analytics notes follow the official unit order, and each unit ends with a quick-revision table and exam questions.
Other papers of the course are under GGSIPU MBA (Analytics) notes.
MBA(A) 216 Unit 1: Introduction
Also covered as "Introduction to Marketing Analytics". Pages 5 to 21. Scope, process, data sources, measurement scales and cleaning come first. The four analytics types follow, from Tukey's EDA to a sales regression (R² = 0.991) and a linear programming budget split, then Excel PivotTables and Solver. Counts: 5 worked illustrations, 10 figures, 1 case study, 17 exam questions.
Quick look: finding an outlier in ten order values with the IQR rule (from page 12)
- Sort the orders: ₹120, 135, 150, 160, 165, 170, 180, 190, 210 and 950.
- With
QUARTILE.INC, Q1 = 152.5 and Q3 = 187.5, so IQR = 35. - Fences: 152.5 − 1.5 × 35 = 100 and 187.5 + 1.5 × 35 = 240.
- ₹950 is above 240, so it is an outlier. It pulls the mean to ₹243 against a median of ₹167.5.
MBA(A) 216 Unit 2: Segmentation, Positioning & Marketing Mix
Pages 22 to 40, the longest unit. Customer analytics brings CAC, churn, NPS and RFM scoring before segmentation. Cluster analysis gets distance measures, linkage methods, k-means and a dendrogram. Perceptual maps, umbrella branding, MDS and marketing mix modelling with adstock close it. Counts: 5 worked illustrations, 12 figures, 1 case study, 16 exam questions.
| Customer (spend ₹'000, visits) | Distance to A | Distance to D | Cluster |
|---|---|---|---|
| B (12, 3) | 2.24 | 28.44 | 1 |
| C (11, 1) | 1.41 | 29.83 | 1 |
| E (42, 10) | 32.98 | 2.83 | 2 |
| F (38, 9) | 28.86 | 2.24 | 2 |
Quick look: first k-means pass, assigning four customers to centroids A (10, 2) and D (40, 8) (from page 28)
- Customer (spend ₹'000, visits)
- B (12, 3)
- Distance to A
- 2.24
- Distance to D
- 28.44
- Cluster
- 1
- Customer (spend ₹'000, visits)
- C (11, 1)
- Distance to A
- 1.41
- Distance to D
- 29.83
- Cluster
- 1
- Customer (spend ₹'000, visits)
- E (42, 10)
- Distance to A
- 32.98
- Distance to D
- 2.83
- Cluster
- 2
- Customer (spend ₹'000, visits)
- F (38, 9)
- Distance to A
- 28.86
- Distance to D
- 2.24
- Cluster
- 2
MBA(A) 216 Unit 3: Pricing & Customer Journey
Pages 41 to 55. Nine pricing goals and price elasticity lead into bundling types, skimming against penetration, revenue management with Littlewood's rule and promotion lift, where a 20% discount raises sales but cuts profit. The journey half covers stages, the Dick and Basu loyalty matrix and CLV formulas. Counts: 8 worked illustrations, 8 figures, 16 exam questions.
| Pricing goal | Price | Units sold | Revenue / profit |
|---|---|---|---|
| Maximise revenue | ₹50 | 500 | ₹25,000 / ₹15,000 |
| Maximise profit | ₹60 | 400 | ₹24,000 / ₹16,000 |
Quick look: revenue-maximising against profit-maximising price for q = 1000 − 10p, variable cost ₹20 (from page 42)
- Pricing goal
- Maximise revenue
- Price
- ₹50
- Units sold
- 500
- Revenue / profit
- ₹25,000 / ₹15,000
- Pricing goal
- Maximise profit
- Price
- ₹60
- Units sold
- 400
- Revenue / profit
- ₹24,000 / ₹16,000
MBA(A) 216 Unit 4: Digital Analytics: Metrics and Measurement
Pages 56 to 70. Traffic, engagement and conversion metrics come first, with a worked month showing 75% cart abandonment. SEO is split into five kinds and SEM into the Ad Rank auction. Social media analytics, network centrality, the viral K-factor and text mining with TF-IDF follow. Counts: 7 worked illustrations, 8 figures, 1 case study, 16 exam questions.
| Metric | Working | Result |
|---|---|---|
| CTR | 5,000 ÷ 2,50,000 × 100 | 2% |
| CPA | ₹1,00,000 ÷ 150 | ₹666.67 |
| ROAS | ₹4,50,000 ÷ ₹1,00,000 | 4.5 |
Quick look: judging a Google Ads campaign with 5,000 clicks at ₹20 CPC and 150 enrolments (from page 60)
- Metric
- CTR
- Working
- 5,000 ÷ 2,50,000 × 100
- Result
- 2%
- Metric
- CPA
- Working
- ₹1,00,000 ÷ 150
- Result
- ₹666.67
- Metric
- ROAS
- Working
- ₹4,50,000 ÷ ₹1,00,000
- Result
- 4.5
Pure bundling with two customers in pricing analytics: excerpt from page 44 of the MBA(A) 216 notes
Excerpt from page 44
Why can selling two products together earn more than selling them one by one? The notes answer with two customers, two products and zero marginal cost.
Pure bundling with two customers
Customer 1 is willing to pay ₹100 for Product A and ₹40 for Product B. Customer 2 is willing to pay ₹70 for A and ₹80 for B. Sold separately, the best price for A is ₹70: both buy and revenue is ₹140, while ₹100 sells only to Customer 1 for ₹100. For B, ₹40 to both or ₹80 to Customer 2 gives ₹80 either way. Separate pricing earns ₹220.
Now sell A and B only as a pack. Customer 1 values the pack at ₹140 and Customer 2 at ₹150. At a bundle price of ₹140 both buy, and revenue rises to ₹280. The gain is ₹60, or 27% more revenue, because the two valuations are negatively correlated: one customer prefers A, the other prefers B, and the bundle averages the difference out.
| Customer | Value of A (₹) | Value of B (₹) | Value of bundle (₹) |
|---|---|---|---|
| Customer 1 | 100 | 40 | 140 |
| Customer 2 | 70 | 80 | 150 |
| Best price | 70 → revenue 140 | 40 or 80 → revenue 80 | 140 → revenue 280 |
Reservation prices and the best price under each option
- Customer
- Customer 1
- Value of A (₹)
- 100
- Value of B (₹)
- 40
- Value of bundle (₹)
- 140
- Customer
- Customer 2
- Value of A (₹)
- 70
- Value of B (₹)
- 80
- Value of bundle (₹)
- 150
- Customer
- Best price
- Value of A (₹)
- 70 → revenue 140
- Value of B (₹)
- 40 or 80 → revenue 80
- Value of bundle (₹)
- 140 → revenue 280
Advantages and limitations of bundling
Bundling captures more consumer surplus, moves slow-selling items along with popular ones, saves customers search effort, lowers selling and packaging costs and builds switching costs, as telecom plans with OTT subscriptions do.
On the other side, customers may feel forced to pay for items they do not want, the discount can cut margin on items that would sell anyway, tying arrangements may be anti-competitive under the Competition Act, 2002, prices become hard to compare, and a badly chosen bundle can cannibalise single-item sales.
How analytics picks the bundle
Market basket analysis (association rules) shows which items are bought together. If 40% of pizza buyers also buy a cold drink, a pizza and drink combo is a natural bundle, and conjoint analysis then tests the best bundle price.
Price skimming
Market skimming sets a high launch price for an innovative product to collect the most revenue from buyers who will pay the most, then lowers the price step by step to reach price-sensitive segments. It is the opposite of penetration pricing. New iPhones, foldable phones, OLED TVs, new drug molecules and premium electric vehicles follow this path.
Do the MBA(A) 216 notes cover the full GGSIPU syllabus?
Yes. Every topic under the four official units of MBA(A) 216 has its own numbered section in the notes; the unit check found no gap.
| Official unit | Official topics | Where in the notes | Status |
|---|---|---|---|
| I: Introduction | Marketing Analytics: meaning and scope, Data for Marketing Analytics, Exploratory analysis, Descriptive analysis, Predictive analysis, Prescriptive analysis, Use of Excel for summarising marketing data | Sections 1-7, pages 5-20 | Covered |
| II: Segmentation, Positioning & Marketing Mix | Customer analytics, benefits from customer analytics, Segmentation analytics, cluster analysis, Perceptual mapping, umbrella brands, Multidimensional scaling, Marketing Mix Modelling: variables and techniques | Sections 1-8, pages 22-39 | Covered |
| III: Pricing & Customer Journey | Goals of Pricing, Bundling, Skimming, revenue management, promotions, Customer journey mapping, Customer loyalty, Customer lifetime value: meaning and calculation | Sections 1-8, pages 41-53 | Covered |
| IV: Digital Analytics: Metrics and Measurement | Important web metrics, SEO and SEM, Social Media Analytics, Networks, Viral marketing, Text Mining | Sections 1-6, pages 56-68 | Covered |
MBA(A) 216 official syllabus mapped to the notes
- Official unit
- I: Introduction
- Official topics
- Marketing Analytics: meaning and scope, Data for Marketing Analytics, Exploratory analysis, Descriptive analysis, Predictive analysis, Prescriptive analysis, Use of Excel for summarising marketing data
- Where in the notes
- Sections 1-7, pages 5-20
- Status
- Covered
- Official unit
- II: Segmentation, Positioning & Marketing Mix
- Official topics
- Customer analytics, benefits from customer analytics, Segmentation analytics, cluster analysis, Perceptual mapping, umbrella brands, Multidimensional scaling, Marketing Mix Modelling: variables and techniques
- Where in the notes
- Sections 1-8, pages 22-39
- Status
- Covered
- Official unit
- III: Pricing & Customer Journey
- Official topics
- Goals of Pricing, Bundling, Skimming, revenue management, promotions, Customer journey mapping, Customer loyalty, Customer lifetime value: meaning and calculation
- Where in the notes
- Sections 1-8, pages 41-53
- Status
- Covered
- Official unit
- IV: Digital Analytics: Metrics and Measurement
- Official topics
- Important web metrics, SEO and SEM, Social Media Analytics, Networks, Viral marketing, Text Mining
- Where in the notes
- Sections 1-6, pages 56-68
- Status
- Covered
Compare the topics yourself in the official GGSIPU MBA (Analytics) Scheme and Syllabus 2022-24 onwards (PDF), printed pages 92-93. The notes also add RFM scoring, Littlewood's rule, TF-IDF and the GA4 engagement rate.
Every course in GGSIPU notes is checked against its official PDF in this way.
How to write MBA(A) 216 answers: CLV and pricing numericals, k-means steps and ten-to-fifteen-mark theory answers
The exam tips in these MBA(A) 216 Marketing Analytics IPU notes point to three answer shapes. Numericals come first: elasticity, Littlewood's rule, lift and CLV in Unit III, adstock in Unit II, and ROAS and the K-factor in Unit IV. Write the formula and assumptions, substitute, state the result with its unit, and end with what it means for the marketer.
Worked answer: simple CLV of a café customer (from page 53)
A customer spends ₹300 a visit, comes 48 times a year at a 40% gross margin and stays 3 years. CLV = 300 × 48 × 0.40 × 3 = ₹17,280 (undiscounted). So a ₹1,000-a-year loyalty offer pays if it keeps such customers longer. Discounted CLV adds a year-wise table.
Technique answers are step answers. A k-means numerical needs five parts: the distance formula with one worked distance, the assignment table, new centroids, a convergence check and a name for each cluster. RFM needs the scoring scale, a customer table and an action per segment.
Theory answers open with a sourced definition (Winston, Kotler or Oliver), then a comparison table and an Indian example such as Jio, IndiGo or Amul.
Try these answer shapes on GGSIPU MBA (Analytics) important questions.
MBA(A) 216 exam corner: 40 short (two to five marks) and 25 long (ten to fifteen marks) questions by unit
| Unit | Short (2–5 Marks) | Long (10–15 Marks) | Worked illustrations / case studies |
|---|---|---|---|
| I | 10 | 7 | 5 / 1 |
| II | 10 | 6 | 5 / 1 |
| III | 10 | 6 | 8 / 0 |
| IV | 10 | 6 | 7 / 1 |
MBA(A) 216 exam questions and worked material per unit, as printed in the notes
- Unit
- I
- Short (2–5 Marks)
- 10
- Long (10–15 Marks)
- 7
- Worked illustrations / case studies
- 5 / 1
- Unit
- II
- Short (2–5 Marks)
- 10
- Long (10–15 Marks)
- 6
- Worked illustrations / case studies
- 5 / 1
- Unit
- III
- Short (2–5 Marks)
- 10
- Long (10–15 Marks)
- 6
- Worked illustrations / case studies
- 8 / 0
- Unit
- IV
- Short (2–5 Marks)
- 10
- Long (10–15 Marks)
- 6
- Worked illustrations / case studies
- 7 / 1
One question per unit, with pointers:
- Unit I: What is prescriptive analysis? Explain its techniques with an example of budget allocation using linear programming. Maximise Z = 3D + 2T: digital ₹6 lakh, TV ₹4 lakh, Z = ₹26 lakh (page 16).
- Unit II: Explain adstock with a numerical example. Adstock = this week's GRPs + λ × last week's adstock; at λ = 0.5, 100 GRPs carry over as 50, then 25 (page 37).
- Unit III: Calculate CLV: m = ₹3,000, r = 75%, i = 10% (infinite horizon). CLV = m ÷ (1 + i − r) = 3,000 ÷ 0.35 = ₹8,571.43 (formula on page 52).
- Unit IV: What is viral coefficient? When is a campaign called viral? K = invitations per user × conversion rate; growth sustains itself only when K is above 1 (page 65).
The MBA(A) 216 notes list 40 short-answer and 25 long-answer exam questions across 4 units, plus 25 worked illustrations, 38 figures and 3 case studies.
A longer practice set sits in the MBA(A) 216 important questions.
Who writes and checks the MBA(A) 216 Marketing notes?
- Prateek Talwar, author: Founder, Unnati Educations · ACS, ICSI (membership no. 58704, since 15 May 2019) · B.Com, University of Delhi (SOL), 2012 · bio
- Ritik Rawat, subject reviewer: Senior Full Stack Developer, Unnati Educations · B.Tech · checks the regression, k-means and CLV working, Excel steps and GA4 definitions · bio
These Marketing Analytics notes in English follow the 2022-24 onwards scheme and draw on Winston's Excel-based text from the suggested readings. The 2026-27 edition uses GA4 metrics, India's DPDP Act, 2023, the Bayesian MMM tools Meridian and Robyn, and Indian cases from Tatkal fares to UPI cashback referrals. Last updated 26 October 2026; read our editorial policy.
The same author and reviewer team also prepares GGSIPU important questions across courses.
MBA(A) 216 and the GGSIPU MBA (Analytics) Sem 4 papers it connects to
MBA(A) 216 has no paired lab; its Excel work is taught inside Unit I. All Sem 4 papers and electives are listed in the GGSIPU MBA (Analytics) syllabus.
The closest link is MBA(A) 208 Multivariate Data Analysis: its Units I and II teach exploratory analysis, multidimensional scaling and cluster analysis, which MBA(A) 216 applies to customers and brands. Unit IV's SEO, viral marketing and social KPIs overlap with Units III and IV of MBA(A) 220 Digital and Social Media, and Unit I's forecasting meets Unit II of MBA(A) 218 Supply Chain Analytics.
The other Sem 4 papers: MBA(A) 202 Major Project Report, MBA(A) 204 Project Management, MBA(A) 206 Ancient Management Philosophy and Indian Ethos (NUES), the HR electives MBA(A) 210, 212 and 214, and the Finance electives MBA(A) 222, 224 and 226.
MBA(A) 216 Marketing notes FAQs
How do I get the full MBA(A) 216 notes?
Tap "Get Full Notes" to message Unnati Educations on WhatsApp at 9899436384. The MBA(A) 216 notes are sold only as a 71-page printed book, not as a PDF. Delivery takes 5 to 7 days at most, depending on your location. The button fills in the paper code and a reference number, so your order matches the right book.
How are MBA(A) 216 marks split, and what is the passing mark?
For MBA(A) 216 the official MBA (Analytics) PDF prints only internal assessment, out of 25: Class Test-I carries 15 and an individual presentation, viva-voce or group discussion carries 10. End-term marks, the total and a pass percentage are not printed there. The scheme follows Ordinance 11, which sets end-term minimums and re-appear rules, so read those on ipu.ac.in.
Is MBA(A) 216 compulsory in GGSIPU MBA (Analytics) Sem 4?
No. MBA(A) 216 is one of three Marketing stream electives in Semester 4, with MBA(A) 218 Supply Chain Analytics and MBA(A) 220 Digital and Social Media. The scheme lists Elective III and Elective IV as 3-credit Skill Enhancement papers and asks students to "select any two from respective streams". Check which electives your institute runs before choosing.
Which MBA(A) 216 unit has the most numericals?
Unit III, with 8 worked illustrations in the MBA(A) 216 notes: revenue against profit pricing, pure bundling, skimming against a single price, Littlewood's rule for a 100-room hotel, a 20% discount, retention with NPS, and two CLV cases. Unit IV follows with 7, from web metrics and the Ad Rank auction to the K-factor and TF-IDF.
Do I need Excel, R or Python to study MBA(A) 216?
No software is needed to follow the MBA(A) 216 notes, because every numerical is worked by hand, step by step. Unit I does teach Excel itself: COUNTIFS, SUMIFS, XLOOKUP, PivotTables, the Analysis ToolPak and Solver, with menu steps. Later numericals also name the matching Excel function, so practising on a laptop helps, but it stays optional.
Are the MBA(A) 216 exam questions answered in the notes?
The 65 MBA(A) 216 exam questions are listed at the end of each unit without separate model answers; the sections of that unit answer them. For example, the Unit IV short question asking you to calculate conversion rate and AOV from web data follows the worked e-commerce month on page 57, where conversion is 2% and AOV is ₹2,500.
Get the full MBA(A) 216 Marketing Analytics Notes on WhatsApp
| Unit | Pages | Page count |
|---|---|---|
| I | 5-21 | 17 |
| II | 22-40 | 19 |
| III | 41-55 | 15 |
| IV | 56-70 | 15 |
Pages per unit in the MBA(A) 216 printed book
- Unit
- I
- Pages
- 5-21
- Page count
- 17
- Unit
- II
- Pages
- 22-40
- Page count
- 19
- Unit
- III
- Pages
- 41-55
- Page count
- 15
- Unit
- IV
- Pages
- 56-70
- Page count
- 15
71 pages in all, with 25 worked illustrations, 38 figures, 3 case studies, 6 exam-tip boxes and a quick-revision table per unit. Printed book only, not a PDF; delivery takes 5 to 7 days at most, depending on your location.
Order the printed MBA(A) 216 book on WhatsApp (4 units, 71 pages)
Unnati Educations is an independent education provider based in Vikaspuri, Delhi. We are not affiliated with, authorised by, or endorsed by Guru Gobind Singh Indraprastha University (GGSIPU). 'GGSIPU' and 'IPU' are used only to describe the syllabus our notes follow. For official information, visit ipu.ac.in.
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