BBA/B.Com Introduction to Analytics JUNE 2026

Description

Introduction to Analytics

Jun 2026 Examination

 

 

Q1. A regional retail chain has recently expanded into three new geographic markets. After several months of operations, the management team wants to understand differences and similarities in consumer purchasing patterns across these regions. Specifically, they want to identify: Product categories that are uniquely popular in each region; Categories that perform well in more than one region; Categories that are consistently successful across all regions. Currently, the company relies on basic tabular sales reports, which make it difficult to identify patterns, overlaps, and regional variations in demand. As part of the analytics team, how would you apply data analytics techniques to segment the markets and identify overlaps and differences in bestselling product categories across the three regions? What analytical methods would you use to compare regional performance, and how would your findings support strategic decisions related to inventory planning, regional marketing strategies, and product positioning? (10 Marks)

Ans 1.

Introduction

When a retail chain expands into multiple geographic markets simultaneously, the challenge shifts from operational expansion to strategic intelligence. Understanding whether the same products resonate equally across different regional consumer bases is essential for making smart decisions about inventory, marketing, and product positioning. Relying on basic tabular sales reports to compare three distinct regional markets is structurally inadequate because such reports display totals and rankings but conceal patterns, demand overlaps, and behavioural clusters that

Fully solved you can download

ASSIGNMENTS JUNE 2026

• Fully Solved, High Quality
• Lowest Price Guarantee: Just ₹199 per Assignment!
• 100% Original & Manually Solved (No AI/ChatGPT!)

Hurry! Last Date: 27 April 2026

• Order Now: com/online-buy-2/

Quick Response Guaranteed!

For Unique Assignment please contact on

• WhatsApp: 8791490301
• [email protected]
• aapkieducation.com

 

Q2 (A). A regional manager for Swift-Mart looks at the year-end report. Store 402 shows a perfectly stable average sales figure, while Store 405 shows wild fluctuations. Descriptive analytics (the ‘What’) suggests Store 402 is the model of consistency and deserves the highest bonus. However, a deeper dive into diagnostic and predictive analytics reveals a different story: Store 402’s ‘stability’ was actually due to chronic under-stocking; they hit a ceiling every month because they ran out of product, leaving thousands in potential revenue on the table. Store 405’s ‘volatility’ was actually the result of aggressive local marketing trials that successfully captured new market segments during holiday weekends. If descriptive analytics only tells us how the sales moved, how can the integration of diagnostic analytics (finding the root cause of the spikes) and prescriptive analytics (optimizing future stock levels) transform the retail chain’s strategy from simply ‘surviving the swings’ to ‘capitalizing on the chaos’? (5 Marks)

Ans 2(A).

Introduction

Descriptive analytics answers the question of what happened by summarising historical data into performance metrics, averages, and trend lines. It is the most widely used form of analytics in retail management. However, as the Swift-Mart case demonstrates, descriptive analytics can produce deeply misleading conclusions when surface-level patterns conceal structurally different underlying realities. Store 402’s apparent stability and Store 405’s apparent volatility look like performance opposites on a year-end report, but they are actually the opposite of what the report

Q2 (B). BrightMart Consumer Goods Ltd. is a mid-sized Indian FMCG company selling 400+ SKUs across 22 states through kirana stores, modern trade, and e-commerce platforms. Despite collecting data from its retail networks, supply chain systems, customer loyalty program, and social media channels, the company struggles to act on it. Last Diwali, stockouts of their bestselling coconut oil cost Rs.4 crore in lost revenue. A Rs.2 crore digital campaign for their new protein bar delivered a 0.4% conversion rate. The Supply Chain VP has no visibility into which distributors may default on payments next quarter. The CEO has asked the newly appointed Chief Data Officer to build an analytics-driven decision-making culture across the organization. Using descriptive, diagnostic, predictive, and prescriptive analytics, explain how BrightMart can transform its data into better business decisions. (5 Marks)

Ans 2(B).

Introduction

BrightMart collects data from four distinct operational sources: retail networks, supply chain systems, customer loyalty programmes, and social media. Yet three separate and costly failures, the Diwali coconut oil stockout worth Rs. 4 crore, the Rs. 2 crore protein bar campaign that delivered a 0.4 percent conversion rate, and the complete blindness to distributor default risk, all point to a single systemic problem. The company is data-rich but insight-poor. Building an analytics-driven culture means deploying all four types of analytics, descriptive, diagnostic, predictive