Real-World Data Analytics Case Study 10: Sales Performance Analysis Across Regions and Teams (Updated May 2026) (Updated May 2026)

TCS cut approximately 12,000 jobs in the first half of 2025. The common thread in those exits wasn't seniority — it was lack of demonstrable analytical value. The analysts who stayed were the ones who could walk into a room with a messy sales dataset and walk out with three clear business recommendations in 45 minutes. Case Study 10 in the ABC Trainings data analytics programme is exactly that scenario. The company in this case study wants to understand four things: how much it's selling overall, which region is performing best, which products are underperforming, and whether the sales team's effort is concentrated in the right accounts. These sound simple, but turning raw transaction data into honest answers requires understanding data quality, aggregation logic, comparative benchmarking and visual communication — in that order. The video linked above walks through the full analysis live; this article provides the analytical framework, the key decision points, and the insight-writing skills that turn a correct chart into a persuasive business finding.

The Case Study 10 Business Problem: What Management Wants to Know

Management's four questions in Case Study 10 are deceptively simple. How much are we selling? This requires a clean total revenue figure — but raw data often has duplicate orders, cancelled returns not netted out, and inter-company transfers counted as revenue. Which region is performing best? This requires not just revenue rank, but revenue vs target comparison — a region that hits ₹50 crore on a ₹40 crore target is performing differently from one that hits ₹80 crore on a ₹100 crore target. Which products are underperforming? This requires margin analysis, not just revenue — a high-revenue product with negative contribution margin is a strategic problem. Is the sales team's effort well-directed? This requires revenue-per-salesperson and account concentration analysis — if 80% of revenue comes from 20% of accounts and only two salespeople cover those accounts, the team has a coverage risk. Frame all four questions explicitly before you open the dataset. Every data pull, calculation and chart should trace back to one of these four questions.

Step 1 — Data Quality Check: What to Look for Before Any Analysis

Before any calculation, run a data quality pass. In Excel or Power Query, check: row count (does it match expected transaction volume?); duplicate order IDs (use Remove Duplicates or COUNTIF to flag); null values in Region, Product, Salesperson and OrderValue columns (how many? are they random or systematic?); date range (are there orders outside the analysis period?); negative values in OrderValue (legitimate returns or data entry errors?). In Case Study 10, you'll find that some transactions are missing region codes and some product names have inconsistent capitalisation. Fix these systematically in Power Query using merge joins and Text.Proper transformations — never by manually editing source cells. Document every transformation step so your analysis is auditable. This data-quality habit is what separates junior analysts from senior ones in interviews at Infosys Analytics, TCS iON and Mahindra Finance.

Analysis Dimension	Key Metric	Visual Type	Business Question Answered
Overall Revenue	Total Revenue vs Target	KPI Card + Trend Line	How much are we selling?
Regional Performance	Revenue Attainment %	Ranked Bar Chart	Which region performs best?
Product Performance	Revenue vs Gross Margin %	Scatter Plot + Matrix	Which products underperform?
Salesperson Effectiveness	Attainment % + Top Accounts	Bar + Tooltip Page	Is team effort well-directed?
Trend Analysis	Monthly Revenue MoM %	Line Chart	Is performance improving?

Step 2 — Building the Analysis: Revenue, Region, Product, Team

Revenue analysis: SUM by month to get the trend line; SUM by region and compare to regional targets; SUM by product category for the product view; DIVIDE(actual, target) for attainment % everywhere. Use pivot tables in Excel or DAX measures in Power BI — both should give identical numbers; if they don't, you have a data model error. Regional analysis: sort regions by Revenue Attainment % descending. The top region by absolute revenue is not necessarily the best performing — attainment % is the honest measure. For a visual, use a bar chart with an overlay line showing target; the gap is immediately visible. Product analysis: scatter plot Revenue vs Gross Margin % per product; the bottom-right quadrant (high revenue, low margin) is the danger zone. For salesperson analysis, a ranked bar chart sorted by Revenue Attainment % shows who's driving performance. Add a tooltip showing each salesperson's top 5 accounts by value when the user hovers over a bar.

Step 3 — Identifying Patterns and Anomalies in the Data

Patterns to look for beyond the basic charts: Seasonality — does revenue spike in Q3 every year? If yes, is the team staffed appropriately for that peak? Account concentration — if the top 10 accounts represent more than 60% of revenue, the business has a retention risk. Product cannibalisation — if a new product launch coincides with revenue decline in an existing SKU, is that planned substitution or unexpected cannibalisation? Salesperson territory overlap — if two salespeople are booking sales in the same region from the same accounts, there's a coverage model problem. These second-order insights are what management consultants at Deloitte, McKinsey and KPMG India build their slide decks on — and they're accessible from any standard sales dataset if you know what questions to ask. At the junior analyst level, demonstrating awareness of these patterns is enough to differentiate yourself from candidates who only know how to produce a bar chart.

Step 4 — Writing Business Insights, Not Just Presenting Charts

A chart without a sentence is incomplete analysis. For every major visual in Case Study 10, write one finding sentence using this structure: what is observed, in which subset, compared to what benchmark, and what is the business implication. Example: "The South region achieved 118% of its ₹42 crore quarterly target, the strongest attainment in the portfolio, driven primarily by three enterprise accounts — territory capacity should be reviewed before adding headcount." That one sentence is more valuable to a business head than a beautifully formatted chart with no label. In your portfolio, include the insight sentences as text boxes adjacent to the visuals. Recruiters at Bajaj Finserv, HDFC Bank analytics, Mahindra Finance and mid-sized analytics firms consistently respond to this format because it demonstrates the candidate understands the business purpose of the analysis — not just the tool.

Maharashtra's CMYKPY scheme provides ₹6,000–₹10,000/month to eligible students during certified data analytics training. ABC Trainings is an approved NSDC and NASSCOM training partner. Call +91 7039169629 or WhatsApp 7774002496 to check eligibility before your batch starts.

FAQs

What is Case Study 10 in the ABC Trainings data analytics programme?

Case Study 10 is a guided real-world analysis of a multi-region sales performance dataset. You clean the data, build revenue, regional, product and salesperson analyses using Excel and Power BI, identify patterns and anomalies, and write business insight statements for each major finding. The objective mirrors what a junior data analyst would be asked to do on day one at a finance or FMCG company.

What is the difference between a data analytics capstone project and a case study?

A capstone project focuses on building a dashboard or deliverable from scratch — the emphasis is on construction and tool proficiency. A case study focuses on analytical reasoning — reading data, identifying business patterns, formulating insights and communicating findings. Both are in the ABC Trainings programme because employers test both: tool proficiency in a practical test and analytical reasoning in an interview.

What jobs does a data analytics portfolio open in Pune and Maharashtra?

A completed portfolio of 10–12 data analytics case studies and capstone projects opens junior data analyst roles (₹3–4.5 LPA), MIS executive positions (₹2.8–3.5 LPA), and business intelligence analyst roles at Infosys, TCS, Wipro, Mahindra Finance, Bajaj Finserv and mid-sized analytics firms. With 2–3 years of experience, senior analyst roles at ₹6–10 LPA come into range.

How long is the ABC Trainings data analytics programme and what does it cover?

The ABC Trainings data analytics programme runs for 3–4 months and covers Excel, SQL, Power BI, Python basics and 12+ real-world case studies and capstone projects. New batches start every month at Pune Wagholi, Hadapsar, Cidco Sambhajinagar and Osmanpura. Call +91 7039169629 for the next start date.