Cracking the Sales Data Analysis Assessment

Section 01

What this assessment actually tests

This is a classic FMCG sales analyst assessment. The recruiter is not just checking whether you know VLOOKUP — they want to see how you think about a business. The six files span two years of secondary sales, distributor (CP) billing data, outlet-level field operations, and territory scorecards.

Before you open a single file, take two minutes to read the question document carefully. The instructions call out specific columns by letter — that is not a suggestion, it is a clue. Assessors who designed this have a model answer in mind, and it references those exact columns.

Section 02

Understanding the six datasets

Most candidates fail this step — they open every file and start building formulas without pausing to understand what each file represents. Here is what is actually inside each one.

SIS (Secondary Information System) — Data_-_SIS.xlsx

This is your territory performance scorecard. The GO sheet contains a region → RGM HQ → AGM HQ → GO HQ hierarchy with columns for secondary sales targets, actual sales, % target achievement, last year actuals, and % growth. Focus SKU data appears in the right-hand columns. This file is your primary lens for identifying underperforming territories.

Sales DSR — Sales_DSR.xls

DSR stands for Daily Sales Report — aggregated monthly over two years in this context. Each row represents a brand-month combination with sales volume in cases. You will derive month-over-month trends and year-over-year comparisons from this file.

SAP Data Dump — SAP_data.xlsx

This is the distributor (Channel Partner) billing file exported from SAP. Each row is one invoice line. The columns the assessment asks about are H (Month), B (Buyer/CP code), and K (Invoice Qty), along with J (Unique SKU description) for the SKU-level question.

Column	Field Name	Used For
Col B	BUYER	CP code — numeric ID of each distributor
Col H	Month	Month label for pivot grouping (Nov, Oct, etc.)
Col J	Uniq Desp / SKU	Unique product description (Frooti Tetra 160ml, etc.)
Col K	Inv Qty	Invoice quantity in cases — volume metric

SFA Stock Report

Covers one field week (Sep 18–25, 2014). Each row is an outlet-SKU-visit combination. The three columns flagged in the question are G (Outlet ID), AA (Available Stock Qty in Cases), and AH (Stock Amount in ₹).

SFA Order Report

Same structure, same field team, same week — but records orders placed rather than stock on hand. Brands include Frooti (multiple variants), Appy Fizz, Appy CL, and Café Cuba. Use this to cross-reference whether low-stock outlets actually placed replenishment orders.

Section 03

Solving the Sales DSR analysis

The DSR task has three deliverables: % growth, % contribution, and monthly trend — all at brand level. Work through these in order.

1. Create a Brand × Month pivot table

   Insert → PivotTable on the DSR sheet. Drag Brand to Rows, Month to Columns, Sales Volume (Cases) to Values (Sum). This becomes your base matrix.

2. Build a Year 1 vs Year 2 comparison layout

   Create two separate pivot tables — one per year — and place them side-by-side so Year 1 and Year 2 figures for each brand-month sit in adjacent columns.

3. Calculate % Growth (YoY)

   % YoY Growth

   = (Year2_Sales - Year1_Sales) / Year1_Sales

   Format as Percentage. Apply conditional formatting: negative values in red, positive in green.

4. Calculate % Contribution by Brand

   % Contribution

   = Brand_Monthly_Sales / $C$18   ← lock the Grand Total denominator with $

   Each brand’s monthly sales divided by all-brand monthly total. Lock the denominator so it does not shift when you copy down.

5. Insert a line or combo chart for monthly trend

   Select the month-wise brand totals → Insert → Line Chart. Add a secondary axis if brands have very different volume scales. Include year range in the chart title.

Section 04

SAP data: counting unique CPs billed

This is fundamentally a deduplication problem. You have tens of thousands of invoice rows, but the question asks: in each month, how many distinct distributors were actually billed?

Method 1 — PivotTable with Distinct Count (recommended)

Insert PivotTable → check “Add this data to the Data Model” → place Month (Col H) in Rows and Buyer/CP Code (Col B) in Values → change aggregation to Distinct Count. One step, no formula needed.

Method 2 — SUMPRODUCT formula (any Excel version)

Unique CPs in a given month (e.g. “Nov”)

=SUMPRODUCT((H2:H10000="Nov") / COUNTIFS(H2:H10000, H2:H10000, B2:B10000, B2:B10000))

This divides 1 by the count of each CP-month combination then sums — effectively counting each unique CP once per month.

Unique CPs billed — with SKU level (Col J)

For the second question you need unique Month + CP + SKU combinations. Add the SKU field (Col J) as a third criterion:

Unique CP-SKU combinations in a month

=SUMPRODUCT(
  (H2:H10000="Nov") *
  1/COUNTIFS(
    H2:H10000, H2:H10000,
    B2:B10000, B2:B10000,
    J2:J10000, J2:J10000
  )
)

Section 05

SIS business review — identifying concern areas

The SIS file has a GO-level performance matrix. Your job is to populate the summary sheet and surface the red flags. Assessors expect 3–5 structured insights, not just a filled-in table.

Key metrics to compute per territory (GO HQ)

1. % Target Achievement

   Verify the column: = Actual_Sales / Target. Flag below 75% as red, 75–90% as amber, above 100% as green. Use a 3-color conditional formatting scale.

2. % Growth vs Last Year

   Territories with negative growth despite a reasonable target have declined both in market and performance — these need the strongest flag in your commentary.

3. Focus SKU performance

   Compare Focus SKU % TGT ACH against overall % TGT ACH. A territory can hit 80% overall but only 20% on Focus SKUs — serious because Focus SKUs are the margin-drivers.

4. 125% CAP analysis

   Territories capped at 1.25x may have had unrealistically low targets. If they grew 40% YoY but were capped, the target-setting process needs review — a different kind of concern.

Section 06

SFA outlet stock & order analysis

The SFA files cover one field week. The assessment asks for an outlet-wise × SKU-wise view showing visit frequency and whether stock quantity is rising or stagnant.

Column reference — Stock Report

Column	Field	What to do with it
Col G	Outlet ID	Primary grouping key in your pivot (Rows)
Col AA	Available Stock Qty (Cases)	Sum per outlet-SKU; track day-wise to see if restocking occurred
Col AH	Stock Amount (₹)	Value of stock held — prioritise high-value outlets with zero stock

Building the outlet × SKU matrix

PivotTable → Outlet Name in Rows, Uniq SKU Desc (last column) in Columns, Available Stock Qty in Values. This creates a matrix view of which outlet holds which SKU at what quantity.

Counting billing frequency per outlet

Add a Count of Outlet ID to your pivot — this tells you visit frequency per outlet over the week. An outlet visited 5 times in 7 days with consistently zero stock is a critical availability failure worth calling out explicitly.

COUNTIF helper column

=COUNTIF($G$2:$G$50000, G2)   ← paste in a helper column to show each outlet's visit count

Section 07

Formula & technique reference

VLOOKUP — linking SAP CP codes to names

=VLOOKUP(B2, SAP_data!$B:$E, 4, FALSE)

B2 = CP code in summary sheet. Column 4 of the range returns Buyer Name. Always use FALSE for ID-based exact lookups.

SUMIF and SUMIFS

=SUMIF(H:H, "Nov", K:K)
=SUMIFS(K:K, H:H, "Nov", B:B, 645)   ← two conditions: month AND specific CP

Array formula — unique count (Ctrl + Shift + Enter)

{=SUM(1/COUNTIF(B2:B10000, B2:B10000))}

Enter with Ctrl+Shift+Enter. Curly braces appear automatically — do not type them manually.

Quick formula reference

Conditional formatting heat map

Select the % TGT ACH column → Home → Conditional Formatting → New Rule → “Format all cells based on their values” → 3-Color Scale → Red at 0%, Yellow at 75%, Green at 100%. The whole column becomes instantly scannable.

Macros (VBA)

Record a macro that refreshes all pivot tables at once and formats the summary sheet. Name it clearly (e.g. Sub RefreshAndFormat()). Add a comment at the top of every Sub explaining what it does — assessors who open macro-enabled workbooks appreciate readable code.

Section 08

Structuring your final output

The question explicitly asks for a presentation (PPT). In a sales analytics context that means: one chart or table per slide, three-second-glance readability, and one sentence of insight beneath each visual.

Recommended slide flow

1. Executive Summary (1 slide)

   3–4 bullets maximum. Top brand by growth, top concern territory, one CP billing insight, one SFA finding. Write this slide last.

2. Brand Sales Trend (2 slides)

   Slide 1: Line chart — monthly trend for top 4 brands, two-year overlay. Slide 2: Bar chart — YoY % growth by brand sorted descending, declining brands in red.

3. CP Billing Health (1–2 slides)

   Month-wise unique CP count as a bar chart with a MoM change line overlay. Table below shows top 5 and bottom 5 months.

4. Territory Concern Matrix (1 slide)

   Table with traffic-light conditional formatting on % TGT ACH, sorted by worst performance. Assessors expect Bhubaneswar and similar lagging territories to be prominently flagged.

5. Outlet Stock & Availability (1 slide)

   Top 10 outlets by visit count with average stock level. Highlight high-visit-frequency / near-zero-stock outlets as systemic distribution failures.

6. Recommendations (1 slide)

   Three to five actionable points tied directly to the data. “Prioritise CP activation in months where billings dropped below X” is actionable. “Improve sales” is not.