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Roshni Consumer Brands (Pvt) Ltd · AI Capability Prompts — Sales Data 2025
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Use these prompts on the 'Sales Data' sheet with Claude in Excel, Microsoft Copilot, or any advanced AI tool. Each prompt is designed to showcase a distinct AI capability beyond basic analysis.
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#CapabilityPrompt (copy & paste into your AI tool)
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1📊 Executive
Dashboard Builder		You are a senior business intelligence analyst. Using the sales data in this spreadsheet, build me a fully dynamic executive dashboard on a new sheet. The dashboard must include: (1) A summary scorecard at the top showing Total Revenue 2025, Total Units Sold, Overall GP Margin %, and Achievement vs Target % — all as live formula-linked KPI cards with colour-coded RAG status (green ≥100%, amber 85–99%, red <85%). (2) A monthly revenue trend chart with a 12-month trendline. (3) A stacked bar chart showing revenue by Category for each month. (4) A pivot-style table showing City × Category revenue with conditional formatting (heat map — darker = higher revenue). (5) A channel mix doughnut chart. (6) A Top 5 and Bottom 5 Products by Revenue table. All charts and tables must auto-update if the underlying data changes. Label everything clearly for a C-suite audience. Use navy and gold as the colour scheme.
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2🔍 Sales Intelligence
& Insight Generation		Act as a Chief Commercial Officer reviewing full-year 2025 performance. Analyse the sales data across all dimensions — category, region, city, channel, product, and sales rep — and produce a structured intelligence report covering: (1) The 3 highest-growth and 3 declining categories or cities (by revenue), with a hypothesis for why. (2) Seasonal demand patterns for each category — which months spike, which dip, and by how much vs the annual average. (3) Channel mix analysis: which channels are growing their share and which are losing it quarter by quarter. (4) Sales rep performance: rank all reps by total revenue, achievement %, and GP margin — flag anyone consistently below 85% achievement. (5) At least 3 data anomalies or outliers worth investigating. Present your output in a clear structured format with an Executive Summary, followed by each section. Use specific numbers and percentages from the data to support every finding. Do not use vague language.
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3📈 2026 Sales Forecast
& Scenario Planning		Using the 12-month 2025 actuals in this dataset as your baseline, build a 2026 monthly sales forecast with the following structure: (1) Apply a trend-based growth rate per category (calculate from 2025 data). (2) Incorporate seasonality adjustments based on monthly demand patterns observed in 2025 (e.g. Ramadan peak in Mar-Apr, summer beverage surge in Jun-Jul, year-end uplift in Dec). (3) Produce three scenarios on a new sheet: BASE CASE (trend + seasonality), BULL CASE (add 15% driven by new city expansion and e-commerce acceleration), and BEAR CASE (deduct 12% driven by competitive pressure in Beverages and rising input costs in Home Care). (4) For each scenario, show monthly Revenue, Units, GP, and GP Margin % for each Category. (5) Summarise at the bottom: Full-Year 2026 Revenue by scenario, variance vs 2025 actuals in PKR and %. Use Excel formulas throughout — no hardcoded values. Add a toggle cell so the user can switch between scenarios.
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4⚙️ Operational Scenario
& What-If Analysis		I need to stress-test our 2025 business performance and model the impact of key operational decisions. Using the sales data, answer the following what-if questions with quantified impact: (1) PRICING: If we increase Unit Price by 8% across Personal Care and Home Care from July onwards, what would full-year 2025 revenue and GP have been? Assume a 5% volume elasticity decline per 10% price increase. (2) DISTRIBUTION: If we had activated E-Commerce as a channel in Peshawar and Quetta from January (matching the E-Commerce revenue rate per city seen in Karachi), how much incremental revenue would that have generated? (3) PORTFOLIO: If we had discontinued the bottom 3 SKUs by GP Margin % and redeployed that sales effort to the top 3 SKUs (assume 20% uplift on top SKU volumes), what is the GP impact? For each scenario, show a Before vs After comparison table with Revenue, GP, and GP Margin %. Add a sensitivity table for Scenario 1 showing the revenue outcome at price increases of 5%, 8%, 10%, and 12%.
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5🏆 Territory & Sales Rep
Optimisation Engine		Act as a Sales Excellence consultant. Using the sales data, conduct a full territory and sales force effectiveness review: (1) TERRITORY ANALYSIS: Calculate revenue per city as a % of national total. Identify cities that are under-indexed vs population size (Karachi ~16m, Lahore ~13m, Faisalabad ~4m, Rawalpindi/Islamabad ~5m combined, Multan ~2m, Peshawar ~2m, Hyderabad ~1.8m, Quetta ~1.2m). Flag where revenue share is materially below population share and estimate the revenue opportunity gap. (2) REP BENCHMARKING: For each sales rep, calculate Total Revenue, Total GP, Achievement % (vs Target), and Revenue per Transaction. Rank them and identify the top quartile vs bottom quartile performers. (3) CHANNEL-CITY MATRIX: Build a matrix showing which channel generates the most revenue in each city. Identify 3 channel-city combinations that are underdeveloped relative to the top performers. (4) RECOMMENDATIONS: Based on your analysis, propose: a rebalanced territory allocation, a revised target-setting methodology, and 3 specific actions to close the performance gap in the bottom quartile cities. Output everything in structured tables with a clear summary of the top 5 actions for the Sales Director.
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Data: Roshni Consumer Brands (Pvt) Ltd — Sales Jan–Dec 2025 | 540 transactions | 5 Categories | 9 Cities | 5 Channels | isacakarachi.org → AI workshop dataset
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