Product Sales Forecasting Through Time Series Analysis (Power BI Visualizations)

2026-02-12 · Source: Towards AI - Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Intermediate, medium

Summary

This article, part one of a series on product sales forecasting, details the initial data exploration and insights derived from an interactive Power BI dashboard. The analysis utilizes a dataset containing store_id, region_code, store_type, location_type, total sales, holiday status, discounts offered, quantity sold, and transactional ID. Key findings include noticeable sales declines in March, November, and February, indicating seasonality. Region R1, store type S1, and location type L1 consistently generate the highest sales. Within Region R1, store type S4 and location type L2 are top contributors. QTD, MTD, MoM, and YoY metrics reveal January, May, July, and December as high-performing months. Holidays show no impact on sales, while discounted days consistently lead to higher sales across regions. Store type S1 records the highest average sales despite moderate orders, contrasting with S4 stores which have the highest order volume but lower sales revenue.

Key takeaway

For retail strategists and business analysts focused on optimizing sales and inventory, these insights highlight the critical role of discount campaigns over holidays in driving sales. You should prioritize investment and expansion in high-performing locations like L1 and L2, while investigating underperforming S2 and S3 store types. Focus forecasting models on specific store type and location combinations, giving more weight to discount days and peak months (January, May, July, December) to improve accuracy and resource allocation.

Key insights

Interactive Power BI dashboards reveal critical sales patterns, regional performance, and the impact of discounts versus holidays.

Principles

• Sales seasonality impacts demand.
• Discounts drive sales more than holidays.
• Average order value varies by store type.

Method

The analysis uses Power BI for interactive visualizations, exploring overall sales, region-wise performance, holiday/discount impact, and store-type specific trends to derive initial insights for product sales forecasting.

In practice

• Identify peak sales months for inventory planning.
• Prioritize investment in high-performing store types/locations.
• Analyze average order value differences by store type.

Topics

• Product Sales Forecasting
• Time Series Analysis
• Power BI Dashboards
• Retail Analytics
• Sales Performance Analysis

Code references

• deeptinikumbh/Product-Sales-Forecasting-

Best for: Data Scientist, Data Analyst, Business Analyst

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Editorial summary, takeaway, and curation by AIssential. Original article published by Towards AI - Medium.