Introduction
Retail small business owners face tough decisions every day. One common challenge is determining the most profitable business hours. Should a store remain open late even when foot traffic is low? Or would adjusting hours save money without sacrificing sales?
Traditionally, these decisions were made based on intuition or basic spreadsheet data. However, with Fusion Analytics, small businesses can now rely on real-time insights to make smarter, data-backed choices.
This article explores how a small retailer used Fusion Analytics, an analytics tool from Top Line Decisions, to optimize store hours, increase sales, and make better business decisions.
The Challenge: Are Late Hours Worth It?
A small retail business noticed slow sales during evening hours but wasn’t sure if it made sense to adjust store hours. The owner needed clear, data-driven insights to determine whether keeping the store open late was financially worthwhile or if adjustments could improve profitability.
Key questions included:
- Do evening sales justify labor and operational costs?
- Which days and times generate the highest and lowest revenue?
- Are there opportunities to drive more traffic during slow periods?
Rather than relying on gut instinct, the retailer turned to Fusion Analytics for answers.
The Solution: Sales Analysis with Fusion Analytics
The retailer uploaded six months of sales data into Fusion Analytics, and created a dynamic, interactive dashboard displaying:
- Sales trends over time – A clear timeline of daily sales fluctuations.
- Total sales, average ticket size, and number of transactions – Broken down by month, day of the week, and hour of the day.
- Interactive filters – The ability to drill down into specific time frames and uncover deeper trends.
The figure below shows the dashboard that was created.The topmost plot (called a widget) is a timeline that plots daily sales. Nine barchart widgets are arranged in a 3×3 grid. The rows show different statistics: the total sales, the average sale, and the number of sales. The columns aggregate the statistics over month, day of week, and hour of the day. For example, the widget in the first row and first column shows the total sales for each month and the widget in the middle shows the average ticket by day of the week.
Fusion Analytics Dashboard Displaying Sales Patterns
Instead of manually analyzing spreadsheets, the business owner can select data points on one or more widgets to see how different time periods compare.
Key Insights: What the Data Revealed
1. Evening Sales Varied by Day of the Week
The dashboard confirmed that late-evening sales were strongest on Fridays and Saturdays and significantly lower on Mondays and Tuesdays.
Total Sales by Hour of Day for Fridays
Total Sales by Hour of Day for Saturdays
Total Sales by Hour of Day for Mondays
Total Sales by Hour of Day for Tuesdays
2. Seasonal Differences Affected Traffic Patterns
Sales dropped sharply after 6 PM in winter months (January and February) but declined more gradually in May when warmer weather encouraged later shopping.
Total Sales by Hour of Day for January
Total Sales by Hour of Day for February
Total Sales by Hour of Day for May
3. Foot Traffic, Not Purchase Size, Drove Sales Volume
The average sale amount remained fairly consistent across all time periods. This meant that variations in total sales were entirely due to changes in customer traffic, not customers spending more or less.
These insights allowed the retailer to test different strategies to maximize sales while keeping costs in check.
The Business Impact: Turning Insights into Action
After reviewing the data, the retailer implemented two key strategies:
1. Maintain Current Store Hours
Rather than reducing hours, the retailer confirmed that the return on sales and customer satisfaction outweighed the cost of staying open late.
2. Launch Targeted Promotions to Boost Slow Periods
To drive more traffic on slow nights, the store introduced special promotions on Monday and Tuesday evenings, particularly during winter months when foot traffic was naturally lower.
These strategic adjustments helped increase sales without unnecessary cost-cutting, ensuring a stronger bottom line.
The Power of Real-Time Analytics for Small Businesses
Fusion Analytics provided the retailer with more than just reports—it delivered real, actionable insights that led to data-driven decisions and measurable results.
Why Retail Small Businesses Need Analytics
- Optimize profit by adjusting store hours – Ensure you’re open when customers are most likely to buy.
- Identify peak sales times – Know when to schedule promotions or adjust staffing.
Uncover hidden growth opportunities – Spot trends that might otherwise go unnoticed. - React in real-time – Make informed decisions without waiting for manual reports.
Conclusion: Smarter Decisions, Better Business Outcomes
By leveraging analytics, the retailer transformed guesswork into strategy and ensured their business remained competitive.
With Fusion Analytics from Top Line Decisions, small businesses can harness the power of real-time data to make faster, smarter decisions—whether it’s optimizing business hours, increasing sales, or fine-tuning operations.Are you ready to take control of your business data? Contact Top Line Decisions today to see how Fusion Analytics can help your business grow.