Overview

Many retailers rely on point-of-sale data to understand performance, yet this shows only half the picture. Without knowing how many people entered the store, it’s impossible to determine true conversion rates or identify where potential revenue is lost.

A mid-sized home décor retailer operating three stores in Oregon wanted to compare in-store visitor numbers from CountR sensors with POS transaction data to understand how effectively store visits turned into purchases, and how the average basket value compared to customer potential.

By combining CountR’s precise footfall analytics with cash register (POS) data, the retailer could finally connect store traffic, sales, and spending behavior into one clear performance picture.

Their Story

The home décor chain averaged around 6 000 visitors per month per store, according to CountR’s system. However, sales data showed only 1 200 monthly transactions, meaning the store-level conversion rate was roughly 20 percent. Management had assumed this rate was closer to 30–35 percent, revealing that nearly one in five potential buyers left without purchasing.

Using CountR’s data integration, they began tracking daily visitor-to-sale ratios, identifying time periods with strong traffic but weak conversion. The insights showed that weekends had 40 percent more visitors, but conversion rates dropped to 15 percent, suggesting staff shortages during peak hours.

When managers adjusted scheduling and added one more cashier on Saturdays, conversion rose to 22 percent, generating an additional $8 000 in weekly revenue.

The team also analyzed average basket values. POS data revealed an average purchase of $84.50, but CountR’s footfall data showed the average potential visitor value (total daily sales divided by total daily visitors) was only $17.20. This gap helped the retailer visualize the untapped spending potential in their stores.

Further breakdowns uncovered key insights:

  • Morning visitors had an average conversion of 26 percent, basket value $92.

  • Afternoon visitors converted at 18 percent, basket $78.

  • Evening visitors had lower conversion (15 percent) but higher basket value ($105).

 

With these findings, marketing and store management teams jointly optimized operations: promotional stands moved to high-traffic morning areas, staff training focused on mid-day conversions, and late-evening sales were paired with bundled offers to lift total spend per transaction.

Results & Conclusion

Within three months, the retailer achieved a 6 percent overall increase in conversion rate (from 20 to 26 percent) and a 9 percent rise in average basket value (from $84.50 to $92). Combined, this delivered an estimated $22 000 additional monthly revenue across the three stores.

By linking visitor-count data with POS system insights, the retailer gained a full understanding of store efficiency, not just transaction totals. CountR’s analytics transformed separate data sources into one clear measurement of true conversion performance, enabling smarter staffing, better merchandising, and stronger profitability.