Global data from Vend shows low sales volumes, but strong spending figures as shoppers continue to support independent retail sector
TORONTO, June 20, 2018 /CNW/ - Sales volumes for North America's independent retailers in the past 12 months were 11% lower than the global average, according to data from retail management software leader Vend. This insight comes from Vend's first Retail Benchmarks Report launched today, which compares data from over 13,000 retailers with a particular focus on North America, the United Kingdom, Australia and New Zealand.
Average monthly sales volumes - the number of transactions processed by each store - in North America were 31% less than sales volumes in the UK, and 16% lower than in Australia.
However, retail spending was strong compared to global markets. Vend's data found that North American retailers' average monthly revenue was on par with the global average, and 15% higher than in the UK. The average transaction value in North America - the amount spent in a retail store per transaction - was 4% higher than the global average.
"It's not an easy market for independent stores right now," says Butch Langlois, Vend Country Manager for North America. "However, it is encouraging to see healthy revenue and spending figures compared to other key markets. This shows that consumers are still clearly choosing to shop with independent stores - its simply value over volume - which is a testament to the unique products and experiences these retailers provide."
Interestingly, beer, wine, and spirits stores experiencing the highest revenue of North American retailers by far, followed by furniture stores, and jewellery and luggage stores. Cosmetics and beauty stores, and office supplies, stationery and gift stores had the lowest average revenue.
"Our data also shows that our local retailers are generally doing a good job of their customer loyalty programs compared to other countries. Some 56% of North American retailers have loyalty enabled in their POS system, and they have 23% more customers stored in their database than the global average - well above British and Australian stores. Knowing who your customers are and being able to use the information you have to give them the best in-store experience possible can make a big difference to sales and repeat business," says Langlois.
Chris Guillot, founder of small business retail consultancy Merchant Method has seen similar trends: "Many of the report findings are consistent with my recent experience. I'm surprised by the higher rate of collected customer information, though. This may be correlated to the national "shop local" movement in the US. When considered with lower margins in the US, this elevated customer capture rate may also indicate price sensitivity — customers being willing to exchange their personal information for discounts."
This data has been analysed by Vend as part of its first-ever Retail Benchmarks Report - a global report looking at the state of retail across key markets and sectors. You can read the full report here.
Vend is cloud-based point-of-sale and retail management software that lets retailers run their business in-store, online, and on-the-go. Vend's software includes inventory management, Ecommerce, customer loyalty, and reporting analytics. Vend integrates with other world-leading business and payments applications including Shopify, Square, Xero and PayPal, and is a key retail partner in Apple's global Mobility Partner Program. Vend is trusted by retailers in over 140 countries and is used in more than 20,000 stores worldwide. Founded in 2010, Vend has offices in Auckland, San Francisco, Toronto, London and Melbourne, and has raised more than US$45 million from top-tier investors. For more information, please visit: http://www.vendhq.com.
About the data
The data and findings in the Retail Benchmarks Report are based on actual anonymised information from a sample of over 13,000 retail stores using Vend. The vast majority of the retailers in this data set are independent retailers with 1-10 stores. All data is in gross-mean, to find the most accurate average figure for each data-point.