To boost product visibility and sales with optimized prices, retailers and brands can use retail data analytics to access real-time insights into the global eCommerce landscape. Let’s look at how Michigan-based furniture manufacturer Herman Miller can use retail data analytics to gain a competitive advantage in the global retail market.
This case study focuses on a specific product: the Herman Miller Mirra 2 chair. Retail data analytics can help Herman Miller pinpoint which retailers around the world offer products that exactly match this chair model, how their prices compare and what Herman Miller can do to remain responsive and competitive worldwide.
Retailers around the world selling exact matches of the Herman Miller Mirra 2 chair include Amazon.com (US), Amazon.co.uk (UK), Flipkart (India), Badbacks (Australia), Rakuten (Japan) and Taobao.com (China). These findings help Herman Miller know its global competitors – for this particular product – with greater certainty.
The scope of competitors expands further when Herman Miller considers other furniture retailers selling similar matches (as opposed to exact matches) to the Herman Miller Mirra 2 chair, including Steelcase and Haworth, which are both US companies.
Data analytics help Herman Miller understand how the Mirra 2 chair compares to these competing products in terms of their attributes. The similar matches and the Herman Miller Mirra 2 have major differences in chair weight and height, and negligible differences in seat width and depth. Thanks to these findings, they could emphasize that the Mirra 2 chair is almost 11 pounds lighter and 2 inches taller than the Steelcase similar item, and a whopping 20 pounds lighter and 2.5 inches shorter than the Haworth chair.
Knowing these global competitors with exact and similar matches can help the company see how its pricing strategy compares to rivals’. They can view product price fluctuations in local currencies, as well as a single currency (USD) for easy comparisons for the Herman Miller Mirra 2 chair.
In addition, Herman Miller can see that an increase in price over time led to a corresponding increase in product visibility on Amazon.com in the US market. This suggests Amazon may favor higher priced products and boost their online visibility, which impacts retailers’ and brands’ online pricing strategies.
By contrast, as Japanese retailer Rakuten’s price for the Herman Miller Mirra 2 chair generally declined over time, the product’s visibility increased on the eCommerce website. This suggests Rakuten may favor affordability, driving the chair’s popularity and online product visibility, which impacts retailers’ and brands’ online pricing strategy in Japan.
Another important online pricing consideration is discounting. A lack of international standards for retail reporting among international retailers’ offerings means Herman Miller would not have a clear picture of the amount of discount offered on the different eCommerce websites. To solve this challenge, Herman Miller can gain access to pricing tools to adapt competitors’ pricing movements. Setting custom pricing rules and generating smart prices – such as always selling for 10% less than Amazon – can help Herman Miller adjust its pricing in real time to remain competitive globally.
Bottom line: Data fuels market dominance
Overall, retail data analytics have evolved into essential resources to compete in global eCommerce. Data insights can help Herman Miller gain a deeper understanding of its competitors and retail pricing strategies around the world. These insights can help them (and other retailers and brands) reduce risk by adjusting pricing in real time to gain a competitive edge, boost agility and remain attractive to online shoppers.
