Many online stores today use geolocation to improve on a wide gamut of functionality. This can range from localized payment gateways, to calculating shipping costs on the fly, all the way to fully translating location-based subsets of the ecommerce experience into local languages. There are a lot of reasons why online retailers focus on geolocation but on a high-level, the aim is usually to improve both customer experience and alleviate purchase friction by exposing familiar services. By leveraging geographically split crowd-logic, we can further build on those efforts and improve the “local” customer experience to match the rest of the look and feel of the website.
There are multiple examples of established online stores trying to open up new markets with a tried and true concept, only to fail miserably because they did not understand the market and failed to adapt to customer expectations. Ebay tried to conquer the Chinese market in 2003 and Amazon struggled to do the same in 2004. To this day, neither companies have a very established presence in China and yet their concepts have worked very well for them elsewhere across most of the world. As a manifestation of this, Jack Ma’s (co-founder of the Alibaba Group) famous declaration rings true:
“eBay may be a shark in the ocean, but I am a crocodile in the Yangtze River. If we fight in the ocean, we lose—but if we fight in the river, we win.”
There are over 6000 spoken languages in the world at the time of writing this article. Most online stores start slow and primarily serve their local population in their native language or choose one of the most widely spoken languages to serve a broader audience. However, when expanding rapidly into multiple continents and individual countries, sooner or later, retailers start to modify the experience to be more localized. Usually, this involves localizing the language, offering local payments, and calculating localized shipping information. At Nosto, we set out to streamline product discovery on a local level and came up with some nice automation to assist the process.
Examples of Trending Best Sellers By Geo Location
We tackled the topic by separating our scoring of trending products by geographical location, leading to an automated way of splitting down product catalogues to either country, region or even city, if the different geographical locations have enough data. Let me show you how it works in practice.
New York based LeSportsac has, for years, primarily sold their “Deluxe” products which are inherently elegant and plain. However, when looking at their Google Analytics data, they noticed a large subset of their traffic comes from Hong Kong. By leveraging “Trending Sellers By Location”, they were able to expose a completely different sets of products to their Hong Kong clientele.
The products aimed at Hong Kong based shoppers are vastly different, playing more on different color schemes and “cute stuff” instead of the “elegant” products that LeSportsac had been selling to their loyal US customers for years.
Another great example is from Canadian fashion retailer Bluenotes who was established back in 1941. Bluenotes have been selling their products to the US for a long time and the difference between New York and California is uncanny.
Starting with the New York example (above) where the climate is slightly colder this time of the year, we can see a clear trend towards winter clothing with a couple of warm parka jackets signaling the clear coming of winter. However with the Los Angeles example (below), we see a slightly different trend.
With warm clothing in the mix as well, there is still room for a t-shirt, signaling the clear differentiation between the two coasts polarising the US.
There are also industries where geolocation can mean the world when it comes to product discovery. One such example is Ice Jerseys which is a one-stop-shop for all of your hockey jersey needs. When it comes to sports, it’s clear that you pick a team and stick with it and for Ice Jerseys, your geography matters. Let’s start again with a New York example.
You can see the clear trend towards both New York Islanders and New York Rangers. If I were to visit Ice Jerseys as a shopper from New York – these teams would probably be the ones I am looking for so they completely nail it with this approach. Let’s look at what happens when we jump the border to Toronto, Canada:
As you can see – this example is spot on. Toronto Maple Leafs is most likely one of the most known Hockey Clubs in the world and if I were a proud Toronto-native, I’m sure i would find what I’m looking for here. Look – there is even a Team Canada Jersey! Also note the change in currency by leveraging Nosto Multi-currency, which adds a very nice local touch for the Canucks compared to US shoppers.
Geolocation mostly works for every industry, however as you can see, there are clear indications that a global Fashion store, or something as polarized as sports teams, can really benefit from this functionality.
How to Deploy Trending Best Sellers by Geo Location
After introducing the Recommendation Gallery, we made it easy to preview what products would be shown to different geographical locations by providing a simple navigator to drill down between countries, areas and even cities – if there is enough data.
Deploying the functionality from here is easy and you can use the Recommendation type called “Geo-targeted Trending Products” on any recommendation campaign you setup in the user-interface. The locality tracking and exposure of different products works automatically.