Thursday, August 4, 2016

Centuple your Market with Language-neutral Product Search

A German and an Italian go on a Polish online shop… Sounds like a start of a bar joke? The situation indeed seems bizarre in spite of all the EU propaganda about the Digital Single Market. The German and the Italian simply won’t understand a single word. They cannot find any products in this Polish shop.

 

Easy Fix Translation?

Translating static content and product catalogues into English would surely help. In some EU countries half of the population has a satisfying passive command of English. However, that percentage becomes quickly minuscule when consumers have to enter the right English search term for a specific domain. Full translation into all or even into mayor languages only, is for most cost prohibitive.

 

Domestic Customers have a Hard Time, too!

For domestic customers it’s not easy to find products either. Today’s string based search often returns no matches. Or it finds way too many, displayed in unintuitively sorted lists, which has the same effect. Instead of searching semantically for what the customers wants, online shops expect their customers to enter the very same strings they have used in their catalogues.

 

Scroll Hit Lists or Explore Product Offering?

Search results are always displayed in a list of matches: a column of product names or tiles of product images. But what if there are many matches in several different categories? An alternative, more natural way would be to display the search result graphically in a product tree with related products closely organized. This way the shopper can quickly find the product, she was actually looking for and is motivated to buy more.

 

Social Shopping

E-commerce has increased the buying options to a degree, which leaves many consumers completely lost. Therefore online shoppers often rely on third party information such as test reports, customer feedback, and blog articles to make a buying decision. Shops should give their customers the comforting feeling of having made an informed and good decision, but better without having to leave the shop.

 

Solution Architecture for Advanced Linguistic Product Search

All the above requirements, particularly the semantic and cross-language search, can be relatively easily fulfilled by deploying Advanced Linguistic Search (ALS) on top of a Multilingual Knowledge System (MKS). The following chart illustrates the architecture for finding products semantically and language-neutral:




The ALS deals with language specifics such as morphology, spelling variations, etc. Deployed with the MKS it can expand searches semantically and across languages. The MKS stores the product information in a knowledge graph. This way, found products are listed by semantic proximity and not by string match scores. Alternatively, the shopper can explore the offering in a product graph. Supporting third party information is provided, machine translated if originally in a different language, to help the consumer to make buying decisions without leaving the shop.

 

Find, Upsell, Advice = Higher Revenue

The above solution, based on a Multilingual Knowledge System such as Coreon, enables online shops to sell much more. Without ongoing translation efforts, shops can drastically extend their customer base in the Digital Single Market. For shops in almost half of the EU countries that increase would be hundredfold!