The E-commerce Taxonomy Challenge

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During the past ten years, e-commerce has exploded and continues to evolve at a rapid pace. Therefore, its a must to have in place an e-commerce taxonomy to meet the customer demands across every channel.

In 2020, the number of global digital buyers is estimated to reach 2 billion1. The total value of global retail e-commerce sales is expected to reach 4 trillion2 – twice as much as in 2016 – and will make up 15% of retail sales worldwide. Moreover, currently, about 50% of buyers prefer to use large e-commerce marketplaces4. According to the forecast, by 2040, 95% of purchases will be facilitated by e-commerce5.

One of the key factors contributing to such growth is the generational shift to global digital buyers. Digital natives started playing a more important part as buyers, and their share will continuously increase.

Standardized Taxonomies as a Big Data Challenge

However, the dynamic progress is not only a great opportunity but also a major challenge, as new market conditions imply new demands. These demands will be faced both by suppliers and retailers. One key ingredient in modern e-commerce is big data: a complete taxonomy to describe products, product data transformations to make data compliant, and data syndication to marketplaces and other sites.

Setting de facto and de jure standards (with GS1) for taxonomies, is a key scalability factor in marketplaces. Catalogs can only efficiently expand to hundreds of millions described products, when Product Information Management is compatible to multilingual taxonomy standards. What are the main topics in a taxonomy standard?

Categorization, complexity and ambiguity

Product categorization should be unambiguous, intuitive, and as simple as possible. Therefore, a product should be correctly classified in only one obvious way. A complicated data model leads to a supplier spending too much time on the correct product categorization and increases the risk of incorrect categorization. Ambiguity results in the situation when the same products appear in a variety of categories. Complexity and ambiguity decrease data consistency as well as browsing and filtering capabilities for shoppers.

Differences per country

Major retailers and marketplaces operate in multiple countries. It is often the case that they sell the same products on several markets or provide cross-border shipping. However, sometimes retailers use different categorizations per country or locale. This makes product categorization, data delivery, and transformation overly complicated. In addition, in the context of globalization, shoppers can move to other countries and keep using the same retailers. Different categorizations confuses a buyer and might dissatisfy them.

Multiple parallel taxonomies

Omnichannel is a future of e-commerce. Nowadays, buyers may use different channels to communicate and to buy with one supplier. It means that retailers should seamlessly integrate different platforms. Currently, over 50% of retailers have omnichannel capabilities6. However, some retailers don’t have a common e-commerce taxonomy as different channels have their own data models. To make things worse, sometimes these taxonomies are even based on totally different data modelling principles. Different company departments can also use their own databases. Such kind of an approach substantially complicates the data delivery, its transformation, and synchronization.

Complicated data-sheets with redundant attributes

Some suppliers want to make their product data as complete as possible. However, a huge number of details can be not only useless, but also harmful. Too much information may scare shoppers away and get them to look for a retailer with more simple and user-friendly data. This point becomes especially prominent when we are taking into account the share of mobile e-commerce, which facilitates about two-thirds of digital sales7. What is more, too many attributes make content production more time-consuming and increase the risk of mistakes in data-sheets. Complexity can also be resolved on user-level by hiding technical or lesser specs behind a button.

Too specific mandatory attributes

Mandatory attributes can be either essential for the category and the product description, or very important for the supplier for some other reasons – legal, regulation, Corporate Social Responsibility, logistics or marketing reasons – and must be applicable for all products within the category. Uncommon attributes with mandatory status may make product imports impossible.

Outdated attributes and attribute values

At any point in time, old taxonomies may contain obsolete fields. For example, as is the case for hardware descriptions: old processor models, and interfaces. Some of them are not used anymore, others are always filled by the same value (e.g., ‘Yes’ or ‘No’). Still they’re a part of a taxonomy and could even be mandatory, overloading editors and making data transformation more time-consuming.

Attribute and value duplicates

The characteristic of a product should be correctly described, and only in one way. Any data redundancy leads to inconsistencies and decreasing filtering capabilities for shoppers.

Missing attributes and values

Despite the presence of redundant data, a supplier data model could still miss some nice-to-have details. Generally, this issue could be caused by insufficient flexibility of a taxonomy. To stay up-to-date, a taxonomy should be continuously updated. Moreover, new hardware and technologies often require taxonomy changes like adding new attributes or new values to existing ones. It may seem strange, but even today some taxonomies could miss USB Type-C ports or USB 3.2 generation spec support. Lack of such information denies shoppers to form a clear vision of the product and make a balanced choice.

High quality taxonomies and PIM (Product Information Management) systems make data easy to access, exchange, and understand, and flexible to apply. That is why suppliers and retailers pay more attention to this sphere and invest money into taxonomy standardization, development and maintenance, data transformation, and syndication. Above all, it’s critical for converting online visitors into buyers.

References


1. [Statista] 2. [Statista] 3. [eMarketer] 4. [Oberlo] 5. [Nasdaq] 6. [Oberlo] 7. [Oberlo]

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