Since the days of Aristotle in the West, humans have used classification to help organize their experience in the world. In the retail world, a good taxonomy has helped us organize and group our products into similar categories so that the products are easy to find. Cereals in a grocery store are never next to detergent. Everything we need is all found in close proximity to each other. That is why taxonomy is what we will be exploring in our blog this week, and how it important it is for the consumer’s convenience.
What is a taxonomy, really? A taxonomy usually will have three elements: a category tree, a facet and a value list. What do these mean? A category tree is a simple hierarchy you might use to arrange your products in groups and sub-groups such as men’s clothes>trousers>jeans. If the jeans are stone-washed, that is a facet or an attribute and if the length is 33 inches, that is a value. If you want to peruse all 1,000+ categories, Ebay, the grandaddy of them all has produced a site here where you can explore such categories.
Taxonimies have evolved over time to fit the online requirements of searchability. We have heard them described as search retrieval mechanisms, and this requires an awful lot of thinking and manual optimization. If you have a keyword-based search function on your site, you are in a straitjacket of pain (for the escape method see below). You have to ensure that your taxonomies are search engine optimized. You don’t want customers who abandon your site because they can’t find a product or come up with zero results! So, in order to optimise your product taxonomy, you include additional content like Meta Titles or Descriptions or stuff your URLs with so many words you lose count. By and large, this is done manually and extremely inefficient, we have named a few faults that
And the list goes on. The end result is a miserable experience for the staff who want to highlight how beautiful and unique a product is, frustration for the customers who can’t find what they are looking for and lower profitability directly related to inferior CX. Fortunately, there is a solution! Drop your worries and bucket and use the power of AI to power your new search functionality. There are multiple examples of AI and machine learning used to rank and group objects efficiently and logically. Computer vision can quickly group similar shaped objects into a group, saving dozens of hours of manual labor.
The real power for search in your eCommerce shop is Natural Language Processing (NLP). You must probably be wondering, how can NLP help?
NLP is one of the most powerful tools in AI today, and it can help in many ways. Through the use of NLP, you can entirely remove the tedious and error-prone manual tagging of products. The first problem that can be solved are the vague, too broad or missing categories in a product feed. Through the use of SVMs (a machine learning method to group similar items), we can classify products into a set of known categories through supervised learning.
Given a product with informational details such as name and descriptions, we can group the product into a particular category with similar products, e.g. ‘Women’s Clothing’ or ‘Automotive’. To do this, we analyse product feed information from different data sets and build a model that will automatically classify your products. This is not perfect, but accuracy rates now reach above 85% and are certainly better than human operators. Delvify improves on these numbers by working with our customer’s unique datasets.
Your products are unique and each item has a story or description attached to it. The description of each product holds the emotional weight of brand designers who spend hours thinking carefully about the products they develop and the merchandisers who work with them to develop concepts and themes for each and every product.
If I looked at Monet’s Water Lilies and described it as ‘Oil Painting > French > Impressionist > Flowers, you would be baffled. So, why do we insist on searching using keywords? With the use of NLP, we can take apart sentences and find the connections and meaning in them. It is a way to match what your customers “feel” with the descriptions of your products. With NLP, you can search your product database with no tags needed! Yes, none at all!
How can you find your products without tags? With NLP, your customers need to only describe what they want and the algorithms get to work matching the search query meaning with the description that most likely matches. For example, in a normal in-site search engine, searching for “natural food for my Dalmatian” will require a lot of faceting! But an NLP engine would understand natural as a synonym for organic and food for a Dalmatian as dog food, and return results for organic dog food from a pet food selection. With these accurate results, what your brand gets is a happy customer (and most likely, returning customer)! The NLP search functionality can match the qualitative meaning of your products so brands can have a super efficient way to avoid manually labelling their products and concentrate on what is important – delivering accurate results to consumers.