What is contextual search?
Contextual search is a technique that utilizes Artificial Intelligence (AI) & Machine Learning (ML) methods to understand the context of a search query to improve the accuracy of the search results.
The advent of the internet and social media has created an explosion of content. This is both good news and bad news for digital businesses. The good news is that there’s more opportunity than ever before. The bad news is that there’s so much noise out there that it can be harder to cut through to your customers and give them exactly what they are seeking
For example, if a user searches for ‘Apple’, the search engines can determine whether the user is looking for Apple products like the iPhone or iPad OR apple, the fruit, or even apple fruit products such as apple juice.
Users want quick answers that are relevant and useful – essentially, they want the search engine to understand them.
How manually added product tags are one way to obtain contextual information
Manual tagging is a laborious process that involves tagging products or content with additional pieces of information that are not directly evident. It allows users to find content more easily by using different words or terms, and it also provides search engines with more context surrounding the content which can help them deliver more accurate results.
This information could include:
- Key phrases, synonymous terms, alt words
- Descriptive information
- Category or genre
- And other information that users could search for
When a document is manually tagged, the searcher enters context by reading through the document and adding tags that describe its content.
This process relies on each and every piece of product or content being extensively pre-tagged. While this process is used highly successfully in digital businesses such as Netflix, it has its downsides.
The drawbacks of manual tagging
Firstly, human behavior is unpredictable, you might think you've already accounted for everything your user might search for but that's far from the case.
Let’s say you sell hardware items and have hundreds of products on your website, you might think that tagging all of these with different key phrases is the best way to ensure each and everyone appears in relevant searches. But what if someone types in 'red nail screwer’ instead of ‘red screwdriver', and the phrase ‘nail screwer’ is missing from the product tags? Your user will be presented with a ‘No Results’ page and will likely leave.
Secondly, it's extremely resource-consuming and expensive. Tagging all the products or content published on your website is a tedious task, and manually tagging even a small percentage can take up massive man-hours, which in turn costs you large sums of money.
And even if you manage to do it perfectly (which is highly unlikely), there’s no guarantee that users will remember to include the right tags when searching for something.
To sum up, manual tagging is time-consuming, prone to human error, makes it difficult to maintain consistency, requires expert knowledge, and even then, your user might just use terms completely unaccounted for.
The drawbacks of manual tagging can be alleviated by using automated methods to add contextual information to content. Automated methods have the potential to be more accurate while also costing you less time and money – so you can focus them on your products.
One such automated method is contextual search.
How contextual search works
Contextual search or semantic search will take into account not just the words someone uses in their search query, but also the context surrounding those words. Contextual search will take into account their location, past user search history, global search history, spelling, and other factors to deliver results that are relevant to your specific needs.
Contextual searching has grown in both usage and its abilities – it's not just a buzzword in the industry. Many search engines including Google, use some form of it to determine what a searcher is looking for and to provide more relevant results. The demand for contextual search has also grown as users expect more from their searches.
The idea behind contextual search is that the meaning of a query can be better understood by matching the search context with the item or content, rather than just matching keywords together.
This approach has several advantages over manual tagging: it is faster, more consistent, and does not require expert knowledge.
3 types of contextual search:
- Basic – only takes into account the query. Users have limited control. Eg: a user searching for ‘menu’ on a restaurant website.
- Explicitly supplied context – the searcher provides additional information to the search engine that is used to improve the accuracy of results. Eg: a user searching for .xls files named ‘budget’ on their laptop.
- Automatically inferred context – the search engine uses algorithms to automatically determine the context of a query and use it to improve accuracy. Eg: if a user searches for ‘pizza’ on their phone, the search engine understands they want to order pizza and show pizza delivery options within a certain distance from their location.
What factors do search engines use to infer contextual information?
A context-based search engine uses several factors, including but not limited to:
- User profile
- Search History
- Time of day
- Last visited website
- Semantic HTML Tags
- Related Phrases
There’s a whole host of categories that determines what the search result will look like.
Broadly speaking, contextual searching uses:
- Semantic information – taking into account alternative phrases, the meaning of words and phrases, as well as understanding relationships between them.
- Artificial Intelligence – Using advanced algorithms to automatically determine the context of a query and use it to improve accuracy.
- Natural language processing – This involves taking into account the grammatical structure of queries, as well as idiomatic expressions and slang. Basically, translating human language into something that machines can understand perfectly.
AI-based algorithms use a combination of these three methods – semantic information, Artificial Intelligence, and Natural Language Processing – to help contextual search engines understand a user’s needs better and provide them with more accurate results.
Coming back to our original question, by understanding what the user wants regardless of how they write it, contextual search gives users the results they are looking for.
An AI-based contextual search approach reduces the need for manual product tagging by using the context of a query to improve the accuracy of results. This approach is faster, more consistent, and does not require expert knowledge.
Contextual search can also be used in conjunction with other methods, such as manual or automated tagging, to further improve the accuracy of results.
A contextual search is a powerful tool for improving the accuracy of search results. It can be used to reduce the dependency on manual product tags, and expand the set of items that are relevant to a search query.
Contextual search is the future of online search, and businesses that don’t employ it will be at a distinct disadvantage. Not only is it less resource intensive than manual tagging, and much more powerful, but once you employ it you may find you don't need manual tags at all!