What makes AI-driven search stand out from normal search

What makes AI-driven search stand out from normal search
Posted by Attraqt | 23 June 2021

It wasn’t long ago that on-site search generated similar results for most queries; text entered into the box offered up only a handful or results, a lot of which wasn’t relevant.

Times have now changed with ecommerce site search. Artificial intelligence in search is making an impact on deciphering browsing history, shopper intent, as well as the meaning and context of real-word information.


Artificial intelligence is a type of computing that imitates human intelligence, learning from and adapting to our search queries. It is increasingly being used in ecommerce merchandising with solid results.

The fact is normal search doesn’t take account of the fact that human searches are unpredictable and messy. Each consumer uses different words to look for the same things. They misspell words and they attribute different meanings and project different ideas on to similar products that they might search for. It’s human nature. But as demonstrated in this PrettyLittleThing.com case study a new breed of AI Search is able to make sense of all this, raising the bar when it comes to ecommerce search.

On-site search is inherently very complex, not only does it have to account for continual evolving user behaviour, it also has to take account of an ever-expanding imperfect data set. This is what makes AI search stand out from normal search, which is not only a broken experience but underperforming. Poor ecommerce search engines fail brands in their quest for better conversions and profitability.

Type in “red dress” – into a traditional on-site search engine and you will get fairly good results. This is an easy query. But if someone types in “red cocktail dress for a party” a normal ecommerce search solution will struggle. These words have meaning, they have context, they have different semantic understandings. AI search is able to make sense of this.

AI search results can be outstanding, with almost limited zero search results. This leads to better conversions, which we find are up by 20 to 50 per-cent on search queries. Revenue will also be elevated in the process. This is because embedded artificial intelligence in ecommerce site search that kicks in pre-query is far more powerful than if AI is deployed further into the search journey.

The AI Is able to look at every key word in a search query and give it context, it understands natural language and is able to process it effectively. It will also look at what the user queried before, what they browsed on in the past, it will also look at what the user interacted with before online. This helps leverage the intelligence in any current search.


Unlike normal search, AI search can also self-learn. Over time with more data, more queries and the mapping of more customer journeys the AI becomes smarter. It allows the AI Search function to work out what users will eventually click on for any specific query. It can also learn what products are the most impactful for a particular query in terms of conversions and revenue.

Artificial intelligence and machine learning can split queries into sub-queries in order to make sense of them and weight sub-queries accordingly. For instance, it can work out the difference between “cocktail” and “party” dress. It can calculate the most important words in any query and utilise natural language processing or NLP to make sense of any word typed into the on-site search function.


Segmentation is in fact vital. AI can segment user types, standard search cannot. This allows merchandisers to find patterns that they would find incredibly difficult to do manually. By segmenting small groups, brands can sell more appropriate and unique products to a niche group of individuals. It can highlight long-tail products, allowing merchandisers to sell low volumes of hard-to-find items to many customers. This is a significant goal for many brands.

The AI in retail can also match customer journeys that are similar. By comparing two consumers and their behaviour, brands are able to predict what the next action will be for any specific customer – users who bought this, buy that. These common patterns matter, they matter because via an AI-generated search result they can generate conversions and revenue.

In conclusion, when it comes to search, AI trumps normal search every time because it’s significantly more accurate. Full stop. AI search is incredibly powerful and its learning with every single query and every single typed word in an on-site search. Nothing is wasted, no data is lost and shoppers don’t encounter a “no-results” scenario. Every last byte of information is utilised and delivered at scale.

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