Intelligent Search is the new future of digital commerce

Shopping from the eCommerce site is going to replace the traditional brick & mortar experience to a great extent. The share of global retail sales generated via eCommerce has steadily been rising and projected to reach 1/3rd by 2024. At such times, it is important not only to provide a clean and user-friendly purchase experience but also to see that users can find the right product without wasting much time in the maze of the navigations or inefficient in-site search engine.

It is important to make sure that the search engine understands the user and gives the most relevant results without having the user type exact jargon that appears in the catalog or be extra careful about common spellings or spacing mistakes.

“Searching should be a seamless experience and not a task in itself”.

According to a study, among 60 top-grossing US and European eCommerce sites in late-2019:

  • 27% of sites are incapable of handling the misspelling of just a single character in the product title.
  • 51% of the sites do not have faceted search filters.

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Just think what happens if a customer is searching for a product that your website has, but your search engine is unable to fetch?

Not only customer bounces off the site in a fraction of a second, but also likely to make a bad perception about the site, and never return. Bad Search experiences not only cost companies their current sales but impacts future sales as well.

Evolution of Search Engine

The evolution of the Search engine can be divided into four major segments based on their maturity. These 4 segments are as follows:

  • 1. Initiate
  • 2. Optimize
  • 3. Transform
  • 4. Innovate

The four phases are classified starting from the basic search engine which takes care of just the hygiene factors such as basic keyword search and sorting to the very advanced search engine which understands user’s intent, context and provides a personalized experience.

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Evolution of Search Engine

Let’s understand the evolution stages in depth.

1. Initial

Search Engines at the initial stage have basic functionalities which would give some search result, but not the best solution in terms of accuracy and ranking. In such a search engine, users can search for products, but search engines might or might not give a very accurate, relevant, and complete result set. The engine of this maturity stage can have features like:

  • Recent search
  • Auto spacing
  • Auto spelling correction
  • Sorting option
  • Basic type-ahead

2. Optimize

Search Engine at the optimize stage can identify user intention and gives correct and accurate result set. It also helps the user in finding what they are looking for, with minimum interaction. Search engines in this stage can have features like:

  • Synonym mapping
  • Faceted search
  • Breadcrumb negation
  • Natural language processing
  • Alternate suggestions
  • Deep search across various dimension

The search Engine in this stage gives a more relevant and accurate result set but is still unable to identify the intricacies of natural language used in the search query.

3. Transform

Search Engine with a much better ranking that helps in better conversion and user experience. Most of the basic in-site search engines work based on keyword matching, and finer details of language in the query and context of the user are not taken care of. For example when User searches for “hooks for shower curtain” or “shower curtain with hooks”, in both the cases most of the search engines would produce the same result as both the queries have almost the same keywords, even though it is apparent that the user is searching for hooks in the first case and shower curtains in the second case. More mature search engines take the context of the user also into consideration while producing results. Such search engines do Natural language processing to understand what exactly the user is searching for, they also try to determine the primary product type or category type to produce the correct result set. Such engines use NER (Named entity recognition) to understand and classify different keywords of the query. E.g. “Red Nike Shoes 7” would be translated into shoes with the color red in brand Nike and Size 7. With such classification search engines can give the most relevant products on the first page which helps in keeping the user engaged and eventually driving more conversion. Advanced type-ahead monitors all the user queries and uses it to predict what the user is planning to type. Correct predictions can save a lot of user’s time and can give a very nice searching experience to the user.

4. Innovate

Search Engine which is very sensitive to the user. Personalized results that can incorporate various business and marketing objectives.

Apart from all the above-mentioned features, advanced eCommerce in-site search engines such as Amazon’s search engine, have the capability to know the user and give tailored and personalized results as per each user’s needs and preferences. Such an Advanced search engine uses various data points like browsing history, purchased products, products added in cart or wish list, and behavior of other similar users to determine user’s gender, brand preference, price sensitivity, and much more. With personalization, each user sees a different search result page for the same query. Personalization helps in achieving customer satisfaction and improving customer Loyalty.

By implementing intelligent search engines, you can make sure that the user gets maximum output in terms of the most relevant search results with minimum efforts. This goes a long way in making sure that the user stays on the site, keeps interested, and moves a step forward in the user’s journey towards purchase.

Personalized search and recommendation is a new mantra to your success.

About the Author

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Gopi Joshi

Product Manager managing products like Search and Recommendation. eCommerce enthusiast, helping customers shop better.

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