Being Amazon: How Does Artificial Intelligence Affect Online Store Search?

How has the behavior of users of online stores changed over the past 10 years?

The Internet is oversaturated with information, and search is the only way to find relevant content, which is why Google controls the Internet. In e-commerce, a similar situation, only in miniature. The number of stores with 350K – 500K products is constantly growing, marketplaces like Amazon, eBay, Alibaba with millions of items in stock are gaining popularity.

10 years ago, we flipped through 5 pages of a search engine in the hope of finding relevant content. Online stores were much smaller, their users were brave and persistent people who preferred shopping online because of a unique product or a bargain price.

Now users only look at the first page, without spending more than 30 seconds searching for the desired content. Search in the online store has transformed with the help of extensions (like Mageworx Company) from an interface design element into a working tool for working with conversion and increase sales.

Behind the usual search line is a powerful engine that forms the user’s path to the desired product. According to Econsultancy, 6% of visitors use site search and bring companies 13.8% of total profits. 42% of companies do not pay attention to search engine optimization.

What statistics say:

  • 70% of users leave the site without finding the right product within 1 minute (Shopify);
  • 72% of online stores do not meet the expectations of users who are looking for goods there (Bigcommerce);
  • 43% of site visitors go directly to the search bar (Forrester Research);
  • Buyers using search spend 2.6 times more in an online store (Salesforce Demandware);
  • 12% of users go to competitors after an unsuccessful search (KISSMetrics).

How do shoppers look for products? One opens the search bar and drives the name. Another uses navigation and using filters selects the desired product. The third surfs through the pages and views the blocks with product offers.

Modern research divides users of online stores into two groups.

Surfers are people in the middle of a sales funnel. They have already heard about the product or have a vague idea of ​​what they want to buy. They need a guide, personalized recommendations, a pre-filled search bar, advanced filters.

“Defined” – people at the end of the funnel, a tidbit for any online store. They are looking for a specific product, in the search bar they drive in the name, category, brand, color, size or even the product code.

The future of smart search

Personalization, voice and visual search.

55% of millennials use voice search once a day, voice assistants Google Home and Amazon Echo are not a fashionable feature, but a necessity. According to Microsoft, voice queries contain longer phrases, and optimization requires processing: content should be viewed through the prism of text and voice.

Google, Amazon, Microsoft, and Apple are working to optimize their artificial intelligence and train voice assistants to ask users questions. The challenge for brands for the next 3-5 years is to effectively use their content in the new environment.

Amazon gradually implements image search: Amazon Firefly uses smartphone cameras to identify products and then searches for them in the online store. Visual search will become the main retail tool in the next couple of years. To fully utilize these technologies, brands must improve their “visual vocabulary” and “share” it with search engines.

Personalization is more than selling the necessary goods or services. Thanks to the technology of machine learning and Big Data, the client can get an answer to his request even before driving it into the search bar. In the foreseeable future, sites will soon offer products and services based on information known about the user.

So, if a user is going to restore his car of the latest model, machine learning systems will select both suitable products and additional restoration services.

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