The State of Ecommerce Technology 2023

8 Mar 2023
Product package boxes in cart with shopping bag and laptop computer with blurred web store shop on screen for online shopping and delivery concept

An in-depth analysis of the trends impacting ecommerce technology in the near future.

Online shopping experiences are changing. They’re becoming more dynamic, personalised and ever more engaging. This, in large part, is due to a huge increase in the use of artificial intelligence (AI), machine learning and algorithms. We’re seeing brands across all sectors, verticals and markets adopt AI to drive growth.

The value of opportunities that could be generated by AI is forecasted to reach $15 trillion, with at least a fifth of this value apportioned to sales and marketing applications.

Already, all of the most noteworthy emerging AI technologies, such as machine learning, computer vision, natural language processing, deep reinforcement learning and knowledge graphs, have applications in ecommerce.

They are at the heart of a new generation of ecommerce tools that are transforming the online shopping experience by allowing brands to curate more inspiring customer journeys.

AI—The Power of Personalization

  • 80% of consumers say they’re more likely to buy from brands offering personalized experiences.
  • 63% say they would stop buying from brands that use poor personalisation techniques.
  • 92% say they’ve been influenced by personalised recommendations on the checkout screen.

Here’s another set of compelling figures that demonstrate the importance of personalisation for online brands: 91% of consumers say they are more likely to shop at sites that provide relevant offers and recommendations, and 47% say they would subsequently use Amazon when brand websites provide irrelevant recommendations.

The takeaway is clear: online retailers have to put serious effort into getting their personalisation strategies right. For those that do, the payoffs can be significant. Attraqt clients report that as much as 80% of their site turnover is generated from 20% of products with recommendations applied.

AI is key to achieving this. Self-learning algorithms, for example, can work seamlessly in the background to analyse data and recommend items based on the proven preferences of similar user profiles.

And because the algorithms are self-learning, they make light work of merchandising strategies by constantly testing, deploying and refining the most effective strategies.

AI—specifically computer vision—is also the power behind image-based recommendation engines that can search vast inventories of product photography to serve up recommendations of visually similar items.

This is good for shoppers, who receive a carefully curated selection of products to complete their chosen outfit or interior design scheme and also good for merchandisers, who no longer have to laboriously categorise inventories semantically using keywords.

Understanding Intent—Even if Shoppers Don’t!

We’ve already seen how AI can leverage data to make intelligent inferences about shopper personas and match them with relevant products without missing a beat.

However, it’s in understanding shoppers’ intent where AI tech really helps brands pull away from the competition. The most sophisticated algorithms can take personalisation to another level by making real-time appraisals of shoppers’ goals—knowing, for example whether they’re searching for a gift or embarked on a self-indulgent spree.

Intelligent Content—The Future of Product Recommendations

Showing shoppers a collection of appealing items at strategic touch points throughout their journey is one thing—serving them fully personalised content and following up with bespoke offers by email is another.

Dynamic content makes this concept a reality by allowing merchandisers to automate the customisation of every element of site pages to appeal to varying audience segments – or even tailored to the individual.

Dynamic content could be as simple as delivery and currency information or seasonal product banners that change according to the user’s location.

But with the right tools, merchandisers and marketers can also use dynamic content generation to display entire web pages with custom banners and search results based on users’ favourite brands, preferred styles, budgetary constraints and more. But even further than this, it can even eliminate irrelevant products and content.

And they can take personalised product recommendations off-site, curating consistent omnichannel messaging across social media, email and into brick-and-mortar stores.

Configurability and Open Architecture

AI is an emerging technology and almost as prone to change as seasonal merchandising KPIs, so it goes without saying that the ecommerce technology stack of 2023 and beyond must be open, highly configurable and ready to scale.

Open architecture will allow merchandisers to unlock the power of AI and data science. In contrast to pre-made ‘black box’ algorithms, which offer a set-and-forget approach, open architecture allows merchandising teams to configure and fine-tune the algorithms that will help them understand their customers better, ensure the right shoppers always see the right products and ultimately meet the KPIs of the hour.

Savvy ecommerce brands will also increasingly look towards ‘headless’ architecture in which the store’s front and back-end run independently of one another. This makes it easy for merchandising teams to integrate their own data, algorithms and analytic platforms, deploying new features fast without affecting existing applications.

It will also keep teams ready to take advantage of exciting new AI technologies as they emerge over the next decade and beyond.

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