The power of AI — five techniques to personalize your ecommerce experience

The Power of AI: Five Techniques to Personalize Your Ecommerce Experience

Personalized ecommerce experiences can delight customers while driving serious results for the companies that deliver them. However, customers’ perceptions of “great” experiences are rapidly evolving — and are guided by the very best brand experiences out there. Today, customers want to feel consistently known, understood, and served with uniquely contextualized experiences across touchpoints and channels, all in real time.

Unfortunately, many companies struggle to deliver real-time one-to-one personalization that customers have come to expect. Without a clear path to success, ecommerce merchants often try to fill gaps in their customer experience with standalone tools — creating a fragmented stack of marketing and commerce solutions, resulting in more complexity, data silos, and a lackluster experience. Further, in the current economic environment, companies are facing shortages of talent and other resources. In fact, 74% of companies said they lack the full range of in-house talent needed for effective personalization at scale.

Personalization is hard — a clear AI strategy can help

If we look at companies that are delivering superior experiences today, they excel in doing two things to manage this challenge:

Starting with a clear strategy and supporting employee efforts with AI enables companies to get more value out of fewer resources and deliver personalized shopping experiences to every customer, every time. In this article, we explore five ways to use AI to implement personalized shopping experiences at scale. With this knowledge, ecommerce merchants will be empowered to set their own AI strategy and seek out solutions that are most relevant for them, driving results for their business — and engagement from their customers.

Strategy 1 — Use AI to segment customers

At its core, personalization is about using customer data to create and deploy experiences tailored to customers’ unique needs and contexts. Segments serve as the critical bridge between customers’ data and the experiences they receive. Today, many ecommerce merchants segment their customers manually based on basic demographic data — like location, age, and gender — and limited behavioral data. Unfortunately, that process is slow, lacks granularity for hyper-personalized experiences, and may not consider other valuable data types, such as real-time behavioral or transactional data — for example, items viewed on the site, categories browsed, historical purchases, and more.

74 percent of personalization leaders create segments using predictive models.

Using AI to identify and create valuable segments

Personalization leaders use AI models to easily identify and create segments without involving data analytics teams. AI tools feed customer behavioral data (ads clicked, emails opened, in-store activity), ecommerce data (product views, preferences, past purchases, returns), and data from other sources (loyalty data from CRM systems) into models that score customers’ likelihood of taking certain actions in the future. This number is called a propensity score.

For example, an AI model could identify customers who previously returned products and haven’t visited the site in six months as “high propensity to churn” and turn that output into a segment. The merchant can then re-engage customers in that segment through customer support, targeted discounts, ads, or other mechanisms. While some AI solutions operate in a black box, the best solutions provide transparency along with automation, showing the reasoning behind propensity scores — such as “customer has not viewed product pages in a certain category.”

Propensity scores and factors driving each score are created using Customer AI in Adobe Real-Time CDP.

Automating segment qualification in real time

In addition to using AI to identify and create powerful segments, personalization leaders often automatically shift customers from one segment to another in response to real-time behaviors. For example, if a customer has only shopped in the “men’s” category, but on Valentine’s Day shops in “women’s,” that customer could be automatically added to a “women’s gifts” segment in real time and immediately receive a different site experience during that browsing session to highlight best women’s gifts and related holiday promotions.

As a shopper, if you’ve ever purchased a one-time product — such as new winter boots — then received endless ads and promotions for those same boots you just bought, you know the headache of backward-looking, poorly designed segments. Instead, propensity-based AI models can understand your past purchase behavior and place you in a high propensity segment for complementary products, such as thick socks that match your boots, empowering the brand to deliver a much more relevant and helpful experience to you.

With AI identifying and creating impactful segments and automating customer segment qualification, merchants can save time while creating more powerful, personalized experiences for their shoppers.

Strategy 2 — Use AI to facilitate product discovery

When shoppers arrive on ecommerce sites, merchants have only seconds to present the most relevant products to them. There are three opportunities that are most impactful for showing customers what they are looking for — site search, category browsing, and product recommendations. AI can play a role in all three to show every customer the right product to drive conversion.

Optimize the search experience

Nearly 40% of visitors use the search bar to find the right products. And those customers convert nearly twice as much as non-searchers — so getting search right is critical. However, over half of top-performing ecommerce sites have weak search performance, and most of those gaps can be addressed with AI-driven capabilities that personalize and optimize ecommerce site search.

Strong search solutions provide suggestions as customers type, seamlessly handle typos, and offer synonyms in cases when a shopper uses different terminology than the brand uses — like searching for “jacket” instead of “coat.” Even more powerfully, AI-powered search solutions can use shoppers’ behavioral actions taken on the site to deliver the most relevant products to each shopper. For example, if a shopper spends time in the “running gear” section of the site, when they search “pants” AI algorithms can re-rank search results to put running pants ahead of denim pants for that shopper in real time.

When searching for a product in a large catalog, shoppers often need help narrowing their search to the products they are looking for (colors, sizes, materials, types, and more). Merchants often set up filters in a sidebar to enable customers to narrow their results and find what they need. AI-powered search solutions dynamically suggest filters depending on the search. For example, a hardware ecommerce store could show “screw length” as a dynamic facet for a search for “screws” rather than a standard set of color, material, or other less useful filters.

Live Search in Adobe Commerce suggests searches as shoppers type and uses AI to surface the most relevant filters for each search.

These AI capabilities create a powerful search experience that allows customers to find exactly what they are looking for, every time.

Optimize the browsing experience

While many customers turn to the search bar, 60% of shoppers prefer using on-page navigation to find the products they need. So, using AI to deliver a sophisticated, personalized browsing experience is important to boost conversion and customer satisfaction.

Many ecommerce merchants use manual merchandising, setting positions of products on category pages or using drag-and-drop tools to set up the experience. However, setting the order of products in this way is often static, meaning that it doesn’t change depending on the customer or context. Instead, AI can be used to dynamically re-rank products for each customer using current and past shopping behaviors taken on the website. Depending on where customers spend time on the page, AI models score category and product affinities and use them to show customers the products they care about most.

Using those same category affinities, ecommerce site navigation can also be personalized to the shopper. For example, if a shopper only looks at “women’s” categories, those categories can be positioned first in navigation menus to expedite a shopper’s hunt for relevant products.

Products can be adjusted up or down on category pages based on AI algorithms.

Deliver relevant product recommendations

Product recommendations are the third piece of the product discovery puzzle and provide an excellent opportunity for AI optimization. Many companies today do not use any customer segmentation to inform their product recommendations. Using segmentation to deliver highly relevant product recommendations in response to shopper behavior is incredibly powerful. To take our earlier example of a customer who shops for running gear, that shopper may be placed in a “runner” segment. The site would then update product recommendations to prioritize running apparel in each category the shopper visits.

Product Recommendations in Adobe Commerce present recommendations for each individual shopper.

91% of customers say they are more likely to order from brands that present relevant product recommendations and offers. Leveraging AI to guide accurate and helpful product recommendations can drive significant business impacts.

Strategy 3 — Use AI to create personalized content

76% of personalization leaders intelligently automate the assembly of modular content. 69% of personalization leaders use artificial intelligence and machine learning to create photorealistic images.

When we think about what we can personalize on an ecommerce site, content is key. Home page banners, content blocks, and other content pieces on the site create the look and feel that forms our experience of a brand. However, before merchants can deploy personalized content on their sites, it must be created, which can be a painfully manual and time-intensive process. As companies grow and need personalized content for different screens, channels, global regions, and brands, the complexity multiplies. In fact, an IDC survey found that 85% of marketing professionals feel pressure to create assets and deliver campaigns more quickly.

AI can help by supporting a content supply chain that automates manual tasks — from creating new assets, tagging existing assets, producing variations by size, and assembling or adapting modular content for personalization.

Automating manual content workflows

Personalization leaders use AI-supported asset management solutions that help to produce and personalize content with automated workflows. For new assets, AI supports content creation with functions such as removing backgrounds, replacing objects, cutting out pieces of existing images, and placing them within existing brand assets. New AI tools can even create 3D models of products and create a series of 2D images of the product. This eliminates the need for brands to engage in lengthy product photoshoots. Once assets are created, AI can identify objects in each asset and tag them, making them easy to find and deploy on ecommerce sites.

Adobe Experience Manager can automatically tag images, making them easy to find and deploy in site content.

Producing content variations to use across channels

Creating many variations of each content piece for different channels and devices — or personalized to different consumers — is a highly tedious task. AI can help by automatically cropping and re-sizing images for different aspect ratios. Best-in-class solutions can even identify text and subjects within content pieces and automatically re-configure, re-size, and render new content pieces to fit different use cases, such as an email campaign, social media ad, mobile screen, site banner, and more. For physical goods, sophisticated 3D tools can create multiple versions of a product with varying textures, colors, and designs — and then produce a variety of 2D images for personalized ecommerce site content.

As content creation gets increasingly time-intensive and complex, AI can help — empowering ecommerce merchants to create and manage assets so they are ready to deploy personalized commerce experiences.

Strategy 4 — Use AI to deliver and optimize personalized content and promotions

75% of leaders personalize experiences based on a customer’s real-time behavior.

Once content has been created, merchants need to allocate the right content, promotions, and messaging to the right customers to drive engagement and conversion. AI can be used as a powerful tool to personalize content and promotions in response to AI-powered segments and shopper actions taken on the site.

Deploying personalized content

When shoppers engage with brands’ websites, content needs to be relevant and feel organic within the shopper’s current context. If a shopper is looking for an iPhone, but the home page of an electronics brand is all Samsung branded, a shopper may click away and look elsewhere. That means content must be shown in a way that adapts to current in-session actions taken on the site and historical data about the customer. Sophisticated brands automatically adjust what content is shown in response to real-time behavioral and profile data to create these impactful experiences.

Personalizing promotions

As with content, promotions can be used as a valuable tool to drive conversion, but only when they are offered to the right customers who might need an extra nudge to make a purchase. Many companies offer discounts to all customers or large subsets of shoppers, even if those customers would convert without the promotion. This lack of personalization not only harms margins but can also train shoppers to wait for a discount before making a purchase.

AI can be used to suggest promotional offers — or no offer — for each visitor based on their profile and behavioral data. For a merchant using AI-driven segments and propensity scoring, the shopper in the example above would have been given a high propensity of purchasing the shoes at full price. Rather than deploying the 30% discount, the merchant could choose to deliver another discount to increase cart value, such as free shipping on a cart value of $150 or more for the $100 pair of shoes.

Optimizing personalized content and promotions over time

The best brands out there don’t just set it and forget it when it comes to personalization. Instead, many use AI-powered tools to test the impact of content and promotions, automatically deploy variations to different customers, and optimize over time. For example, AI can send a banner or promotion to different audiences and then automatically pick the winner of the test and drive traffic to that winner.

When brands use AI to deliver rich personalized experiences, customers feel uniquely understood. With AI testing and optimizing over time, leaders can continuously improve how they serve their customers and unlock value.

Adobe Target can conduct A/B or multi-variate testing to determine the most effective content for each shopper

Strategy 5 — Use AI to deliver the next best interaction in the shopper journey

79 percent of personalization leaders use a decisioning engine to determine which customers receive which experiences

Zooming out beyond the commerce site experience, customers expect every interaction with brands to be personalized, from introductory communications, to email campaigns, learning content, loyalty offers, upgrades, mobile notifications, and more. Orchestrating all these touchpoints manually without the support of AI can feel like an impossible task and often results in a fragmented and inconsistent customer experience. Further, merchants often need to rely on data teams to understand the interactions between various touchpoints and business KPIs.

Delivering the next best experience and targeting KPIs

AI-powered tools can help by using real-time customer data to determine the next best experience for each customer across channels and devices, such as emails or mobile push notifications. Trained AI models rank each message for a given customer, then adjust ranking as customers engage with the brand. For example, if a customer purchases an item, a brand can follow the purchase quickly with a targeted ad that acknowledges the purchase and suggests complementary products. Best-in-class solutions even use goal-based models that optimize based on a merchant’s prioritized KPIs — such as maximizing conversion rate (CVR) or revenue.

By using AI to determine the next best interactions with customers, brands can deliver consistent and personalized experiences that span the customer lifecycle, even as customers move across channels and devices.

Ready, set, go — Set your AI strategy for successful personalization at scale

Delivering on your personalization vision can feel complex, but it doesn’t have to be. While there are many ways to create powerful personalized experiences, AI can help you automate, orchestrate, deliver, and optimize. Personalization tools can work with your existing technologies and data to transform your business and customer experience. The strongest digital commerce solutions have many of these AI capabilities built-in, allowing you to focus on your strategy and the metrics that matter for your business.

Adobe Commerce delivers powerful personalization tools spanning segmentation, product discovery (with live search and product recommendations), personalized content and promotions, and more. Through new connectors with Adobe Real-Time Customer Data Platform, Adobe Target, Adobe Journey Optimizer, and other products in the Adobe Experience Cloud family, you can deepen your AI-powered personalization over time.

Watch session recordings from Adobe Summit to learn how to create personalized commerce experiences.

If you are focused on creating powerful personalized commerce experiences for your customers, be sure to replay sessions from the "Commerce Made Personal" track from Adobe Summit! The track included 20 in-person sessions, which are available for replay now on the Adobe Summit website. Through those sessions you can get the latest on Adobe Sensei-powered capabilities across Adobe Experience Cloud.

For those interested in AI and personalization, be sure to replay the following sessions:

  1. Activate Commerce Data to Personalize, Optimize, and Learn: In this replay, you’ll learn how you can use your commerce data to personalize the customer experience and campaigns while improving visibility into your business performance.
  2. Personalizing the Commerce Experience: This session breaks down personalization into actionable strategies and demonstrate the foundational and advanced capabilities that Adobe Commerce delivers.
  3. Commerce Made Personal Super Session: Discover how to execute end-to-end personalized commerce journeys and hear the latest best practices, case studies, and strategies to grow revenue and increase productivity.

These are just a few of the action-packed sessions that aired at Adobe Summit. Be sure to check out the replays today!

Rohan Bhatt is a product marketing manager at Adobe, specializing in personalized commerce experiences. Prior to Adobe, Rohan spent over five years in technology and payments consulting, advising some of the world’s largest technology companies on product and go-to-market strategies. Rohan has an MBA and MS in Design Innovation from the Kellogg School of Management at Northwestern University.