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Customers can be delighted by personalized e-commerce experiences, and the businesses that provide them can see significant financial gains. Customers impressions of great experiences are, however, quickly changing and are influenced by the finest brand experiences currently available. Customers today demand real-time, individually contextualized experiences across touch-points and channels that make them feel continuously recognized, understood, and cared for.

The power of AI, Personalization is hard — a clear AI strategy can help

Unfortunately, a lot of businesses have trouble providing the real-time, one-to-one personalization that clients have come to want. E commerce merchants frequently attempt to use standalone products to address customer experience gaps because they lack a clear path to success. This leads to a fragmented stack of marketing and commerce solutions, which increases complexity, creates data silos, and degrades the user experience. Additionally, in the current economic climate, businesses are struggling with a lack of talent and other resources. In reality, 74% of businesses claimed they lacked the wide spectrum of internal talent necessary for efficient mass personalization.

Companies are able to give personalized shopping experiences to every client, every time by starting with a defined plan and assisting employee efforts using AI. This article examines five ways that AI might be used to scale up personalized shopping experiences. With this information, online retailers will be better equipped to determine their own AI strategy and look for solutions that are most appropriate for them, resulting in outcomes for their companies and increased customer engagement.

Strategy 1 — Use AI to segment customers

Personalization is fundamentally about developing and deploying experiences that are specifically catered to the wants and situations of the client utilizing their data. The crucial link between customer data and the experiences they have is provided by segments. Today, a lot of e-commerce businesses manually categorize their clients based on simple demographic information (such location, age, and gender) and sparse behavioral information. Unfortunately, the approach is cumbersome, lacks the granularity needed for hyper-personalized interactions, and might not take into account other valuable data types, such real-time behavioral or transactional data, which could include things like items viewed on the website, categories searched, previous purchases, and more.

Using predictive models, 74% of personalization leaders develop segments.
utilizing AI to find and build profitable sectors. Without integrating data analytics teams, personalization leaders quickly identify and develop segments using AI models. 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) are all fed into models that score customers' propensity to take particular actions in the future by AI tools. A propensity score is used to describe this figure.

For instance, an AI model could create a group for "high propensity to churn" customers—those who have previously returned items and haven't visited the website in six months. The retailer can then re-engage clients in that segment using customer service, special offers, advertisements, or other tactics.

Strategy 2 — Use AI to facilitate product discovery

Merchants only have a few seconds to show customers the most pertinent products when they visit ecommerce sites. The three most effective ways to show clients what they are looking for are product recommendations, category browsing, and site searches. To present every consumer the appropriate goods and increase conversion, AI can be used in all three scenarios.

Optimize the search experience

The search box is used by about 40% of visitors to locate the desired products. Search must be done correctly because those clients convert almost twice as much as non-searchers. The majority of these performance gaps can be filled by AI-driven capabilities that personalize and optimize ecommerce site search, however more than half of top-performing ecommerce sites have poor search performance.

Strong search systems offer suggestions as users write, effortlessly handle errors, and offer synonyms when a consumer uses vocabulary other than that of the brand, for as when they search for "jacket" rather than "coat." Even more effectively, AI-powered search systems may present the most pertinent products to each shopper based on their online behavior. For instance, if a customer spends a lot of time in the "running gear" section of the website, AI algorithms can instantly reorder search results so that running trousers appear above denim pants for that customer.

Customers frequently need assistance when trying to focus their search on certain items in a wide catalogue (colors, sizes, materials, types, and more).

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

Once content has been produced, merchants must target the appropriate customers with the appropriate content, promotions, and messaging to increase engagement and conversion. In reaction to AI-powered segmentation and customer behaviours on the website, AI may be a potent tool for personalising content and promotions.

Deploying Personalized Content
When consumers interact with a brand's website, the content must be pertinent to their current situation and feel natural. When an electronics brand's home page features only Samsung logos when the customer is seeking for an iPhone, the customer 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 utilised as a beneficial tool to boost conversion, but only when they are presented to the relevant people who might need an extra shove to make a purchase. Many companies provide discounts to all consumers or big categories of shoppers, even if those customers would convert without the promotion. This lack of personalisation not only lowers margins but can also encourage shoppers to wait for a bargain before making a buy.

AI can be used to recommend promotional offers — or no offer — for each visitor based on their profile and behavioural data. The customer in the aforementioned case would have had a high probability to buy the shoes at the merchant utilising AI-driven segments and propensity scoring 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.


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