A key component of most effective ecommerce marketing strategies, behavioral segmentation is a method that divides customers into smaller groups based on their actions and purchasing needs. Thinking beyond the traditional demographic and geographic segmentation methods, and using psychographic and behavioral data, allows for the execution of more intuitive, humanized marketing campaigns and recommendation AI. The process of effective behavioral segmentation allows you to create greater connection between people and the products they’ll love. Behavioral segmentation also allows for a more personalized online experience for the consumer, as well as increased brand loyalty.

As the term suggests, behavioral targeting is the method of targeting consumers by their behavior and segmenting them in groups in order to deliver the most relevant ads and content to them. When undertaking behavioral targeting, customers are grouped based on activities and actions such as browsing behavior, pages visited, searches performed, links clicked, recency of visit, device used, geographic location, and products purchased.

Visitors with similar behaviors are then grouped into defined audience segments, allowing retailers or advertisers to target them with specific, relevant ads and content based on their behavior. Behavioral targeting matters not only because the advertising is more likely to lead to a conversion, but it makes for a better, more personalized ecommerce experience. When carried out well, providing a sense of knowing to the consumer makes each interaction feel special.

A common approach for executing product recommendations using machine learning, collaborative filtering looks at the collective set of preferences across users and items to learn from users that have similar behavior patterns. Recommendation systems collect both explicit data actively provided by users (such as numeric rating) and implicit data inferred by the system based on a user’s behavior (such as a preference for a certain product after viewing similar ones in the past). These massive datasets allow systems to craft predictions and serve relevant product recommendations tailored to users’ individual affinities and shopping behaviors.

A field of artificial intelligence (AI), computer vision enables digital systems to process, analyze, and make sense of visual data in the same way that humans do. Computer vision uses deep learning and convolutional neural networks (CNN) to derive meaningful information from digital images, videos, and other visual input, and take actions or make recommendations based on that information. A computer vision system uses large amounts of data that is analyzed over and over until it discerns distinctions and ultimately recognizes images. Computer vision is transforming ecommerce as it can identify products along with their characteristics such as color, size, shape, texture, print, and style.

The content-based filtering method uses intrinsic information about items and users to understand what features of an item a user might like. In contrast to collaborative filtering, content-based filtering does not require other users’ data to provide personalized recommendations to a user. This flexibility allows the system to provide highly specific recommendations to the current user, but it is also limited in the sense that it is unable to expand on known user interests.

A standard pillar of an effective marketing strategy, customer segmentation is simply the technique of dividing a company's customers into groups that share a similarity among customers in each group. The overarching goal of customer segmentation is to be able to make decisions about how to engage with customers in each of the segments in order to maximize both the value to the customer, and of each customer to the company. When carrying out customer segmentation, companies can separate consumers by personality traits, psychographic data, certain interests, and habits, as well as a host of other demographic factors, such as where they live, their income levels, and age bracket.

A subset of machine learning, deep learning has infiltrated and made its way up every benchmark across most machine learning subfields, such as natural language processing, computer vision, and so on. To achieve this, deep learning uses a multi-layered structure of algorithms called “neural networks”. The key aspect of deep learning is that these layers of neural networks are able to construct additional features in an automatic manner, instead of being manually designed by human engineers. In contrast to traditional machine learning, within ecommerce, deep learning uses an initial set of given features usually used to predict purchases (such as age, or region) to build more abstract and compressed features based on different combinations of those initial features. A growing technological field, deep learning is a core element when developing predictive modeling, building precise recommendation engines, and shaping personalized content.

The data relevant to a company’s customers that is collected and owned by that company is referred to as first party data. In this case, information about customers is compiled through software and systems that the organization itself owns. The organization can then use this data (demographics, purchase history, digital interactions, behavior, preferences, and so on) to deliver highly targeted experiences to users and increase brand loyalty. First-party data is considered to be one of the most valuable sources of user data because of its cost-effectiveness, accuracy, and relevance to the brand.

A broad umbrella, hyperautomation combines multiple automation technologies, tools and platforms — such as artificial intelligence (AI), machine learning (ML) and robotic process automation (RPA) — with the aim of automating sophisticated human tasks and streamlining complex business processes. Whereas standard automation usually occurs on a smaller scale, creating solutions that address individual tasks, hyperautomation encompasses multiple automation tools to really scale initiatives. As such, hyperautomation is perfect for maximizing productivity, efficiency, and accuracy within organizations. In addition, by delegating low-value and time-consuming tasks to bots, hyperautomation enables businesses to dedicate their attention to higher-value initiatives, such as understanding customer preferences and enhancing the customer experience.

A subcategory of computer vision, image recognition consists of a set of techniques for detecting, analyzing, and interpreting images to favor decision-making - the task of recognizing what an image represents. Using deep learning and trained neural networks algorithms, image recognition is capable of identifying objects of interest within an image (such as people, features of a product, or places) and recognize which category they belong to. Some of the current and future applications of image recognition that can help improve personalized experiences include targeted advertising, face analysis, smart photo libraries, and enhanced research capability.

A branch of artificial intelligence (AI), machine learning uses a vast amount of data to find patterns that enable computers to learn the way that a human would, without explicitly being programmed. Machine learning is rooted in the idea that a system can learn from data, identify patterns and make decisions with minimal human input. Machine learning allows software applications to become more accurate at predicting a variety of outcomes. Currently used in many applications, machine learning is the driving force of everything from recommendation engines, websites that make personalized recommendations and personalized product recommendations, to banking software, fraud detection, speech recognition, and computer vision.

When carrying out a customer segmentation strategy, there are two key types: macro-segmentation, which focuses on dividing consumers by high-level customer data such as language, gender, geographic location, or source, and micro-segmentation, which is more specific and granular, such as the use of behavioral and psychographic data. Macro-segmentation results in larger segments because it looks at broader attributes, but is better than no segmentation at all. Macro-segmentation and micro-segmentation, are both important strategies for executing personalization.

The metaverse is a shared digital environment that allows users to interact virtually. Combining multiple elements of technology, the metaverse integrates aspects of social media, online gaming, cryptocurrencies, augmented reality (AR) and virtual reality (IR) to build highly immersive digital experiences. It is expected that, as the metaverse grows, it will enable multidimensional interactions in which the physical and the virtual worlds will converge. When it comes to digital fashion, personalization opportunities are infinite, as a metaverse environment can yield millions of individualized combinations to create highly tailored multisensory experiences.

When devising a customer segmentation strategy, there are two key types to keep in mind: macro-segmentation, which generally splits up consumers by top-level customer data such as gender, geographic location, gender, or device, and micro-segmentation, which uses more specific behavioral input and psychographic data, such a values and lifestyle choices. Micro-segmentation ensures you have clear sub-groups of your audience, in order to create a relevant, and personalized experience. When carrying out any kind of online personalization, both macro-segmentation and micro-segmentation are important approaches.

In the simplest terms, non-fungible tokens, or NFTs, are one-of-a-kind cryptographic assets that exist on a blockchain and cannot be reproduced. Due to their unique nature, NFTs cannot be traded or exchanged at equivalency. While fungible tokens, such as cryptocurrencies, are identical to each other and, therefore, mutually interchangeable, no two non-fungible tokens are not the same. In this way, non-fungible tokens enable the opportunity to generate one-off, highly valuable digital assets, such as digital artwork or digital fashion items, that can be tailored to consumer preferences.

Presenting many opportunities, NFTs can be also used to represent real-word items, such as artwork or real estate. By converting a physical asset into a digital one, non-fungible tokens remove intermediaries and simplify transactions, which leads to increased efficiency and streamlined processes. As they cannot be copied or substituted, NFTs also reduce the probability of fraud.

Describing a retailer's efforts to provide a consistent and coherent customer experience, omnichannel retailing is a multichannel retail approach that typically includes brick-and-mortar stores, apps, mobile, laptop, tablet, and any online platforms. When crafting an omnichannel retailing strategy, the focus is on providing a seamless customer experience wherever the consumer is interacting with the brand or retailer. Creating a seamless omnichannel retailing experience is important because it makes it easy for consumers to feel a connection with the brand at many points in their day. An omnichannel retailing strategy not only creates a better consumer experience, but also gains increased revenue, and better attribution data.

Providing deeper context about individual users and consumers, personalization engines employ data science to deduce patterns that allow businesses to edit, buy, select, tailor and deliver personalized messaging, content, product selections, and other interactions. Personalization engine software solutions typically use machine learning algorithms such as collaborative filtering, which depends on the choices of similar individuals. A variety of variables can be used to better understand the consumer and provide accurate recommendations when creating a personalization engine, including past purchase history, site interactions, and psychographic data. Using an effective personalization engine increases conversion, ad effectiveness and consumer satisfaction, hence improving business outcomes.

A personalized product discovery experience is a customer-led approach that uses artificial intelligence (AI) to ensure shoppers get the products they really want quickly, easily, and intuitively. From homepage to checkout, implementing personalized product discovery means delivering the most relevant products, content, and services for each shopper. This streamlines the ecommerce buying journey ensuring that each individual customer’s preferences are identified, understood, and catered to. Applying personalized product discovery is the key to building a more holistic user experience and driving customer engagement, which will ultimately translate to increased conversions and revenue per visit.

A branch of advanced analytics, predictive analytics combine a variety of statistical techniques that analyze both current and historical facts to predict future outcomes more accurately. Using historical data, statistical algorithms, predictive modeling, and big data machine learning techniques, a predictive analytics system is able to reliably forecast future events, trends and behaviors. In general, businesses apply predictive analytics to find patterns in data that help them identify potential risks and opportunities. In marketing, one of predictive analytics's main assets is that it allows businesses to develop clear-cut customer segmentation and make precise conversion and purchase predictions.

To enable machine learning, personalization, or even simply search and filtering, product tagging in ecommerce entails descriptive tags assigned to products to help to organize, document and track their progress. In essence, product tags are keywords, attributes, or qualities used for product identification within the website, which can range from the very broad to the ultra specific. For example, a fashion product can be tagged with a price category, brand, material, color, style, or psychographic property. In this way, product tags sort products by a certain feature and allow a specific, narrow product search so that products can be easily found by consumers. In ecommerce, product tagging must be consistent, updated, and accurate to enable relevant product search and discovery and a great customer experience.

A recommender system, or a recommendation system, or most commonly, a recommendation engine, is software that uses data and machine learning algorithms to recommend the most relevant items to a particular user or shopper by predicting the preference or rank that a user would give to a product. In the past, the art of recommending products would come from a sales associate or personal shopper. Today, algorithms have taken on the task of deciding what product, service, or experience to recommend to consumers. There are three key types of recommendation engines: collaborative filtering, content-based filtering – and a blend of both.

When a retailer begins to attempt to implement greater personalization on their platform, a solid product recommendation strategy is crucial in order to create a recommendation engine that can maximize marketing ROI. In order to help your system make the best algorithmic decisions possible so that the right products are recommended to the right user or consumer, the most effective type of recommendation strategy for each context needs to be considered. Generally, the more data the better, but which product data, such as color, product type, style, and so on, and which user data, segments, age, demographics, operating device, or psychological data, is most important to each context, will be critical to consider.

Data that has been collected by a business or other entity that doesn't have any direct link to the visitor or customer, is referred to as third-party data. Third party data is often collected, aggregated, and sold to companies to help them build better digital customer experiences and develop retargeting strategies. As it's not collected directly from the company's actual customers, is available to potential competitors, and the data quality is often unclear, third-party data is not considered as valuable as first-party data. However, third-party data can be useful if used as a complement to first-party data when developing personalized experiences.