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Personalization has altered the way of online shopping experience. This practice is keenly used by the online retailers to create personal interactions and experience with its customers on e-commerce sites. Using personalization, the retailers dynamically show contents, recommend products depending on the consumer’s browsing behavior, purchase history, psychographics and demographics. This demographic-based approach to personalization helps for customer segmentation and showcase the most relevant products in their shopping account. As the practice of E-commerce personalization has evolved in the world of online shopping — it’s time for the small business and online brands to get evolved along with it too.
To understand its customer better, Brands collects data such as preferences, age, location, and shopping history, and uses it to segment its audience into groups, compiling similar customers together. They name each segment as persona and then create tailored content exclusively for them. The brand’s Digital Experience Platform (DXP) then sends tailored messages and products solely to each persona — hence giving a personalized update on products to each persona-member in the process.
Research reports suggest that 98% of businesses that adopted E-commerce personalization as their market strategy registered a significant improvement in their customer engagement processes.
Online shopping websites which showcases exclusive products representing a person’s personality and uniqueness attracts the maximum traffic. All humans are conditioned is such a way that, they prefer to have customize experiences as it fulfils their desire to control. This characteristic can be well utilized by personalizing the products keeping in mind the customer’s needs. E-commerce personalization not only provides a good opportunity for customizing the products that people like to use but also is a smart financial move.
There are plenty of products which can be customized easily in terms of size, color, logos, name or offering personalized designs. The main reason for E-commerce personalization is that it follows a low investment inventory model. Products are ordered from the inventory only when an order is placed which means there is no need to find a place for storing large volume of products and wait for its purchase order. Another way of doing it is to outsource a third-party supplier who can operate a “print-on-demand” type of e-commerce business and handles all the logistics in a similar way of drop-shipping model.
Every customer shows an extra loyalty for a brand that they like using. If these brands start treating its customers with tailor-made products, then retailors can easily expand their customer base. Personalization is about reaching out to its right set of customers with the right content and offers – at the right time. This can be done by automating some processes, keeping in mind when to step in and engage with the customers in a person-to-person, authentic manner.
To optimally utilize the features of advanced personalization, the companies having a transactional e-commerce site should capture data and their personalized experiences based on the following parameters; Context such as (Device type, Time, location, Referral source), Behavior (Recent views & product categories, Items rejected, purchase history.
Apart from these data points, there are thousands of other parameters that needs to be captured. However, this is beyond the scope of human capability. This is where machine learning can play a key role. It can help the personalization platform to consider all these different data points as context to determine the best choice for the customer in real-time.
However, Companies are still facing lots of challenges in finding ways to make the most out of this strategy. Almost half of the marketers still believe that a big volume of their customers base inaccessible. In fact, more than half of the online retailors accept of not successfully delivering personalization to their audience. Amazon personalization provides an out of box service for retailers to leverage the personalization capabilities of Amazon.com on their digital platforms.
Amazon Personalize is a machine learning service that makes the developer’s life easy in building individualized recommendations for its users. A lot of activity is revolving around machine learning to improve customer engagement by driving personalized product and content recommendations, search results and specific kind of marketing promotions. However, due to the complexity of the process, not every company is able to utilize the machine-learning capabilities to produce these sophisticated recommendation systems. But with Amazon Personalize, the developers with no prior machine learning experience can also easily build sophisticated personalization capabilities into their applications, by making use of machine learning technology developed from years of use on Amazon official site.
Amazon Personalize offers a gamut of activities through its application such as – clicks, page views, signups, purchases. It also provides an inventory for the recommended items such as articles, products, videos, or music. The user can also provide additional demographic information from its targeted customers such as age, or geographic location. Amazon Personalize draws meaningful insights from these data and applies the right algorithms, educate and enhance a personalization model that is customized for the given data. There are no safety issues as all data analyzed by Amazon Personalize is kept private and in a secure mode. It is only used for customized recommendations. Users can start displaying personalized recommendations by making simple API call. And the best part is that users pay only for what they use, with no minimum fees or upfront commitments.
Using Amazon Personalize is almost like having Amazon.com machine learning personalization team 24hours available in a day.
To create high-quality recommendations, there is a need for the right set of data and the right technology. Amazon Personalize algorithms are designed in such a way that it removes all the common problems coming in the way of custom recommendations.
To closely understand the customer’s need it is important to track the time spent while browsing the products. This helps in making the right recommendations until they move on different sites. Amazon Personalize meticulously combines the actual time activity data with existing user profile and product information to identify the right product recommendations for that moment.
Amazon Personalize provides consistent information about each user and their behavior across all channels and devices. All these Personalized recommendations can be easily integrated into websites, mobile apps, or content management and email marketing systems with the help of a quick API call. Everything can be personalized based on the individual tastes and needs right from on-site search, product sorting, recommendations and offers.
Amazon Personalize generates custom personalization model in just a few clicks. It automates and accelerates the complex machine learning tasks required to build, train, tune, and deploy a personalization model.
Everyone wants to be treated special and E-commerce personalization coupled with machine learning is making it possible. When a business delivers personalized experiences, it is more likely to convert its potential customers and can witness an increase in their revenue by 26%. Furthermore, with their customized product recommendations, they can make customers spend more time and money on their website.
