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How E-Commerce Personalization Converts Browsers Into Buyers

Co-founder, CEO
E-Commerce Personalization
Tags :
Product Recommendations, Product Ranking
3 min read

Office manager Jane is shopping for a red dress; she has already browsed through multiple websites and is finally on yours. She’s still checking the images on the product detail page, when she is distracted by a pop up about the latest air fryer. What will Jane do? Will she buy? Will she abandon? This question is topmost on the mind of every ecommerce retailer. Is ecommerce personalization the answer?

According to Statista, while in the US alone, we expect to have around 278.33 million online shoppers by 2024, in comparison, the average conversion rate for an e-commerce store is estimated at just 2.1%. This is unsurprising considering that users go through several different phases during the buying process.

Imagine the consumer buying process as a funnel.

conversion funnel
Source: Argoid

Based on the funnel, we can say that customers who make a purchase have successfully navigated competing sites, products, prices, and personal commitments to engage for a few minutes with a brand.

As a brand, the challenge not only includes how to make sure that the few minutes of customer engagement leads to a confirmed purchase; but also, how early in the checkout journey should it offer to help the customer in the decision-making process. And that’s where Artificial Intelligence can assist with personalized recommendations and also what is known as conversion rate optimization (CRO)

Know Thy Customer with AI

User experience analytics deals with data points that relate to the user, at various points of interaction with the platform. A user who shops for a hat may need to interact with search, price filters and sorts, thumbnails and the cart. Machine learning algorithms can leverage user behavior analytics to learn user behavior and provide businesses with deep insights, and users with effective personalization.

Such insights provide valuable inputs for user experience testing, through A/B Testing, Search and Recommendation Systems. These systems boost user engagement and target the audience for personalized marketing efforts and help boost customer engagement with business.

Artificial intelligence is used to gather data related to user behavior and preferences, and tailor campaigns to users’ preferences. Customer behavior analytics can then be leveraged for effective data discovery, segmentation and classification of customers based on data points provided to the system.

Nail the Messaging with Ecommerce Personalization

ecommerce personalization

In the previous example, our user Jane was searching for a red dress; sometimes users may get even more specific or vague. Users predominantly use search engines to access products they would like to buy and are often driven away by irrelevant listings. A user’s intent could be complicated for a simple keyword-based search, and often the system must consider the user’s preferences, behaviors and buying habits to tailor their results.

A good recommendation system helps a customer find products that best match their needs, by using their past search, onsite behavior, purchase history among other things . Often, search and recommendation complement each other to deliver customers with the best experience.

Target. Engage. Excite

Gone are the days, when retailers had to painstakingly collect data on mailers, paper and survey forms. AI/ML is not rocket science, and with a few recommendation and personalization tools, you can turn your ecommerce business around and boost user engagement. Businesses from all around the world have begun to move to AI-based digital platforms to better understand their customers and give them a distinguished experience, tailored to their needs.

You can leverage big data analytics and machine learning models to rapidly scale up consumer analytics. This enables you to target advertisements to your target audience and make campaigns that resonate with users’ sentiments like AMP email, which provides users with personalized shopping experiences, and buying recommendations, all within the confines of their inboxes.

You can use ML models to find the best advertisement thumbnails, locations and time intervals that result in the best rates of conversion. For example, Home Depot analyzes customer’s buying patterns over 2300 stores to determine the optimum item locations in-spite of different shop layouts. THD empowers their employees with state-of-the-art search for efficient in-store navigation. Argoid’s AI technology predicts E-commerce consumer behavior reliably, leading to a 90% improvement in click-through rates and 50% uplift in conversions.

AI based software can help you test your user interfaces, so it stands the test of user volumes, and provides them with the most intuitive experience, young and old alike. Your store can be appealing to all and easy to shop, converting browsers to buyers.

In conclusion, personalization and recommendation turns your brand buyer into a better shopper while building a differentiated personalized experience for your customer, In turn, significantly improving the key performance indicators such as conversion rate, average order value and reducing abandonment, drop rates, for your ecommerce store.


What is eCommerce personalization?

eCommerce personalization is about creating a unique, tailored shopping experience for buyers by adding different personalization features like product recommendations, personalized product pages, personalized emails, personalized push notifications and so on.

Why does eCommerce personalization matter?

eCommerce personalization reduces time and search efforts of the buyers and provides them with most desired products with just a few clicks. Hence, it can improve conversion rate easily for eCommerce stores.

What is the latest eCommerce personalization trend?

The latest eCommerce personalization trend is to add a recommendation engine to your eCommerce store to generate product recommendations easily and engage more customers.

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