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E-commerce Product Recommendations - A Simple To Understand Guide

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As an e-commerce platform, don’t you feel your customers are spoilt for choice? Whether it be groceries, accessories, home decor, or anything under the sun, consumers are exposed to unlimited options. This makes the decision-making process relatively more complex. So how can you make your customer’s life easy? Of course, through AI-powered product recommendations, so that they finalize what to buy based on ‘what’s trending’, ‘‘top sellers’, etc. 

Further, suggestions like ‘similar products’, ‘frequently bought products’, or ‘customers who bought this item also bought’, simplifies the buyer’s journey. Suppose, a customer who buys a textured tote bag by Zara will be suggested similar products from the same brand. So our purchasing activity is considerably influenced by personalized product recommendations.

In this article, we talk about how AI can be used for e-commerce product recommendations to create a seamless customer experience.

What is product recommendation?

Product-ReccomendationsProduct recommendation is a filtering-based tool that predicts and shows relevant items to users based on their buying behavior.

Though it may not be fully accurate, the job is done right if the user likes what they see.

E-commerce product recommendation systems are like a well-trained shop counter that fosters cross-selling and up-selling.

In recent years, prominent e-commerce platforms, like Amazon, eBay, etc. have been using proprietary recommendation algorithms to improve customer satisfaction. As there is a lot of information on the world wide web, like research articles, news, movies, it’s crucial that companies segregate information according to user’s preferences and interests.

How do AI-powered recommendation systems work?

AI-powered recommendation systems use 3 different techniques for accurately predicting customer behavior and providing a personalized shopping experience. Depending on your requirement, you can pick the one that best suits your business needs.

1. Collaborative filtering

This technique involves collecting and analyzing data related to user behavior, activity, or interests. The main feature of collaborative filtering is that it analyzes the behavior of multiple customers and their purchase histories.

2. Content-based filtering

It relies on the characteristics of the products themselves, so the results are highly relevant in terms of user interests. Technically, such systems are convenient to implement and offer good quality recommendations even at initial stages.

3. Hybrid filtering

Hybrid systems combine collaborative and content-based recommendation systems. Hybrid recommenders have proved to be more effective and accurate than pure methods. Netflix is an excellent example of hybrid recommendation systems. It compares what users are searching for and what they love to watch for making recommendations (collaborative filtering). The website also offers movie suggestions by taking cues from the films rated highly by the user (content-based filtering).

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Next, we discuss a few AI-enabled recommendation system use cases.

AI-enabled recommendation system use cases

In the era of AI and big data, recommendation systems play a significant role in informed decision-making. Here, we have categorized e-commerce product recommendation system use cases under two heads - computer vision-based and NLP-based.

1. Computer Vision-based use cases

Computer vision simplifies the visual search process by providing intelligent product recommendations. Amazon uses computer vision-powered feature StyleSnap, where you can click a picture and instantly search for similar items. Deep learning and computer vision algorithms are used for detecting images and classifying clothing into ‘tapered pants’, ‘linen shirts’, ‘maxi dresses’, etc. Images are classified into different categories based on colors, patterns, brands, types of clothing and accessories. This helps in predicting demand for a particular brand, color, etc. depending on emerging fashion trends. Through this process, the e-commerce industry can work towards enhancing the overall customer experience, boost sales and profitability.

2. Natural Language Processing (NLP)-based use cases

A recommendation engine using NLP approach assigns attributes to different items. Fashion e-retailer, Alibaba Cloud implements natural language processing to analyze customer reviews and short-text content. This makes it easy for new customers to decide whether they should buy a particular product based on earlier customer reviews. Most e-commerce platforms use NLP to analyze customer sentiments through social media listening. This helps e-retailers to come up with special promotions, innovative product ideas, based on travel trends and festival seasons.

Taking the example of music streaming platform, Pandora plays songs based on user's interests. So if you like a particular song you can give a 'thumbs up' and receive more recommendations from a similar genre. Pandora also initiated a 'Music Genome' project that identifies 400 different musical characteristics for recommending songs. NLP (Natural Language Processing) is used to derive a correlation between a song's lyrics and genre. Similarly, global music streaming platform Spotify uses NLP to skim through news stories, articles and song metadata for producing “tags” linked with each song or artists. Comparing the tags, it offers listeners a pool of personalized song recommendations.

Lastly, we discuss a few best practices for effective product recommendations

E-commerce product recommendation systems - Few Best Practices

Every e-commerce company must follow certain best practices to leverage latest technological trends and boost conversions. Below are mentioned a few common best practices:

  1. Tracking user profile, spending habits, and site traffic data.
    Initiate personalized email campaigns using e-commerce product recommendation engines.

  2. Introduce ‘ Recently viewed’, ‘featured recommendation’, or ‘frequently brought together’ suggestions to encourage your buyers to explore more.

  3. Offer product recommendation when the user adds items to his cart that need accessories (eg. shoes require socks, smartphone require screen guard/back cover).

  4. Create peer-generated product recommendations for social relevance. For example, if a customer bought a hair shampoo, you can suggest that other customers also bought a hair conditioner along with a shampoo.

  5. Keep your online viewers updated about any latest version of a product.

  6. Feature best-selling products based on collection, season, recent fashion trends. This strategy has proved to be highly effective in terms of customer retention.

  7. Display product bundles that club together frequently purchased items in a particular category (eg. Kid’s activity books). You can offer attractive discounts to customers who purchase the bundle.

  8. Showcase items with maximum customer reviews and highest ratings to build confidence.

  9. Give a personalized touch to product recommendations so that the user feels valued.

Product recommendations play a crucial role at each stage of the e-commerce conversion funnel. If you’re actively looking to provide your customers with an improved online shopping experience, invest in product recommendation engines. As an e-commerce platform, if you desire to leverage AI and ML capabilities to optimize the process of product search, Skyl.ai is just the right partner for you.

Check out the various solutions that can be built using Sky Platform here.

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