*I frequently get asked questions about Data Science, so in the interest of helping as many people as possible, I’ve started this blog to answer those questions as simply as possible. This is a robust topic, and if you want a more in-depth discussion, please revisit my blog, where we will be going into greater depth at another time.
Personalization in e-commerce is the best way to tap into a customer’s individual wants and needs, recommending products and services that are tailored to them specifically. Powered by data science, personalized recommendation systems are revolutionizing e-commerce, driving sales, and increasing brand loyalty. Read on to learn how you can enhance your company’s e-commerce experience.
Personalization is a great way to serve your consumers and attract new ones to your business. Personalized recommendations are powered by data science as they pull real-time customer data, like demographics, browsing history, and past purchases, to make product recommendations tailored to the individual consumer.
Personalized recommendations are revolutionizing the e-commerce space, providing consumers personalized messages and offers, cart abandonment emails, and more, customizing the e-commerce experience to fit their needs. If you want to level up your e-commerce and better serve your consumers, this blog will teach you everything you need to know.
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When it comes to personalization, data science is key. Data science techniques are what help you to analyze your consumers’ behaviors. Whenever you interact online, there’s a trail of data following you, from your product search history to reviews you’ve read to previous purchases you’ve made.
This data can then be collected, stored, sorted, and analyzed to generate meaningful insights about what consumers want and when — which is the heart of data science. Companies can then use these data points to add personalization to the e-commerce experience and make predictions and recommendations for individual consumers — all in the hopes of making more sales and driving profits.
Personalization in e-commerce can not only help drive sales but also aids in customer retention and brand loyalty and can even provide a competitive advantage, all by tapping into widely available customer data.
Like personalization, data science is also key in developing recommendation systems, which use user preferences and activity data to make recommendations. For example, say you watch an action movie on Netflix; once the movie is over, Netflix will recommend other related titles to you based on what you just watched. This is also present on YouTube and even on e-commerce sites like Amazon.
There are various data science techniques used to drive these recommendations, two of which include:
- Collaborative Filtering
- Content-Based Filtering
Generally, collaborative filtering is used by recommendation systems to identify similar patterns among users and filters out recommendations a user might like based on reactions and ratings from similar users. Collaborative filtering basically works by taking a large group of people and then finding a smaller set of people within that larger group that have similar tastes.
Content-based filtering involves using similarities in features to make decisions, revolving around comparing user interests to product features. Products that have the most overlapping features with user interests are the ones that get recommended. Content-based filtering is an easy-to-scale data science technique because it doesn’t require much data.
While there are different techniques used for personalization and to make recommendations, there are also different types of data that can be used, including:
- User Data
- Product Data
- Contextual Data
User data is any data that is created by the people interacting with your product or platform. In a lot of cases, this can include personal and sensitive data points like personally identifiable information, financial information, health data, and device location.
User data can be explicit, meaning it was provided by the user directly, or implicit, meaning it was not provided directly. User data can also be obtained through external parties. User data can be extremely valuable during product development and design, providing insights on how you can improve your products and offers.
Product data is simply all the data available about a specific product and includes attributes about what makes the products different from each other, like size and color. Product data and attributes are often displayed on product detail pages and help consumers identify their options when shopping online.
Product data can also be used when companies are determining the best way to organize their products on shelves in stores or in categories online.
Contextual data provides perspective and broader understanding by showing how pieces of data relate to each other and form a larger picture. Your customers can provide contextual data through their previous buying behavior, preferences, location, and even their social media activity.
Contextual data can be used to provide relevant recommendations. For example, think about how seasonal weather patterns affect sales of certain products and services. Consumers need to make certain purchases at specific times — like snow shovels in the winter and bathing suits in the summer. Businesses can use this data to respond accordingly and make better recommendations based on context.
When looking to improve the e-commerce experience and make better connections with your consumers, there are several benefits to implementing personalization and tailored recommendations for both your business and your consumers, including:
- Increased Sales and Revenue
- Improved Shopping Experience
- Builds Brand Loyalty
- Increased Customer Retention
- Competitive Advantage
One of the biggest advantages of adding personalization to your e-commerce is its ability to generate more sales, thus increasing your revenue. Offering features like personalized recommendations and special discount codes can help increase conversion rates and also increase the likelihood that consumers will shop with you again.
It has also been found that adding personalization to e-commerce can decrease shopping cart abandonment rates. There are several reasons why a consumer may abandon their cart, but by providing them with personalized recommendations at check-out or sending them reminder emails with discount codes, you can increase the number of consumers that return to their cart and complete their purchase.
Personalization can also include the e-commerce shopping experience as a whole. When you tap into data to gather insights about who your customers are and what they want and need, you’re better equipped to cater to their desires and meet their needs.
With data science, you can create a personalized shopping experience for each of your consumers. You can learn what they like and what they’re looking for and provide relevant recommendations and also send notifications about promotions they would be interested in.
When you add personalization to e-commerce, you’ll be improving your consumers’ individual shopping experiences, thus building brand loyalty over time. Consumers like to feel seen and cared for by the companies and brands they frequent, and providing them with in-depth personalization and a tailored experience will do just that.
With an improved shopping experience and increased brand loyalty, you’ll likely also see an increase in customer retention. When your customers are happy and feel like your company cares about them individually and also hits the mark when it comes to recommendations, they’re likely to keep choosing you over the competition.
At this point, personalization in e-commerce is a requirement for a lot of consumers; that said, there are still some companies that haven’t implemented e-commerce personalization successfully yet.
If your company is already on top of using data science to drive personalization and recommendations for your consumers, you’ll have a sizable competitive advantage in the market.
While there are many benefits to adding personalization to the e-commerce experience, there are a few challenges to be aware of as well.
When faced with challenges, data science techniques like matrix factorization and deep learning can help address issues that may arise. Matrix factorization is a mathematical algorithm commonly used in recommendation systems to predict what users might be interested in. Deep learning can be used in more complex recommendation systems because they process information and data in a nonlinear way.
Some of the challenges in generating personalized recommendations include:
- Cold Start Problems
Cold start problems can occur when new users and items are added to the e-commerce system but can’t be recommended to shoppers yet because it is newly introduced and doesn’t have any reviews or ratings attached to them. This makes it hard to make recommendations for the new entry or predict who may be interested in it.
Sparsity issues can arise when many users don’t give ratings or reviews for products they’ve purchased. A lack of data here makes for a sparse rating model and can decrease the likelihood of finding sets of users with similar interests. Essentially, if you have a sparsity issue, you have insufficient data.
Overfitting occurs when a machine learning model can give accurate predictions or recommendations based on training data but not new data. Overfit models are often inaccurate. This means that if you’re dealing with an overfitting issue, your recommendation system likely won’t be able to recommend new products to consumers.
Using data science to power personalization in e-commerce isn’t a novel concept and has been implemented by several major companies. These systems they’ve developed have greatly impacted the e-commerce space and the broader world of consumer products, revolutionizing how people shop online and interact with brands.
Some successful and widely-used personalized recommendation systems in e-commerce today include:
Amazon truly tops the list when it comes to the most successful, effective e-commerce personalization. The entire Amazon homepage is full of personalized recommendations based on previous purchases and items you’ve viewed or favored. This ensures that every person with an Amazon account has their specific interests reflected in their recommendations, creating a highly personalized shopping experience for everyone.
Netflix is best known for its viewing recommendations, thanks to its massive, innovative algorithm that is constantly taking in data points and evolving to perform better. Netflix makes recommendations for shows and movies based on what you’ve previously watched and how you’ve rated titles.
If you watch a lot of horror movies and rate them highly, Netflix will make more recommendations in that genre, but say, for example, you never engage with romantic comedies and rate them poorly; you won’t receive recommendations for those. The recommendations are highly personalized and tailored to each viewer’s unique preferences.
Starbucks started implementing personalization when it deployed the rewards system on its mobile app. The app is very intuitive and designed to make the ordering experience more personalized. The app can remember a user’s favorite drinks and customization preferences and then rewards them with perks and discounts based on their activity. The app also uses demographic data to identify the closest Starbucks to users at any given time and uses AI to send out varying personalized messages to consumers to promote unique offers.
Sephora adds personalization to the e-commerce experience by making product recommendations by comparing products that are similar to the ones you’re currently viewing. The company’s “Compare Similar Products” feature on the website compares products with similar ratings, price points, and ingredients to help consumers when making purchase decisions.
Data science-powered personalization is the future of e-commerce. It offers several benefits to both businesses and consumers and allows companies to maintain relevancy and stay on top of trends in an increasingly digital world that prioritizes personal touches.
Personalized recommendation systems have grown a lot over the years and are likely to continue evolving with the help of AI and deep learning, creating more complex, accurate, and effective systems. There is currently research underway about how deep learning and reinforcement machine learning approaches can be implemented to take recommendation systems to the next level.
As technology and data science, and analytics continue to advance, businesses will be able to create more human-like systems, thus improving the quality of the recommendations they’re making. There will also likely be shifts to make personalization feel even more personal and more complex — technologically advanced recommendation systems can make that happen.
Personalization is key if you want to drive more sales and improve the e-commerce experience for your consumers. Personalized recommendations are powered by data science as they pull real-time customer data, like demographics, browsing, and purchase history, to make product recommendations tailored to an individual consumer.
Personalized recommendations are revolutionizing the e-commerce space, providing consumers with personalized messages and offers, product recommendations, and more, customizing the entire e-commerce experience to fit their needs.
In the realm of e-commerce, data science-powered personalization will shape the future. Most all consumers want the brands and businesses they engage with to offer some level of personalization. Learning to implement these systems will help you position your business for success in an increasingly digital world, giving you a competitive advantage over other businesses that are slow to adopt.
About the Author
Tiffany Perkins-Munn orchestrates aggressive strategies to identify objectives, expose patterns, and implement game-changing solutions with agility that transcends traditional marketing. As the Head of Data and Analytics for the innovative CDAO organization at J.P. Morgan Chase, her knack involves unraveling complex business problems through operational enhancements, augmented financials, and intuitive recruiting. After over two decades in the industry, she consistently forges robust relationships across the corporate spectrum, becoming one of the Top 10 Finalists in the Merrill Lynch Global Markets Innovation Program.
Dr. Perkins-Munn earned her Ph.D. in Social-Personality Psychology with an interdisciplinary focus on Advanced Quantitative Methods. Her insights are the subject of countless lectures on psychology, statistics, and real-world applications. As a published author, coursework developer, and Dissertation Committee Chair, Tiffany still finds time for family and hobbies. Her non-linear career path has given her an exclusive skill set that is virtually impossible to reproduce in another individual.