Best Practices for using Machine Learning on E-Commerce today
Providing a good customer experience online is one of the essential ingredients for the success of online businesses.
Customer expectations have been greatly increased and these needs should be met with our products or services. Personalization can be used across a small part or entirely in category listing, emails, push notifications and through stores.
After the user clicks on the ad, navigate an e-commerce, searches or click on some products merchandising, the machine learning system will use the purchasing and behavioral data and use them for future recommendations or for clustering similar customers.
“35% of what consumers purchase on Amazon and 75% of what they watch on Netflix come from product recommendations based on such algorithms”
– McKinsey
Larger brands have the human and financial resources to create their own systems, using enterprise products, but some other companies must wait for a while to find a simplified plug-and-play system.
Although collecting customer data to improve future marketing efforts is not a new approach for most of you, modern strategies currently using machine learning and artificial intelligence algorithms to process huge amounts of information to provide a better customer experiences.
Personalization in E-Commerce
Dynamically provide personalized content, products or promotion offer to users, email subscribers or application users based on the characteristics and intentions of website visitors.
Automated recommendations, personalized search and enriched personalized marketing campaigns delivered at different points in the customer journey can be based on rules or artificial intelligence based on machine learning of user interactions.
Product Recommendations
This is the most typical usage of Machine Learning in e-commerce, based what previously bought and discarded carts grouped by users, it recommend the most product probably to buy to a specific customer.
Email
In this field, the machine learning, can help for creating personalized messages delivering results much faster and more effectively than simple A/B testing.
Notifications Management.
Machine Learning can define when send a specific communication to a customer, choosing by itself the right time of the day to send a push notification or send an email.
Personalized Navigation and User Experience.
The main goal is to personalize the e-commerce replacing a standard merchandising listing and carousel with a dynamic and more consistent showing for the customer experience.
According to a Accenture report, 75% of consumers are more likely to buy from a retailer that recognizes them by name, recommends options based on past purchases, and knows their purchase history.
ChatBot Automation.
The Natural Language Processing (NLP) currently used today in several area such as cart, checkout, for activation or retention phases or easily for tech and customer service ticketing.
Shipping and Inventory Management.
Even the Inventory can be managed using Machine Learning to better predict the timing of purchase for every product while minimizing the cost of the excessive stock.
What is the difference between Personalization and Hyper-Personalization?
Personalization is the integration of personal and transaction information (such as name, title, organization, purchase history, etc.) into your communication.
Hyper-personalization goes a step further, using behavior and real-time data to create highly contextual communication relevant to the user.
Companies can send highly relevant communications to specific customers through the right channels at the right time and place. With the increasingly fierce competition in digital marketing, hyper-personalized marketing provides organizations with opportunities to effectively attract customers, deepen existing relationships, build new relationships, and improve customer experience.
"Recommendations" for your
E-Commerce
Research increasingly shows the value of product recommendation and its key role in personalization strategies. Recommendations not only increase the conversion rate, but also improve the user experience, so that visitors come back and increase the average order value.
- HOMEPAGE: best-selling products on the homepage for hooking your users attention as soon as they reach your site.
- PRODUCT PAGE: related to items you’ve viewed to increase number of products added to cart.
- PRODUCT PAGE: "Customers who bought [this item] also bought [that item]” provide a relevant product suggestions to user in order to increase number of products added to cart.
- CATEGORY PAGE: best-selling category products in the first part of the page for hooking your user attention on what most probably interesting him the most.
- CART PAGE: frequently bought together recommendations (Cross-Selling) to increase average order value (AOV). Before your customers move into the checkout process, you have one last opportunity to show them recommended products, so make sure the products you’re offering don’t distract users from completing the purchase.