Product Recommendation Engine - AI in eCommerce
Intelligent product recommendation engine for a home decor eCommerce company from London. Over 32% increase in CRR in Q1 2020 (compared with Q1 2019) and improved Net Promoter Score.
Background
Improve CRR
Our client, a fairly new home decor eCommerce business, wanted to create a product recommendations engine to achieve better sales and make the buying process more convenient for its customers.
The ultimate goal of the solution was to retain more customers, as well as engage them more through individual recommendations.
Value Delivered
32% Increase In CRR
Thanks to the individualized content, the client managed to achieve over 32% increase in CRR in Q1 2020 (compared with Q1 2019).
Improved
NPS
The client regularly gathers data on customer satisfaction. Net Promoter Score has increased after implementation (as of Q1 2020).
Happy Clients Spend More
Visitors who engage with product recommendations generate much higher revenue per visitor.
Business Problem
With the multitude of eCommerce businesses nowadays, each company is trying to stand out. The more products you offer, the more extensive the customers’ choices, and a bigger chance they will find something relevant. But searching through thousands of products can be exhausting. For that reason, companies (the most known are e.g. Amazon, Netflix, Spotify) are using recommender systems, which allow customers to find the products relevant to them quicker and easier.
Our client, a fairly new home decor eCommerce business, wanted to create a product recommendations engine to increase the retention rate, achieve better sales, and make the buying process more convenient for its customers.
Solution
Initial Analysis
Most recommender systems take either of two basic approaches: collaborative filtering or content-based filtering. Other methods, such as demographic-based or hybrid approaches, also exist. We chose to mix collaborative and content-based ones.
Collaborative filtering is a recommendation based on a model of prior user behavior. It uses knowledge of other users to form a recommendation. This is based on the assumption that if user A likes chocolate (or in our example a golden lamp) and user B like chocolate (golden lamp) and popcorn (red curtains), then user A will also like popcorn (red curtains). Essentially, recommendations are based on the collaboration of multiple users, based on which you can filter those who show similar preferences.
Content-based filtering is based on the user’s behavior. This relies on the product’s characteristics. So if a user often buys a specific product (e.g., almond chocolate by company A), we will show him/her the same product by another company (almond chocolate by company B) and also similar products by different companies (hazelnut chocolate).
Therefore, product recommendation runs on predictive analytics, meaning correlating information and making predictions based on it.
Implementation
Data
- Site traffic data: customer’s journey through the eCommerce website, including which items they look at
- Transactional info: historical customer data such, spending habits, past sales, list of items checked out
- Customer profile: demographic data and info about probable interests
- Product listings: names, quantity and a possible target group of a product
- Prices: Pricing information on all products, including past and future sales and which demographics should see the sale price
- Time-sensitive product data: seasonal product launch dates, etc.
“Searching through thousands of products can be exhausting. For that reason, companies (e.g. Amazon, Netflix, Spotify) are using recommender systems, which allow customers to find the products relevant to them quicker and easier.”
Additional Features
Additionally to creating the recommender system, we added recommendation-related options to our client’s website:
- Alternative product recommendations: similar items to the product you browse / alternatives for the product that is no longer available.
- Related product recommendations: proposing a matching product, e.g., a vase that looks good on the table you chose.
- Top weekly items: a tab with the best sellers.
- Better product search experience: we helped our client to add more characteristics of the products, so the search is now more accurate.
Conclusion
We developed a product recommendation system for a home decor eCommerce company. The solution helped the client achieve over 32% increase in CRR in Q1 2020, improve the NPS score, and generate higher revenue per visitor. We used a mix of collaborative and content-based approach, leveraging machine learning for a detailed pattern detection.
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