The Usage of Recommendation Engine. Kipling Success Story
The Main Challenge
- Provide personalized recommendations based on customer visitors previous actions, demographics and other personal data.
- Boost sales and Increase conversion rate.
About The Client
Kipling offers a colorful array of designer handbags, luggage, backpacks and travel accessories.
The products are lightweight, lasting and above all functional.
Kipling creates fun, colorful designs that are not a slave to trends because at Kipling they feel it more important to have personal style than perfect style.
Kipling was founded in 1978 by three designers in Belgium, who discovered nylon fabric.
Today Kipling’s well-known bags and accessories are available around the world in 436 stores in 80 countries and can be found in more than 7500 shops including the best department-stores, and their online store: kipling.com.
Kipling relies on SmartHint recommendation system for its individualized offers, product recommendations and other content based on their visitors’ previous actions, demographics and other personal data.
It’s impossible to over-emphasize the importance of personalization in eCommerce.
Kipling has an audience that understands receiving personalized recommendations matching with what they are looking for as an incentive to actually buy the item. SmartHint recommendation system uses artificial intelligence to identify a consumer’s shopping profile. That’s the main reason it always provides accurate and personalized product recommendations to its customers.
How SmartHint Helped
– SmartHint identified a consumer’s shopping profile, offering a personalized recommendation to customers.
– Improve Kipling’s customer experience.
– Content-based recommendations showed in the “You may also like” section. The main idea is that you like a product, then you’ll also like “similar” products. As the user takes more actions on the website, the recommendation engine becomes more and more accurate.
– By using a collaborative filtering method SmartHint suggests what other people have purchased to make recommendations for other users at “Also bought with” section.
Kipling and SmartHint cooperation are here to show the power of relevant personalized recommendations in various contexts, using all data available.
As a result, SmartHint recommendation engine increased shopper engagement and optimized conversion rate. The conversions rate increased by +192,86% in one month after integrating SmartHint. Hereby, smart recommendations represent +21,61% of the whole revenue of Kipling.