The holiday season is round the corner and the shopping bags are out. With more and more people willing to buy online, E-commerce industry is going from strength to strength. Also, with the increase in reach of technology there are various e-commerce websites who are trying to increase their share of pie. They are trying their best to deliver a good user experience and finding innovative ways to engage their customers.
E-commerce companies are serving recommendations to the customers when they buy online. Most of the recommendation systems, consider customers’ previous buying trends and click stream i.e. pages they have visited while browsing the website, to predict the items that the customers may be interested in buying. These suggestions are used to upsell or cross sell other products that are a part of the inventory.
However, these sites are also increasingly facing one problem with recommendation engines known as “the cold start problem”. As mentioned earlier, recommendation systems rely on the past buying trends as well as the click stream data to recommend other products. But this approach fails especially in case of a new visitor. The system is unable to personalize the recommendations as there is no historical data available about their previous buying behaviors or their site browsing history. The recommendations in this case would be generalized recommendations and might not be of much interest or any relevance to the user. They normally push the most popular items which may not cater to the preferences or tastes of the buyer in such cases. Also, in case of a new product to be launched there is no past data to support. The buyers haven’t had a chance to look at the product let alone it being recommended.
The best way to encounter the cold start problem would be to gather more and more knowledge about the website visitors irrespective of whether they are new user or a returning user. This can be done by generating a holistic view of the user based on various data points and not just sticking to their click-stream data.
At Infinite Analytics we are able to resolve the cold start problem by focusing on various parameters of a buyer. We create a complete view of every customer, by mapping the user profile to the :
- Product catalog
- Macro-trends from the web
- Online as well as offline data (both structured and unstructured)
Through the use of NLP, Machine Learning and a lot of predictive analytics, we can predict user’s behavior for retail and e-commerce applications. This allows us to serve personalized recommendations to the customers even in absence of any previous data. Also, once the user browses various sections we analyse their click stream and refine the recommendations in real time to constantly deliver the best recommendations to them. This not only improves online conversions but also provides great user experience as they are able to view the products and offerings personalized as per their tastes and preferences.
The companies would benefit enormously by understanding their customers quickly and ensure that they would be the number one name for shopping on their priority list. And it is the quality of personalization that will help them in this endeavor to a great extent.
In case you would like to understand how we apply our technologies for resolving this issue you may write to us at firstname.lastname@example.org for more information.