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Give your ECommerce ML based Predictive Recommendation for your products

Give your ECommerce ML based Predictive Recommendation for your products


Online shopping has expanded its spectrum to groceries over the last five years. And the responses received have handed eCommerce yet another victory. Buying Indian groceries for Indians in the USA was a nightmare a few years back. But now, Grocerybabu offers Indian grocery delivery in the USA. With Grocerybabu, you could buy Indian groceries in the USA and get the same flavors and taste as you got in India.

The service is highly data-driven. To provide a seamless and personalized user experience, Grocerybabu uses machine learning predictive selection. It offers recommendations based on your previous activity and orders. The recommendation system has added to the superior user experience.

 

What is a Product Recommendation System?

A product recommendation system is a software tool designed to dynamically populate products that the user might be interested in buying or engaging with. This system uses advanced machine learning techniques such as predictive selection to create a complex connection between users and the products to generate the best possible results.

The product recommendation system relies on a net of connections generated. The following are the types of connection a product recommendation system creates:

1: User-product relationships-

It is based on users’ individual product preferences. For example, Grocerybabu recommends products based on your previous orders or search history.

2: User-user relationships-

It maps a connection between users based on the fact that similar people are likely having similar product preferences.

3: Product-product relationships-

We build a connection based on similar or complementary products that can be put into relevant groups. For instance, for every dry fruit you buy, we recommend similar nuts or dry fruits that you might buy.

 

Why is it necessary?

As internet users, we interact with a lot of recommendation systems. Right from Google search to online shopping, all our actions offer us another set of preferred products that we might be interested in engaging with. The product recommendation system is one of the most successful and widespread applications of predictive product selection techniques under the roof of machine learning. When implemented properly, it can effectively boost sales, click-through rates, and other metrics.

Not just an increase in the company’s revenue, recommendation systems create positive feedback from the users. This is because of the personalization of products and ease of shopping on the website. It creates a sense of loyalty towards the company without compromising on the quality of service received.

 

What Grocerybabu aimed at?

The only question we wished to answer was “How to personalize user-experience at our website?”. Though it was a simple question, it was much more important to generate positive feedback from the users. Recommendations are embedded at every stage of the shopping experience at Grocerybabu.

The recommendation system triggers as soon as you place an item in the cart. A list of similar products is generated and is displayed in the catalogue. It provides you with a similar experience of shopping groceries physically as you can always find similar items lined up on the same shelf. If you are a regular buyer, you can shop by history and order weekly groceries at a time.

Data Sources

The database used for our recommendation system mainly comprises the cart records of various users. We build a complex net of connections and relations to recommend to you similar and relevant products based on items in your cart. We also provide personalized suggestions based on your previous orders.

The category of products provides useful information to group the products mentioned under the title of frequently bought together. The search keywords you type are also considered. Other features such as demographics and culture are also taken into account to provide the best possible suggestions.

Results Achieved

As a result of the strategy to provide supreme user experience, the following observations were recorded-

1: Increased user engagement-

The overall engagement rate of users with the shopping portal has increased drastically. This led to an increase in the number of deliveries per day and the average revenue generated.

2: Doubled value-

The value of the online grocery market doubled between the years 2016 and 2018. Grocerybabu played a major role in setting up the numbers as it holds an extensive position in USA online grocery stores.

3: User satisfaction-

Users are highly satisfied with the feature as it can be seen with the increased user base over the past year.

 

Final Thoughts

By a thorough case study of the recommendation system of Grocerybabu, we can conclude that resembling techniques deliver good results. Instead of using just a single method, different sets of connections can be defined to hand over the best outcome. The product recommendation system not only boosts up your business growth but also develops a strong bond of Loyalty with the buyers with the personalized interface.

We at SNDK Corp help your eCommerce business with  ML-based Predictive Recommendation for your products. Inquire more by logging on to our official webpage.

 

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