Shopping is a very essential part of every human being living out in the world. Our purchasing behavior is influenced by the products we find attractive or the products preferred by our friends. We are inclined towards products praised by people because we have faith in them. In the present day, many successful e-commerce sites and streaming platforms make use of recommendation engines. Any website or company recommends a product with a perception that customers will have more choices and it will enhance their shopping experience and increase sales.
What are product recommendations?
The user data that is available on the website will help to generate product recommendations. Products which are more likely to be purchased by the customers are the only one which have been shown up. It is very beneficial when it is done in a proper and accurate way. It saves money and helps in customer retention. The more personalized the experience is, the better is customer satisfaction and retention. A 2018 report shows that returning customers who engaged with a suggested product (by clicking on it and reading the description, for example) were 55% more likely to make a purchase during that shopping session. For new customers, that figure was 70%.
There are 3 main types of recommendation engines:
1. Collaborative filtering systems
In this method, data is being analyzed about collective foreseen opinions shared by customers regarding the products which will be more opted by the customers. It also considers other factors such as what are the searches done by the customers, whether the customer is a first-time buyer or not and the location of the buyer. It is divergent because it examines the behavior of more than one customer and analyzes their purchase history.
2. Content-based filtering systems
In this, the product is recommended after analyzing the previous choices made by the same customer. It does not relate the users with the products used by similar users, rather than this it uses its own analysis to develop a customer preference profile. For example, the pop-ups coming on the screen which says “If you liked this, you might also like…”.
Content-based focus on one data from one customer at a time. It does not classify the trends prevailing in a group of similar customers.
3. Hybrid recommendation systems
This method combines the collaborative filtering systems and content-based filtering systems. It generates recommendations of products using data from similar users and past preferences of the specific user. These recommendation systems consider the data of collaborative and content-based predictions individually, then combine them. It can be considered as the largest storage of data and most versatile which is capable of generating accurate results to a larger extent.
What do recommendation engines look at?
In the present time, the user data is increasing and is easily accessible, so it is effective to make use of a recommendation engine to put forward a very explicit proposal to your customers.
Recommendation engines standardly examine the data such as:
- A customer’s quest queries.
- Customers purchasing habit and past records
- The wishlist they have added in their shopping cart
- Social practices (such as shares)
- Their geographical location
- Demographics of the customers
Various ways in which an online recommendation engine can increase sales
When everyone is on an e-commerce site and using it for their growth, it is difficult to say that you are ahead of your competitors. There are various things executed by everyone for their products such as innovation, offering an incredible shopping experience but your customer needs personalized shopping these days. Major five ways in which an online recommendation engine helps to increase sales with the practice of a personalized recommendation engine.
1. Offer personalized shopping experience to your visitors
Personalization plays an imperative role in showing relatable products to visitors. It offers an irresistible shopping experience, turning it into greater conversions. It is very important to focus on that recommendation engine adequately examine the behavior of the people visiting the site and what they are looking for.
2. Cross-sell complementary products to your visitors
Cross-selling complementary products to your customers involves selling products that enhance or increase the value of the original product by adding a new range of capabilities. For example, while selling phone sellers always try to sell insurance.
3. Show recommendations that deliver social proof
Social validation is the result of the modern internet generation as they count more on what opinions their friends or relatives have about the product and how they feel about it. For instance, recommendation widgets such as ‘People who viewed this also viewed’ highlight recommendations based on the understanding of the crowd and use social proof to engage and retain customers.
4. Upsell alternate products to your visitors
Offer the visitor with more products and options which they have not known about otherwise. It can be very helpful as sometimes it may lead the visitor to spend more and it results in increasing the average order value across your site.
Product recommendations make the products of their choice easily accessible and help them to analyze what they want to buy. The more effortless and peaceful experience you make for them, the more confidently they will think for you. The usage of sales data to focus the attention to recommendations and highlight the relevant products, the chances of visitors buying that product increases and retention of customers also increases.
5. Personalization based on location and time
It has been discussed in the above points the way in which eCommerce sites understand the behavior of users and take into consideration social proof to offer a relevant recommendation. While giving any product suggestions it is important to keep in mind that it is location-based recommendations as it helps in the conversions.
Product recommendation best practices are valuable only when you are getting the most out of your recommendations. The most efficient manner in which you can maximize your conversion is to adapt certain things in your product recommendations. The best practices of product recommendations are subsequent to analyzing the right behavior, testing strategies, introducing recommendations in unexpected places, giving prominence to the right number of recommendations and the usage of social proof.