NaXum Explains How You Can Take Advantage of Personalized Recommendation Engines on Your E-Commerce Website

E-commerce technology has developed a great deal over the past ten years. The advancement of areas like referral marketing has brought the entire industry forward with it.

Today, technology allows e-commerce merchants to refer customers to products in which they may be interested. This related product marketing is responsible for higher sales among e-commerce companies and brings customers more relevant suggestions for their shopping pleasure.

NaXum explains how technology can help salespeople create relevant suggestions for the customer.

How E-Commerce Works

E-commerce is defined as the practice of selling merchandise or services over the Internet. The basic unit of e-commerce sales is the transaction. To handle an online transaction, merchants must process a sale online (usually with a credit card) and deliver the goods to the customer.

E-commerce combines three systems: a server that manages the online storefront and processes transactions, linking to bank computers to verify customers’ credit cards, a database that can keep track of the items that are in stock, and a dispatch system for the warehouse where items can be located and sent to the buyer.

How Do Customers Find What They Want?

While some customers are laser-focused on finding one particular item with a specific make or brand, others are more open to exploration. It is these customers who NaXum and other referral marketing companies target.

Customers who search for products may be served with a set of listings for items that are close to the search results. Some of the criteria that could lead to these suggestions include the things that similar customers have bought, the things that the customer has searched for in the past, and items that are tagged to correspond to the item the customer is viewing at the time.

This form of marketing is highly effective. Product recommendations account for up to 31 percent of e-commerce revenues. Up to 12 percent of customers’ purchases are comprised of products that an e-commerce website recommended to them.

Salesforce found that while e-commerce customer visits that resulted from a shopper clicking on a recommendation make up only 7 percent of site hits, they are responsible for 26 percent of the average site’s revenue.

How Personalized Recommendation Engines Work

The process of creating personalized recommendations for each website visitor may seem self-explanatory, but it contains some complex steps. This system is useful for e-commerce websites of any type, especially those selling goods at retail.

Choosing Data to Formulate Suggestions

First, it is necessary to decide which data your recommendation engine will use to predict a customer’s wants. Some of the data points that may be useful include aggregated data for the category and product views, what items customers add to their carts, and the internal record of product searches.

User-specific data is even more useful. The website should be able to collect the user’s viewing and purchase history.

Finally, product data is the third piece of the puzzle. Price, brand, availability, and other product attributes are in this category.

Selecting an Algorithm

The selection of an algorithm that recommends products is different based on a number of criteria. First, the website determines whether the visitor is new or returning. A new visitor will be shown a list of the most popular products on the site. A returning visitor will receive information about products they have browsed in the past, products similar to those they have bought in the past, and special recommendations from the built-in AI based on their user information.

Applying Special Rules

There may be overarching rules that a store wants to prioritize above the personalized recommendation engine’s suggestions. Some examples include avoiding brand conflicts, promoting seasonal items, and keeping low-stock products from being recommended.

Suggestions for Product Recommendation Engines

Here are several suggestions from seasoned online retailers that will help you set up your personalized recommendation engine.

• Display a list of suggestions based on the browsing history of the visitor. Adding a shopper’s name gives this item extra punch.
• ”Frequently bought together” recommendations are excellent and help to increase sales.
• Product recommendation engines can also be used in your email marketing. You can use the results of the recommendation engine to target customers with relevant marketing emails. Abandoned cart emails are particularly effective.
• Featured recommendations can give shoppers ideas that they may not have thought of on their own. The customer will welcome these recommendations as new possibilities.
• Using the shopper’s own browsing history can help them remember items they intended to buy and consider buying.

Understanding How E-Commerce Suggestions Work

NaXum believes that websites’ e-commerce capabilities can be enhanced through the use of personalized recommendation engines. Using the criteria in this article, you will decide on the best features for your website and then turn to a professional to have them implemented.

Personalizing a website can raise your sales figures and gain more repeat business. It makes sense that every e-commerce retailer would want to participate.