Product recommendation algorithms present online shoppers with a personalized choice of the most relevant products in real-time. They have become indispensable for the operation of e-commerce websites, along with consistent design, a virtual phone service, and other crucial features.
Such product recommendations accounted for up to 31% of e-commerce revenues, according to research conducted by Barilliance. On average, customers saw 12% of their overall purchases coming from products that were recommended to them.
E-commerce personalization tools offer intelligent product suggestions based on both individual and aggregate browsing behavior and purchase history.
These tools help to shape unique experiences across the buying process for each of your customers. Visits where the shopper clicked a recommendation comprise just 7% of total site traffic but make up 24% of orders and 26% of revenue, the study by Barilliance revealed.
Here we discuss eight crucial ways you can optimize product recommendations and improve your online sales.
1. Use personalized data to enrich the ‘Recommended for you’ field
One important function of recommendation engines is to take into account the browsing patterns in the current session to determine a customer’s intent. This information is then combined with their historical habits and purchase history.
Source: Remarkety.com
For example, if a shopper sorts a category by low to high price, the engine can conclude that they are price-sensitive and recommend them cheaper products or those that are on sale. If they have bought small-sized clothing in the past, they will only be shown products that are available in that size.
This is also combined with data from other visitors who have made similar choices in order to provide the best recommendations. It is important to note that purchase history offers a far stronger signal than browsing history. Therefore, recommendations based on purchase history are of the highest quality.
2. Take advantage of aggregated data
Data, not just on this customer but on previous ones, helps define which products are popular and are best sellers. It also helps bundle together information on several products. Examples of aggregated data are:
- category views
- product views
- purchase data
- search queries.
Powering recommendations by such aggregate data - which can come from various sources, including your call center as well as website - can help determine the relevance of a certain item in relation to a given context. For example, this helps add items to the “Frequently bought together” category.
This type of recommendation is especially effective when shown on the cart page, as it reaches the customer when they have committed to a purchase and triggers further impulse buying.
3. Use recommendation engines data for cross-selling
The data supplied by recommendation engines helps sell related products and compatible accessories. This strongly improves online sales by boosting average order value. A humorist Erma Bombeck once said: “The odds of going to the store for a loaf of bread and coming out with only a loaf of bread are three billion to one.” Funny as it sounds, it has also become a reality of modern-day e-commerce shopping.
Using recommendation engines to cross-sell on the cart page is also helpful for increasing your average order value and number of items bought per order. Such recommendations help match chosen products with their corresponding accessories. For example, a dress with coordinating shoes and a bag, so shoppers can buy a whole outfit in one go. Combined with a user-friendly product search system, such recommendations can go a long way in boosting sales.
Cross-selling-type recommendations are also very effective when used in an automated follow-up email sent after a customer has made a purchase. For example, six months after a customer has purchased a printer from you, get in touch with them to inquire if they need to buy more of the related toner.
4. Rely on pop-ups to direct attention
Pop-ups are a great way to direct customers that have just arrived on a site. For example, a category-specific popup recommending seasonal bestsellers helps focus the attention of those shoppers that are not certain about what specifically they are looking for.
In fact, this strategy is good for any bestsellers, regardless of seasonality. But mentioning when products are selling well adds a degree of specificity, meaning a more focused search and more clicks.
Another great method to use popups generated by a recommendation engine is when a customer has selected a few items and has added them to a cart. During this step, a popup recommending items other customers have also bought is appropriate.
5. Use product recommendation emails to turn subscribers into customers
With such behavior-based emails, you inform subscribers of any changes to their wish-list items, such as availability or price reduction. Sending wishlist-related emails is much less annoying for customers than general promo emails that might be seen as random or spam.
Apart from wishlist-related emails, occasional personalized emails with product recommendations based on recent purchases are also helpful for boosting online sales.
Source: Sleeknote.com
6. Use product recommendations to remind shoppers about upcoming holidays
Recommendations generated by an algorithm are a fine way to remind users about the upcoming occasions to buy a particular item. These occasions could be holidays or other special events.
Product suggestions and deals based on past shopping behavior are a great way to simplify shoppers’ life in the run-up to the gift purchasing season. According to research done by Google, 85% of e-shoppers are more likely to shop from brands that offer personalized discounts.
Real-time notifications based on users’ intent, demographics, preferred product attributes, on-site interactions, past behavior, and other data are a useful tool to drive holiday sales.
7. Present the right number of recommendations
The goal of incorporating product recommendations into your site is to help visitors find something they might like. There are never too many items that a particular person might wish to buy. That is why it is important not to overdo it with the number of suggestions.
Flooding a user with suggestions might be distracting and hurt the overall experience. It diverts his or her attention away from the actual purpose of a page. By keeping it minimal and focused, users are helped to accomplish their goal – choosing and buying an item.
That is why ‘less is more’ is a highly recommended strategy for user suggestions. In case there is a need to boost several recommended items, it is better to feature them across your entire site. But do so in a subtle way, placing them below the fold or in a sidebar.
8. Embed social proof to build trust
Adding a social proof element to recommendations goes a long way in helping brands showcase the credibility of the products they are suggesting.
A HubSpot study suggests that 57% of consumers prefer a product or service that has at least a 4-star rating or has good reviews. Shoppers are willing to spend 31% more on a business with better reviews. Good reviews are a sign of social proof, but they are also a result of effective customer engagement strategies and superb customer service.
Rating and review tools are a crucial and innovative element of modern-day e-commerce, along with headless commerce or options to make digital magazines.
Retailers can also add star ratings to their best-selling product recommendations. Labels like “bestseller,” “top picks,” or “editor’s choice” are also effective.
Source: Blog.3dcart.com
Product suggestions are good for your site, sales, and user experience
Personalized product recommendations, which mathematical calculations determine on the back end, are a key feature of today’s e-commerce websites, and they can be matched well with themes for e-commerce. Setting up the right product recommendation strategy is essential in order to drive sales – be it cross-selling, upselling, or increasing order volume. They are also a crucial element of a smooth user experience.