The art of personalized product recommendations

In today's world, customers expect a personalized experience when shopping online. A crucial element of that experience comes from delivering product recommendations that are tailored based on an individual's preferences, interests, and purchasing behavior.

Understanding how to implement effective recommendation strategies can drive more conversions, foster customer loyalty, and make all the difference your revenue.  

In this article, we'll explore why personalized recommendations matter and some examples of strategies for effectively implementing them.‍

Why personalized recommendations matter‍

Personalized recommendations play a huge part in creating better customer experiences.

By providing tailored product recommendations, rather than broad or irrelevant suggestions, a brand can show its customers that they understand their needs and preferences.

This leads to increased customer satisfaction and loyalty, as discussed in our article covering the link between loyalty and personalization.

Having effective product recommendations translates to several positive outcomes: ‍

#1 Better engagement‍

Recently, a study by Epsilon found that personalized emails have an open rate that is 29% higher than non-personalized emails. When it comes to making a strong impression, tailoring interventions - like recommendations, emails, and messages -  can go a long way.‍

#2 More sales

Personalized recommendations can also drive conversions. According to a study by Certona, retailers who provide personalized recommendations see a 3x increase in their conversion rate compared to retailers that don't.

#3 Higher average order value (AOV)

Finally, there's the benefit of getting customers to spend more. The main focus of product recommendations is often to encourage bigger purchases. While irrelevant suggestions are likely to be ignored, personalized ones get noticed and used.

In addition, around 36% of consumers believe brands could be doing more to personalize their experiences. Considering how much of a contribution they can make to a retailer's bottom line, recommendations are a great place to start.

Data required for personalized product recommendations

The first step in implementing personalized recommendations is to gather the type of insight you'll need to understand who you're talking to.

There are lots of examples of information you can utilize, but retailers tend to base their recommendations on a mix of the following:

• Purchase history
• Recently viewed products
• Website behavior (e.g. exit signals, categories browsed)
• Cart contents
• Demographic data (e.g. age, gender)

It's important to stress about the 'mix' of data used to create valuable suggestions.

For example, if you know that a customer has bought a product from you before (e.g. a certain bottle of wine) and doesn't have it in their cart, you can recommend they 'buy it again'.  Here, we're using a blend of cart contents and past purchase data to deliver the right message.

Types of personalized product recommendation strategies

The next step is to use your data to create customer segments in order to categorize customers based on their preferences and behavior. From here, you can bring recommendations out to different audiences.

With the recommendations themselves, there are several strategies that can be utilized, each doing things in a slightly different way. Here are some of the more popular choices:

‍Collaborative filtering

Collaborative filtering involves recommending products based on the behavior of similar customers.

An example of collaborative filtering is the recommendation system used by Amazon. This analyzes the purchasing behaviors of customers who have bought similar items to the one a person has just viewed or bought.

For instance, if customers who purchased a specific book also bought another related book, the system will recommend the latter to anyone who views or purchases the former.

Content-based filtering

Content-based filtering is a method of making recommendations based on the characteristics of products themselves.

By analyzing the attributes of these items - such as their category, brand, or specifications - a profile of the user's preferences is built. The system then uses this profile to recommend other products with similar attributes.

For example, if a customer frequently purchases mystery novels by a particular author, a content-based filtering system might recommend other mystery novels, or books by the same author.

This approach is particularly effective when an individual's preferences are unique and do not necessarily align with the broader trends of the customer base.

Hybrid approaches

As you might guess, hybrid approaches combine both collaborative filtering and content-based filtering. By using a mix of both methods, brands can provide customers with highly accurate and relevant recommendations.

The importance of real-time recommendations

Regardless of which method you choose, we cannot understate the importance of reacting to signals from your customers in real time.  

Providing customers with recommendations at the precise moment they are most likely to be used is crucial to success. To do this, you'll need to trigger real-time recommendations based on a number of different events, responding as soon as a product is added to the cart, or a product page is viewed.  

By reacting to customer behavior in real time, you will significantly increase your chances of driving conversions and extra spend.

Measuring the effectiveness of your recommendation strategy

Most retailers will start serving recommendations, see a few extra sales and believe their work is done. If we were to make a recommendation ourselves, it would be to measure the effectiveness of your strategy on a regular basis.

You can do this in a few different ways, but don't forget these three core metrics:

Click-through rate (CTR): Measuring your number of clicks against the total number of impressions will give you a firm idea of how often customers interact with your recommendations.  

Conversion rate (CVR): Measuring your number of conversions against the total number of impressions gives you an idea of how often your recommendations are driving purchases. It's a very important metric if you are highlighting products to someone who doesn't have an item in their cart, as your main role is to get them to convert, rather than spend more.

Average order value (AOV): If you're recommending additional products to your customers (e.g. matching tops to someone checking out with bottoms), it's wise to track AOV to see how much extra spend you're driving.

By measuring the effectiveness of your recommendation strategy, you can make adjustments, improve your approach, and the best bit: get better results!

Top practices for personalized recommendations

When implementing personalized recommendations, there are a number of best practices to keep in mind.

1. Make sure you're providing relevant recommendations. Take into account the customer's past behavior and purchase history to ensure you're adding value to their journey.
2. Focus on the user experience. Recommendations should be easy to understand and relevant to the customer's needs. If they have a negative impact on their experience, you've taken one step forward and two steps back.
3. Don't overload customers with too many recommendations. Reels and reels of suggestions can be overwhelming and lead to decision paralysis. Just a few tailored choices can make all the difference.
4. Test and learn: Finally, make sure you're continuously testing and optimizing your approach.

Final thoughts

Personalized recommendations are a crucial component of any eCommerce strategy. We can't think of many investments that can drive conversions, increase customer loyalty, and provide a better overall shopping experience, all at the same time.

For us, it's important to understand the different recommendation strategies before you start implementing something that doesn't suit your site. It's also important to use real-time recommendations to maximize your effectiveness.