The use of personalized product recommendations can double the profit
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The section on Amazon that recommends similar products accounts for 35% of their total sales.
How does it work?
The recommendation algorithm predicts the most relevant products based on the product a customer is interested in. It uses sales data and product descriptions to show a mix of products. Over time it becomes more accurate as new orders and product data become available.
Another way that big ecommerce stores like Amazon do this, is by bringing up items that you’ve already viewed and influences its recommendations to you. After all, why would you look at something if you weren’t going to buy it? Solid logic, right?
Types of Product Recommendation Engines
1. Collaborative filtering techniqueThis is the most common technique. Most websites like Amazon, YouTube, and Netflix uses it as a part of their recommendation systems. With this technique, you can filter out items that a user might like on the basis of reactions by similar users. It works by searching a large group of people and finding a smaller set of users with tastes similar to a particular user. It looks at the items they like and combines them to create a ranked list of suggestions.
2. Content-based filtering techniqueThis technique is based on a description of the item and a profile of the user’s preferences. It treats recommendation as a user-specific classification problem and learn a classifier for the user’s likes and dislikes based on product features. It works with data that the user generates, which is then used to make suggestions to the user. As the user provides more inputs or takes actions on the recommendations, the engine becomes more and more accurate.
3. Hybrid recommendationsMost recommender systems now use a hybrid approach, combining collaborative filtering, content-based filtering, and other approaches. Netflix uses hybrid recommender systems.
Recommend Related ProductsIn essence, it comes down to the fact that if you were to buy a 15” laptop, for example. Logic would dictate that you might want a laptop cover or sleep, maybe an external keyboard, a wireless mouse and a cooling platform on which to place your laptop. A study of the top 100 Internet retailers shows that recommending products in the shopping cart based on user purchase behaviour results in a 915% increase in conversion.
Recommendations on product pages are increased by 411%, on the homepage, that number is 248% and within search results, 192%.
These numbers are huge!
They don’t even need to be directly related to work well. Sometimes you can even link the weather with certain products. One popular retailer in the UK had their product recommendations related to weather. So, whenever the rain is about, and they’re looking to buy something, some waterproof goodies would be recommended.
If you want to effectively make moves with product recommendations to help your ecommerce business grow, take a look at some of our helpful hints.
- Use ‘Best Sellers’ to Attract New Visitors
If you’re heading to an ecommerce site and you want to buy something, not anything too specific, this is likely the first place you’ll go. It helps because as a store, you have no idea what to recommend just yet.
- Put them Above the Fold
Once you do have a few recommendations for a couple of users, but those things above the fold. You only have a few seconds to make an impression and above the fold, recommendations are almost twice as effective.
- Place Recommendations into Email
After you have got the user in your database, you can start personalising the information to work best for you. If they’re shopping for something specific, you should be able to email them about that particular product when it goes on sale. Putting recommendations into email can go a long way.
How Socital can help you personalise product recommendations?
We create data collection points, such as welcome or exit-intent pop-up with social login.
When visitors subscribe, we collect data from their social media profiles and analyse them.
Instantly your visitors see a “success screen” alongside a set of recommendations, based on their interests or their browsing behaviour.