Shopping online can be overwhelming and often difficult. Many times, you’ll find yourself searching for items that have nothing to do with what you were originally looking for. Having a road map by your side to help guide you in the right direction can be necessary, especially as more and more advertisements pop up to steer you towards their products.
So how do we, the website developers, implement this road map to help make the shopping experience easier for our consumer base? Many sites have turned their efforts towards machine learning recommendation algorithms. These are internal processes that gather the information provided by a consumer’s searches and purchases and create a recommendation list that is tailored to their desires.
By offering this recommendation list, you are providing the consumer with a map that guides them towards the items that might better suit their needs. This way, they can enjoy a quick and personalized shopping experience, and your site will be remembered for the assistance. Many different recommendation systems include different features, so how do you design one that matches your website perfectly? Understand your consumers.
ML Recommendation systems
The best way to understand your customers is to tailor their experience towards them. Through the implementation of a machine learning recommendation system, you’re better able to provide users the interface that makes them feel as though their experience on your site is their own. Machine learning is the process of the program behind the website taking in user information and using it to provide the perfectly recommended items.
The use of a recommendation system provides benefits to both the consumers and developers involved in the process. For the developers, you’re able to provide your users with more reason to come back. By showcasing them the recommended products, you’re able to offer them the concept that your website has what they need. If they bought a product and liked it, through a recommendation system, they will know you offer similar items for similar prices.
In the case of the customer, shopping is made simple. They will be alerted to products that are in stock that match their previous searches. This keeps the consumer on track and allows them to continue their search for the perfect item. Consumers who receive a recommended section on a website are more likely to continue shopping with you than those who don’t.
The basic feature of a recommendation system is product recommendations. This will come with most systems implemented on a website – it’s the feature that your consumer base will interact with the most. This feature showcases the products that the ML system has determined to be similar to those that are purchased or searched for by the user. Product recommendations will help sell your users on more products.
Another feature that should be implemented with a recommendation system is a price range suggestion. If you offer quality recommendations to your users but the prices are far beyond what they usually purchase, your recommendation system will be useless. If the products are within the consumer’s price range, they’ll be more likely to purchase the recommended product.
Having an ML recommendation system as part of your website will help customize the shopping experience towards your consumer base, creating a wider range of loyal shoppers.
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