Getting the Most from eCommerce Recommendation Systems

Getting the Most from eCommerce Recommendation Systems

eCommerce recommendation systems have become exceptionally popular, and effective, for businesses selling online over the last couple of years. However, if you have missed out on what they can do for businesses and brands, it is not too late. In fact, as good as they are in analyzing consumer behavior and providing personalized product recommendations for customers, many businesses are not maximizing their potential.

With those personal recommendations rapidly becoming an essential part of many online purchasing solutions, it is important for businesses to make full use of everything an eCommerce Recommendation System can do, and ways in which they can be improved. This is important, as the systems improve the customer experience, and can increase brand loyalty and boost conversion rates, improving profitability for the online store.

Here are several ways in which eCommerce Recommendation Systems can be allowed to reach their full potential.

Integrating Artificial Intelligence, Deep Learning and Machine Learning Algorithms

Using machine learning, deep learning, and artificial intelligence algorithms

All eCommerce Recommendation Systems make use of algorithms to analyze data and develop recommendations for an individual. By integrating the latest technology such as Artificial Intelligence or Machine Learning into that analysis, an eCommerce Recommendation System can make use of more data, faster, and offer more accurate and effective recommendations. When combined with a feedback system that lets the AI understand how well the recommendation was received, it can continually refine the process to deliver even more useful recommendations for any individual.

Making use of Contextual Data

Most eCommerce Recommendation Systems use a limited amount of data, including browsing and search history as well as previous purchases. However, by making use of contextual information as well, systems can increase the usefulness of the recommendations. That contextual data could mean time of day or date, which would allow recommendations for warmer clothing as the cold weather is due for instance, which adds value to the experience for that individual. BY preempting their own needs, the recommendations become part of their experience, something to rely on, and that brings brand loyalty and higher engagement.

Continually Refining System Performance

Any eCommerce business should be in a constant state of evaluation and refinement. It is the only way to ensure that you stay ahead of the competition by providing the optimal experience for visitors to your site. From click-through rates to average order values, data can show you where you are winning, and where there are areas for improvement. The same applies to eCommerce Recommendation Systems, with optimization of the system and data enabling improved performance over time.

Utilizing Image Recognition

Image recognition

Any eCommerce Recommendation System can be enhanced by the incorporation of image recognition technology into the process. This technology can pick out images similar to those being viewed, and make recommendations based on that similar image. This could be product images, but could also be used to analyze colors, textures and shapes too, helping to identify the best match with a consumer’s searches.

As eCommerce Recommendation System experts, we can help you integrate and optimize a recommendation solution to work for your business. Talk to use today and see how we can help your eCommerce solution reach its full potential.

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