Making the online shopping experience unique with personalization and recommendations

Responding to current trends and delighting customers

Buyers have less and less patience and no time to spend on a website. They have clear ideas and are looking for the right product. In addition, there is a certain expectation of personalization that customers are used to from Netflix, Spotify, Amazon, and so on:

• 35% of Amazon's revenue comes from personalized recommendations
• 80% of Netflix's streaming time is based on personalized recommendations

Creating an on-site experience

Online retailers are struggling to deliver the quality of an in-store customer experience online and humanize online business. The proliferation of online stores also makes it expensive to buy loyalty.  Personalization can make the online experience more human.

Customers benefit by getting the right products to the right customers at the right time. They can also create a special shopping experience through excellent recommendations.

Displaying relevant results

When an online store can't deliver relevant product search hits, customers get frustrated. AI-powered personalization enables 
This allows our clients to meet and exceed customer expectations, resulting in more loyalty and conversion.

Saving resources

Complex ranking rules cost a lot of time and effort if you want to balance a large product catalog (e.g. balance high and low performer products) and reach many buyer groups (e.g. Star Wars fan, Lego enthusiast, gamer and the complex overlaps of customer groups).
Thanks to automated personalization and recommendations, a wide range of products and heterogeneous buyers can now be targeted with a single click.

Manual control combined with AI creates success stories

Loop54 combines human behavior with machine learning

At the core of Loop54 is a machine learning algorithm that personalizes the user experience without relying on Big Data. To better understand why Loop54's algorithm is unique, let's look at the differences between humans and machines:

How do machines function?

In order for a machine to output a relevant result, e.g. a classification "That's a dog in the photo", there has to be an input, its processing and machine learning. Machine learning is based on labeling the input, i.e., you have to tell the machine that this is, for example, a picture of a dog. Initially, there will be errors and the machine will not be able to tell dog and cat apart, for example. However, this changes as it learns, adjusting parameters until a model is created that can distinguish dog and cat. As models become more complex, the number of parameters that the machine must learn increases.

In conclusion:

• High volume, repeating information to learn models.
• To achieve some relevance, models need long learning times
• Properties must remain the same to get them the same answer

How do humans function?

Through natural functions in our brain, we can quickly associate and infer information between objects. When we see a picture of a dog, parts of the brain are stimulated that store information about the dog, e.g. size, color, breed. The more pictures we see, the more complex the concept and pattern of the dog becomes. We learn that characteristics such as size, color and breed can vary. Therefore, we are not surprised when we see a dog of a different color than those we have seen before.

To summarize:

• Small sets of data are enough to understand traits
• Gradually learns the complexities of pattern recognition
• Understands properties even if they change

How does the Loop54 algorithm function?

Instead of focusing exclusively on products and their attributes, Loop54's algorithm uses product-independent neural linking. This means that machine learning has been combined with human-like property learning of product attributes based on small data sets. This enables personalized sorting of search results based on personal preferences and intentions within a browser session. 

Individual products can be replaced at any time without affecting the relevance of the search results. Complex product relationships are understood and, for example, new products can be classified directly without these products in particular requiring a learning period, as is the case with conventional machine learning.

User behavior also passes on information about personal preferences and intention within a browser session to neighboring neurons. Thus, interactions do not only affect a product or a specific category, but the entire model domain around the controlled neuron.

How is Loop54 different?

Special requirements for the use of personalization and recommendations powered by Loop54

In order to benefit from the results of AI personalization and recommendation, there are some requirements that need to be met.