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Insights / Blog / Data Science & AI

Solving problems and improving customer relationships with Machine Learning

January 28, 2020
Mario ErazoMario ErazoDeveloper

The interaction between Machine Learning (ML) and intelligent search engines can help businesses to improve processes, business performance and customer communication. In this two-part blog series, I will take a look at how ML can influence E-Commerce offerings as well as Machine Learning’s applicability to Search Engines.

Machine Learning can be found in different contexts from photo tagging and loan approvals through speech recognition and language translation to self-driving cars. While sounding like futuristic technology that has recently prevailed in the enterprise IT market, its history dates back to the late 1950s, when IBM researcher Arthur Samuel first developed a Machine Learning program that successfully learned to play checkers.

Today, Machine Learning is capable of far more than becoming proficient at a simple board game. One of the many ways in which Machine Learning is used is to provide meaningful insights into raw data. This enables companies to quickly solve complex, data-rich business problems. This is done through learning from the data in increments, which allows computers to identify different types of hidden patterns without being explicitly programmed to do so. In this two-part series, we will take a look at ML and E-Commerce as well as Machine Learning’s applicability to Search Engines.

Steven Bailey

Steven Bailey

Machine Learning is an excellent tool to help companies arrive at a “reasonable digital product” faster and more efficiently. Products are thus more likely to solve the problem for which they were created, as a broader base of more intelligent, digitalized information is utilized during development.

Every second retail company uses ML to improve its E-Commerce

ML has proven to be such a helpful tool that 45 percent of all retail companies are using it to engage their customers – a value that is expected to grow in the next few years.

In E-Commerce, Machine Learning can be used to track and understand customer-related interactions in association with a product. Algorithms used for this purpose can be extended to predict customer behavior, such as knowing which target audiences are most likely to buy a specific product – thus building better customer relationships.

Additionally, product information can be improved through ML to better match user queries and make a product easier to find for customers. Helping target audiences find relevant information faster and with more accuracy creates a better overall customer experience.

With all of this in mind, it is important for companies to understand the requirements of Machine Learning and the benefits that the ML can provide. Among others, ML: 

  • helps companies make use of data that is already available
  • eliminates manual tasks
  • allows for an effective analysis of customer behavior
  • improves the user experience
  • simplifies data management

ML and Advanced Search with Searchperience

In E-Commerce processes, Machine Learning helps to address the following use cases:

  • Recommendation Engines
  • Upselling and cross-channel marketing
  • Market segmentation and targeting 
  • Customer ROI and lifetime value
  • Improved Business Models and Services

When talking specifically about our own search and recommendation engine, Searchperience, there is a range of Machine Learning methods that can be applied to improve the user experience and the quality of search results displayed on the results page.

A key differentiator to other search solutions on the market is that we adapt our algorithms to the specific needs of our clients, which results in more precise and accurate outcomes. 

Searchperience features that use ML

Handling data management through AOE’s own tracking solution

Searchperience can track and record user interactions by embedding a dedicated JavaScript into the online shop or by using the tracker’s API. Interactions are saved for later processing. This information is used as data in the different Machine Learning algorithms that enrich search results.

Continuously learning about relationships between search queries and products, brands, retailers, etc.

Interaction from customers in the system provide a concrete foundation for the discovery and computation of relationships between search queries and specific products, online shop categories, brands and even retailers. These relationships are used to make these entities more discoverable while searching.

Keeping track of user interactions in the system in order to enrich product information

Through the analysis of tracking data, or in other words, the different user interactions around the system, it is possible to compute which search queries result in a purchase. It is also possible to calculate the view count- or sales information about any product in the online shop. Depending on the case, this information is available per variant and per product. The computed results are used to enrich products.

Intelligent data indexing

Searchperience’s indexer is able to analyze text and automatically recognize language and terms (Named-Entity Recognition). In addition, appropriately trained supervised learning models can automatically classify documents during indexing. By using AI APIs, images can also be automatically annotated.


The data collected by the tracker is used to learn different recommendation models, for example to give personalized product recommendations to users with so-called collaborative recommendations (“Users who bought x also bought y”).


In today's business world success is all about the customer. Machine Learning is ideally suited for getting to know target audiences better, which helps companies build better customer relationships. At the same time, a more automated approach to understanding the market and the competitive environment a company operates in, creates a more solid foundation to problem solving and product development. 

In my next post, I will further explore Searchperience features that use Machine Learning, including being able to compute product recommendations and product similarity; take a dive into personalization through the use of search widgets; and take a brief look into the future with learning to rank.