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Insights / Business

Search & Machine Learning: More Sales for Your Online Shop

June 22, 2022
Author Steven BaileySteven Bailey
Chief Strategy Officer

Provide the right information at precisely the right time: The search is what brings customers and products together. Therefore, it makes a crucial contribution to part of the customer experience in a webshop. The better the search, the more satisfied the customers – and in the end, of course this affects sales. But what exactly characterizes a good search, and what roles can artificial intelligence (AI) and machine learning (ML) play here?

The task of the search is to take customers to the pages that interest them as quickly and easily as possible – or to ones that could interest them. In order for the search to work, it must access various information from different sources and be able to evaluate it. On the one hand, of course this includes the product information and other data from the online shop. On the other hand, this is data that results from user behavior. And this is where AI comes into play in the form of ML.

Machine learning can help use existing data optimally, for example, in order to analyze customer behavior effectively and improve the user experience. But how? Thanks to incremental learning from data. This way, different types of hidden patterns can be detected – and this is how complex problems can be solved. Naturally the possibilities for deployment are many and varied; at the same time, given the background of the individual situation, for example the business goals and technological starting point, not all possibilities are of equal value.

The theory: Which areas of application for machine learning actually make sense?

In its “AI Use Case Prism for Digital Commerce,” the market research and consulting company Gartner evaluates various areas of application where AI (and thus also ML) can assist with e-commerce by examining the relationship of the resulting business value and technical feasibility.

In the context of search and machine learning, the following areas of application are relevant:

  • Price optimization
  • Keyword search
  • Product recommendation
  • User interface optimization
  • Customer segmentation
  • Product categorization
  • Offer personalization

Noteworthy is that according to Gartner, many of these are in an area with relatively high business value with great technical feasibility – and they are therefore very promising.

Furthermore, if you look at these areas of application against the background of the four phases of the sales funnel (awareness, evaluation, purchase, post-purchase), it becomes clear that ML in searches is especially relevant during the evaluation and purchase phase, so it can provide meaningful help for a customer making a purchase.

Therefore, it is worthwhile for online retailers to equip their shops with an optimal search function.

The practice: Where specifically can the search be optimized with machine learning?

Tracking: Of course the basis for the deployment of ML in the search is that user interactions are tracked and recorded. ML algorithms then use this data to enrich the search results.

Continuous learning: Via the interaction of customers with the system, it is possible to draw conclusions about their relationship to products, categories, brands, or retailers. Such interactions can be search queries or purchases, for example. They show what is especially interesting for customers.

Enriching of product data: The tracking data can also be used to analyze which search queries lead to a purchase. For example, view figures and sales information can be calculated, and the product data enriched with this.

Intelligent indexing of data: In the best case, an indexer can analyze text and detect language and terms automatically (named-entry recognition, NER). Appropriately trained, monitored learning models can also classify documents automatically during indexing. Pictures can also be annotated automatically using ML/AI.

Recommendations: With data collected by the tracker, various recommendation models can be learned, for example for making personalized product recommendations for customers with so-called collaborative recommendations (“customers who have purchased product A have also purchased product B”).
 

The learning: Investment in machine learning in searches pays off

For long-term business success, a shop’s functions must be oriented according to the customer’s needs. A search function that uses ML in the appropriate places is an important tool for online shops in order to better understand customers and their needs. At the same time, its quality makes up a decisive part of the user experience.

Now not every shop system automatically has a high-performance search – but this does not mean that operators of online shops must convert their whole system or be satisfied with the integrated search. Anyone who examines the market will surely also find an appropriate external search solution and/or additional AI applications that will fulfill individual requirements and can be integrated with the existing shop system – and thus contribute to customer satisfaction, customer loyalty, and increased sales.

This article first appeared on etailment (in German). We appreciate your feedback and sharing the article.

Original article (in German)