When you see particularly appealing products on the web it means that machine learning algorithms have selected them for you. 

Smart technologies sneak into e-commerce and boost profits with smart personalized recommended lists, smart dynamic pricing, and prediction of customers’ buying behavior. This is very valuable information for sales and marketing and the topic of this article. 

To get machine learning data science solutions, e-commerce companies share their domain expertise with developer companies, and the latter rig it with algorithms and models from AI, mathematics, and statistics fields. With the help of these kits e-commerce companies can leverage more aware business strategies and make evidence- and insight-based decisions. 

Use Cases

Recommender Еngines

This part is about the types of recommender engines and how e-commerce companies apply them.

Recommender engines in machine learning e-commerce can show /”recommend” to the customers the products that they are most probable to purchase based on their prior buying choices. 

3 steps that AI recommender engines run:

  1. Recommender engines analyze users’ prior buying behavior. 
  2. Recommender engines predict new probable purchases of the users.
  3. Recommender engines select or show the products or services that users are most probable to purchase again. 

These systems crawl on the Internet and analyze either users and items or user-item interactions. There are different types of recommender engines. 

Types of recommender engines

Collaborative Recommender Engines 

The logic behind collaborative engines reads: if someone liked something in the past they will like this or a similar product again. In this type of engine, the user “collaborates” with the system. 

Applications of Collaborative Recommender Engines 

  • The most vivid application of collaborative recommender engines is Facebook. This wide arena for e-commerce recommends millions of its users new friends and groups based on their prior choices. 
  • CNET considers AI use cases to be the puppet master of 70% of all videos people watch on Youtube riding the recommender chain.

Content Filtering Recommender Engines 

Content filtering recommender engines on your website use the properties of your products and a temporary profile of an unregistered user. The system creates a temp user profile to analyze the user’s prior preferences in order to recommend to them the products on your website.

Application of Content Filtering Recommender Engines

  • You often see retail applications of recommender engines that are based on content filtering as all kinds of lists of recommended items in online catalogues. Here, the AI system selects and shows the products similar to those that users have already clicked on. 

Hybrid Recommender Engines

Hybrid recommender engines are the most popular and blend collaborative, content, and other types into one. This engine aims to form a more convincing recommendation list for users and achieve better engagement and conversion rates. Its power lies in retrieving substantially different data about users. A hybrid recommender engine can develop its constituent types of engines separately and then blend them to form one. Or it can develop one of the constituent types and further enhance it with elements from other types. 

Application of Hybrid Recommender Engines

  • To make recommendation lists of movies for their users, Netflix is using the hybrid recommender engine designed by BellKor’s Pragmatic Chaos company. This approach blends 107 approaches into one prediction. In particular, it combines the watch, search, and rate behavior of users.

Machine learning use cases in recommender engines are capable of significantly raising conversion and driving sales. For large e-commerce companies, there is no question of whether or not to use automation in marketing. The question is what type of automation they will apply to better compete on the market. 

Softengi develops effective machine learning e-commerce use cases with recommender engines to solve easy and complex marketing tasks.

Pricing Algorithms in E-commerce

This part is about the types of machine learning pricing algorithms and how e-commerce companies apply them. 

Effective pricing is one of the milestones of effective business strategies. Machine learning e-commerce can develop pricing algorithms that automatically adjust the prices of items/services depending on data changes in the market or in the company. For example, AI use cases can make smart entry prices, discount prices, promotion prices, and similar.

Types of price adjustment in the companies:

  1. The businesses do smart dynamic pricing when they change prices depending on the market (competitors). 
  2. And they can perform smart price optimization when they change prices to maximize operating profit in different scenarios.
Types of pricing algorithms

Applications of Smart Pricing Algorithms in E-commerce

“Third eye” pricing algorithms

The “Third eye” machine learning pricing solution will analyze for example a customer on your site, see opportunities for sales and adjust the price accordingly. These smart pricing algorithms can find correlations in enormous volumes of data that your business may need. Meanwhile, your competitors with non-smart types of pricing solutions cannot do this. On their sites, they will not even recognize this potential buyer. Fast reaction on market opportunities promptly raises the return on investment. 


“Black Fridays” pricing algorithms

When retailers sell off at the end of the season, their main goal is to get rid of the old products to give a way for the new ones. The price drop is dramatically low. Still, a profit-driven business wants to be smart about the price cuts. “Black Fridays” machine learning pricing algorithms make analysis to form a reasonably low selling price for the products that the personnel and common solutions can underestimate.

AI pricing algorithms for operating profit

To maximize the operating profit of the business and raise business resilience, e-commerce businesses may apply certain prices to particular products. In this scenario, the price of the product does not depend on outer conditions as competitors, and demand. It depends on the inner company’s goal to raise the profit. To do so, AI solutions developer companies design and train flexible pricing algorithms that can learn the set of required patterns and further do price optimization based on them. 

“Joint-products” pricing algorithms

Different types of products that customers usually buy together usually correlate in price. “Joint-products” pricing algorithms are based on machine learning retail and are capable of adjusting prices not only in product categories but also correlate them by product types. This means that a large online retailer will no longer adjust products’ prices in a product category and then manually correlate items among different product types. But they will run at once through the whole assortment of product types and categories while correlating items in different product types. Thus, you will promptly get the precise prices for your entire assortment that do not need your further attention.

Algorithms for price elasticity prediction

The question of whether or not to raise the prices and for how much is now much easier to answer. Your machine learning pricing algorithms for elasticity will analyze big data and predict the quantity of products that you will need to supply if you raise the price, what price will result in what demand, and how price optimization in your company can look like.

Softengi develops effective machine learning pricing solutions for SMEs and large businesses.

AI Algorithms for Predictive Segmentation of Customers

This part is about AI algorithms that can predict customer interest in your products and how e-commerce companies can apply these algorithms.

AI algorithms for predictive segmentation of customers analyze many parameters of the data comparing users, items, and history of their prior interactions. Based on this information, these solutions predict the customers’ possible reactions to your products. 

Data Science in E-commerce Use Cases

Application of Predictive Segmentation of Customers in E-commerce

Companies often make marketing campaigns in search engines or social media platforms. They target their campaigns and make customer segmentation to reach the goals. AI algorithms can analyze the customer and make a grounded prediction of their interest in your campaign. Then, it makes a grounded decision whether or not to include this user in the target audience of your marketing campaign.  Thus, the companies, for example, will not pay per view of an irrelevant user or for a click of a user with potentially low conversion. 

AI Algorithms for a Personalized Search of Product Images 

This part is about machine learning solutions for e-commerce that do a personalized search of product images and how on-line retailers can apply these solutions.

Forbes 2020 research gives convincing coverage of the customers’ want in personalized offers. It quotes Accenture as saying:

“91% of consumers say they are more likely to shop with brands that provide offers and recommendations that are relevant to them.” 

Thus, the task of the online retailer companies is to show the correct customer their most fitting image – that means to personalize the image search results by applying AI algorithms.

Applications of Personalized Search of Product Images in E-commerce

  • Among popular tools for product browsing via product images are Google Lens and Image Search. It is an easy way for the customers to skip keywords-play with the search engines and go directly to what they want. Both clients and the companies upload so many images that machine learning algorithms have no difficulty matching demand and supply. 
  • Machine learning retail applications in SMM ground on images whether companies do campaigns or display their products. Thus, in Pinterest via Lens Your Look clients can upload the image of their clothes, and find, and buy the matching clothing items.

Smart Assistants AI Chatbots

This part is about the current demand of e-commerce companies in AI chatbots and how e-commerce companies can apply AI chatbots.

Many large chain retailers, for example in the cosmetics industry, had to close their physical shops and go online. This led to an enormous number of online orders. One of the constituent parts of their business solution was:

  • to move all the products online, even the ones that were not supposed for online sales,
  • to enhance online shops with extensive filters, 
  • and to enroll more personnel.

Applications of AI Chatbots in E-commerce

  • AI chatbots in your e-commerce company will text or talk with your clients online. They will help with personalized purchasing by navigating the clients through the massive volumes of products and their properties on site, comparing products, and combining them for customers. They solve the situations when clients cannot find what they need using filters on your site or the personnel is not responding. 
  • AI chatbots can emulate entire customer service and take online orders, take the customer’s details for shipment and inform them of their order details. 

Softengi AI software development company designs and trains successful AI models. E-commerce companies can apply them to reach their outer and inner goals. 

Conclusion

This article gives coverage of the main AI models for e-commerce. They encompass recommender engines, pricing algorithms, predictive customer segmentation, personalized search of product images, and AI chatbots. These models can apply as single solutions or in combination to solve different business needs. E-commerce companies are successfully using them for marketing and business objectives. 

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