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Cyril Dorogan
CCO at Gepard
Cyril Dorogan Gepard PIM E-Commerce Consultant
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Machine Learning In PIM Software: Benefits For eCommerce

8 min read
Published: May 10, 2023
Updated: July 31, 2023

With growing digitalization, ML-powered PIM software has become a popular trend in eCommerce: with this technological advance, human work becomes more effective, the companies can do more with fewer resources, meanwhile, innovation forces businesses to boost their products and services.

According to McKinsey’s “The Future Of Personalization” paper, businesses grew their revenues by applying an ML-driven product recommendation system by 5%-15% and increased marketing expenses efficiency by 10%-30%. In this article, we will explore the benefits of ML-powered product data management, and its benefits for eCommerce and review the use cases of Machine Learning PIM systems for online sales.

What Is Machine Learning in eCommerce?

Machine Learning (ML) is a department of AI that lets computers and applications learn automatically without being programmed to do so. It is widely used in eCommerce to optimize the shopping experience in online stores in many ways: buyer segmentation, personalization, targeted marketing campaigns, product recommendations, managing product supply and demand, and much more.

How Does Machine Learning Work In PIM Software?

ML-powered PIM software improves the quality of product data management in many ways by using different algorithms. Let’s review some of the most important ones.

Automated Data Classification Algorithms

These algorithms are used in eCommerce PIM to categorize goods based on their attributes such as color, size, price, design features, and other characteristics.

Natural Language Processing (NLP)

These techniques are used to take out the information from disorganized data sources such as commodity descriptions, buyer reviews, or social network posts, which helps to fill product fields with more precise and comprehensive information.

Recommender Systems

ML algorithms first collect and then analyze data on customer behavior, including shopping history, search history, and product views, and then select algorithms for recommendation tasks. After model training, it can generate personal recommendations for users.

Data Cleansing

These algorithms are used to recognize and amend data errors or inconsistencies. After cleaning the data (correcting spelling or typos, removing duplicate content like product descriptions or listings, and replacing missing values), the algorithm validates and verifies the information to ensure it’s accurate and complete. Then, the algorithm will offer suggestions for correction or updates and any detected inconsistencies or errors will be reported to the PIM user for further improvement.

Sentiment Analysis

This algorithm is used to analyze the text data, such as buyer reviews or social network posts to define the overall feeling and attitude, expressed in the text. In eCommerce, it helps to understand how customers feel about certain products or services, by analyzing customer reviews about them. It helps businesses to discover the areas for product/service improvement and increase buyer satisfaction.

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Benefits Of Machine Learning PIM Software for eCommerce

  • Automated content creation and data enrichment

The correctly trained ML algorithms reduce the manual resources and expenses related to data gathering.

  • Improved data matching and linking

The more data is generated and gathered, the higher the need for error-free and efficient data matching and linking. These processes are significantly improved with the help of Machine Learning. ML algorithms can be trained in large databases to learn data patterns and relationships, and then use this expertise to match and link information from different sources. Here are the examples of applying Machine Learning to data matching and linking:

  1. Feature extraction. Artificial intelligence can be used to take out applicable data characteristics, to spot matches between datasets.
  2. Entity resolution. ML can be utilized to detect and resolve conflicts between different variants of the same entity in different datasets (e.g. multiple spellings of the same product name).
  3. Record connection. AI algorithms can connect the records across different datasets based on common attributes (e.g. name, size, delivery address, etc).
  4. Data classification. Machine Learning is used to put data into different categories (matching or non-matching), based on the learned relationships.
  • Better personalization

According to McKinsey and Company, 35% of purchases made on Amazon were due to product recommendations.

ML-driven product recommender systems can process previous sales information, acknowledge recurring buying models among typical customer archetypes and forecast the goods that might grab the attention of particular users to offer them personalized shopping suggestions.

Overall, retail businesses can use ML PIM and eCommerce personalization tools to navigate goods in online stores, get relevant product information, and see marketing messaging tailored to their preferences and needs.

Also, with Machine Learning, businesses can personalize the content of their websites, for greater conversion and customer engagement. ML considers different factors such as favorite colors and style, visual intensity, preferences, and activities history, to adapt the website results to the personal liking of each potential customer, which leads to better buyer satisfaction and increased profits.

  • Predictive analytics

With Machine Learning algorithms, businesses can learn different things about viewers visiting their online store and purchasing products from them. Moreover, ML can predict what catches customers’ interest and how likely they’re going to buy certain goods. Here are some of the things that can be forecasted with Machine Learning techniques:

  1. Customer lifetime value
    Artificial intelligence can predict how much money particular clients will spend in a shop over the whole estimated timeframe they would be a business’s customers. With the accurate assessment of a customer’s lifetime value and identifying the most valuable clients, businesses can effectively distribute marketing budget, look after the most high-value clients and minimize loss exposure.
  2. Probability of a purchase
    This real-time prediction help to find out if a user is going to purchase a good of a certain product category, which helps a seller choose the right reaction to convert a user into a buyer – for example, sending an email with appealing content to engage users, or offering a personal discount. In such a way, businesses can increase conversions while potential buyers are considering a purchase.
  3. Client returns
    Machine Learning is whether a buyer will be returning for more purchases and what they will buy at certain times. With this information, businesses can prepare a special marketing message with dynamic pricing for these customers, to encourage them to return and buy more.
  4. Customer churn
    These Machine Learning predictions help businesses find out what customers are at risk to leave. With these insights, companies can promptly react to the buyers who are likely to stop purchasing from them. With such a Machine Learning solution in place, businesses can grow the retention rate and ensure a stable source of revenue.
  • Enhanced search capabilities

Smart search engines are an important technology enhancement, especially for companies that have a huge merchandise stock and offer customers millions of products to choose from. Meanwhile, the traditional search solution stands on matching the keywords from the search bar with the ones in the product description, ML algorithms are more complicated. Integrated with deep learning and NLP, machine learning can entitle search engines to a deeper level of context apprehension. It can use a wider range of synonyms or fine-tune the search process to suggest results that are matching users’ shopping behavior or product preferences.

According to the Forrester research report, 43% of website users go straight to the search bar, meanwhile, it’s a 2-3 times higher probability that searchers will convert into buyers, compared to non-searchers. That’s why investing in a Machine Learning search system is a great way for businesses to boost their sales and improve buyer experience.
Sergey Shvets
Sergii Shvets
Founder & CEO at Gepard
  • Automating manual tasks

Machine learning, as a branch of artificial intelligence, contributes a lot to automating manual tasks in eCommerce. Here are a couple of automation examples:

  1. Automated data enrichment. ML algorithms can take out and analyze insights from big sets of data and then use this information to enrich content inside PIM. Businesses can optimize product data according to their requirements, and connect to third-party data suppliers for data quality improvement, enhancing its accuracy and searchability.
  2. Fraud detection. ML can analyze large volumes of data and recognize patterns that may be a sign of fraudulent actions (e.g. unusual shopping behavior or billing data). With its help, businesses can protect sensitive clients’ data and prevent fraud.
  3. Price optimization. Machine Learning algorithms can find and analyze competitors’ price data, product demand, and other elements that can optimize pricing. It helps businesses boost their sales and keep pace with competitors.
  4. Inventory management. ML algorithms can be used to forecast product demand and manage inventory levels accordingly. This information helps companies lower stockouts or understocking and prevent lost sales and increased expenses.
  • Chatbots and virtual assistants to improve customer service

Another ace in the hole of ML solutions is its capability to significantly improve the quality of customer interaction with eCommerce platforms. Chatbots and virtual assistants are the most striking example of it. According to Invesp, online retail shops have the highest rates of chatbot integration.

With natural learning processing and deep learning, chatbots are not limited to solving simple inquiries but understand the context of operations and learn from their previous experience to improve future ones, such as interactions with the users. With such a technology, eCommerce businesses can offer 24/7 customer support in a form of a real conversation. The virtual assistants imitate knowledgeable consultants to facilitate a smooth online shopping experience: they can show and compare prices, find the best deals, clarify the shipping options, or perform the transaction process.

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Use Cases For Machine Learning In PIM Software

  • Recommendation engines of eBay and Amazon

Popular eCommerce chains, such as eBay and Amazon use Python-based ML recommender systems that allow easy interaction between their microservices. eCommerce businesses that sell on these platforms benefit from Machine Learning, as the buyers can easily access the goods and services that fit their needs and choices. The recommending systems also can carry the shopping experience of other buyers and recommend commodities that are bought by other customers that live nearby. With its item-based collaborative filtering, each Amazon user can see different products when they enter the platform UI. This tool, which started in 1998, helped Amazon outpace other eCommerce leaders like Alibaba and contributes to 35% of Amazon’s profits.

  • Language translation on eBay

eCommerce giant eBay built a machine learning technology called eBay Machine Translation. With this tool, product names on the marketplace are 90% accurately translated, which is key to a successful eCommerce localization strategy. The machine translation also concern product reviews, which helps shoppers to understand whether this good is a good fit for them, considering the review of buyers from other countries. This ML-based solution boosted the company’s sales by 10.9%.

  • Alibaba’s built-in fraud detection system

With the help of Machine Learning algorithms, Alibaba’s eCommerce platform enabled fraud risk monitoring and management system. This system apprehends large volumes of real-time data on website user behavior, analyzes it with the help of ML, and predicts the fraudulent actions of suspicious users.

  • Magento Elasticsearch engine

With ML-powered Magento Elasticsearch extension, businesses can enable scalable full-text search tool that immediately shows the query results. It offers different search settings such as multi-match or simple-match queries, search suggestions, spell correction, and different filter types. All these contribute to enhanced search options and increased sales and buyer satisfaction, as they can seamlessly find the goods they need.

  • Facebook chatbot

Facebook, one of the leading social commerce players, enables sellers to add chatbots for greeting, product search, showcase, recommendations, or not found options and also offers instant replies. Businesses can automatically communicate with their customers, and show their gallery of goods, meanwhile, the buyers can scroll through products without even leaving their messenger application, which makes for highly mobile-friendly shopping and increases conversions.

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Machine Learning In PIM Software FAQs

What types of Machine Learning algorithms are commonly used in PIM software?

ML algorithms used in PIM help optimize product information management and reduce manual efforts related to this process. Some of the most popular ones are natural language processing (NLP) (for example, to select relevant data from product descriptions and reviews), recommendation algorithms for ML-driven product recommendations, predictive analytics to forecast the price of a good based on its attributes, and classification algorithms, for automated assignment of product attributes based on their descriptions or images. The image recognition algorithm can be used to classify product images into groups based on their visual features.

How does Machine Learning help PIM software ensure data accuracy and consistency?

Machine Learning helps to improve PIM systems and automatically maintains data validity and uniformity. With machine learning algorithms, eCommerce businesses can automate the constant process of information updates, as AI continuously extracts and verifies new data from multiple sources. Artificial intelligence can identify and amend errors in product information, such as inaccurate product names, features, descriptions, and prices.

Can Machine Learning help PIM software identify and prevent fraudulent product information?

Machine Learning fraud detection uses algorithms that allow to process of immense datasets with many variables and expose the hidden connection between user behavior and the possibility of deceitful actions. It minimizes manual work and reduces the number of verification stages.

Data-Driven eCommerce Fueled by Machine Learning Technologies

Successful eCommerce businesses are powered by quality and compelling product content and require effective information management. Nowadays technologies allow us to minimize the burdensome manual data-related work and automate most of the business workflows.

ML-powered PIM software is the right choice for any manufacturer, brand, or retailer who wants not only to automate their data routine but also to get the most out of their data insights and provide a one-of-a-kind shopping experience. Are you ready to benefit from AI-driven product information management? Book your free personalized demo and Gepard experts will explain what your business can gain by implementing our PIM solution.

Ask Us How Gepard PIM Solution Can Help You To Automate Product Data Management

Cyril Dorogan Gepard PIM E-Commerce Consultant
Written by Cyril Dorogan
CCO at Gepard
Dedicated professional with more than 15 years of professional background in the eCommerce domain. Helping companies with Product Data syndication and eCommerce solutions, striving for success, and focusing on dedicated customer service. Cyril has implemented a range of eCommerce solutions for a variety of companies as Amazon, GS1, HP, Dell, Rakuten, Elkjop, Lazada. He is passionate about making business processes streamlined and growing the companies' operational efficiency by fulfilling eCommerce automation.

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