The Role of AI in Product Information
In this Article
PIM stands for Product Information Management, a software solution that centralizes and manages the information related to a company’s products. With the rise of AI, there are questions about AI in Product Information Management and its influence on eCommerce.
In eCommerce, PIM is essential because it ensures that accurate and consistent product information is available across all channels and touchpoints, such as the company’s website, online marketplaces, social media, and mobile apps. This is particularly important for businesses with many products, as managing product information can become time-consuming and error-prone if done manually.
With the right PIM system, eCommerce businesses can improve the efficiency of their product management processes, reduce errors, and enhance the customer experience. Customers are more likely to make a purchase when they have access to complete and accurate information about the product they are interested in. Additionally, PIM can help businesses streamline their supply chain management by providing suppliers with the information they need to produce, package, and deliver products.
Artificial intelligence, on the other hand, is a branch of computer science that focuses on the development of intelligent machines that can perform tasks that typically require human intelligence, such as learning, reasoning, problem-solving, and decision-making.
This article explores AI in Product Information Management.
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How AI Enhances Product Information Management
To best understand how AI can enhance the management of product information in the context of your business, it’s important to look at it from the angle of the routine tasks that go into preparing and organizing your own product information. The next question then becomes: How can you use AI to make those tasks faster, simpler or less costly?
With this line of thinking, you are not restricted to only a few known ways to apply AI. Instead, you should aim to utilize it in a tailored approach that works best for your processes.
Having said that, here are some of the top applications of AI in PIM at the moment.
AI-driven Data Extraction and Cleansing
Obtaining and maintaining accurate product data can be a challenging and time-consuming task, particularly for large retailers with extensive product catalogs.
But with AI, algorithms can be used to automatically identify and extract relevant data from various sources such as product catalogs, spreadsheets, websites, and other data repositories. The extracted data is then cleansed and standardized to ensure that it is accurate, across all channels.
The models used in this process can learn and improve over time, enabling you to continually improve the quality of data.
In a typical PIM platform, of course, it goes without saying that the platform must have a built-in AI-powered tool for data extraction and cleansing. The feature can work in different ways depending on a PIM platform’s design, but typically, it begins by identifying and classifying the data sources based on their format and structure. Once the sources have been classified, the AI algorithms within the tool will analyze the data and apply natural language processing techniques to extract the relevant product information, such as product titles, descriptions, pricing, and images. The extracted data can then be cleansed and standardized. The cleansing process involves mapping the extracted data to a standardized format and applying business rules to validate the data against predefined standards.
Automation of Product Categorization and Classification Tasks
Machine learning algorithms can be used to automatically assign products to relevant categories and subcategories within a PIM system. The process involves training a machine learning model on a large dataset of products and their associated categories. The model uses this training data to learn the relationships between products and categories, allowing it to accurately categorize new products based on their attributes.
The first step in automated product categorization is to define a set of categories and subcategories that will be used to organize products. These categories can be based on various attributes, such as product type, brand, material, size, and color. Once the categories have been defined, the PIM system will start collecting data on products, including their attributes, descriptions, images, and other relevant information.
Next, the model will analyze the product data and assign products to the appropriate categories. With time, the model will identify patterns and relationships between products and categories. It will then use these patterns to make predictions about the categories that new products belong to. And this right here is the beauty of AI – the ability to learn and do things without relying on routine. It means that once the model learns to some good level of accuracy, all you need to do is upload all kinds of products into your PIM, activate the feature and you’ll have all those new products categorized and classified automatically.
To ensure accuracy, the model will need to be regularly updated and retrained with new data. This is because product attributes and categories can change over time, and the model needs to be able to adapt to these changes in order to remain effective.
Enhanced Product Search and Discovery
The PIM platforms with this capability use AI in various approaches. The most common is to leverage Natural Language Processing (NLP) techniques to understand the context and meaning behind product queries. NLP involves parsing text data and extracting meaningful information to enable machines to understand human language. PIM platforms can use NLP to analyze the language used in search queries to better understand what users are looking for and return more accurate search results.
Image recognition is also effective. AI can identify key features in images, such as colors, patterns, and shapes, and use that information to automatically tag product images with relevant keywords, making it easier for users to find what they’re looking for.
Another way is through visual search. This technology allows users to search for products using images instead of text. AI-powered visual search can analyze the visual features of the image and provide recommendations for related or similar products. This can help users find products that they may not have been able to describe using text-based search.
Personalization of Product Recommendations
Based on the customer’s interests and preferences, an AI-powered recommendation engine built into a PIM platform can be used to recommend products that are most relevant. The recommendation engine is designed to use different algorithms such as collaborative filtering, content-based filtering, or hybrid filtering to personalize recommendations.
By continuously updating the engine with real-time data, the system can ensure that the recommendations are always matching users’ current preferences.
These recommendations can then be tailored and distributed through PIM to various channels, such as email marketing, social media, web stores, or marketplaces.
Top Use Cases of AI in Product Information Management
Many companies, including global giants in the eCommerce space, are already using AI to manage their product information.
Let’s have a look at the top examples.
Amazon’s Use of AI for Product Recommendations and Search
Amazon was one of the very first companies, if not the first ever to start using personalized recommendations. In fact, many in the eCommerce space believe it’s indeed Amazon that may have pioneered this concept of product matching in eCommerce.
Back in 2013, a report by Mckinsey indicated that up to 35% of what is purchased on Amazon is a result of recommendations. If this was way back in 2013, what percentage could we be looking at today? Food for thought.
So, how exactly does Amazon use AI for product recommendations and searches?
- Collaborative Filtering: Collaborative Filtering is a type of recommendation system that predicts user preferences based on their past behavior and similarities with other users. Amazon collects user data, analyzes it using collaborative filtering algorithms, and suggests products that other similar users have purchased or shown interest in.
- Natural Language Processing: Amazon uses natural language processing (NLP) to improve its search capabilities. This technology helps the search engine understand the user’s intent and context when they perform a search. For example, if a user searches for a “smartphone,” the search engine will take into account the user’s past purchase history and show them relevant products, such as the latest iPhone model.
- Image Recognition: This technology uses machine learning algorithms to analyze product images and identify key features, such as color, shape, and texture. This allows Amazon to recommend visually similar products to users and improve the accuracy of search results.
- A/B Testing: Amazon uses A/B testing to determine which product recommendations and search results are most effective. Different users are shown different variations of recommendations and search results to see which ones perform the best.
- Personalized Pricing: Users’ browsing and purchase history is analyzed to determine the optimal price point for a product. For example, if a user has a history of purchasing expensive electronics, Amazon may offer them a higher price for a new electronic product than it would for a user who typically purchases lower-priced items.
IKEA’s Use of AI to Bring Showroom Experience Online
IKEA is driven by the principle of enabling customers to experience in real-life the feel of how the furniture they intend to buy will look and feel in relatable spaces. Their showrooms are designed in a way that is inspired by this goal. If you want to buy bedroom furniture, for example, the showroom gives you the opportunity to get a feel of how that furniture feels in a typical bedroom. Based on this, you can make a decision on whether it’s a good fit for your bedroom or not.
To advance this philosophy, IKEA is taking advantage of AI to bring this unique store experience online. Here, customers can find predefined sets and showrooms both on the website and in-app.
Walmart’s Use of AI for Product Tagging and Metadata Enrichment
Across its Sam’s Clubs, Walmart uses floor scrubbers to capture real-time images of every item in the store as they clean the floors. The scrubbers are equipped with inventory intelligence towers capable of taking more than 20 million photos of everything on the shelves every day. These images enable the company to tell exactly what products are where, and what brand. With this precision, it’s then possible to tag each image correctly and present it accurately to both customers and inventory managers.
According to Anshu Bhardwaj, senior vice president of tech strategy and commercialization at Walmart, the accuracy is well over 95%.
Writing in Medium back in 2020, Karthik Deivasigamani revealed that Walmart was building a retail graph that can capture information about products and related entities with the aim of helping customers to better discover products in the catalog. The entities for NLP-based AI models were extracted from metadata such as product titles and descriptions.
Walmart is also using AI in some interesting ways like suggesting substitutes whenever customers miss what they wanted. Let’s say a customer who wants to buy a specific flavor of a particular dry dog food brand discovers that the flavor is out of stock when shopping. How do we recommend the correct substitute? This is a complex task. But Walmart is cracking it with AI. They are using deep learning AI to analyze hundreds of variables such as brand, size, pricing, historical shopping by the customer, etc. Based on this information, AI can recommend the best substitute in real time. In place of guesswork, precision takes over. The customer is asked to approve or decline the recommended substitute. If they decline, the response is fed back into the AI – an important metric that will be used to improve the algorithms so that future substitutes for similar customers can be more accurate. Read the rest of this case study on Walmart.
Challenges and Limitations of AI in PIM
While Artificial Intelligence is powerful in Product Information Management, it also comes with significant challenges that you need to take note of:
Lack of Human Oversight and Potential for Bias
AI algorithms learn from the data they are trained on, and if this data is biased or skewed, it can lead to biased decisions. For example, if the product information data used to train an AI algorithm only includes information about products that are typically purchased by a certain demographic, the algorithm may be less effective in making accurate recommendations for customers outside of that demographic.
It means that if these algorithms are not designed with fairness in mind, they may perpetuate or amplify existing biases, leading to recommendations that are not inclusive or equitable.
Secondly, AI algorithms are often seen as “black boxes” because it can be challenging to understand how they arrive at their decisions. Since one critical aspect of PIM is the accuracy and completeness of product data, the AI algorithms that are used to automate many aspects of product information management, including data entry, classification, and enrichment, can give inaccuracies in the absence of transparency. For example, an AI algorithm that automatically categorizes products based on their attributes may make mistakes, leading to products being categorized incorrectly, making them difficult to find.
Dependence on Quality Training Data
The training data for AI algorithms should be relevant, representative, and diverse to ensure that the algorithms learn from a broad range of examples and can accurately generalize to new data. However, sourcing and preparing high-quality training data can be a time-consuming and resource-intensive process. In fact, data preparation is often the most significant cost associated with implementing AI systems.
Secondly, maintaining the quality of the training data is also a significant challenge when dealing with large amounts of data. The training data must be regularly updated to ensure that the algorithm continues to learn and make accurate decisions. Additionally, the quality of the data must be continuously monitored to identify and correct any errors. This requires a significant amount of time and resources, which can be a limitation for businesses with limited budgets.
AI in Product Information Management (PIM) is a great asset for businesses looking for a great tool to support them in developing a good competitor analysis. Quality training data is essential for AI algorithms to be effective and accurate, as it serves as the basis for the AI’s decisions. Having a competitor analysis data and utilizing it in PIM training data sets, businesses can create AI algorithms that can outperform the competition and gain an edge in the marketplace. This will help businesses increase their revenue while also improving their customer experience. Investing in the right training data sets can help businesses stay ahead of the competition and take their marketing to the next level.
Cost and Complexity of Implementing AI-powered Integrations
AI models require access to large amounts of data, and PIM systems may not have the necessary data structure or processing capabilities to effectively integrate with AI technologies. Furthermore, AI-powered integrations often require custom development and configuration, which can add to the complexity of implementation.
The integrations may also require significant changes to existing business processes, including data governance and data management policies. These changes can be difficult to implement, not forgetting the additional resources and expertise that would be required.
The potential benefits of AI in PIM are diverse and far-reaching, with the ability to transform how businesses manage and present their products to customers. AI-powered algorithms can be utilized for data extraction and cleansing, streamlining the processing of vast amounts of product data. Successful automation of processes such as categorization and classification can free up valuable time for other tasks, while also providing more accurate and relevant search results. Additionally, AI can personalize product recommendations by analyzing customer data and identifying patterns, leading to higher sales and increased customer satisfaction.
As AI continues to advance, we are potentially staring at a future where AI-powered eCommerce systems could become fully autonomous, with AI handling everything from product sourcing and pricing to customer support and logistics. This could lead to significant efficiency gains for businesses and a more seamless and personalized shopping experience for customers. For PIM in particular, it could herald an era where the volume of product information management processes is significantly reduced. PIM platforms are already doing a lot on this and the combination with AI will make them even much more efficient in ways that would never have been possible.
It is difficult to predict precisely how long it will take for a complete change, but the pace of technological advancement in recent years suggests that we may see significant progress not so long from now.
AI can automate various tasks such as data cleansing, classification, and enrichment, as well as providing insights into customer behavior. Errors are a leading challenge in product information management, especially in eCommerce where businesses deal with so many products. With AI integrated into PIM solutions, the process of identifying errors can be automated. This means that even the tiniest of errors will always be caught and corrected automatically where possible or flagged for manual correction. AI can also use natural language processing (NLP) to understand product descriptions and categorize them correctly, reducing errors and improving the overall quality of product information.
AI can automate the monitoring and enforcement of data standards and regulations in the following ways:
- Data quality management: AI can be used to monitor data quality in real-time, identify data anomalies, and flag errors.
- Regulatory compliance monitoring: AI can be used to monitor regulatory compliance requirements and flag any violations. Machine learning algorithms can identify patterns, anomalies, and potential violations based on predefined rules or training data.
- Product classification: AI can be used to classify products according to different regulatory requirements, such as safety standards or environmental regulations.
Yes. AI can analyze customer behavior across different markets. The AI tools can then use this data to identify which products are most popular and which features are most important to customers in different regions. With this information, businesses can create targeted product descriptions and marketing campaigns that are more likely to resonate with customers in different markets.
- The AI algorithms used in PIM systems rely on large amounts of training data to learn how to make decisions. However, this data may include sensitive information, such as customer purchasing history or product pricing. If this data is not properly secured, it could be vulnerable to hacking or other data breaches.
- AI-powered PIM systems may impact the entire lifecycle of a product, from design to end-of-life. For example, AI algorithms may be used to optimize product design or to forecast demand for certain products. However, if these algorithms are not designed with ethical considerations in mind, they may lead to unintended consequences, such as overproduction or environmental harm.
- AI algorithms may generate new product information or insights that could be considered intellectual property. It may be unclear who owns these insights and whether they can be freely used or shared.
- Human workers who previously performed product information management tasks may be replaced with AI. This could have negative social and economic consequences, particularly for workers in low-skilled or routine-based jobs.
A combination of technical and business skills are necessary. Technical skills may include expertise in machine learning, natural language processing, and data analysis, while business skills may include understanding customer behavior and preferences, product marketing, and data governance.