Table of Contents
How AI and ML Technology are Changing the PIM System?
A well-organized and efficient Product Information Management (PIM) system is crucial for any eCommerce business. Without a centralized hub to manage and distribute product data seamlessly across platforms, maintaining operational efficiency becomes a daunting task.
However, setting up a PIM system traditionally involves tedious manual tasks like entering product descriptions, managing attributes, and ensuring data consistency. These tasks are not only time-consuming but also prone to errors, leaving little room for creativity and strategic focus.
The emergence of Artificial Intelligence (AI) and Machine Learning (ML) solutions has revolutionized the automation of product information. According to recent trends in eCommerce, integrating AI into your PIM strategy can significantly streamline operations and drive profitability.
Here’s how AI and ML are reshaping PIM systems:
1. Attribute Mapping and Classification
Efficiently managing product attributes like colour, size, and material is critical for eCommerce businesses to deliver accurate and consistent product information across platforms. Traditionally, this process involves manual entry and categorization, prone to human error and time-consuming repetitions.
However, with the integration of Artificial Intelligence (AI) and Machine Learning (ML) technologies, this landscape is rapidly evolving. AI algorithms can analyse vast datasets—including product data and images—to automatically identify, classify, and map attributes within Product Information Management (PIM) systems.
2. Merging Data from Different Sources
Launching a dropshipping eCommerce business involves integrating product data from multiple suppliers, each with their own data formats and delivery methods. Traditionally, this diversity complicates the data integration process, requiring manual effort to standardize and import information into a Product Information Management (PIM) system.
Suppliers often provide product information in varied formats such as CSVs, XLSXs, JSONs, PDFs, and website links. Each format may have different structures, naming conventions, and data hierarchies, posing a significant challenge for seamless integration into a unified inventory system.
AI can interpret and process diverse data structures and file formats, including unstructured data like images and PDFs. This flexibility eliminates the need for manual data manipulation and allows for seamless integration of data into the PIM system. Machine Learning (ML) models learn from corrections made by users, continuously improving data interpretation and integration accuracy over time.
3. SKU (Stock Keeping Units) Matching
A great use of AI and ML in PIM systems is finding and automatically matching duplicate products’ SKUs. For example, BetterCommerce PIM offers an AI-based solution that uses product titles for accurate matching. This is particularly useful on marketplace eCommerce platforms where multiple sellers might offer the same product.
Consolidating SKUs benefits both you and your customers. For you, it means a more organized and efficient database that’s easier to maintain and faster to run. It also enhances your ability to moderate the marketplace.
If 10 sellers offer the same product, but one has it at 30% of the usual price, it might indicate something suspicious, and you might want to investigate. An AI-powered PIM system can identify these outliers in real-time and flag them for human review.
For customers, matching SKUs with AI allows for several quality-of-life improvements. They can compare different listings of the same product and choose the one that best fits their priorities, whether that’s the best price, quickest shipping, or the most generous return policy.
4. Dynamic Pricing
Product pricing is another area where AI can transform your business approach. PIM solution allows you to monitor your direct competition and adjust your prices as often as every 10 seconds. But the potential doesn’t stop there.
You can also use AI to monitor and analyse customer behaviour and real-time global trends, suggesting optimal pricing strategies.
For instance, if there’s a shortage of a product on the market, it might be a good idea to increase your price for that item. If weather forecasts predict a cold and wet summer, you could start a sale on your outdoor sports-related products. These types of analyses and decisions can now be effectively managed by AI.
5. Image Recognition & Tagging
Imagine running an eCommerce business that sells decorative pillowcases. With just the pictures, AI can determine crucial tags and attributes to make your store easy to search. It can classify the colour, shape, and pattern like floral or geometric, and even identify the fabric with some training.
You only need to provide the system with the appropriate product photos. AI-powered image tagging is already a substantial industry, offering solutions tailored to specific uses or product types.
Another area where AI can save you time is filling out image metadata. Entering image titles, file names, and alt texts for every photo is tedious but necessary for making your store accessible and SEO-friendly. Fortunately, this task is perfect for an automated system. It’s a simple process of “describe what you see,” which is easy for AI and a big relief for you.
6. Demand Forecasting
Demand forecasting involves monitoring both your store’s internal data and external factors. Machine learning systems can identify patterns such as running out of stock of your products. Naturally, you’d want to increase your stock for the following year to maximize profits.
But by how much? You don’t want to overestimate either. AI can consider various factors, such as:
- Your sales data to determine if sales were declining or increasing when you ran out of stock.
- Website analytics to see how many users visited the product pages only to find the item unavailable.
- Global reports on the bike market.
- Seasonal weather forecasts.
- Data on your customers’ overall financial situation.
Based on these factors, the system can predict how many more bikes to order for the next year and automatically adjust your supplier orders accordingly.
7. Data Extraction & Cleansing
AI and ML significantly enhance data extraction and cleansing in PIM systems. These technologies can automatically pull data from various sources—such as PDFs, images, and websites—and clean it by correcting errors, standardizing formats, and filling in missing information. This ensures that the data is accurate, consistent, and ready for use, reducing the time and effort required for manual data processing and improving the overall quality of your product information.
End Note
Introducing AI and ML solutions is the next logical step in the evolution of the PIM tool. The benefits in terms of efficiency and profitability make this decision an obvious one for most eCommerce business owners. Automating tasks like dynamic pricing and demand forecasting can save countless hours and boost profits.
AI isn’t a one-size-fits-all solution, and you don’t have to implement everything at once. You can choose the systems that excite you and ignore those that don’t fit your needs. Since most popular PIM systems don’t yet include AI functionality, you’ll need to connect AI to your PIM system’s API. Look for robust integration capabilities, like those offered by BetterCommerce, to ensure success.
AI becomes more effective the more you use it. Initially, automated systems might not deliver perfect results, as each store has unique requirements. It takes time for the technology to learn your business logic. However, with continued use and sufficient data, AI will become increasingly precise with each new item it processes.
