By definition, Product Information Management or PIM is a systematic approach to organize all the data an organization has or uses about their products. Last week I discussed how organizations have the ability to make experiences better by adding discipline through enhanced use of their Product Lifecycle Management (PLM), in this article we will discuss how using the train depot of PIM can drive more effective use of product information when machine learning or artificial intelligence gets involved.
At its core, PIM systems take the data an organization has about its products and organizes it within a system for use on eCommerce platforms. On a broader level, a PIM can activate ancillary systems that are in use such as Site Search, Recommendations or Personalization engines, Outfitting, Fit algorithms, etc. etc. These systems are typically driven by complex algorithms to enhance the customer experience and ultimately drive conversion. The PIM is the restaurant that will serve up the food for the AI-driven mechanism to do its work.
Let’s dig a bit deeper. In the PIM typically, the data within it ranges from the ordinary, such as Product Descriptions and Summaries, to the more complex, such as category relations or meta data relations. Taking this arsenal of data, the PIM can directly interface with the eCommerce platform but also via API or data-feed can populate the AI-based systems.
Let’s use Site Search and Out-fitting as an example. The PIM system is going to populate the eCommerce platform with base level product data that it has stored to allow a customer to purchase the product. We could stop here, and a customer would be able to purchase a product, but to really enhance the experience let’s go further. Sophisticated, AI-based site search tools will also interface with PIM to get as much information about the product that it can and begin the learning process as customer engage with site search to find what they are looking for. While this is going on, PIM will also interface with outfitting to pull together the appropriate outfits based on the criteria and relationships establish within the PIM. Pairing a hat, gloves, and scarf with a jacket and pants, for example completes the outfit. The smart Site Search program will then take the input from the customer and not only return results for “Winter Jacket” but also return the completed outfit that was funneled from the relationships of product that were established at the PIM. As more customers, search and browse the site the smarter the Site Search and Outfitting machines get, and the conversion rises. All based on the fundamental data that is organized and maintained with the PIM.
So ultimately, as companies are building out their eCommerce eco-system they want to think about how the hierarchy of needs progresses. What I mean by this is they all sell products or services of some kind. In order to make the most of that they want to get the information about those products as organized as possible. A PIM will do that. The product then goes to the website for customers to find and purchase the product. On top of that, the company will seek out and purchase the coolest and fanciest tools to make finding and purchasing even easier, faster, smarter, etc. It starts with the PIM and makes its way to the cool stuff, i.e. AI and machine-learning systems, quickly. Bingo-bango just like that you have a sophisticated ecommerce eco-system that marries the diligence and efficiency of Product Information Management with the wonders of Artificial Intelligence.
I hope you found this insightful and see you again next week.