PIM
Manage all product content in one central system of record.
Syndication
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Enhanced Content
Enrich product pages with below-the-fold content and rich media.
GDSN Data Pool
Synchronize standard supply chain, marketing, and ecommerce attributes globally.
Grocery Accelerator
Leverage the first-ever category-wide PXM accelerator in the grocery industry.
Digital Shelf Analytics
Continuously optimize your organization’s product content syndication.
Catalog Sites
Share secure, on-brand, and always up-to-date digital product catalogs.
Automation and AI
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PXM Platform, Integrations, and APIs
Integrate the PXM platform with the rest of your enterprise systems architecture.
Supplier Onboarding
Accelerate supplier onboarding while ensuring your schema requirements are met.
Product Listing
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Content Enrichment
Increase online conversions with Content Enrichment.
Automation
Save time and increase operational efficiency with retail automation.
SXM Platform, Integrations, and APIs
Integrate the SXM platform with the rest of your enterprise systems architecture.
Syndication Network
Automate how you exchange product content data to the digital shelf.
Enhanced Content Network
Turn product pages into product experiences with Enhanced Content.
Commerce Platform Integrations
Create winning product experiences everywhere shoppers are, including on owned sites.
GDSN Data Pool
Synchronize standard supply chain, marketing, and ecommerce attributes globally.
Open Catalog
Connect to the digital shelf faster with an open, standardized, and free product catalog.
Resources
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API
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Machine-learning personalization is the process of using data sets and data modeling — typically within an artificial intelligence solution — to tailor a webpage, product, or piece of content to a person or group.
Machine learning involves data-gathering algorithms that help artificial intelligences learn and adjust accordingly. In today’s ecommerce world, where consumers increasingly demand customized marketing and purchasing options, machine-learning personalization is a boon.
This technology gathers, organizes, and derives insight from users to create tailored experiences for them. Classifier algorithms, for example, use information from users (such as their demographics, interests, or engagement with a brand) to classify them into groups. From there, they display marketing and product information best aligned with the needs and interests of that group.
Other forms of machine-learning personalization include technology that uses past purchases to market future ones; algorithms that “score” user behavior and categorize accordingly; and technology that can search manually entered data points for the most relevant. In each of these cases, the tech offers businesses a way to personalize their marketing and sales efforts to particular people or segments.