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Product Data Management
PIM has evolved from an initial data onboarding and publishing solution into a powerful digital commerce solution. With “experience” projected to be the key differentiator over “product” and “price”, the Contentserv PIM capabilities extend beyond facilitating solid product information management to delivering exceptional product experiences.
The first step to getting a single source of product truth, is the importing and consolidation of product data that is siloed across an organization. Product data usually comes from different internal and external sources and systems, such as suppliers, third party content aggregators or data pools.
Product data usually arrives in high volumes, using different formats and with varying levels of quality. To reconcile inconsistencies and irregularities, rules for standardization, accuracy, completeness and overall quality are created, managed and enforced using automated workflows.
Many organizations struggle to manually maintain product information and digital assets. With automated data management capabilities such as data modeling, enrichment, localization and publication, organizations are able to improve operational efficiencies toward obtaining rich, relevant and channel-optimized content.
Today’s customers expect a seamless experience across multiple touch points, so marketing and sales teams are hard-pressed to meet and exceed this expectation. With automated publication capabilities, teams can share data across the organization and ensure accurate, complete, consistent and timely content published across all channels, at any time.
Forget the endless internal sharing of spreadsheets and manual processes that lead to delays, errors and inconsistent information. With Contentserv’s PIM solution, you can stay focused on delivering accurate, rich, and enhanced content to your customers – anywhere and everywhere.
Bring products to market faster
Convert shoppers into customers
Drive more traffic to your product pages
Improve customer satisfaction
Increase sales while building loyalty
Localize your products in every market
These accelerators are our advantage
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In this blog, we will discuss about the classification of data and describe the various categories of data (reporting, transactional, master, golden, reference, metadata, unstructured and big data).
In this blog, we will discuss about the classification of data and describe the various categories of data (reporting, transactional, master, golden, reference, metadata, unstructured and big data).
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Data mapping and transformation are critical components of any Product Information Management (PIM) implementation. In this blog post, we will discuss best practices for data mapping and transformation in the new multi-tenant SaaS Informatica PIM.
Data mapping and transformation are critical components of any Product Information Management (PIM) implementation. In this blog post, we will discuss best practices for data mapping and transformation in the new multi-tenant SaaS Informatica PIM.
The need to integrate data management services and take decisive decisions to run businesses is increasing day by day. Now is the time for your organization to understand the true value of master data management and implementation. But before that, it’s more important to avoid these 5 common mistakes while managing a 360 view of business data.
The need to integrate data management services and take decisive decisions to run businesses is increasing day by day. Now is the time for your organization to understand the true value of master data management and implementation. But before that, it’s more important to avoid these 5 common mistakes while managing a 360 view of business data.
In this blog, we will discuss about major misconception we build while dealing with data and the confusion we usually build between Master Data and Reference Data. Most will tell you that reference data is a subset of master data, and it is, sort of. But...
In this blog, we will discuss about major misconception we build while dealing with data and the confusion we usually build between Master Data and Reference Data. Most will tell you that reference data is a subset of master data, and it is, sort of. But...
