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What is Melissa Clean Suite?
The Melissa Clean Suite, formerly known as Melissa Listware, is designed to tackle the challenge of inaccurate data within platforms like Salesforce®, Microsoft DynamicsCRM®, and Oracle CRM® and ERP systems. This solution not only verifies and standardizes customer contact information but also corrects and enhances records to ensure that your data remains clean and usable. With the ability to create pristine datasets, it empowers businesses to excel in omnichannel marketing and boost their sales performance.
* Ensure contacts are accurately verified and autocompleted prior to entering the CRM system.
* Enrich records with essential demographic information to enhance lead scoring, segmentation, and targeting strategies.
* Maintain up-to-date and immaculate contact details to facilitate effective sales follow-up and marketing efforts.
* Safeguard the integrity of your customer data through real-time data cleansing at the point of entry or through batch processing.
In an era where data is pivotal for effective customer communication, decision-making, and analytical insights, having clean data is crucial. Neglecting this aspect can result in operational inefficiencies and distortions in understanding customer behavior.
What is Data Quality on Demand?
Data plays a vital role in multiple sectors of a business, such as sales, marketing, and finance. To fully leverage this data, it's important to maintain its integrity, protect it, and manage it effectively throughout its entire lifecycle. At Uniserv, we believe that data quality is a core principle of our identity and the services we offer. Our customized solutions convert your customer master data into a crucial asset for your business. The Data Quality Service Hub ensures that your customer data remains of the highest quality across all locations within your organization, including those overseas. We offer services that align your address information with international standards, utilizing the best reference data available. Furthermore, we validate email addresses, phone numbers, and banking information with meticulous attention to detail. If your data has duplicate entries, we can quickly pinpoint them according to your defined business requirements. Often, the identified duplicates can be merged automatically using predefined rules or categorized for manual assessment, which promotes an efficient data management workflow that boosts operational productivity. This thorough strategy for ensuring data quality not only aids in compliance but also builds trust and credibility in your customer relationships, ultimately leading to stronger business outcomes. An unwavering commitment to data quality fosters a culture of accountability within the organization, encouraging all departments to prioritize accurate data handling.
Integrations Supported
Inte Q Genesis
Microsoft Dynamics 365
Oracle CRM On Demand
SAP Store
Salesforce
Syniti Data Connectivity
Syniti Knowledge Platform
Integrations Supported
Inte Q Genesis
Microsoft Dynamics 365
Oracle CRM On Demand
SAP Store
Salesforce
Syniti Data Connectivity
Syniti Knowledge Platform
API Availability
Has API
API Availability
Has API
Pricing Information
Pricing not provided.
Free Trial Offered?
Free Version
Pricing Information
Pricing not provided.
Free Trial Offered?
Free Version
Supported Platforms
SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux
Supported Platforms
SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux
Customer Service / Support
Standard Support
24 Hour Support
Web-Based Support
Customer Service / Support
Standard Support
24 Hour Support
Web-Based Support
Training Options
Documentation Hub
Webinars
Online Training
On-Site Training
Training Options
Documentation Hub
Webinars
Online Training
On-Site Training
Company Facts
Organization Name
Melissa
Date Founded
1985
Company Location
United States
Company Website
melissa.com
Company Facts
Organization Name
Uniserv
Date Founded
1969
Company Location
Germany
Company Website
www.uniserv.com/en/business-cases/customer-data-management/data-quality/
Categories and Features
Data Quality
Address Validation
Data Deduplication
Data Discovery
Data Profililng
Master Data Management
Match & Merge
Metadata Management
Categories and Features
Data Quality
Address Validation
Data Deduplication
Data Discovery
Data Profililng
Master Data Management
Match & Merge
Metadata Management