Efficient duplicate detection is crucial for businesses looking to manage their customer and address data optimally. A significant portion of the challenges in today’s data landscape involves ensuring that the information you hold about your customers is accurate, up-to-date, and unique.
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Well-maintained address lists and customer master data are the foundation for successful marketing and sales initiatives. Moreover, an error-free address database is crucial for meaningful analyses and statistics. Only with accurate data can you make operational and analytical decisions that propel your business forward. Therefore, it is essential to regularly conduct duplicate checks to identify duplicates and clean the addresses.
Data classification is a crucial step in maximizing the value and relevance of information within a company. By systematically categorizing data into classes or groups, not only is the handling and utilization of data improved, but a solid foundation is also established for accurate analyses and decision-making.
Identifying duplicates in address databases is a crucial step in ensuring data quality and optimising your customer data. Successful methods use innovative approaches to detect duplicates accurately and efficiently. Various techniques are used, combining automated processes and intelligent algorithms.
In the digital age, data enrichment has become an indispensable part of customer management. Companies face the challenge of not only collecting their customer data, but also enriching it in a way that enables targeted communication and personalised marketing campaigns.
In a world where data is the new currency, the quality of information can determine a crucial advantage. Imagine entering a scheduled meeting with a potential client only to discover that the contact details are inaccurate and key information is missing. This situation can not only waste time but also valuable resources and jeopardize your credibility.
Despite advancing technological developments and a growing awareness of data management, many companies have still not fully realized that high data quality is an essential prerequisite for effective customer relationship management.
TOLERANT Software Managing Director Stefan Sedlacek was interviewed by the online magazine WebsitePlanet.
The half-life of customer data is seven years on average. This means that after seven years, half of the data is already out of date, for example because customers have moved house, got married or taken out a new telephone contract. The data quality tools TL Match, TL Post, TL Name, TL Move and TL Bank from TOLERANT Software considerably increase the half-life of customer data.
Migrating data from multiple sources into a new information management system is a complex and often headache-inducing undertaking. Data migration is usually necessary to keep pace with technological advances and industry standards, but it requires a great deal of effort. Data from different storage areas – both on-premises and in the cloud – must be evaluated, analyzed, cleansed and organized before it can be combined and matched.
Data quality products from TOLERANT Software tap the full potential of CRM systems, making AI and business intelligence applications possible in the first place.
Contact us.
We will be happy to help you with your data quality issues.
TOLERANT Software
GmbH & Co. KG
Büchsenstr. 26
70174 Stuttgart, Germany
Phone: +49 711 400 4250
Fax number: +49 711 400 425 01
info@tolerant-software.de
www.tolerant-software.de