What does poor quality data cost?

The price for poor address data is high

But: Faulty addresses and poor quality are a cost factor that companies could simply avoid.

In Germany, about 14 million addresses change every year due to relocations and about 990,000 due to deaths. Many of the total 370,000 marriages and 150,000 divorces per year are associated with name changes. In addition, there are thousands of changes in street names, postcodes and towns every year. Most changes occur at the address - the CRM is not necessarily up-to-date. However, correct postal addresses are of central importance for companies. Only correct addresses ensure the deliverability of mailings, minimise postage and advertising expenses and are an indispensable prerequisite for identifying duplicates.

At what point do companies have a problem with the quality for their address data? When is the time reached when action must be taken and optimisation measures must be implemented? It is often not recognisable at first sight whether an address is postally correct or incorrect.
 

Poor address data is noticeable at various levels

These symptoms should be taken seriously, as they give an indication of the actual state of address data: 
 

• High return rate for mailing campaigns due to incorrect or outdated addresses. 
• Customers complain about receiving advertising mail more than once because the contact data are duplicates.
• Form of address keys are poorly maintained or not maintained at all, so that letters are sent to customers with the wrong form of address. 
• Legal regulations such as the anti-terrorism ordinance cannot be complied with because the technical possibility to implement matching against sanctions lists is missing. 
• The company's own employees complain about the time-consuming data record maintenance in more than one system and moan about a high manual effort when searching for certain customer groups or other relevant information.

Good address data is also a prerequisite for data integration and data synchronisation. If this is not the case, the company master data cannot be meaningfully enriched or integrated. When merging two or more data sets, errors can occur that distort the information of the master data set. This in turn can have fatal consequences for all areas of the company: in support, the customer is advised incorrectly, invoices are issued for products that the customer has not purchased or the customer is advertised with products in which he has no interest or which he has already purchased. Especially in the CRM environment, this can annoy the customer - in the worst case, the business relationship is jeopardised.