Intro, why is this topic relevant, why is it strategically so important?
For all companies seeking direct contact with their customers, the customer database is the linchpin of coordinated sales and marketing activities. The conviction that quality – in particular the simple accuracy of address data – plays a key role in this is finally gaining ground.
Customer relationship management, data-driven marketing and sales, value to the customer or database marketing virtually demand uncompromising quality and up-to-dateness. The usual buzzwords include address validation, data cleaning, data cleansing, data quality tools, data analysis and optimal data collection by your own employees. But also optimal support for self-service data capture by interested parties and customers. This is the only way to ensure that e-mail addresses for e-mails and e-mail marketing are captured without errors right from the start.
In July 2020, we created and published the first German address and data quality software and service provider landscape. The wide variety of technology, tools and software solutions does not make product and/or service provider selection any easier. We are here to help.
Here, in this document, you will learn the most important steps for pragmatic data management and how to improve the quality of your customer addresses step by step.
Best practice examples – How does the analysis usually start?
At some point, the CEO receives a letter in which the surname or first name is misspelled. Then the “inner” question automatically arises:
What does this look like in my own company and who takes care of data quality management in our company?
At the next management meeting, you then take it in turns to ask: Is it IT, marketing, sales, customer service or database marketing that is responsible?
If you find someone responsible for data quality management at all, the next question is: “Is our database or are our addresses in order? What are we doing to keep it that way? Is there any incorrect data? Who pays attention to them, who corrects them? And what about all the other data?”
At this point, the person addressed often uses politically tinged sentences such as, “Don’t worry! In the last campaign, no mailings came back with “Undeliverable”.” (How could they, because there was no advance directive printed on the mail as information for the letter carrier, so no mailings can come back. An advance directive is the text above the address field, “If moved, please forward and return to us with an address correction card”). An advance directive is a premium address service provided by Deutsche Post. In the end, they often try to leave the impression that everything is in order.
Data quality management: Why are well-maintained addresses so important?
The recipient of the letter or message does not like to read his name misspelled. Correct and sensible personalization in the letter or e-newsletter leads to an increase in the response rate. All analyses are severely impaired by poor address quality and therefore also the basis for decision-making. Incorrect addresses lead to increased mail returns, unnecessary waste of budgets and lost sales. Duplicate addresses or letters frustrate the recipients (“Oh, they must have money”).
If, for example, mother and daughter receive letters or catalogs at the same time, but with different offers, this leads to a loss of sales, as they naturally always choose the cheaper offer.
Only with standardized, cleansed and up-to-date addresses can external data be added that leads to further segmentation or qualification (e.g. micro-geographic or lifestyle data).
Data quality management: definition of address and data quality
Data is information. Important information. Data is the new oil! This guiding principle has become increasingly prevalent in recent years.
Data, including addresses of course, is the basis for good dialogue marketing, targeted sales, perfect service, customized products, sophisticated reporting and detailed analyses, determination of key figures … and much more. From the company’s point of view, it is all about individualization and personalization. The prospective customer or customer wants a “perceived closeness, they want to be understood …”
The more valid this data is, the more effective these measures are and the better the prospective customer or customer feels they are in good hands. Most companies are concerned with improving the status quo. High data quality is therefore the long-term goal.
In addition to the data protection components, which we will not go into here but will refer to, companies should also consider the aspect of motivation:
What does mindfulness mean in relation to this topic? What motivates employees to achieve good address and data quality? Ultimately, this is a management task and a question of attitude and mindset.
All data quality management efforts have one goal:
To achieve and maintain the best quality of existing data in an efficient manner.
And we do not leave it at a one-time maintenance, but try with everything that is available to a company or with the help of a prospective customer and customer to keep this data always up to date.
Wikipedia writes about this:
“Information quality is the measure of the fulfillment of the ‘totality of requirements for information or an information product that relate to its suitability for fulfilling given information needs.’[1] Statements about the quality of information refer, for example, to how accurately it ‘describes’ reality or how reliable it is, i.e. the extent to which it can be used as a basis for planning one’s own actions.
The term data quality (as a quality measure for data) is very close to ‘information quality’. Since the basis for information is ‘data’, ‘data quality’ affects the quality of the information that is obtained from the corresponding data: No ‘good’ information from bad data.”
A little further down in this Wikipedia article is written:
“Quality criteria for data quality differ from those for information quality; criteria for data quality are as follows[7]:
- Correctness: the data must correspond to reality.
- Consistency: A data set must not show any contradictions within itself or with other data sets.
- Reliability: The origin of the data must be traceable.
- Completeness: A data set must contain all necessary attributes.
- Accuracy: The data must be available with the required accuracy (example: decimal places).
- Up-to-dateness: All data records must correspond to the current state of the reality depicted.
- Freedom from redundancy: There must be no duplicates within the data records.
- Relevance: The information content of data records must meet the respective information requirements.
- Uniformity: The information in a data set must be structured in a uniform way.
- Unambiguity: Each data record must be clearly interpretable.
Comprehensibility: The terminology and structure of the data records must correspond to the ideas of the specialist departments.”
Data governance as a meta-level – Wikipedia writes:
“Here the focus is on an individual organization. Data governance here is a data management concept regarding the ability of an organization to ensure that high data quality is present throughout the lifecycle of the data and that data controls are implemented that support the business objectives.
Key focus areas of data governance include availability, usability, consistency[2], data integrity and data security. It also includes establishing processes that ensure effective data management across the organization, such as accountability for the detrimental effects of poor data quality and ensuring that the data a company holds can be used by the entire organization.
A data steward is a role that ensures that data governance processes are followed and policies are enforced, as well as making recommendations for improvements to data governance processes. Translated with www.DeepL.com/Translator (free version)
This section could also be included in section 6 “Address and data quality is a management task”. Because it is about responsibility. Only below is it about management responsibility. A data steward or similar corresponds partly to a data protection officer and partly to an operational manager.
We will be taking a more in-depth look at this in the coming weeks.
Data quality: Is there a difference between addresses and data?
There is not a big difference, but we would like to make a few key points.
Address quality refers to the data that belongs to the address. These are usually variables such as salutation, title, first name, surname, street and house number or the PO box, zip code and city. The zip code can be further differentiated according to zip code street and zip code PO box. This also includes variables such as the e-mail address, telephone number, smartphone number, fax number, etc. This is because address quality is about the delivery of the message. Regardless of which communication channel is used.
We consider all other data that does NOT belong directly to the address separately under the name of data quality. This is not entirely free of overlaps and therefore contradictions. Nevertheless, we would like to summarize the topic of data quality management as follows.
- Data is information -> information quality as an overarching concept
- Data and addresses for a prospect and customer -> data quality as a bracket
- Addresses Contact information for a prospect and customer -> address quality
- Data (such as purchasing behavior, industry, preferences …) on a prospect and customer -> data quality
In addition, there are the criteria that cannot be directly assigned to a prospect or customer, but are used in the context of transactions. Terms such as Product Information System (PIM)
