No direct digital marketing without data. If you wish to approach a customer, e.g., via email marketing, you will need at least his email address (and his consent to use it for marketing purposes). For truly customer-centred marketing, you will need more data to obtain e.g., information on the customer’s interest, marketing preferences or contexts of use. However, in order to effectively use this data, it must also be correct. A high data quality is therefore an essential success factor in marketing. We will demonstrate to you the significance of the data quality in marketing and how you can ensure it.
Low data quality can be caused by a variety of reasons. The data could be incorrect (e.g. an email address with reversed letters). It could be incomplete (e.g. men and women should receive different campaigns, but the gender of most customers is not known). It could be used in a legally incorrect manner (e.g. because opt-ins were obtained without the adequate data protection declaration):  Data could be set in the wrong context or even without any context (e.g. a customer receives a discount via email for a product which he has just purchased in the online shop).
According to a current study by Veritas, on average, only 14% of data stored by companies is actually useful. The remaining 86% is either not properly assigned, is not legally watertight, is redundant, incorrect, incomplete or simply useless for the intended application purpose. Dan & Bradstreet Net Prospex estimate the deliverability of emails due to insufficient data quality in 62% of companies as questionable at best. Deutsche Post assumes that 14.2% of postal addresses in customer data bases are undeliverable.

Impact of Insufficient Data Quality

What impact does insufficient data quality have on marketing? Incorrect (email) addresses or telephone numbers prevent the campaign being delivered in the first place. It doesn’t need explaining that this goes against the marketer’s intention. However, the marketer might have to explain why his performance figures are so low, why targets are not reached which were set based on the customer number, assuming that these customers would all be targeted. If data have not been collected in a legally-compliant way, this can lead to cautions which are not only costly but can also have a negative impact on the company’s reputation as well as the trust in marketing in general. If data exists, but is incomplete, incorrect or in the wrong context, you don’t only waste the great potentials of customer-centred marketing (especially in comparison with the competitors who work better with data), you might even confuse or annoy your customers through unsuitable communication. “Bad” data can be worse than no data.
And also: The capturing, storage and processing of data generates expenses, regardless of the data quality. Even more expenses arise from the venture of transforming existing “bad” data into “good ” data in retrospect. Customers will have to be followed up digitally or over the phone. Data stocks will have to be analysed and cleaned up in an elaborate effort. Missing consent will have to be obtained retrospectively etc. Datamatics has calculated the cost of a low-quality data stock in B2B email marketing in an example. The cost to bring a lead to a business transaction is 145% higher with a proportion of 50% incorrect data compared to a proportion of 90% correct data.

Ensure Data Quality

How can I ensure data quality in marketing? We have compiled a few measures for you.
Guarantee Legal Security: Personal data can only be used with the explicit consent (opt-in) of the customer. This means, at all touchpoints you consistently need to obtain opt-ins from customers for data usage.
Set a Data Strategy: Many companies still operate according to the principle of first capturing as much data as possible and subsequently checking how this can be used. The result: Useless data is collected, the correct consent is missing, data must subsequently be processed and linked correctly for the future purpose, etc. You should therefore first define a data strategy of which data is actually needed for the set purpose and how to link it in a sensible way. Further information can be found in our article From Big Data to Right Data
Bounce Management: When a dispatched email is not delivered, e.g., because the email address is incorrect, it creates a bounce which is returned to the sender. Modern marketing automation solutions use a bounce management to automatically identify incorrect email addresses and remove these from the mailing list or notify an operator so that he can check the address and possibly follow it up. Further information on bounces can be found in our checklist: What Can Cause Email Bounces
Use Opt-ups and Self-service: The best customer data is obtained when you enable customers to adapt data themselves. Customers can amend incorrect data or add new data. To do this, we recommend a self-service portal. Customers with incomplete profiles should frequently be encouraged during the communication to update their data. This is called “opt-up”. A welcome series, for example, would be very suitable here.
Centralise Data: The most important challenge in the correct linking of data lies in compiling all data in one central customer profile which can be updated in realtime. You can find more information on our article Single Customer View – Consistent Dialogue Throughout the Customer Journey