Why implement a Data Quality strategy?

27% of company data is inaccurate. Capency reveals the 4 steps to succeed in your Data Quality strategy.
27% of company data is inaccurate. Capency reveals the 4 steps to succeed in your Data Quality strategy.

The quality of data is measured through various characteristics, whether internal or external to the company: coherence, validity, timeliness, integrity, clarity, or even the security of the data. 56% of companies face data issues, and poor management can result in a cost of $11 million*.

In our case, we will focus on customer data, whether collected in-store or through web forms. These tips are applicable to any business that is concerned with improving data management in its database.

Firstly, data quality can be compromised at two levels:

  • At the time of collection (33%): misunderstanding of the data during input, incorrect field(s) filled, typos (4% error rate).
  • Due to the data itself: duplicates, outdated data (the person no longer lives at the indicated address, etc.).
Capency reveals best practices to ensure data quality.

How to assess the quality of data?

To succeed in a data quality strategy, it is first necessary to identify mistakes to avoid and data that could be considered erroneous:


Poor-quality data includes:

  • Inaccurate: Missing information on a customer record, typos, etc.
  • Non-compliant: Postal address not standardized upon entry into the database, data not compliant with current standards (e.g., GDPR).
  • Uncontrolled: Duplicates.
  • Unreliable: Without the implementation of secure software.
  • Static: Data is not updated; it is also called dormant data. Example: a change of address or phone number.
    Exemple : un changement d’adresse ou de numéro de téléphone 

Only an organizational approach can improve data quality, with a very simple process: prevention of non-quality data before entry into the database; cleaning process for data already in the database and control of data integrity.

The steps of the data quality management process:

  1. Define the data to collect and control
  2. Evaluate the quality of data
  3. Improve data quality through better collection and data updates
  4. Calculate the Return on Investment (ROI)

This Data Quality Management (DQM) work offers numerous advantages, provided that you use your data in the right way. They are essential to improve your business and marketing operations, making them more effective and profitable, as 40%* of business initiatives fail to achieve their goals if the data is not of high quality.

1) Improve the deliverability rates of your campaigns across all your channels:

MAILING: By standardizing postal addresses and adhering to the standards expected by the postal service, you can optimize your deliverability rates and reduce the number of Undeliverable-As-Addressed (UAA) mail. A process for handling address changes can also be implemented to retrieve the new addresses of your customers.

Results obtained following the implementation of our address standardization solutions: +30% of postal addresses corrected with CAP ADDRESS.

EMAILING: Contacts considered inactive (static data) or hard bounces (non-existing or incorrect email addresses) penalize you: the email is not delivered. Moreover, ISPs keep the history for several months; an email campaign to inactive contacts will take time to be forgotten, impacting your future campaigns. By routing to valid emails, you can ensure the success of your campaigns and preserve the reputation of your IP addresses.

Results obtained following the implementation of our email validation solutions: +20% of emails corrected with CAP EMAIL.

SMS: SMS is the most effective means of communication for 58%* of consumers. With a DQM strategy, you reduce the number of incorrect numbers to improve your deliverability rates.

Results obtained following the implementation of our mobile number control solutions: +15% of phone numbers validated with CAP PHONE.

2) Building a 360° customer view:

To set up a Customer 360° View and thus obtain a comprehensive vision of your customers, the implementation of DQM solutions is essential to standardize, structure, and qualify your data.

3) Optimal customer satisfaction:

With quality data, you can stay in touch with your customers by offering them personalized deals, for example, with the ultimate goal of building a lasting relationship and fostering customer loyalty.

4) Reduction in non-quality costs:

due to the distribution of messages or packages to outdated, incorrect, or duplicate contacts in the database, for example.
(a faulty data costs 100 times more than when it is verified and correct)

Adopting a DQM strategy means recovering 20% of your revenue!

Solutions
Data Quality
Identité & Consentement
Offres data complémentaires​
Connecteurs
Logiciel de caisse | POS​
CRM
CMS | Ecommerce
Solutions
Data Quality
Identity & Consent
Complementary data offers
Connectors
Checkout software | POS​
CRM
CMS | Ecommerce