Increase consent rates through data analysis, A/B testing and marketing skills
In order to avoid having to work with less data after the integration of a CMP, the CMP can be configured from the beginning in such a way that the highest possible number of users gives as many approvals as possible. Targeted opt-in optimization protects advertising revenues, evaluates a larger set of statistical data and better targets customers.
Opt-In: A granted consent - the user agrees to the use of his data
Opt-Out: A withdrawn consent - the user refuses the use of his data
Explicit Opt-In/Opt-Out: An explicit given/revoked consent - for example by clicking on the "Accept" or "Reject" button
Implicit Opt-In/Opt-Out: An implicitly granted/withdrawn consent - e.g. based on default values ("Google Analytics is disabled by default and will only be played after an opt-in = implicit opt-out" or "Trusted Shops is enabled by default but can be disabled = implicit opt-in") or after a timer ("If no decision has been made after e.g. 30 seconds, the user accepts everything").
WHY DO I NEED AN OPT-IN OPTIMIZATION?
An implicit opt-in (see definition) sounds cunning and has been/is (still) practiced a lot - but since the latest legal decisions it is no longer allowed and therefore subject to legal action. If you still want to receive as many (explicit) consents as possible and thus data that can be used for marketing purposes from your users, you should configure your cookie banner cleverly and find out the best possible variant for your website / company by means of test runs.
OPT-IN OPTIMIZATION WITH THE COOKIEBOX
ANALYSIS & DATA EVALUATION
We combine data from different sources and create accurate statistics and datasets. The data is then evaluated together with the customer. With the help of our experience, we then put the results into context and make suggestions for further action.
2. EXTENDED CONTENT-TRACKING WITH COOKIEBOX ANALYTICS
With the help of our own analysis tool, we can collect specific anonymized information about the interaction with the banner – completely in accordance with the applicable GDPR and without further consent of the user.
A/B TESTS TO OPTIMIZE THE RATES
Using the A/B test, we evaluate approval data and have different banner configurations tested against each other to achieve the best possible result. Even small changes such as the color of a button or the position of the cookie banner can cause remarkable changes in the approval rate. Especially for companies whose marketing is based on individualized advertising, an opt-in optimization can mean a large additional profit. For this reason, A/B testing has become one of the most important test methods in online marketing.
PROCEDURE OF THE A/B TESTING
- Analyze of the website to be optimized
- Creation of an A/B testing concept
- Layout & implementation of the test variants
- Performing the test
- Evaluation of the results
- Implementation of the best variant
EXAMPLES OF TWO POSSIBLE BANNER VARIANTS OF A/B-TESTING
WHAT ARE THE DIFFERENT RATES?
The term "opt-in rate" must first be put into perspective. Depending on the calculation, you get different results. In the following, we will briefly explain the differences:
Total Opt-In-Rate (TOIR)
Shows the total ratio of Opt-In and Opt-Out - whether explicit or implicit. The rate is calculated using the following formula:
Sum of all Opt-Ins divided by the sum of all Opt-Ins + Opt-Outs.
Explicit Opt-In-Rate (EOIR)
Shows how many of the total users give explicit consent. The rate is calculated using the following formula:
Sum of explicit opt-ins divided by the sum of all users.
Interaction Opt-In-Rate (IOIR)
Gives information about the relationship between "Accept" and "Reject". The rate is calculated using the following formula:
Sum of explicit Opt-Ins divided by the sum of all explicit consent (Opt-In/Opt-Out).
Source of the figures: Usercentrics (various CMPs, 106,109,588 individual users)