Getting Around the Consumer Data Privacy Landscape in Digital Marketing

Consumers’ awareness of data collecting in their daily lives is growing, along with their discomfort with it. Given that 97 per cent of consumers are somewhat or extremely worried about securing their data, it’s likely that you are one of those consumers on your day off. The way that marketing is practised as we currently know it is about to alter as a result of the widespread anxiety surrounding the gathering and use of consumer data.

This blog will discuss how to adjust to the new data privacy and digital marketing restrictions as well as how they affect data-driven attribution models.

How to Adjust to the New Digital Marketing Constraints and Data Privacy

1. Give transparency and consent a top priority

Transparency is the cornerstone of moral data handling in digital marketing. Consumers should be made aware of your data gathering and usage policies. Before obtaining any personal information from them, get their approval in writing. Make it simple for users to consent to or refuse the acquisition of their data. The necessity of permission is emphasised by both the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA).

2. De-identify and combine data
When possible, think about anonymizing and aggregating data to safeguard individual privacy. You can still gain useful insights without compromising privacy by combining data points into bigger databases that cannot be traced back to single persons. This strategy fits with the principles of “Privacy by Design.”

3. Upgrade Data Security Procedures
To protect the data you acquire, make a significant investment in data security methods. In addition to causing financial damage, data breaches can reduce consumer trust. Establish incident response procedures, regularly patch systems, and encrypt important data. Establishing a commitment to data security helps win over customers.

4. Create user profiles that can be customised
Think about allowing users to edit their profiles and privacy preferences. Give them discretion over the sharing and handling of their data. Having a say in how they choose to interact with brands online, gives consumers more power and fosters trust.

Data-driven attribution models Are Impacted

Data-driven attribution models, which are crucial for comprehending customer journeys and optimising marketing efforts, are significantly impacted by consumer data protection legislation.

1. Reduced Data Granularity
The level of detail in the data that can be used for attribution may be restricted by privacy laws. The use of personally identifiable information (PII) in attribution models is, for example, discouraged by GDPR. It may be more difficult to precisely track individual client touchpoints due to this decreased granularity.

2. A focus on consolidated insights
Because of the restrictions imposed by data protection laws, marketers are depending more and more on compiled data. Without compromising privacy, broadening data segmentation can still offer insightful data on consumer activity. This method, however, might not fully capture the subtle distinctions between each customer journey.

3. Juggling Personalization and Privacy
Marketers need to strike a balance between personalisation and privacy. While user consent and data minimization are encouraged by privacy legislation, customization depends on comprehensive consumer information. Emerging technologies that enable personalisation without disclosing sensitive information include federated learning and differential privacy.

The Final Words

In the world of digital marketing, consumer data privacy is no longer an option but a requirement. Transparency must be prioritised, user permission must be respected, and data security measures must be strengthened to respond to the new data privacy restrictions. To strike a compromise between personalisation and privacy, these restrictions are useful in attributing models and cutting-edge methods.