Fluent is a leader in performance marketing, delivering customer acquisition solutions across untapped channels. Leveraging diverse ad inventory across partner ecosystems and owned sites, Fluent helps marketers connect with highly engaged audiences and convert them into loyal customers.

Consumers voluntarily opt into sharing their information when they engage with these digital properties, allowing Fluent to build a first-party data asset fully compliant with the latest privacy laws and regulations. In a world without cookies and other user-level identifiers, access to first-party data will continue to be important as marketers search for alternative targeting and measurement strategies.
 

What are your recommendations for the right Targeting Strategy in the new world of privacy?

As Apple and Google continue to roll out new privacy updates, app marketers will need to get creative with their targeting strategies. While upcoming versions of SKAN and Google's Privacy Sandbox promise targeting and measurement tools, they won't provide the full picture. These solutions restrict access to user-level data across in-app environments, making it harder for marketers to truly understand their audience and tailor ad campaigns effectively.

To overcome these hurdles, marketers should focus on diversifying their media mix. By leveraging alternative channels like influencer marketing and rewarded web-to-app campaigns, they can unlock new audiences while maintaining greater control over targeting and visibility into performance. 

Tapping into existing first-party data and user behavior will also be important as marketers look to create personalized ad experiences, improve retention, and inform targeted campaigns across other channels. Overall, creativity and a willingness to experiment will be key tools in the new era of app marketing. By embracing a multi-channel approach, app marketers can access incremental audiences, gain valuable insights, and ultimately, achieve sustainable growth.



 

How can marketers make sure they follow the rules about privacy while still making ads that feel personal and work well?

Privacy regulations are making deterministic data a thing of the past. However, contextual targeting offers a powerful alternative for delivering personalized ads. This approach leverages the content and user context within an app to display highly relevant ads – all without relying on individual user data. Ads seamlessly align with the user's current activity and app environment, boosting engagement and effectiveness. 
While first-party data is not a requirement for contextual targeting, marrying contextual data with first-party insights allows for even more precise targeting and optimization opportunities. By focusing on contextual relevance and leveraging valuable first-party data when possible, app marketers can continue to create successful campaigns that resonate with users while respecting their privacy.
 

As the privacy rules change, what factors should marketers think about when picking the tools to advertise via, so they keep our information safe and still reach the right customers?

As privacy rules tighten, mobile app marketers face a challenge: balancing user privacy with effective reach. When selecting tools or advertising partners, it’s important to ensure they adhere to all current privacy regulations (GDPR, CCPA, etc.) and any upcoming changes from platforms like Apple and Google. Working with MMP partners and ad platforms actively involved in shaping the future of app marketing in a post-IDFA/GAID world can help marketers navigate forthcoming policy updates and pivot when necessary.
 

How can marketers use the right technology to make ads that reach the right people without being too nosy about their personal data?
 

This is an area where AI and machine learning will really shine. Traditionally, marketers identified valuable users based on past behavior (e.g., purchase history). However, in a privacy-focused world, access to user-level data is limited. With predictive modeling, advanced algorithms analyze user behavior within an app or game to identify individuals with high LTV potential. Based on this analysis, marketers can then focus their advertising efforts on users with a higher chance of converting. As machine learning algorithms continue to evolve, expect developers to double down, tapping into AI to optimize ad placements, improve bidding strategies, and deliver personalized in-app experiences that further encourage engagement and conversion without the need for personal data.



 

Why is it important for marketers to be open and honest about how they use data, and how can they talk to users in a way that makes users trust them more?

Trust is at the center of any lasting relationship between an app and its users. Part of building that trust is being upfront about the data you intend to collect, how it will be used, and what users gain by opting in. Today's privacy-conscious consumers know their data is valuable, and expect something in return for sharing it. It’s important to create a clear value exchange, offering personalized recommendations, access to advanced app features, or other incentives tailored just for them. And for those who you aren’t quite sold, make it easy to opt-out of data collection or request their data be deleted.