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Research Direction: Understanding the Dynamics of Influencer Marketing Through Metadata Analysis
Research Direction: Understanding the Dynamics of Influencer Marketing Through Metadata Analysis
In the competitive world of online marketing and e-commerce, understanding and engaging potential leads is critical for success. My research delves into lead management strategies with a focus on marketing automation and hyper-targeting, leveraging advanced data analytics to transform how businesses connect with their audiences.
What methods do we use?
What methods do we use?
Attribution models
Attribution models
Development of custom attribution models and revenue simulation
Dynamic retargeting
Dynamic retargeting
To measure the effects of different levels of ad personalization
Audience similarity
Audience similarity
Measuring advertising performance for custom and lookalike audiences
How advertising algorithms work?
How advertising algorithms work?
What topics do we focus on at the moment?
What topics do we focus on at the moment?
Behavioral Pattern Analysis: Identifying high-intent leads based on their online activity
Cross-channel targeting: Tracking customer journey across multiple online nad offline channels
Native advertising: The effects of context and the poisitioning in the TOFU-MOFU-BOFU funnel
Dynamic ads vs. manual optimization: Study on Meta and Google Ads dynamic formats
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