ML models for an Influencer marketing platform
Overview
This case study will discuss how our solution helped the customer build a data-driven influencer marketing platform that cut costs for brands on digital advertising while increasing engagement rates and customer loyalty.
Challenge
The task
Implementation
Our team worked closely with the client to understand their requirements and challenges. We developed a tool for data collection and segmentation that allowed the client to grow their personal database of influencer data. We utilized modern machine learning approaches such as OCR, NLP, neural networks, and more to build ML models that could filter influencers and their audiences more effectively. We designed an intuitive user interface that would make it easy for the client's clients to reach the most appealing audience.
Our team collected data from various sources such as social media platforms, blogs, websites, and more. We used OCR to extract data from images, NLP to extract data from text, and neural networks to predict the interests and preferences of influencers and their audience. We developed ML models that could filter influencers and their audience based on various factors such as interests, gender, location, age, and more. The ML models allowed the client to identify the most relevant influencers for their clients' campaigns, which helped to increase engagement rates and customer loyalty.
Conclusion
The client was satisfied with the outcome of our collaboration and was able to provide their clients with a high-quality service that helped them to grow their customer base and loyalty.
