During my time with the dating app Flutrr, I worked closely with the sales and marketing teams to significantly increase their user base. The sales team's targets were ambitious, and the primary roadblock was inefficient ad spend on Meta (Facebook and Instagram). My role was to provide the data-driven insights needed to optimize their campaigns. ​I started by conducting a deep analysis of their existing Meta Ads performance. I pulled data on a wide range of metrics, not just the basics. I looked at click-through rates (CTR), cost-per-click (CPC), and cost-per-install (CPI), but more importantly, I segmented the data by demographics, ad creative, and campaign objective. What I discovered was a clear trend: the sales team's broad-based campaigns were not only costly but also had low conversion rates. Certain ad creatives were resonating with specific, younger male demographics, but failing to engage others, especially female users, which was a critical target market for the app's growth. ​I presented these findings to the sales team, highlighting which ad sets were underperforming and why. I recommended a new, data-informed strategy: instead of a few large, generic campaigns, we should launch several smaller, highly-targeted ones. We A/B tested new ad creatives and messaging tailored to the interests and pain points of distinct user segments, and allocated budget based on real-time performance. For instance, we created campaigns with different messaging and visuals to appeal to users interested in specific hobbies, rather than just "dating." ​The collaboration was a continuous feedback loop. The sales team would provide qualitative feedback from their interactions with potential users, which I would then use to refine my data analysis. Within two months, this strategy resulted in a 40% reduction in the Cost Per Install (CPI) and a 30% increase in app downloads. By leveraging data analysis to provide a clear, actionable roadmap, we not only met but exceeded our targets, demonstrating how a data-driven approach can directly empower a sales team to achieve phenomenal results.

My approach to developing and executing outreach strategies for client engagement is a multi-step process rooted in data, personalization, and continuous optimization. ​First, I begin with a deep dive into goal setting and audience definition. I work with the team to clearly define what success looks like—whether it's generating new leads, increasing brand awareness, or re-engaging past clients. Simultaneously, I create detailed ideal client profiles and segment the target audience based on factors like industry, role, and specific pain points. This segmentation is crucial for tailoring messaging and channel selection. ​Next, I focus on personalization and value delivery. I move away from generic, one-size-fits-all communication. Instead, I craft messages that directly address the client's unique challenges and highlight how our offerings provide a specific solution. This involves referencing their recent work, sharing relevant case studies, or offering valuable, free resources. The goal is to demonstrate genuine interest and expertise, establishing a foundation of trust and respect. ​I then execute a multi-channel campaign. I don't rely on a single method. I use a strategic blend of channels, which may include targeted email sequences, personalized LinkedIn messages, and, in some cases, even phone calls or direct mail. The channels are chosen based on where the target audience is most active and receptive, ensuring the message reaches them at the right time and place. ​Finally, I emphasize measurement, analysis, and refinement. I set up clear Key Performance Indicators (KPIs) to track every step of the campaign, from open rates and click-through rates to conversion rates and client feedback. Using this data, I perform A/B testing on different subject lines, messaging, and calls to action. This iterative process allows me to continuously optimize the strategy, learning from both successes and failures to make each subsequent campaign more effective at driving meaningful and lasting client engagement.

As an AI researcher, I have extensive hands-on experience with a wide range of AI tools. I've used TensorFlow and PyTorch for developing and training deep learning models, and I've worked with the Hugging Face Transformers library to fine-tune large language models like GPT-4 and LLaMA for specific research tasks. My work also involves leveraging scikit-learn for machine learning algorithms and NLTK for natural language processing, allowing me to build and analyze complex AI systems from the ground up.