Context: ManoMano is an unicorn start-up in the e-commerce industry, specializing in selling DIY products. As a marketplace their goal is to match clients with DIY sellers, taking a fee in the process, they also generate a great quantity of data which can be used to tailor recommender systems, optimize retention, acquisition and User Experience. They worked with Pyramind to build their paid acquisition systems, increase their conversion rate and industrialize their data science models with industry-standard craftsmanship.
Testimonial
"As Data Science manager at ManoMano, I had the chance to manage Bryce for several years. Don't be fooled by his young age! He successfully worked on complex and strategic data subjects for the company. User impact and production oriented, Bryce has always been proactive with innovative but pragmatic solutions across the entire value chain from data collection to production, including Data Engineering and Data Science. Bryce has a rare multidisciplinary profile allowing him to have a precise understanding of the challenges of each data team (Engineering, Analytics, Science, MLOps, ...) and therefore to work with each of them."
I developed an algorithm to identify Google keywords to target, manage ManoMano's adwords campaigns, score each keyword daily to determine the optimal budget and select adwords best parameters.
Keywords were identified using a mix of Google APIs, ManoMano's own search database and business recommendations, they were then uploaded to Google Adwords campaigns using Google Adwords API. New keywords were discovered everyday (for example if a new kind of product is on sale) and campaigns were updated accordingly.
I crafted a dataset containing every query that eventually led to a conversion using ManoMano's historical data. I used LightGBM, a Gradient Boosting Tree Ensemble model, to model the query revenue on a daily granularity. Feature Engineering included: category of query in ManoMano's taxonomy, Device used by user, Country of user and cumulative past revenue.
The result was an automated campaign spending around 1M€ monthly while profitable.
I improved the existing Google Shopping bidding algorithm by: crafting new features, rewriting feature engineering compute engine and analyzing dataset and model's output by hand.