A provider of innovative marketing campaigns to the financial sector
Marketing, Financial Services
Data Science, Data Management, Data Analytics, ML
AWS Services (AWS S3, AWS Glue, AWS SageMaker), MySQL


Leads are the lifeblood of any business. No matter the size of the firm or the marketing budget, targeting the right prospects is a key priority for lenders in the financial sector, too, where competition for new customers is fierce. A US-based provider of innovative marketing services to banks and other financial institutions was using the power of ML to take lenders to the leads that matter. Specifically, they offered ML-powered direct mail lists for mail marketing campaigns. The company had an engineering team that maintained a data warehouse storing raw customer data. However, they had to outsource data science services to a vendor for ML-driven lead scoring to rank prospective customers who are most likely to convert. The external vendor’s ML approach to lead scoring was what the client called a black box for them, with the processes from input to output lacking any transparency. The client wanted to bring ML capabilities for marketing in-house and was looking for a trusted partner in ML development. They found us.

Our task was to:
Dive deep into the client’s business context to identify key business priorities, risks, and constraints
Evaluate their current models and map data
Design an end-to-end ML solution in AWS
Build a roadmap for further improvements
Provide training for the client’s engineering team
Prepare comprehensive documentation for ML knowledge transfer

Our approach:

An end-to-end AWS-based ML solution for marketing campaigns that has provided the client with in-house ML capabilities for scoring leads while getting better accuracy than delivered by their previous ML vendor. Our approach to building the solution can be summarized as follows:
Evaluation of old ML models to identify metrics for each model
Exploratory data analysis
ETL processes using AWS Glue to extract and prepare data for two data pipelines: ML model training and data scoring. The automated processes were designed to save the effort and time of the in-house engineering team on data preparation and give the client more operational flexibility
ML model training with AWS SageMaker, with dozens of experiments organized; creation of one comprehensive ML model trained using all historical data
ML model deployment in production
Product improvement roadmap outlining recommendations on enhancing ETL processes, ML model optimization, and using the solution as the basis for building a Software-as-a-Service platform that would allow the company’s clients to score leads on their own, with no engineering skills required


Better lead scoring accuracy
Cost savings, with the client now paying only for actual AWS resources consumed instead of engaging an external vendor every couple of weeks for a lead-scoring task
Operating model flexibility
In-house ML capabilities with complete ML knowledge retention
Transparency in ML model training
The foundations for transforming the product into a SaaS solution

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