With the exponential growth of data, there’s a huge underlying potential to apply AI techniques and tools to transform data into actionable insights. There’s probably more in your data than what meets the eye.
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"Understanding citizens and how they interact with government services is, in part, a big data problem. Collecting vast amounts of data from various government systems and integrating them into a cohesive infrastructure, when combined with AI, can improve citizens lives."
We’ve built a segmentation model to classify companies into different groups according to several dimensions such as Financial Health, Innovation, Potential, Company X-Ray, and Exports.
Our recommendation system enabled our customer to provide a better service to their stakeholders using advanced machine learning techniques. The goal of the recommendation engine was to provide recommended markets for companies to export based on their sector and historical information.
The Exports Forecast Model is a tree-based approach that builds a probability of a company being able to grow their exports in the next couple of years, helping government entities shortlist companies with a higher probability of growing.
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Our Azure Synapse and Airflow-based Data Lake setup infrastructure was the solution that took our customer data management to the next level. With a highly customizable data management system, the government agency gained access to advanced data analytics and insights that helped them make data-driven decisions, ensuring a best outcome and service for their stakeholders.
Industrial Operations
The Routing Matching was a data engineering initiative that aimed to help a logistic startup company. The project harvested logistics data from an API or web scraping and delivered recommendations of loads to purchase. The system was able to find good loads with trucks that were on the road in almost real-time and match them with cargo in the spot market. This enabled the company to secure loads that were 20% above the current market rate, giving them an advantage in terms of revenue.
Marketing and Sales
The Cross Sell Recommendation Engine is a project that involved building a recommendation engine for a major telecommunications company in Iberia. The company was struggling to identify which customers to contact with new offers for premium TV channels, leading to outbound calls that resulted in complaints from clients. The project aimed to predict which customers were more likely to buy the new products by consuming large amounts of past customer data to build a gradient boosting model that incorporated client characteristics, current customer packs, and TV usage data.
Big Data & Cloud Management
We helped our customer migrate part of their data to Google Cloud Platform (GCP), kickstarting a cloud revolution at the company. One of the key challenges faced during the transition is maintaining the quality of data while ensuring compliance with regulatory requirements.To address this, the team wanted to build an architecture and solution that was able to address the topics of data quality, lineage, discovery and privacy.
Training
Our Learning Paths project played a crucial role in equipping our customer's employees with the essential foundational skills needed for data science and data engineering. By identifying these crucial skills and their dependencies, researching and evaluating courses that covered them, and designing proficiency tests, we were able to train over 20 individuals in the company. Today, our Learning Paths serve as the go-to blueprint for onboarding new hires, setting the standard for every new Data hire the company brings on board. Our project has contributed significantly to building a skilled workforce, ensuring the company has the talent it needs to thrive in today's data-driven business landscape.
Marketing and Sales
Our data-driven sales project aimed at increasing a specific brand penetration. The team used a nearest neighbor approach and K-means clustering to rank points of sale that will likely have more demand for a premium brand, resulting in an extra 30% sales in the target group compared to the control group.
Marketing and Sales
Shelf Space Optimization is data-driven sales project that aimed to understand which brands can be replaced on the shelf for new products using a simulation model. The team used a Random Forest Simulation Model to increase the average sales and profitability of each shelf in the supermarket, maximizing expected revenue by using data. The goal was to choose the most appropriate SKUs that minimized the risk of cannibalization and achieve higher ROI.
Marketing and Sales
The goal of this project was to optimize promotions for specific SKUs by determining the ideal promotion percentage and timing to maximize sales while maintaining margins. The team used a boosting model that analyzed past prices and sales data to simulate a grid of prices for the future. These simulated prices were then used to predict a sellout and the chosen promotion was the one that achieved an optimal balance between market share and margin.
Marketing and Sales
This project aimed to build an assortment optimization tool that provided guidance to the commercial team on which SKUs were appropriate for each client. The team used Maximum Likelihood Estimation per Cluster of Clients to determine which SKUs were most suitable for each customer group. The result was a guidance framework for assortment recommendations that improved sales performance. The team worked with 100GB of data and used DataBricks FileSystem to extract and pre-process data, perform K-Means clustering, and calculate Maximum Likelihood Estimation.
Marketing and Sales
We helped our customer developing a pricing model using artificial intelligence to predict the best price for a Silicon Metal / FerroSilicon product. The model uses past proposal data to determine the probability of winning new proposals based on price. The aim was to create a pricing governance system based on the balance between expected revenue and margin.
Marketing and Sales
Our customer developed a model to predict which customers are likely to buy new financial products, using a Tree-Based Classification Model. This helped them rank customers and increase sales commissions on new products by 7.8%, particularly by mapping customers with higher likelihood of buying other products from their portfolio.
We’ve built a segmentation model to classify companies into different groups according to several dimensions such as Financial Health, Innovation, Potential, Company X-Ray, and Exports.
Our recommendation system enabled our customer to provide a better service to their stakeholders using advanced machine learning techniques. The goal of the recommendation engine was to provide recommended markets for companies to export based on their sector and historical information.
The Exports Forecast Model is a tree-based approach that builds a probability of a company being able to grow their exports in the next couple of years, helping government entities shortlist companies with a higher probability of growing.
Our Azure Synapse and Airflow-based Data Lake setup infrastructure was the solution that took our customer data management to the next level. With a highly customizable data management system, the government agency gained access to advanced data analytics and insights that helped them make data-driven decisions, ensuring a best outcome and service for their stakeholders.
Industrial Operations
The Routing Matching was a data engineering initiative that aimed to help a logistic startup company. The project harvested logistics data from an API or web scraping and delivered recommendations of loads to purchase. The system was able to find good loads with trucks that were on the road in almost real-time and match them with cargo in the spot market. This enabled the company to secure loads that were 20% above the current market rate, giving them an advantage in terms of revenue.
Marketing and Sales
Our customer developed a model to predict which customers are likely to buy new financial products, using a Tree-Based Classification Model. This helped them rank customers and increase sales commissions on new products by 7.8%, particularly by mapping customers with higher likelihood of buying other products from their portfolio.
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