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Automated Machine Learning
Machine learning has typically been a powerful tool limited only to those with a particular set of skills in computer science and statistics. Automated machine learning (AutoML) is the end-to-end automation of machine learning model development – a process usually carried out by data scientists – including data cleansing, feature engineering and model tuning. Bluebeak’s AutoML tool allows anyone with basic knowledge of structured data to quickly develop industry-standard machine learning models ready for deployment without writing any code.
ABC Company wants to reduce its customer churn rate (the number of customers who are unsubscribing to their services). They drag and drop their anonymised customer data into bluebeak (identifying both customers who left and those still subscribed), and hundreds of different optimised models are immediately built and tested. Bluebeak selects the best model, gives insights into why customers are churning, and provides a list of recommendations of how to reduce the churn rate into the future. ABC company decides to deploy the model and can now easily identify and take preemptive action to retain customers who were otherwise at risk of leaving.
Optimisation is the mathematical process of calculating the optimal combination of a set of variables to minimise or maximise another – a powerful tool for any industry when faced with difficult decisions. Bluebeak’s optimisation tool puts this powerful concept in the hands of everyone, without needing to know how to code or understand the complex mathematics that is happening under the hood.
XY Hospitality Company (XYH) is concerned about their growing wage bill and the pressure on their wait staff during peak times. They drag and drop their rostering and sales data into bluebeak and specify that they want to optimise staff roster times to minimise cost. Bluebeak runs a series of optimisation models and provides XYH with a list of recommendations of how many staff members they need for their daily rostering. XYH is now able to operate more efficiently and more profitably.
Forecasting is the process of learning from historic data to predict trends and movements into the future. Bluebeak uses intelligent forecasting methods built on the most modern machine learning frameworks to learn from your data and provide accurate forecasts that can be broken down and explained.
ABC Company wants to know how much traffic their website will have during an upcoming campaign that they’re running. They’ve run the campaign successfully over the past 3 years but a lot has changed this time with traffic being affected by COVID19. They drag and drop their historic daily website traffic data into bluebeak which includes information about their prior campaigns. Bluebeak immediately runs sophisticated forecasting algorithms over the data and provides a detailed breakdown of how much web traffic ABC Company can expect during their campaign.
Clustering is a common and useful tool in the data science industry that is used to identify data observations that are similar to one another. Like other machine learning processes, clustering can be computationally expensive which limits its application on large datasets to those with specific programmatic skills. Bluebeak’s clustering tool is built on sophisticated machine learning techniques that allow you to apply clustering over any dataset, and gain a comprehensive understanding of what separates each cluster.
BIG Bank wants to understand the different groups of people who apply for loans from their bank. They upload all of their anonymised customer data to bluebeak which analyses and detects 6 distinct groups of people who behave in different ways. By understanding these differences, BIG Bank is now able to provide more personalized communication and tailored solutions for each group to create a more enriched customer experience for everyone.