Revolutionising Customer Loyalty: Unleashing the Power of Machine Learning for Unprecedented Engagement and Retention!
How a Leading British Retailer Harnesses Data Science for Deeper Customer Understanding and Market Domination
Website Customer Loyalty Improved in Performance and Reliability by 50% using Machine Learning Tools.
New Scaler services enables ML solutions to analyse data from more than 30 million customers.
Overview
A large British retailer previously used machine learning capabilities to gain predictive customer insights and take action to build brand loyalty. For example, recommending products based on past purchases, sending loyalty emails to rewards members, and creating targeted campaigns. However, this previous solution for data science workloads relied on many manual processes, resulting in large workloads for staff members which affected productivity and efficiency, especially for IT & Data Science teams.
Challenge
The retailer enterprise was facing repetitive errors and failures in the previous machine learning model, which was affecting their customer experiences, especially those using their customer loyalty program. The organisation wanted to produce a faster and less labour-intensive method of creating models ready for production. The company decided that the adoption of Azure Accelerator with increase the speed of their model development and deployment time. The retailor aimed to reduce the manual processes and improve the continuous integration and continuous delivery (CI/CD) process.
The company needed to scale the model and train hundreds of models in parallel, this would result in processing massive amounts of data faster by distributing the workload across multiple processors. This can be especially helpful in retail, where processes may need to be replicated for each individual store.
Solution
To solve this challenge, the retailor organisation partnered with New Scaler to create a successful adoption of Azure Artificial Intelligence tools and services to support their digital transformation journey. New Scaler aided the business in enhancing their existing customer loyalty rewards program, helping them to separate the production code from the development code, so that the company could experiment and develop their model, whilst the production code remained stable and isolated. This ability to separate the production code from the development code allowed the companies’ developers to focus on updates and improvements without affecting the final product.
New Scaler helped the retailer’s data science team build a solution using Azure Machine Learning and Azure MLOps v2. This set of deployment templates enabled their data science team to quickly set up and deploy machine learning models that were training large amounts of models. The new templates were integrated into the continuous integration/continuous delivery (CI/CD) pipeline so that deployments could be made directly in Azure Machine Learning. The CI/CD pipeline created an efficient process that sped up time to production through automated tests that helped developers review code quality and perform improvements more efficiently.
Results
The MLOps team with the support from New Scaler, were seamlessly able to manage two Azure Machine Learning environments—one for development, another for production, with the CI/CD pipeline making deployments directly.
- Increased Efficiency: the migration achieved significant improvements in performance, reliability, and reduced execution time by 50%. Inspired by the results of the migration to Azure Machine Learning, the retailor organisation leveraged more solutions from New Scaler to reduce manual processes and fulfil the goal of separating production and development code.
- Enhanced Reliability: The new solution significantly reduced time to deployment across the organisations’ environments. This made it easier for the data science team to run the models, reducing time constraints and bottlenecks for other teams in the retailor that used the output from the models. This helped employees who directly worked in the customer loyalty program deliver rewards, products, and campaigns to customers sooner and more accurately. The increase of the maturity of their machine learning solutions through testing, monitoring, and establishing best practices enhanced the reliability of their solutions.
- Improved Customer Experience: The MLOps team gained a system that largely improved the serve of customers and internal processes. The solution increased the robustness of the ML system and therefore made the retailer’s system more aligned with other environments. The solution allowed their Machine Learning to analyse data from more than 30 million customers to build accurate trends to give customers personalised offers. New Scaler’s solution allowed the retail giant to continue to deliver on their promise of providing high-quality goods to millions of customers, while using machine learning models to continuously innovate and improve on their customer experience.
Conclusion
The ability to scale their architecture is extremely important in the retail industry. It allows businesses to keep up with rapid demand and momentum, especially during massive spikes in user traffic around holidays and sales days, without compromising on quality or efficiency. With the effective configuration of Azure Machine Learning by New Scaler, this retail enterprise was able to adopt a system that could handle the everchanging workflows of customer sales and meet customer traffic without disruptions in customer service.
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