Customer Lifetime Value (CLV) is a crucial business metric for e-commerce businesses. It measures how valuable each customer is to your business, not just on an individual transaction basis but throughout their relationship with your brand.
CLV is a valuable metric for several reasons:
- It guides your customer acquisition efforts
- It lets you segment your customers based on the value
- It offers a better understanding of customer behavior
High CLV indicates good product-market fit, brand loyalty, and regular revenue from existing customers. ECommerce businesses should monitor and optimize their CLV if they are looking for steady growth.
However, statistics reveal that only 42% of companies can precisely measure customer lifetime value. And this is even though 89% agree that CLV is vital for increasing brand loyalty.
Accurate CLV calculation requires extensive data, including historical transaction data, customer behavior, and retention rates. Several companies need help to collect and compile all this data accurately. Besides, CLV calculation involves complex statistical and mathematical models that can be challenging.
Microsoft Azure offers a suite of cloud services, including advanced analytics and machine learning capabilities. The secure and scalable solution streamlines data storage and facilitates the development of predictive models to calculate CLV easily. Let’s understand this in detail below.
How Can Azure and Machine Learning Help?
The first step to calculating CLV is efficiently collecting and storing customer data. Azure offers a range of services that help you do just that.
- Azure Data Lake Storage: This is one of the most significant components of the Azure Cloud that help to handle large volumes of structured and unstructured customer data. It is a single platform for ingesting customer data from demographic records, customer interactions, and transaction history.
- Azure SQL Database: This is a database engine that helps to streamline data. You can organize customer data with an SQL database by designing separate tables to store customer profiles, transaction history, and other relevant data.
- Azure Event Hubs: Event Hubs is a big data streaming platform that gathers real-time customer data from multiple sources. This includes mobile apps, IoT sensors, and website interactions. This real-time data is then used for understanding dynamic customer behaviors.
- Azure Databricks: Azure Databricks is a data analytics platform. It ensures that the gathered customer data is thoroughly cleansed, transformed, and structured for predictive analysis.
Once you have gathered and prepared all customer data with Azure, it’s time to deploy machine learning models. These models enhance CLV prediction by examining historical data and detecting patterns that help forecast future customer behavior.
Azure Machine Learning tools help to create meaningful customer data features such as purchase history, transaction value, and engagement metrics. Also, Azure’s AutoML helps determine the best CLV prediction algorithm. The CLV model is trained and validated using historical data. Azure ML also allows for the creation of automated pipelines for continuous learning. This ensures that the CLV model stays up-to-date and efficient.
Moving on, machine learning algorithms available in Azure can help to segment customers based on critical criteria such as demographics, purchase habits, product preferences, and engagement levels. This segmentation enables businesses to tailor their marketing messages and strategies to match the different target groups, leading to increased engagement and retention.
Real-World Use Cases
Let’s look at real-world use cases where businesses have benefited from using Microsoft Azure to boost their customer lifetime value.
ASOS, a leading fashion retailer, leveraged Azure’s cloud capabilities to improve CLV. The brand used Azure Data Lake Storage to collect and consolidate all customer data from different sources, enabling a unified view of customer behavior. The brand also employed Azure Machine Learning to build predictive models to predict customer preferences and deliver highly personalized experiences accurately.
Majid Al Futtaim is a Middle East-based retail conglomerate. Last year, Microsoft announced that the company significantly improved CLV by implementing the Microsoft Azure Data Platform. The move streamlined the company’s financial planning and analysis and addressed the challenge of data silos. Furthermore, Azure Synapse and PowerBI automated financial reporting and streamlined decision-making across divisions. This transformation gave business teams instant insights that ultimately improved their CLV efforts.
Maersk is a global shipping and logistics company. Recently, the company announced a boost to its cloud-first tech approach by leveraging Microsoft Azure as its cloud platform. The company employed Azure to optimize CLV through supply chain efficiency. It used Azure IoT devices to monitor the conditions of shipping containers and Azure Machine Learning to forecast potential delays and optimize routes. This has enabled Maersk to deliver more efficient and dependable services, enhancing customer satisfaction and CLV.
Conclusion
The successful integration of Azure and Machine Learning offers a robust toolkit for businesses to calculate their customer lifetime value accurately. With Azure’s data processing and analytics, companies can unlock the full potential of customer data. Besides, Azure’s machine learning capabilities empower predictive modeling and accurate CLV predictions.
That said, navigating this technological transformation can be challenging. And that’s where Algoscale can help you as a trusted partner. We are among the reputed machine learning companies known for their extensive expertise in transforming businesses through data-driven strategies and machine learning solutions.
Our team of experts can help you fully leverage Azure and machine learning capabilities to calculate and enhance CLV and keep your business ahead of the competition. Contact us today for a personalized consultation so we can embark on this transformative journey together.
