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How AI Improves Customer Lifetime Value and Makes It a Primary KPI

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He's just become a customer of your B2B software company and agreed to buy your product, which comes with a year of support and maintenance. After 12 months, John will have to renew his contract to keep his licenses active. Now it's time to record the sale and move on to acquiring the next customer, right? With fragmented audiences, expensive advertising, and fierce competition, marketers must become more strategic in how they view customers' revenue potential. Today, once a sale is closed, many marketers consider their job (mostly) done.


Metrics that matter.

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Recently, a company approached us, so we could help them understand their customer data. Their specific data was unique, and they wanted to understand the pandemic's impact on their customers. There is no playbook for this. There is no curated dataset describing "customer profiles during a pandemic." Diving into the data, we uncovered trends.


Create a Python App to Measure Customer Lifetime Value (CLV)

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"IF YOU ARE NOT TAKING CARE OF YOUR CUSTOMERS, YOUR COMPETITOR WILL" – Bob Hooey Customer Lifetime Value is the profit that a business will make from a specific customer over the period of their association with the business. Every industry has its own set of metrics that are tracked and measured to help businesses target the right customer and forecast their customer base for the future. The CLV enables various departments in marketing, sales, etc to plan their strategies and offer specific products or customized services to the most valuable customers. It also provides insights to customer service teams with a framework for knowing the effort that needs to be put into nurturing and retaining customers. The CLV is most effective and adds immense value when it is applied along with other tools such as customer segmentation, pricing & marketing strategy meaning it tells us who are our most profitable customers but it doesn't tell us which product needs to be sold at what price and quantity.


In the Driver's Seat: Driving Value Realization in Customer Experience

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How customer experience can help drive value realization for customers. It's no secret that customer success is critical for business. With customer experience being at the forefront of every interaction, it's important to get it right every time. When striving to get it right, the focus has always been on optimizing touchpoints. Customer touchpoints matter, but they only make up individual interactions you have with customers.


Tutorial: Machine Learning

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Now that you have learnt how to manipulate data in the tutorials Basics & From Lab to Flow, you're ready to build a model to predict customer value. In this tutorial, you will create your first machine learning model by analyzing the historical customer records and order logs from Haiku T-Shirts. The goal of this tutorial is to predict whether a new customer will become a high-value customer, based on the information gathered during their first purchase. This tutorial assumes that you have completed Tutorial: From Lab to Flow prior to beginning this one! From Dataiku DSS home page, click on the Tutorials button in the left pane, and select Tutorial: Machine Learning.