The Dataiku Solution for Customer Lifetime Value (CLV) Forecasting offers a deep dive into customer purchase history and predicts lifetime value based on transaction history, offering ready-made insights to build higher-impact customer strategies.
Get accurate CLV predictions from historical transactional data. Connect RFM scores from the Dataiku RFM Segmentation Solution through Snowflake, Google Cloud Platform, or your choice of data environment with the Dataiku App.
Turn transactional data into actionable insights with a comprehensive dashboard. From past trends to forecasting, ensure full alignment between data experts and marketing specialists with outcome-focused visualizations.
Define customer groups based on low, medium, or high CLV with quantile or k-means clustering. Quickly understand how CLV groups change over time, then share findings with the larger team.
Explore how much value customers create over time, then align different approaches to fit customer needs, predicting future CLV. Analyze the CLV of valuable customers and compare to other clusters, and compute monthly CLV increases to show shifting customer dynamics.
Understand predicted values and trends for CLV clusters, perform what-if analysis, and track model performance and results with the Data Science Validation and Business Insight dashboards. Benefit from a fully explainable approach to machine learning with eased adjustment, customization, and extension possibilities to adjust to your business specificities.
The Dataiku Solution for CLV Forecasting helps answer a broad range of questions like:
The Dataiku Solution for CLV Forecasting is just the start. Quickly tap into more Dataiku Solutions and build customer analytics and AI use cases to rethink your entire marketing strategies, leveraging data and AI at every step.
A composite organization in the commissioned study conducted by Forrester Consulting on behalf of Dataiku saw the following benefits:
reduction in time spent on data analysis, extraction, and preparation.
reduction in time spent on model lifecycle activities (training, deployment, and monitoring).
return on investment
net present value over three years.