Forrester Total Economic Impact Study of DataRobot: 514% ROI and Payback within 3 Months
With AI proving to be the most transformative technology of our time and companies today need to pivot faster and drive near-term impact from their tech investments, many organizations are looking to drive higher ROI from AI as quickly as possible. From predicting customer churn to reducing fraud and avoiding supply chain disruptions, the possibilities are virtually limitless as to how AI can increase revenue, reduce costs, and improve profitability. But how can companies predict the expected value of an AI application or use case so they can justify new investments in the face of tight budgets and headcounts?
To help answer this question, DataRobot today announced the results of a new study: The Total Economic Impact (TEI) of DataRobot. Conducted by Forrester Consulting on behalf of DataRobot, the commissioned study reveals that organizations using DataRobot’s AI platform achieve a net present value (NPV) of $4 million and a 514% return on investment (ROI) with payback often within as short as 3 months.
Forrester interviewed four customers with experience using DataRobot in the retail, healthcare, and manufacturing sectors as the basis for the report to help them better understand the benefits, costs, and risks associated with using the platform. These customers were looking to overcome key challenges, such as forecasting demand and tackling fraud.
Prior to using DataRobot, the customers relied on their data science teams to do the heavy lifting of data preparation, model development and training, and model deployment using traditional open-source technologies, such as the Python and R programming languages and their associated libraries and frameworks.
Some customers were also hindered by their use of legacy data analysis technologies that failed to keep pace with the advancements in AI and machine learning over the past decade. This created environments with lengthy AI project timelines and frequently missed deadlines where organizations often never deployed and operationalized the models they developed.
Forrester Consulting designed a composite organization based on the characteristics of the organizations interviewed. They then constructed a financial model representative of the interview finding, using the TEI methodology based on four fundamental elements: benefits, costs, flexibility, and risks. Forrester’s TEI methodology serves to provide a complete picture of the total economic impact of purchase decisions.
The results were remarkable, with firms reporting significant value relative to cost.
- Cost savings from reduced seasonal overhiring in retail: $500,000
- Cost savings from reduced healthcare fraud: $10 million
- Increased revenue from improved demand forecasting in manufacturing: $50 million – $200 million
- Significant cost savings from avoidance of hiring a data science team 3x as large
Determining ROI of AI
Many of our customers ask for help in estimating the value of AI when it’s being used to augment an existing process where some predictive analytics are already in place. The methodology in this report, drawing on data from 4 real-world customer deployments of AI, should provide a useful framework for anyone looking to justify an AI investment.
Today’s business climate has never been so challenging, and organizations need agile trusted solutions that can steer intelligent decision-making in the face of market turbulence and continuously evolving customer needs.
DataRobot’s end-to-end enterprise AI platform addresses these demands. By preparing data, automating the training of machine learning models, operationalizing AI, and continuously monitoring AI assets, DataRobot enables organizations to enhance prediction accuracy, accelerate time to insight, reduce risk, and increase revenue – all without requiring heavy investment in data science teams.
Read the study to learn more.