Yes, ML models can help reduce costs, but they are also brilliant at uncovering market opportunities and exposing new ways to attract customers. The key is to deploy higher ROI models that deliver more actionable insights and stronger outputs, thanks to the right inputs.
Fiddler empowers you to unlock new revenue opportunities by validating and deploying even the most complex data modeling examples. In fact, Bank of America unlocked $50M in potential revenue by using Fiddler to align ML models with business context.
Everyone wants to make decisions based on descriptive and prescriptive analytics derived from ML models. However, the value of ML models decreases rapidly when they don’t reflect the actual needs and challenges of the business. Relevant market and business dynamics must be included.
Fiddler allows you to understand the causal drivers of the model outputs behind business decisions and to explain decision-making factors. Best of all, it’s simple to share information with key stakeholders using a rich set of dashboards, charts, and reports.
Whenever market dynamics shift (and they always do), it’s important to recognize changes early, understand the business impact of an ineffective model, and update models with speed and efficiency.
Fiddler enables you to perform what-if analysis, test new theories and data modeling concepts, and return predictions with altered inputs. With predictive modeling techniques, you can conduct fast analyses that compare features within models and measure possible impacts on training and production data — without ever leaving the platform.
Increase business alignment and confidence in decision-making by enabling teams across the organization to glean insights and connect ML metrics to business KPIs in a unified view
Build custom reports with the insights you need to gain deep understanding of your models and their impact on business outcomes, from monitoring metrics, feature impact, correlation, and distribution to partial dependence plot (PDP) charts
Drill down on problem areas to uncover the root cause of underperforming segments
Drill down into specific segments to perform exploratory or targeted analysis, and find underperforming cohorts
Evaluate your model’s performance and validate it before deploying it into production