Apple’s App Tracking Transparency (ATT and SKAdNetwork) and Google's decision to disable third-party cookies have made it more difficult for marketers to track and measure user behavior. These changes have created many challenges: reduced ability to target ads to specific audiences, increased difficulty measuring the ROI of marketing campaigns, and increased risk of fraud and abuse.
Pecan AI offers a compelling solution using the power of machine learning and explainable AI to solve these problems.
Pecan AI's platform generates predictive insights at an individual user level, enabling precise understanding of customer behaviors and attributes even with limited data. This helps marketers identify high-value target audiences, model customer lifetime value, forecast churn risks, and optimize campaigns.
Let’s get into it.
Explainable AI for insights
Explainable AI capabilities allow Pecan AI to surface the key drivers and features behind each prediction. For instance, it can pinpoint that a particular customer is predicted to churn due to recent late payments. These personalized explanations allow marketers to take targeted actions.
Explainable AI for compliance and regulation requirements
Compliance teams also benefit from explainability to ensure models align with regulations and corporate policies. As AI adoption grows, model interpretability will only become more important. In just one example of regulations that require explainability, the California Department of Insurance released Bulletin 2022-5, which requires that “when insurers use complex algorithms in a declination, limitation, premium increase, or other adverse action, the insurer must provide the specific reason or reasons for that decision to the consumer.”
That means the insurer needs to be able to explain a decision at the customer level, which requires an explainable approach to AI.
Monitoring Drift in production:
Pecan AI monitors models in production for data drift. That means the platform automatically detects when real-world changes cause performance drop-offs. Analysts can then recalibrate models to current data patterns, keeping predictions accurate.
Data preparation and feature engineering automated with Auto MLOps
I had to call this out since it has been a challenge in the industry for the last 15 years. Pecan automates key tasks like data preparation, feature engineering, and model building with Auto ML & MLOps. AutoML here is applied to deliver real value.
Pecan is a great example of tangible business value delivered with machine learning and explainable AI. Pecan AI is overcoming the limitations of restricted data access with user-level predictions and insights. The machine learning approach makes personalization and modeling possible even amidst the privacy changes disrupting digital marketing.
