Client Experience
Machine Learning
Readiness Assessment
Any trip worth taking requires a bit of preparation.
We'll outline the key steps and resources required to help you start your journey toward leveraging AI for greater client insights.
We address the following items
CX Data Integration
We evaluate your data sources—client feedback, usage history, and profile data—and identify what needs to be integrated. This is often the most time-consuming phase, as it involves coordinating with internal data engineers to set up APIs and create ETL scripts for migrating data to your CX Datalake.
Vendor API Capabilities
Assessing the data extraction capabilities of your SaaS vendors, which often vary significantly.
CX Data Lake Storage
Evaluating your readiness to implement a secure data lake (AWS, Snowflake, etc.) for centralized data storage.
CX ML Model Ownership
Assess your in-house data science team resources and availability to manage the machine learning model or if ongoing assistance is required.
Primary Key Evaluation
Ensuring that a unique identifier exists to link all necessary datasets for seamless integration
Vendor Data Model Mapping
Identifying which datasets are stored in vendor systems vs. internal systems.
Python Development Environment
Ensuring you have the environment in place to develop, run, and modify machine learning models for client experience optimization.
Take Action Readiness
Assess how prepared your people, processes, and systems are to act on machine learning insights, including identifying "next best actions" for client engagement.