Answer
A model is only as good as the data feeding it, and the platform must be able to ingest usage logs, support tickets, subscription data, and tie them to a customer ID. AI customer success platforms need at least 6-12 months of historical data to train effective models. Clean and standardize data to eliminate inconsistencies that undermine AI model accuracy, and according to McKinsey, data quality issues remain one of the primary barriers to AI adoption.
AI can aggregate and analyze data from multiple touchpoints, such as product usage, email communication, support tickets, and even sentiment in chat conversations, with AI able to aggregate and analyze data from multiple touchpoints to provide a nuanced picture of each customer's likelihood to churn. AI pulls customer information from dozens of systems into one place, eliminating manual work across product analytics, support tickets, email threads, and billing platforms.
Studies show that up to 85% of AI projects fail with poor data quality being the main culprit, as AI systems are only as good as the data they are trained on, and using flawed, incomplete, or biased datasets leads to unreliable outputs that cannot deliver consistent results.