AI is transforming customer success — retain more, scale better
AI chatbots, churn prediction, automated health scoring, AI-assisted onboarding. Get a brief on the tools and techniques that actually improve retention.
Curated from 20+ industry labs and publications
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AI chatbots can help or hurt CX
Bad AI support destroys trust. Good AI support scales your team. You need to know the difference before deploying.
Churn signals are getting more complex
AI can analyze usage patterns, support tickets, and engagement data to predict churn. But most tools overpromise on accuracy.
Scaling without losing the human touch
Automation is necessary for growth but customers still want relationships. AI needs to augment, not replace, your team.
AI news through the Customer Success lens
CS-focused AI filtering
We cover AI through a customer success lens — retention, engagement, support quality, and scalability.
Tool evaluations from CS teams
How AI support and success tools perform in practice — CSAT impact, resolution rates, and customer feedback.
Implementation playbooks
Practical guides for adding AI to your CS workflow without damaging customer relationships.
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News Relevant to You
Gainsight Launches AI-Powered Churn Risk Engine with Real-Time Alerts
Gainsight's new machine learning module automatically surfaces at-risk accounts using behavioral signals and engagement patterns. The tool integrates with existing Gainsight deployments and claims a 34% improvement in early intervention rates.
Why this matters to you: Your focus on churn prediction means this AI health scoring update directly impacts how quickly your team can act on at-risk customers before they disengage.
Intercom Reports 42% Faster Resolution Times Using AI-Assisted Customer Support
In their latest case study, Intercom shared data showing CS teams using their new AI response suggestions reduced ticket resolution time while maintaining CSAT improvement. The feature learns from your team's communication style.
Why this matters to you: If you're experimenting with customer automation, seeing real CSAT data from teams using AI support tools can help you build a stronger business case for your own implementation.
What To Test This Week
Run a Churn Prediction Pilot on Your Lowest-Engagement Segment
Select one customer cohort (e.g., SMB accounts inactive for 30+ days) and apply a churn prediction model to score them. Compare manual risk assessments from your team against the AI's predictions over 4 weeks.
Why this matters to you: Testing churn prediction on a contained segment lets you validate AI health scoring accuracy before rolling out to your entire book of business, with measurable CSAT and retention outcomes.
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Topics we watch for you include
- 🔍AI support tool launches and real CSAT data
- 🔍Churn prediction and health scoring AI tools
- 🔍Automation workflows for onboarding and engagement
- 🔍Case studies of CS teams using AI effectively
- 🔍Weekly experiments to try with your customer base
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