Hey,
This week, we’re highlighting a voice from the broader CS community - please welcome Justin Chappell, currently the Head of Digital Strategy, CX and Operations at OneTrust.
He’s a seasoned leader who has a sharp perspective on how to build a customer experience that drives retention and loyalty.
So without further ado, take it away, Justin…
Reactive ➡️ Proactive ➡️ Predictive
That’s the evolution every modern Customer Success org should be aiming for.
Too often, “digital” is miscast as a department or a segment. But digital is a strategy. A deliberate, scalable, AI-enabled strategy for delivering value consistently, whether you serve 100 enterprise accounts or 100,000 long-tail customers.
For years, Customer Success has talked about “being proactive.” But in a market shaped by tighter budgets, rising expectations, and accelerating AI capabilities, proactive is no longer enough.
The real evolution looks like this:
Phase 1: Reactive — Solving the problem after it happens
Every CS organization starts here. A customer submits a ticket. A CSM responds to an email. A renewal goes sideways after weeks of low engagement. This phase is defined by lag. Customer effort is high, and the CS team operates in a constant state of catch-up.
Phase 2: Proactive — Anticipating needs based on known signals
Proactive CS introduces structure and foresight: onboarding milestones with automated nudges, health score drops that trigger tasks, and usage trends prompting a check-in. This is where digital-first teams begin to shine. But proactive CS still relies heavily on pre-defined rules and human interpretation. It doesn’t adapt dynamically. It doesn’t learn.
Phase 3: Predictive — Personalized AI models + autonomous actions
Predictive CS goes further by leveraging AI models that are personalized and always on to detect early warning signs, while AI agents take pre-approved actions. It goes beyond signals and forecasts outcomes, automating the right intervention before the customer even realizes there’s a problem.
Predictive CS is powered by two core components: AI Models (personalized, time-aware models that detect shifts in product usage, stakeholder engagement, or risk signals that no rule-based system would catch) and AI Agents (autonomous digital workers that execute pre-approved actions — nudges, guides, articles, micro-trainings, ticket creation — at any hour, across any segment).
A Real-World Scenario
Imagine this sequence, which is already happening inside advanced digital-first CX organizations: Your AI model spots a 26% drop in engagement from key personas, along with a shift in who is engaging with your organization. It correlates this with slower time-to-value for similar accounts, signaling early churn risk.
Instantly, an AI agent delivers a personalized video walkthrough to the primary admin, surfaces a “next best action” adoption guide tailored to their product tier, and opens a low-effort support request on behalf of the customer to keep friction near zero.
No human bandwidth required. No guesswork. Just timely, intelligent intervention.
Why Predictive Is Now a CEO and CFO Priority
Predictive Customer Success isn’t a CS initiative; it’s a business initiative. Executives care because it unlocks specific outcomes. It ensures GRR & NRR stability, where early risk identification directly improves renewals, protecting revenue well before “red flags” appear. It drives scalable cost efficiency, as AI handles the repeatable while humans handle the nuanced (meaning headcount growth decouples from ARR growth). It ensures a consistent customer experience where every customer gets timely help, regardless of CSM availability. And it improves forecast accuracy, as predictive signals feed the rev-intel ecosystem, improving board reporting and tightening revenue predictability.
Moving to Predictive: Where to Start
CX/CS leaders often ask me where the journey begins. Here’s the blueprint:
Start with the data you already have (product engagement, ticket metadata, onboarding milestones, persona shifts), as most companies underestimate the value of the signals they already capture. This is where you’ll also identify the first predictive use cases — pick one or two (e.g., onboarding drop-off, feature drift, renewal risk, etc.), prove impact, and scale.
Then, pair AI models with AI agents… because models without agents create alerts, not outcomes, and agents without models create noise. Together, they create autonomous, intelligent experience delivery.
You must also redefine team roles so your people become strategic advisors, value engineers, and facilitation partners — not ticket chasers or usage-checkers. Finally, measure the right KPIs like reduction in manual touches, increase in digital deflection, faster time-to-value, and improved GRR/NRR.
Digital-first is customer-first.
Customers want accuracy, speed, and ease. When AI models + agents work in tandem, you don't just scale CS... you future-proof it.
Based on my experience leading CX at scale, this isn’t about replacing people; it’s about freeing them up to focus on high-impact conversations.
What phase is your CS org in today: reactive, proactive, or predictive?
– Justin Chappell
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