Beyond Orientation - Navigating the Radius of Possibility (part 2)

Author’s Note: If you’re joining from elsewhere, this is Part 2 of Article 3. Part 1 covered how we define customer state via the Enhance × Increase matrix and Motion Modifiers. Here, we dive into using that orientation to plot viable destinations, navigate constraints, and expand each customer’s Radius of Possibility.

Mapping the Radius of Possibility

Picture your customer like a ship anchored in a vast sea chart. The spot where they sit is fixed by the very same winds and currents you’ll later use to plot their course. In CS terms, those “winds and currents” are your Motion Modifiers: the mix of internal enablers and limitations, vendor friction, signal clarity, performance validation, and timing conditions.

These modifiers do two things at once:

1. Anchor the Ship (State): They define exactly where on the map the customer currently rests.

2. Chart the Waters (Motion): They carve out the surrounding sea, the Radius of Possibility, which represents every potential route the customer might take next.

A CS Agent’s task now becomes constraint-based planning:

1. Identify the optimal destination within the current radius, using historical patterns and modifier signals.

2. Sequence inputs that stand the highest chance of success given those constraints.

3. Continuously monitor for changes in modifiers that expand or shrink the radius over time.

Reminder: “Inputs” are the discrete actions or interventions, such as automated nudges, configuration prompts, targeted analytics reports, micro-training modules, or summary emails, that the CS Agent and CSM can deploy to orchestrate customer motion.

When applied across a vertical or cohort, mapping these radii reveals shared patterns: which modifiers block upward movement in logistics clients, or which paths succeed most often in fintech scale-ups. This enables not just individual orchestration, but segment-level learning and AI-driven refinement.

Relevance Before Coverage: A New Rule for Value

One of the most pervasive assumptions in Customer Success is that more adoption equals more value. In reality, value is a function of relevance, not coverage:

• Deploying what’s relevant and doing it well increases value realization.

• Failing to deploy relevant capabilities leaves impact on the table.

• Deploying irrelevant features creates noise, confusion, and dissatisfaction.

Adoption without relevance = waste. Relevance without adoption = regret. Only the intersection delivers meaningful value.

This principle transforms how Customer Success teams approach value realization: the CS Agent’s role isn’t to push adoption indiscriminately, nor to measure success by feature count. Instead, it ensures each customer has deployed what matters and can prove it’s delivering outcomes.

If relevance is your true north, you need a precise understanding of the terrain you’re working with. You can’t deploy what matters unless you have a complete inventory of your product’s capabilities.

Product Capability Awareness

Before plotting any motion, the CS Agent needs an up-to-date inventory of the SaaS platform’s capabilities: products, modules, key features and a clear understanding of the specific business outcomes each one is designed to enable.

Why it matters:

• If a feature is already deployed but its expected outcome isn’t showing up in the data, the Agent can flag an adoption-depth motion (an Enhance move up the value axis; e.g. tighten configuration, retrain users).

• If a goal is unmet and the catalog shows an adjacent capability that directly addresses it, the Agent can surface an expansion motion (an Increase move along the growth axis; e.g. pilot the forecasting module).

Behind the scenes, an internal product capability reference lets the Agent link each customer goal or blocker to the right slice of the platform. Think of it as an always‑learning playbook: feature → prerequisite → expected outcome → supporting motions.

With this knowledge in place, relevance truly beats raw coverage and motion plans stay anchored to what the product can deliver today.

But knowing what your product can do is only half the story. True relevance comes from measuring how those capabilities translate into real customer outcomes. That’s why the CS Agent must track the right metrics, not just internal usage figures, but the signals that show which features are driving the value your customers actually care about.

Metrics That Matter

For each capability in the reference, the CS Agent tracks a small set of signal metrics, quantitative clues that tell it when to go deeper (Enhance) or when to expand (Increase).

Customer-Centricity: Focus on the metrics your customers care about. These outcome-oriented signals belong at the top of the list, because what truly drives SaaS success is delivering the real business impact your customers measure.

Category	Metric	Example Signals	What They Reveal
Enhance	Business Impact	% cost savings, efficiency gains, process throughput, error‐rate reductions	Is the product actually moving the needle on core customer KPIs?
Enhance	Adoption Depth	% active seats, feature‐mix penetration	Is the deployed feature being used deeply enough to matter?
Enhance	Time-to-Value & Efficiency	Report turnaround times, hours saved	Are promised operational gains materializing?
Enhance	Reliability & Performance	SLA adherence, error/latency logs	Are technical frictions blocking value delivery?
Enhance x Increase	Sentiment & Advocacy	NPS/CSAT trend, champion engagement	Does the customer recognise the value delivered, and are they willing to invest further?
Increase	Adoption Spread	# of teams, regions, or functions using the product	Is usage expanding beyond isolated pockets and becoming organisation-wide?
Increase	Revenue & Consumption	License uptake, expansion pipeline	Is there commercial headroom or renewal risk?
Increase	Integration Health	API success rate, data-sync freshness	Is the product fully embedded in their stack for growth?

► Why Sentiment & Advocacy Sits in the Middle

While most metrics align naturally to either Enhance or Increase, Sentiment & Advocacy is different, it straddles both.High advocacy usually means the customer has already experienced real, measurable value (Enhance) and is open to doing more with the product (Increase). It’s both a confirmation of success so far and a signal of expansion readiness.

In practical terms, a customer can be using the product well without advocating for it, and that’s a warning sign. Conversely, advocacy without deep usage is often unstable and can collapse if expectations aren’t met quickly.

That’s why we treat this metric as a bridge between the two axes: it captures the emotional and relational state that fuels both value retention and growth momentum.

► Clarifying Adoption Spread vs. Adoption Depth

Although they sound similar, Adoption Depth and Adoption Spread measure very different things:

• Adoption Depth (Enhance) focuses on how well the product is being used within a given footprint. It’s about quality of use, feature penetration, frequency, and the extent to which deployed functionality is truly embedded in workflows.

• Adoption Spread (Increase) looks at how far the product’s reach extends across the organisation: more teams, regions, or functions coming on board. It’s about coverage, not depth.

A healthy account needs both: deep adoption in early teams to prove value, and broad adoption to create resilience and expansion opportunities. Without depth, spread can collapse; without spread, depth can stagnate.

The Agent evaluates each signal against clearly defined, customer-valued targets and trend lines, beginning with Enhance metrics, proof of value from already-deployed features and functionalities (for example, improvements in time-to-value, process efficiency gains, or error-rate reductions agreed up front). Because customers rarely expand before they see real outcomes, the Agent only turns to Increase metrics once those Enhance signals are in positive territory.

When an Enhance metric misses its customer-aligned goal, the Agent identifies a potential depth motion (for example, tightening configuration, retraining users, or redesigning workflows). A “depth motion” here means any intervention designed to improve outcomes from already-deployed capabilities.

Conversely, when Enhance trends are healthy but key objectives remain unmet, it identifies an expansion motion (for example, piloting a new module), a move aimed at adding relevant capability to drive new value.

By pairing business-impact signals with usage and growth indicators, the Agent translates raw data into precise, outcome-focused motions, ensuring we pursue real customer outcomes rather than internally convenient KPIs. This turns capability knowledge into targeted, data-backed next motions instead of blanket adoption campaigns.

Destination Matrix Overview

To support orchestration, the CS Agent must recognize not just the quadrant a customer falls into, but their current state on the matrix. Below is a summary of common states, organized by quadrant, with the typical modifier patterns that define or constrain each one:

► About the Labels

If you’re thinking the names aren’t very intuitive, remember: the matrix is a thinking tool rather than a strict classification system. The labels are shorthand, quick anchors for identifying a customer’s position and starting the conversation about what it means.  The deeper value comes from the CS Agent’s profile: the signals behind the position, the context shaping it, and the motion paths most likely to create progress. Over time, the Agent can adapt these labels to mirror the actual patterns and language found in your portfolio, making them instantly recognisable and relevant.

The Hidden Currents of Risk

At times, we’ve all been caught off guard. A customer looked stable, happy, and engaged, until churn arrived without warning. We learned from those moments, and the framework now takes them into account.

Some states are obvious danger zones: low Enhance and low Increase almost always point to brand churn risk, while high Enhance but low Increase can lead to revenue churn if left unaddressed. Low Enhance with high Increase can split either way depending on whether blockers are internal or external.

But there’s a less obvious danger: the False Calm. Sitting between states like Post-Project Drift and Potential at Risk, it feels safe because performance is solid and expansion potential is visible. Yet without proactive motion, effort shifts heavily onto the CSM to maintain momentum. The brand doesn’t consciously slow down, it drifts, and churn here often comes as a surprise.

The Sea Never Stands Still

When a state is identified, there’s always a motion to take, a play the CSM could run. Choosing not to act might feel like it preserves the status quo, but in reality, it never delivers “nothing.” The sea can’t be frozen. Movement is constant, sometimes dramatic, with visible waves and winds, other times subtle, driven by unseen currents or deep tectonic shifts.

Brands make their own decisions, launch their own changes, and react to forces beyond the CSM’s influence. Even without CS intervention, a customer’s position can shift for better or worse. The Radius of Possibility reflects this: it’s a circle around today’s position, not a one-way arrow. Desirable motions are up and to the right, but negative drift can happen through inertia, idleness, or external pressures.

The CS Agent factors this in. It knows the next state analysis might not match the intended trajectory, and in some cases, might show movement in the opposite direction. Anticipating where and why that could happen is as important as planning the next move. Life in the matrix is neither static nor linear.

Why the CS Agent matters here

Spotting the False Calm and other subtle risk zones depends on tracking dozens of small signals such as usage trends, sentiment changes, leadership engagement, or strategic intent, across an ever-growing portfolio. These signals are rarely in one place, often unstructured, and can change week to week.

Even the most experienced CSMs can’t manually connect every dot in time. The CS Agent does that continuously, combining structured, unstructured, and derived signals to surface when a customer that “looks fine” is actually at risk. It’s not replacing judgment, it’s making sure the important shifts aren’t missed.

From hidden risk to charting the way forward

With those states defined, the Agent returns to its chart, its Radius of Possibility, this time with concrete signals in hand. Having the customer’s current state as the epicentre grounds the radius in reality, and live modifiers then refine it. We'll see in the next article how the Agent superimposes these modifiers on the map, filters out infeasible routes, and narrows the field to the Next Best Motion: the single, highest-leverage set of inputs most likely to move the customer forward.

This sample of common states illustrates how a full, exhaustive catalog gives the CS Agent the complete vocabulary needed to diagnose exactly where a customer is today. Across a vertical or customer cohort, comparing these radii exposes where friction routinely appears, which transitions succeed most often, and which sequences of inputs have historically delivered the greatest impact, powering AI-driven, segment-level refinement of CS strategy.

The orchestration logic is sound. But its effectiveness depends entirely on one thing: data. Without access to the right signals, timely, structured, and trustworthy, the Agent can’t diagnose state, simulate motion, or plan what happens next. That’s the real constraint. Before any motion begins, the data has to make orientation possible.

The Customer State Is Not a Gut Check, it’s a Data Puzzle

In a perfect world, the customer state would be easy to assess. In reality, the signals that matter are often:

• Fragmented

• Inconsistent

• Buried in tools, documents, and conversations

To calculate Enhance and Increase (calculations are introduced in part 1), the CS Agent draws on three types of signals:

• Structured signals, for example usage data, license consumption, and feedback scores

• Unstructured signals, for example executive quotes, meeting notes, and regional blockers

• Derived insights, such as observed shifts in behavior, mentions of future expansion, or changes in team structure around the product

This is not a scorecard problem. It’s a reasoning problem. And that’s why CS platforms alone often fall short. Most CS platforms excel at tracking activity, but struggle to assemble and interpret the full story. They process structured data well, but lack the reasoning ability to draw conclusions from fragmented, cross-source, and often unstructured signals.

But it's not just a platform limitation. Even the most advanced AI tools, without the right context and structure around them, hit the same wall. Understanding the Customer State isn’t just a data problem, it’s a comprehension problem. And comprehension requires more than signals. It requires synthesis.

Why AI Struggles With A and B

Most AI tooling excels at analyzing surface behavior using usage patterns, support volume, sentiment scores, and correlating them with outcomes. But true “A” (where the customer really is) and “B” (where they could go next) require context that often isn’t captured in those metrics:

• Behavior ≠ Intent: A drop in logins might mean they’ve fully adopted a feature, or it might mean they’ve abandoned it altogether. AI can’t distinguish without human-sourced context.

• Segments ≠ Stories: Bucketing accounts into “high risk” or “high growth” ignores each customer’s unique business goals and blockers.

• Inputs ≠ Understanding: Raw data points tell you what happened; they don’t explain why. Without executive mandates, timing factors, or unstructured feedback, AI only sees part of the puzzle.

• Data Environment Limits: This isn’t AI’s fault, it’s a reflection of fragmented, siloed, and incomplete data. Until platforms fuse structured, unstructured, and derived insights, AI will keep reacting to scraps rather than reasoning end-to-end.

  
  

AI can predict, nudge, and summarize, but without rich context, it can’t fully orient on a customer’s true state or chart the best path forward. That’s where the human element, and our CS Agent’s orchestration logic remain essential.

Still, knowing how to reason is only half the battle. The harder part is finding the signals in the first place.

Where the Data Lives And Why That’s a Problem

The signals we need to determine customer state exist across multiple platforms:

• CRM (e.g., Salesforce): notes, contacts, opportunity info

• CS Platforms (e.g., ChurnZero, Gainsight, Totango, Planhat): health scores, usage trends, customizable workflows, BI-style dashboards

• Product Analytics: feature usage, login frequency

• Support Tools (e.g., Zendesk, Intercom): ticket types, CSAT

• Conversation Intelligence Tools (e.g., Gong): transcripts, objections, real-time intent signals

• Enablement Tools (e.g., Highspot): playbooks, templates, and guidance

• Survey Tools (e.g., SurveyMonkey, Qualtrics, Typeform): NPS scores, verbatim feedback, open-text fields

• Slack, Docs, Presentations, Emails: buried context, scattered insights

• Ad hoc reports and screenshots: .xlsx exports, PDFs, even JPEGs of dashboards, often the only visible trail of impact or blockers during exec syncs

The data exists, but it’s fragmented, inconsistently structured, and often trapped inside tools, formats, or silos that no platform, AI, or CSMs can easily reason through.

Many CS platforms claim they can consume data from any system and technically, they can. But unless that data is semantically understood, linked to business context, and used to drive decision-making, all you’ve done is centralize noise. The real challenge isn’t access. It’s comprehension.

Access may give you the key, but without reasoning, you still don’t know what door it opens.

From Here to There

Everything in Customer Success is about helping customers get from where they are to where they need to be. But if you misread Point A, or underestimate the forces surrounding it, even the best-planned motion won’t deliver. That’s why orientation comes first. In this article, we explored how the CS Agent maps each customer’s Radius of Possibility, defining where they are today, what’s holding them back, and what’s available around them. We saw how Modifier signals, not just feature usage, determine motion potential, and we acknowledged the hard truth: the data required to build this map is often scattered, inconsistent, and incomplete.

Again, this isn’t a scorecard problem. It’s a reasoning problem. And it’s why the CS Agent was designed not just to track activity, but to interpret context across silos, formats, and signals, and generate a clear, actionable view of customer state.

But identifying Point A is only the beginning. In Part 3, we’ll explore how the Agent moves from orientation to motion: filtering viable paths, sequencing high-leverage interventions, and selecting the Next Best Motion that’s most likely to create real progress.

A good map matters, but it’s only when you begin to move that the journey starts to really mean something.