The CS Input Model – Making Motion Real (Part 2)

Author's Note: This is the second part of Article 2 in the CS Motion Framework series. If you haven’t read Part 1 yet, which introduces the Input Model, tagging system, and early AI orchestration insights, you can find it here: The CS Input Model – Turning Insight Into Motion (Part 1).

This part puts the model into action, walking through a realistic customer scenario to show how a CS Agent can support a CSM in driving coordinated, scalable progress.

Context and Application

Every account is different. Every team is different. Every moment is different. The Input Model isn’t rigid. It flexes. But that flexibility isn’t generic, it’s intentional.

Transactional vs Transformational CS

• Transactional CS focuses on clear, repeatable paths to value, often for a single product or a limited use case. It's about efficiency, coverage, and ensuring adoption of core functionality.

• Transformational CS supports strategic, multi-threaded change, with layered stakeholders, evolving goals, and long-term business impact. It’s less about checklists and more about guidance through ambiguity.

The Input Model adapts to both:

• In transactional accounts, inputs might be automated nudges, guided self-service, or structured playbooks.

• In transformational accounts, the same category of input could take the form of facilitated alignment sessions, co-authored value plans, or real-time signal-based orchestration.

It also flexes across delivery models:

• High-touch: Human-led motion, deeply contextualized, with AI acting as a strategic assistant.

• Tech-touch / Digital CS: Automated input sequencing with lightweight human reinforcement as needed.

And it always responds to signal: A usage drop doesn’t always mean risk. The Input Model checks the customer’s current state, based on the Enhance x Increase Matrix, before assigning meaning or motion

Inputs aren’t selected in isolation. They’re chosen based on the profile of the customer, the triggering signals, and the target motion. That’s what makes the Input Model operational, not theoretical.

In reality, no CSM manages just one account. Depending on the model, portfolio sizes vary widely: from over 50 low-touch customers to fewer than 10 high-touch, transformational ones. The challenge isn’t just in knowing what to do for one account, it’s in maintaining motion across many, each with its own state, timing, and constraints.

The CS Agent’s value isn’t in suggesting a plan. It’s in doing it again, every day, across the entire portfolio, without loss of context or momentum.

Note: You’ll see the term “CS Agent” used in this article. Ultimately, the goal is to build one.

The Input Model, Matrix, and tagging system introduced here are not theoretical. They form the operational blueprint that a future Customer Success AI Agent will follow. The orchestration work described throughout includes sequencing, prioritizing, and adapting across portfolios. This is exactly what the CS Agent will be expected to do. Future articles in this series will explore how to make that real.

How the Input Model Powers the CS Momentum Loop

The Input Model defines how motion begins. It selects and sequences the right actions to move the customer from current state to desired state, creating the input that initiates the loop.

If the Enhance x Increase Matrix shows where to act, the Input Model determines how to move, but motion doesn’t always lead directly to outcomes. Sometimes the state improves. Sometimes it doesn’t. Sometimes it stalls or even regresses.

That’s why this isn’t a one-time loop; it’s a feedback system. The Input Model watches for change (or the lack of it) and reshapes the next sequence accordingly, maintaining momentum even when progress is uneven.

For a breakdown of the full loop (Input, Output, Outcome, and Income), see Article 1: Rethinking Customer Success. We’ll revisit the loop itself in more detail in a future article in this series.

Applying the Input Model to a Real Scenario

Movifret Global is a customer of Transityx, a SaaS company that provides TransityxOS, a platform for logistics orchestration, route optimization, and freight analytics. The platform offers real-time dashboards, customs automation tools, and regional rollout support for global supply chains.

Their primary point of contact is Lena, a senior CSM at Transityx. Lena is responsible for strategic accounts with high expansion potential, and she’s working with the CS Agent to maintain momentum following Movifret’s pilot success.

► Background

Global Scale, Local Complexity

Movifret is a global logistics and freight technology provider operating across 40 countries, with over 12,000 employees. The company operates with high regional autonomy, and tech rollouts are typically decentralized, resulting in a fragmented and inconsistent tech stack. Some regions still use in-house tools, others rely on aging third-party platforms inherited through mergers and acquisitions.

The Case for Change

While these systems remain operational, they lack the consistency, visibility, and scalability needed to support Movifret’s global ambitions. Integration is limited, coordination across borders is inconsistent, and innovation is often slowed by the need to support legacy infrastructure.

The Pilot That Set Things in Motion

To address this, Movifret recently ran a six-month pilot of TransityxOS in France and Benelux. The effort was championed by the VP of Operations in France, who had previously worked with Transityx at another company and recognized its potential to unify fragmented systems. This internal sponsorship helped accelerate approval and drive alignment across the pilot regions. The results were promising: delivery operations were streamlined, customs workflows simplified, and visibility improved through unified dashboards, generating measurable time savings and strong end-user feedback.

Preparing for Broader Motion

Lena has captured as much insight as possible from relevant systems including Salesforce and her success planning workspace in NoChurnInSight, to form a full picture of account readiness. She is now working with the CS Agent to interpret these signals and identify the right motion path forward.

The Enhance x Increase Matrix flags the following current state:

• Enhance = True: Tangible value is emerging: shipment processing times fell by 12%, manual steps dropped by 5 per route, and pilot NPS jumped from 38 to 52. Adoption of key features rose 44% across the French and Benelux teams.

• Increase = True (Potential): Appetite to scale exists, but structural readiness varies.

► Primary Challenges

• Value is isolated, not yet socialized beyond pilot regions.

  France and Benelux have seen measurable improvements, like a 12% drop in delivery delays and more automated customs workflows—but those results haven’t reached other regions. There’s no central communication or case study circulating internally, so success remains local.

• Executive understanding of ROI is fragmented.

  The CFO has heard about efficiency gains, the COO has seen a few usage stats, and some regional GMs are vaguely aware of a pilot, but there’s no unified view of impact. This patchwork makes it difficult to generate urgency or executive sponsorship for broader rollout.

• Regional buy-in is uneven and decentralized.

  Italy and Spain are asking when they can start. Germany is currently trialing a different tool, creating potential divergence. Poland has other committed priority initiatives until Q4. Movifret’s federated structure means each region interprets the pilot independently. For Lena, who isn’t inside the org, mapping these variations requires stitching together scattered signals.

  
  

► What Lena Is Up Against

These aren’t side issues, they’re the conditions shaping every decision Lena makes. This is the terrain the CS Agent must support her across:

• No clear rollout structure exists to guide expansion.

• The pilot offers lessons that clarify what works, but scaling requires more coordination, not less.

• As more regions come online, the coordination burden is quickly outpacing what Lena or any single CSM can realistically support.

• Without visible momentum after the pilot, stakeholders, notably the CFO whom Lena has met before, will start to question value. Doubt tends to surface within 3 to 4 months, making expansion harder to justify.

• Every quarter of hesitation increases the chance this account becomes a well-liked pilot that never scales.

• The champion who pushed for the pilot is region-bound. His influence helped in France and Benelux but holds little sway outside them, limiting his ability to unblock or accelerate progress in other regions.

• Movifret signed a 12-month contract, and Lena knows the clock is ticking, every delay compresses the window for measurable impact, expansion planning, and executive conviction.

Why Timing Matters

The pilot took six months end to end: two for setup, three for usage and value, one for analysis and wrap-up. That leaves just six months on the contract. If the right input sequence doesn’t land fast, there won’t be enough time to execute, show progress, and convert early gains into strategic traction. For Lena and the CS Agent, orchestration isn’t theoretical, it’s a race against a deadline. Miss the window, and even the best plan won’t move the needle.

That’s why this isn’t just a scaling task, it’s a complex progression opportunity.

The CS agent must build a sequence of actions that span storytelling, alignment, planning, enablement and coordination. It must also stretch the Input Library and introduce new actions to fully address the nuances of Movifret’s state.

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When the Library Isn’t Enough: AI’s Role in Designing Motion

The Input Library is robust, but it’s not exhaustive. That’s by design.

Real-world customer scenarios are messy and nuanced. To orchestrate meaningful progress, the CS agent needs to do more than select from a predefined list, it must synthesize: combining existing inputs, generating new ones, and adapting actions to the situation at hand.

When appropriate, these synthesized actions can be formalized and stored in the Library, allowing the system to grow smarter over time.

This includes:

• Compounding inputs: Combining multiple fragments into a coherent, high-value activity (e.g., “Compile impact summary and align across 3 regional leads”).

• Filling gaps: Proposing new steps not yet defined in the library (e.g., “Model ROI of full deployment using pilot data”).

• Customizing sequences: Adapting the shape, effort, and delivery of each input to the customer’s context (e.g., adjusting training for cross-functional rollout with staggered timing).

• Maintaining motion: If the initial sequence doesn’t land, AI loops in new inputs or reorders the existing ones to maintain traction.

This means orchestration isn’t just about “what comes next.” It’s about diagnosing friction, projecting effort, and creating a tailored pathway, even if that means coloring outside the lines of the library. Once the gaps are filled and the plan is shaped, AI can act as both copilot and conductor proposing, assisting, and, where appropriate, executing.

Let's go back to Lena and Movifret.

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The Motion Plan for Movifret

Here’s how the CS Agent supports Lena in co-orchestrating a motion plan, surfacing timely suggestions, sequencing actions, and even synthesizing new inputs. While Lena leads the relationship and makes key decisions, the Agent works behind the scenes to maintain context, spot friction, and propose what comes next.

► Input (prioritized and sequenced actions)

1. Aggregate pilot usage and outcome data

   • Tags: Pre, Pull, Data, Medium effort

   • Source: Existing Input Library

   • Pull reports across KPIs: time savings, user satisfaction, automation rates.

2. Synthesize business impact story (with visuals)

   • Tags: Pre, Push, Data, High effort

   • Source: Existing Input Library

   • Craft a compelling narrative with before/after comparisons, stakeholder quotes, and pilot KPIs.

3. Model ROI of full deployment using pilot results

   • Tags: Pre, Push, Data, High effort

   • Source: Newly Synthesized

   • Extrapolate financial impact (time saved × regional headcount × hourly cost) to forecast benefits at scale, taking into account regional subtleties, strengths, and limitations.

4. Facilitate cross-regional leadership session

   • Tags: During, Push, People, High effort

   • Source: Existing Input Library

   • Present findings, highlight impact, and frame the value of coordinated rollout.

5. Capture alignment gaps and regional concerns

   • Tags: During, Pull, People, Medium effort

   • Source: Existing Input Library

   • Log objections, blockers, and readiness signals by region.

6. Draft updated rollout project plan with Gantt structure

   • Tags: Post, Push, Data, Medium effort

   • Source: Newly Synthesized

   • Define owners, stages, and timelines for each region’s onboarding.

7. Equip regional leads with value decks and action kits

   • Tags: Post, Push, Data, Medium effort

   • Source: Existing Input Library

   • Create editable assets they can reuse with their own teams.

8. Coach regional champions and monitor momentum

   • Tags: Post, Push, People, Medium effort

   • Source: Existing Input Library

   • Establish a rhythm of support and signal scanning.

Sequencing Motion

Each of these eight actions represents a deliberate input, chosen, adapted, and ordered by the CS Agent to match Movifret’s evolving state. The value isn’t just in the list itself, but in the system that built it, and the orchestration logic that keeps it alive as real-world signals change.

This example demonstrates the Input Model’s operational depth. Some activities are drawn directly from the library. Others are synthesized, requiring AI to stretch beyond predefined inputs. And the sequence itself, the prioritization, timing, and adaptive pathing, is where orchestration becomes strategic. This isn’t about ticking tasks. It’s about constructing motion that works.

Scaling Orchestration

While this sequence may appear straightforward, its true value emerges in context: the same CS Agent supporting Lena is helping orchestrate her full portfolio of accounts including five others alongside Movifret, each with its own state, sequence, and signals.

What’s remarkable isn’t that the plan exists, but that it can be generated, adapted, and rebalanced dynamically, without human burnout.

That’s where orchestration becomes strategic. It’s not about knowing what to do. It’s about being able to do it again, at the right time, across 20+ accounts, with no loss of fidelity.

Delivering the Work

Its role isn’t fixed. It adapts to support the CSM, not just in what it suggests, but in how it shows up. Sometimes it recommends, sometimes it prepares, and sometimes it simply does what needs doing.

In the case of Movifret, that might mean auto-compiling the pilot data, drafting the value story, or even kicking off the asset creation for regional leads, so Lena can focus on strategic alignment or navigating objections. The goal is the same: to keep motion going, and the CSM in control.

The CS Agent’s value isn’t just in knowing what to do. It’s in being there when the CSM needs it, helping keep motion alive across every account, every change, every day.

But how does this motion actually create value?

How do actions like these become shared outputs, visible outcomes, and measurable return? We’ll explore that fully when we revisit Movifret in a future article dedicated to the CS Momentum Loop. For now, what matters is this: motion isn’t theoretical anymore. It’s sequenced, contextualized, and real.

The sequence is complete.

But the motion it represents is just one example, one expression of a system built to handle many. So what powers that system?

The Engine of Modern CS

The Input Model is the brain of the CS Motion Framework. It translates insight into action. It defines what happens next. It supports a shift from reactive to proactive, from checklist to choreography. It is designed to support:

• Human-led orchestration: CSMs using experience and context to guide motion.

• Machine-augmented execution: AI surfacing the right actions at the right time.

• Situational flexibility: The Agent adapts, sometimes stepping in, sometimes stepping back, depending on what the moment, and the CSM, need most.

Together, the Input Model, the Enhance x Increase Matrix, and the CS Momentum Loop form the operational structure of modern CS.

Strategic Implication: CS as a Shared System

This clarity enables shared orchestration between humans and machines. Not by replacing CSMs, but by relieving them of repeatable prep, reinforcing follow-through, and helping them prioritize the next best action.

• Automation handles the routine.

• AI assists in complexity.

• And orchestration ensures it all connects.

  
  

In the next article, we’ll explore the Matrix itself: how to define "the customer state", how to profile it with data, and how to map motion to it effectively.