CS operations that catch churn while it is still just a signal in your data.
Most of the churn a team takes in a year has been sitting in its data for months before anyone looks: usage sliding, the champion gone quiet, the billing feed already carrying the contraction. NRR Partners designs and builds the data, automation and reporting layer inside Planhat and the systems around it, so those signals reach the right person in time, and retention and expansion stop being a feeling and become numbers the CEO, the CFO and the CSMs all trust. Senior-led, fixed fee, delivered by a former Planhat employee.
You own the number but not the system that produces it.
Your health score is half CSM sentiment, your renewal forecast lives in a spreadsheet, and every board deck starts with a reconciliation exercise. You need the platform to carry the operating model, not the other way round.
Planhat is one more estate you inherited without documentation.
Dozens of automations nobody can explain, rollups that disagree with the CRM, calculated metrics with no owner. You want it audited, rationalised and written down so your team can maintain it without calling anyone.
You want to know which accounts will expand before the renewal call.
The data exists in product analytics, billing and the CRM. It has never been joined into one view that tells you where the risk is and where the money is. That is a data architecture problem, and it is a solvable one.
The instance is not broken. It was configured before anyone designed the operating model.
Most engagements start with a diagnostic. These are the patterns that show up in nearly every instance we open, whether it was self-implemented, agency-built or inherited through a reorg.
Automations that multiplied instead of composing
Thirty or forty active workflows, many of them near-duplicates written by different people over eighteen months, firing on overlapping triggers. Nobody will touch them because nobody knows what breaks.
Hierarchies that do not roll up
Parent companies, business units and projects modelled as flat accounts, so usage and revenue at the top level are either missing or double counted. Leadership sees a number that the account team cannot reproduce.
Health scores that describe the CSM's mood
A red, yellow, green field driven mostly by a pulse column, never backtested against who actually churned. It makes the dashboard look managed while predicting nothing.
Dashboards built before the data model was right
Beautiful views on top of unreliable properties. The first time a number is visibly wrong in a leadership meeting, the whole platform loses credibility and the team goes back to spreadsheets.
Integrations that land data nobody shaped
HubSpot, billing, product analytics and a warehouse all syncing into Planhat, with no data dictionary, no ownership and no propagation rules. Every field is a guess about which system is the source of truth.
Four builds, one entry point, and a retainer that keeps it all true.
Every engagement is fixed fee with a written scope, a working agreement on change control, and a handover your team can maintain. Start with an audit if you are not sure where the problem is.
Planhat implementation
A new instance built around your revenue model, or a rescue of one that stalled. Operating model first, then data model and integrations, then scoring and automation, and dashboards last, because that is the order that survives contact with real users.
- Data model, custom objects and hierarchy design
- Integrations with CRM, billing, product analytics and warehouse
- Playbooks, sequences and calculated metrics
- Team enablement and documented handover
Automation and data architecture
An audit of every active automation, a data dictionary for every property, consolidation of what should never have been separate, and rollups that make the parent-level numbers reconcile with the child-level ones. This is the work most instances actually need.
- Automation catalogue: trigger, action, properties, intent
- Multi-level hierarchy rollups and cascades
- Consolidation with written rollback plans
- Living estate map handed to your team
Health scoring that is backtested
A scoring model designed from your renewal and churn history, weighted by what actually predicted the outcome, and validated on accounts it has not seen. Numeric, explainable, and surfaced where the CSM plans their week.
- Dimension design from your data, not a template
- Retroactive scoring and correlation analysis
- Thresholds, trajectory and alert routing
- Parallel run against the existing score before cutover
Team cockpits and NRR reporting
Pod or team level views of ARR, usage and targets with gap-to-target and week-over-week movement at every level of the hierarchy, plus the prioritisation layer that turns the view into a decision about where to spend the next two weeks.
- Target import and forecast versus actual
- Lifecycle-phase slicing and movers views
- Portfolio prioritisation and action logs
- Executive rollups that reconcile to the CRM
Planhat estate audit
Two weeks inside your instance. You get a written map of every automation, property and integration, a ranked list of what to fix, and a fixed-fee proposal for the fixes. Useful whether or not we do the fixing.
See the audit After the buildEstate stewardship retainer
A monthly block of senior hours to keep the estate healthy as the platform, the upstream data and the team change. Direction and QA on views your team assembles, new builds drawn from the block, and a weekly call.
How retainers workA construction-tech SaaS with a three-level hierarchy and thirty-six automations nobody could explain.
The client had been on Planhat for a year. Target companies, their organisations and the projects underneath were modelled across three levels, with cross-level property propagation nobody had documented. Leadership wanted pod-level visibility against quarterly targets. The instance could not produce it reliably.
Over seven to nine weeks and three sequential fixed-fee statements of work, we audited and rationalised the automation estate, built rollups so usage moved cleanly from project to organisation to target company with gap-to-target and week-over-week movement at every level, shipped pod cockpit dashboards, and added a prioritisation layer with project-level targets, relationship strength and an action log. The engagement converted into an ongoing stewardship retainer.
Client name withheld under our engagement terms. Integrations in scope: HubSpot, Chargebee via BigQuery, Mixpanel.
Production discipline, because your instance is production.
Most CS platforms have no sandbox. The work happens where your team is working. That shapes everything about how we operate.
Written rollback before every change
Every automation, property or view change ships with a rollback plan written before deployment and communicated before it goes live. Nothing outside the agreed list gets touched without written approval.
Documentation that matches reality
The estate map, the data dictionary and the automation catalogue are deliverables, not afterthoughts. They are handed over in your tools, and on retainer they are kept current as the estate changes.
Weekly thirty-minute sync, async between
One short direction call a week, Loom walkthroughs on completed work, and day-to-day questions in a shared Slack channel. Your team's time is spent deciding, not watching.
Sequential scopes with decision points
Larger engagements are split into statements of work that each deliver something usable on their own. You call the next one when the previous one has landed and proven itself.
Fixed fee, sized honestly
You know the number before we start. Hours are a sizing mechanism for the scope, never a meter. If demand outgrows the scope, you hear about it before it is spent, never on an invoice.
Your team assembles, we direct and QA
Where your ops team can build views themselves, we specify each one with exact fields and logic and review before it ships. Your team keeps the skill and the numbers stay right.
A boutique by design. Senior-led, and built by someone who has sat on every side of the table.
NRR Partners is led by a former Planhat employee who then ran enterprise accounts as a customer success manager at an AI security company, building the customer assurance function for some of the fastest-growing AI infrastructure companies in the world. Before customer success, a data engineering career and a master's degree in mathematics.
That combination is the point. Time inside the vendor means knowing what the platform will and will not do, and where the documented behaviour ends. Time as a CSM means knowing what a health score has to look like to be trusted by the person whose quarter depends on it. The engineering foundation means the integration, the calculated metric and the rollup logic get built by the person who designed them, with nothing lost between the strategy deck and the instance.
Operational thinking for teams running CS as a revenue function.
Why most Planhat rollouts stall, and the order of operations that fixes it
The tool is rarely the problem. Teams configure the instance before they design the operating model it is supposed to carry, and dashboards get built first when they should be built last.
Your health score is probably a mood ring. Here is how to make it predict something.
Red, yellow, green driven by CSM sentiment was never designed to forecast outcomes. A model built from your own churn history and validated on a holdout set is a different instrument entirely.
What customer success contributes to revenue, in numbers a CFO will accept
Retained ARR attribution, expansion influenced, cost to serve and revenue at risk managed, on one slide, refreshed quarterly.
Straight answers.
Who is this for, and who is it not for?
B2B SaaS companies from Series A through growth stage with a customer success or account management function that is expected to own retention and expansion numbers. The best fit is a team on Planhat, or moving to it, with real data in product analytics, billing and the CRM that has never been properly joined.
It is not a fit if you are looking for hourly admin support, a chatbot, or someone to run your CS team for you. We build the system the team runs on.
Do you only work on Planhat?
Planhat is the platform we know from the inside, and most engagements are Planhat work. The data architecture, health scoring and automation design carry to other stacks, and a lot of the value is in the warehouse, the integrations and the model rather than in any one tool. If you are on something else, the strategy call will tell you quickly whether we are the right people.
How is pricing structured?
Fixed fee per statement of work, sized from a written scope, invoiced fifty percent on signature and fifty percent on acceptance. Ongoing work runs as a monthly retainer with a block of hours that pools across a quarter. We do not bill hourly. The pricing page explains the three engagement models and typical ranges, and every number is confirmed in a written proposal after the call.
What does the strategy call actually involve?
Thirty minutes. You describe the setup and the pain, we ask specific questions about the data model, the integrations and the automations, and you leave with a point of view on what is wrong and what order to fix it in. If it makes sense to go further, the next step is usually a two-week audit with a fixed fee.
We already tried Planhat and it did not stick. Can it be rescued?
Usually, and usually faster than starting over. The instance almost always has salvageable structure underneath a layer of unowned automations and unreliable properties. The audit tells you what to keep, what to consolidate and what to rebuild, with a rollback plan for every change.
Who does the work?
The principal does. NRR Partners is a boutique firm by design: the person on the strategy call is the person in your instance, writing the calculated metrics and the rollback plans. When your own team can assemble views, we specify and QA them so the skill stays with your team.
Where are you based, and what hours do you work?
NRR Partners LLC is registered in New Jersey, across the river from Manhattan. Work runs across US and European hours, and engagements to date have included UK-based clients under GDPR-compliant data processing terms.
Do you sign an MSA and a DPA?
Yes. Engagements run corp-to-corp under a master services agreement with a data processing agreement where required. We have executed UK GDPR-compliant DPAs and are comfortable with client paper if you prefer yours.
Tell us what the instance is supposed to do.
Thirty minutes, no deck. You will leave with a point of view on what to fix first, whether or not we end up working together.
- A specific read on your data model, automations and scoring
- An order of operations you can act on immediately
- A fixed-fee proposal if there is a fit, sent within two business days
Prefer email? Write to general@nrrpartners.com. Replies within one business day.