RevOps integrations & reporting

Why CRMs rot

No one decides to let a CRM go bad. It degrades from three directions at once, and each is invisible on its own. Data decays underneath you — roughly 30% of B2B records a year, with 65.8% of contacts changing title or function and 42.9% changing phone number within twelve months. Manual entry drifts, because a field that a rep has to fill in from memory at the end of a Friday will be filled in inconsistently or not at all. And the schema accretes: every campaign, integration, and well-meaning admin adds fields, and nobody ever removes one.

The result is a system where three fields could answer the same question and none of them agrees. Reporting built on it produces numbers that are technically derived from data and substantively fiction. The tell is usually that leadership has stopped trusting the dashboard and started asking people directly.

The fix isn't a cleanup project. A one-time deduplication makes the numbers right for a quarter and then the same forces resume. What holds is a system where the data arrives correct without a human maintaining it, and where the definitions are enforced by the schema rather than by discipline.

• Decay: ~30% of records a year, mostly job changes
• Manual entry: inconsistent, incomplete, and unevenly enforced
• Schema sprawl: duplicate fields, no shared definitions
• Cleanup projects fix a quarter, not the cause

Making the CRM the source of truth

We start by auditing what's actually there: which objects and fields are in use, which are dead, where the same concept is recorded in three places, and how records currently get created. That audit is usually uncomfortable and always useful, because it surfaces the definitional disagreements a team has been working around for years — what counts as an opportunity, when a lead becomes qualified, which field the forecast actually reads.

Then we agree a single definition for each of those and enforce it in the schema. Required fields where a decision depends on them, picklists instead of free text where the values are known, validation on the handful of fields the forecast reads, and a documented data dictionary so a new hire doesn't have to reverse-engineer it.

With definitions settled, the integrations do the maintenance. Enrichment writes into the CRM on a schedule rather than at import time, so records refresh against decay instead of aging in place. Deduplication and entity resolution run continuously rather than as an annual purge. Scores land as first-class fields that routing and reporting can read. Provenance is recorded, so any field can answer where it came from and when it was last verified.

Reporting that explains pipeline, not activity

Most dashboards count things: calls made, emails logged, opportunities created. Activity metrics are easy to build and easy to game, and they tell you almost nothing about why the number at the bottom moved.

We build the reporting that answers the questions leadership actually asks. Where deals stall, by stage and by segment, and how long they sit there before they die. Which segments convert and at what rate, so territory and headcount decisions have something behind them. What a scored account is worth in practice, which is how you find out whether the scoring model is earning its place. And cohort views, so a change made in March can be evaluated in June instead of argued about.

Forecasting sits on top of that. A forecast is only as defensible as the stage definitions and the data underneath it, which is why the schema work comes first. Once it's clean, we build the pipeline coverage and conversion views a revenue leader can walk a board through, and we review them with you rather than shipping a dashboard and moving on.

• Stage-by-stage stall and velocity analysis
• Conversion by segment, source, and score band
• Cohort views so changes can be evaluated, not debated
• Pipeline coverage and forecast a board can follow

HubSpot and Salesforce, in the instance you already have

We work inside your existing instance. No migration, no parallel system, no forcing a team onto a platform because it's the one we like. Your objects, your schema, your permissions and sharing rules stay as they are unless there's a specific reason to change one, and then we change it deliberately.

The integration work is mostly plumbing done carefully: connecting your data sources and warehouse, mapping fields explicitly rather than by convention, handling the edge cases where two systems disagree about which record is authoritative, and making sure automated writes are attributable and reversible. Sync failures alert rather than fail silently, which sounds obvious and is the most common gap we find.

Where you have a warehouse, we treat it as the analytical layer and the CRM as the operational one, so reporting doesn't fight the CRM's limits and the CRM doesn't carry history it was never meant to hold.

What the engagement looks like

The first phase is audit and definition — a few weeks of reading your instance, interviewing the people who use it, and writing down what everything means. You get a documented picture of the current state and a shortlist of the things that are actively producing wrong numbers.

The second phase fixes the foundations: schema, validation, deduplication, and the integrations that keep data current. This is the least glamorous and highest-leverage part, and we sequence it so reps feel improvement early rather than enduring a long quiet rebuild.

The third is reporting, built on data that can now support it, and reviewed with you regularly. After that we stay on for operations — watching sync health, keeping definitions current as the business changes, and adjusting reporting as the questions change. RevOps isn't a project that finishes; it's a function, and the whole reason to embed rather than consult.

When you don't need us for this

If you have a competent RevOps person and your CRM is clean, you don't need an agency — you need to leave them alone and give them budget. We're useful when there's no one in the seat, when the seat exists but is buried in tickets, or when the work needs skills a single generalist hire won't have across data engineering, analytics, and CRM administration at once.

If your problem is genuinely a process disagreement rather than a systems one — sales and marketing don't agree what a qualified lead is, and no schema will settle it — fix that first. We can facilitate it, but we can't engineer around a definition your team hasn't agreed to.