When people sign a new deal, they are excited. The first 30 days are about turning that excitement into confidence that the forecast will reflect how their business actually works.
That is harder than it sounds, and it has little to do with software. A forecast you can trust is more than a data connection. It depends on understanding the business process, configuring the system around it, validating the data and training the models until the outputs reconcile with how the company operates. At ORM that takes four to six weeks to a fully trained model.
Here is how that time is spent.
What happens in the first week?
The kickoff call happens as fast as possible, with the key stakeholders and the working group in the room. It sets the delivery plan, the roles, the expectations and an owner for every step. Momentum matters here. A kickoff that slips a few weeks lets the energy from the sale drain away.
The customer's side of week one is simple to list:
| What ORM needs | Why |
|---|---|
| Key stakeholders at the kickoff | They set the definitions and approve the outputs |
| A working group with named owners | They answer the day-to-day questions |
| Access to the CRM and marketing data | The models train on the company's own history |
| People who can explain the business rules | Definitions matter more than the data connection |
What happens between the kickoff and day 30?
The first part is unglamorous. The APIs get connected and data starts flowing. At the same time, the real work starts: documenting how the business runs. That means the sales process, the stage definitions, the metrics, and the particular way the company uses its systems. This detail gets captured in ORM's presentation layer, so the platform speaks the customer's language. Then the machine learning models are trained on the customer's own historical sales performance.During implementation the teams meet for 30 to 60 minutes every week, with working documents, emails and data questions going back and forth in between.
| Stage | What happens | What the customer sees |
|---|---|---|
| Kickoff | Delivery plan, roles, owners | A clear timeline |
| Data connection | APIs connected, data flowing | Nothing yet, and that is normal |
| Business definitions | Stages, pipeline, bookings and lead stages documented | Questions about how the business defines things |
| Configuration and training | Presentation layer set up, models trained | The platform in their own terminology |
| Validation, around day 30 onward | About two weeks of checking and training | The numbers, tested against their own |
What slows an implementation down?
Meaning, not plumbing. Connecting to the data is rarely the hold-up. Working out what the data means is.One ORM customer could not say which field held the closed-won booking amount. It took five to seven iterations, and some custom logic, to get it right. That is why an implementation is more than a technical integration. The technology has to be reconciled with how the business defines things.
It is also why the vendor's first answer matters. Ask any forecasting vendor, "How will this work for my business?" If they cannot explain how the tool adapts to your stage definitions, your idea of qualified pipeline, the way expansion differs from renewal, and who owns the forecast, be careful. And if they say they can have a forecast running in 15 minutes, walk away.
Does the data need to be clean first?
No. The objection ORM hears most is "I don't think our data is ready," usually followed by some version of "garbage in, garbage out."
Machine learning looks for predictive signal. If the business records data in a way that is imperfect but consistent, those patterns still predict what happens next. The job is to find the reliable signals, model them, and improve the data over time. Once something is being measured, the data gets better. Perfect data never arrives. Predictive data is what matters.
What gets validated, and how?
Around day 30 the system is delivered and a roughly two-week validation and training period begins. It is comprehensive:
- The data is right. Add up bookings for the year in the platform and they should match what the company reports internally. - The business logic is right. If the customer calls a qualified lead an MQL or an SAL, that is how the platform reports it. - The outputs make sense. The model's results and the user experience have to work for the people who will use them every week.
Once the working group is comfortable with the data and the logic, the first insights go to the key stakeholders.
What does a good first 30 days feel like?
The goal is more than getting the software live. It is making sure the excitement from the sale has not worn off, and that the customer comes out of implementation thinking the partner really understands their business.
That matters because the work does not stop at go-live. After delivery, ORM moves from weekly implementation meetings to working sessions every two weeks, with data science support and help interpreting the platform for the length of the subscription. The implementation gets the system live. The partnership is what makes it useful.
What should you ask before you sign?
Two questions protect you before any implementation starts:
1. Is my data secure, and are you using it to train your models? 2. Do the results come from our own systems, with a trace back to the raw data?
For more on choosing a platform, see sales forecasting software compared and what to ask an AI forecasting vendor.
---
Pete Furseth is COO of ORM Technologies, which builds custom revenue forecast models on a company's own CRM data.Frequently Asked Questions
How long does it take to implement sales forecasting software?
At ORM, four to six weeks to a fully trained model built on your own sales history. The system is delivered around day 30, followed by about two weeks of validation and training. Be wary of any vendor that promises a trustworthy forecast in 15 minutes.
What slows down a forecasting implementation?
Understanding what the data means, far more than connecting to it. One ORM customer could not say which field held the closed-won booking amount, and it took five to seven iterations with custom logic to get it right.
What should you validate before trusting a new forecasting tool?
Three things. The data reconciles: a year of bookings in the tool matches what the company reports internally. The business logic matches: a qualified lead shows up the way you define it. And the outputs make sense to the people who will use them.
Does our CRM data need to be clean before implementation?
No. Every company thinks its data is uniquely bad. Models look for predictive signal, and data that is imperfect in a consistent way still carries it. The data improves once it is being measured.
What should the customer provide in the first week?
Key stakeholders for the kickoff, a working group, access to the data, and people who can explain how the business defines its stages, pipeline, bookings and lead stages.
What happens after the software goes live?
At ORM, weekly implementation meetings become working sessions every two weeks. Data science support continues through the subscription, including help reading what the platform shows.
See how ORM turns these insights into action
ORM builds custom revenue forecast models for B2B SaaS companies. Not dashboards. Prescriptive analytics that tell you what to do next.
Schedule a Demo