If you’re in sales ops, a revenue leader, or anyone who’s tired of “gut-feel” forecast meetings, you’ve probably heard of Clari Co-Pilot. But most guides either hand-wave about “AI-powered insights” or drown you in generic dashboards. This walk-through is for folks who want to set up a real, custom pipeline forecast—one that actually fits your team’s needs, not just what the vendor demoed.
Let’s get your forecast out of the spreadsheet jungle and into something you can trust.
Step 1: Know What You Actually Want to Forecast
Before you even open Clari Co-Pilot, get clear on what you want out of your custom forecast. Don’t skip this. If you just want a pretty chart, save yourself the time.
Ask yourself: - What’s the main question you want to answer? (e.g., “Will we hit $1M this quarter?” or “How much pipeline is real?”) - What counts as ‘pipeline’ for you? (Don’t blindly use the default stages.) - How do reps currently update deals? (If your CRM data is garbage, Clari won’t magically fix it.)
Pro tip: Write down your “must-haves” and “nice-to-haves.” Otherwise, you’ll end up tweaking for weeks.
Skip: Trying to copy a “best practice” template without thinking. Your business is different.
Step 2: Clean Up Your CRM Data (Yes, Really)
Clari Co-Pilot pulls straight from your CRM. If your opportunity stages are a mess, or reps are sandbagging, your forecast will be junk—just more automated junk.
- Audit your pipeline data. Look for missing close dates, weird stage names, or deals that haven’t been updated in months.
- Decide which fields matter. At minimum: opportunity name, amount, close date, stage, owner.
- Fix what you can. It’s not fun, but it’s faster than unpicking bad forecasts later.
What to ignore: Fancy AI features promising to “fix” bad data. They can help, but they’re not a magic wand.
Step 3: Set Up Your Custom Fields and Definitions
Clari Co-Pilot lets you pull in custom fields from your CRM. This is where you can tailor things—like forecasting by region, product line, or whatever matters for your team.
- Map your CRM fields. Go into Clari’s admin/settings area and connect the fields you actually use.
- Clarify stage definitions. Make sure your team agrees on what each stage means. If not, now’s your chance to fix it.
- Tag custom metrics. Want to track “confidence level” or “deal risk”? Set those fields up now.
Don’t bother: Overcomplicating with ten custom fields if your team only cares about two. Complexity kills adoption.
Step 4: Build Your Forecast Categories
This is where the “custom” part comes in. You’re not stuck with just “Commit,” “Best Case,” and “Pipeline.” Create categories that match how you actually run your business.
- Go to Forecast Setup. In Clari Co-Pilot, you’ll find an area to define forecast categories.
- Add or edit categories. Maybe you want “Upside,” “Likely,” or “At Risk.” Name them whatever makes sense.
- Set inclusion rules. For each category, pick which opportunity stages or custom fields roll up into it.
- Test with sample data. Make sure deals fall where you expect.
Reality check: If you have more than 4-5 categories, your forecast meeting will get sidetracked. Keep it simple.
Step 5: Define Your Rollups and Hierarchy
Forecasts are only useful if you can see them sliced the way you need—by team, region, segment, whatever.
- Set up your team hierarchy. Clari lets you mirror your org chart, so managers can see their team’s rollup.
- Choose rollup logic. Decide if you want sum totals, weighted pipeline, or other math.
- Filter views. Set up filters so you can quickly see pipeline by segment, rep, or product.
What to ignore: Creating a view for every possible question. Start with what you actually review in forecast meetings. Add more later if people actually use them.
Step 6: Tune Your Forecasting Model (Optional, but Worth It)
Clari Co-Pilot has some AI/ML forecasting tools. Honestly, these can be hit or miss, depending on your data quality and pipeline volume.
- Try AI predictions, but don’t bet the farm. They’re good for a second opinion, not gospel.
- Customize your model inputs. Most teams start with stage, amount, and close date, but you can add fields like “last activity date” or “deal risk.”
- Compare Clari’s forecast to your gut. If the model is way off, check your inputs—it’s often a data issue.
Pro tip: Use AI to flag deals that need a closer look, not to replace rep or manager judgment.
Step 7: Set Up Your Forecast Submission Process
If you want reps or managers to actually use this thing, you need a clear, simple workflow.
- Decide who submits forecasts. Reps, managers, or both?
- Set a schedule. Weekly is common, but pick what works.
- Automate reminders. Clari lets you nudge folks to submit forecasts on time.
- Lock in your process. Make it part of your sales cadence, not an extra chore.
What to ignore: Overly complex approval chains. You want speed, not bureaucracy.
Step 8: Build and Share Your Dashboards
Visualization is where the pain of setup starts to pay off. Focus on views that actually help you take action.
- Create summary dashboards. Show top-line forecast, gap to goal, and changes week-over-week.
- Drill down views. Make it easy to click into teams, reps, or specific deals.
- Highlight exceptions. Use color or flags for deals slipping, stuck, or missing updates.
- Share links or set up auto-emails. Clari makes it easy to send dashboards to execs or teams.
Reality check: Don’t chase dashboard perfection. If a view isn’t used after a month, kill it.
Step 9: Train Your Team (Without the Eye Rolls)
Even the slickest forecast system is useless if your team ignores it. Keep training short and focused.
- Show them what’s in it for them. Faster forecast calls, fewer “update your pipeline” nag emails.
- Run a dry run. Walk through a forecast submission and review live.
- Document the process. One-pager or short video > 50-slide deck.
- Keep feedback lines open. You’ll need to tweak things as people actually use the tool.
What to ignore: Endless training sessions. Most folks learn by doing, not by watching.
Step 10: Review, Adjust, and Don’t Overthink It
Your first custom forecast won’t be perfect. The good news: You can (and should) keep tweaking as you go.
- Review after each cycle. What worked? What was confusing? Did you actually use the outputs?
- Cut what doesn’t add value. Be ruthless. If a category or view isn’t used, drop it.
- Stay close to the front line. Reps and managers will tell you if it’s working—if you listen.
Pro tip: Iterate, don’t overhaul. Small, regular updates beat big bang changes.
Keep It Simple, Keep It Honest
Don’t get lost chasing the “perfect” forecast or every shiny AI feature. The best pipeline forecasts are clear, simple, and actually used by your team. Build the basics, get feedback, and improve. And remember: no tool fixes bad data or unclear process.
Good luck—and don’t let your forecast become another unused dashboard.