This webinar explores how forecasting has fundamentally changed in today's dynamic go-to-market environment. Drawing on their combined experience in revenue operations, sales leadership, and rev tech, Katie Stanforth and Chad O'Connor walk through why legacy forecasting approaches fall short, what plays are working in modern sales organizations, and how to use data-driven signals to move from reactive postmortems to proactive deal management. This article includes time-stamped explanations of key moments in the video so you can quickly jump to the topics most relevant to you.
Note: This webinar was recorded prior to the company’s rebrand from People.ai to Backstory. As a result, presenters and on-screen materials refer to the company as “People.ai.”
The Real Stakes of Forecast Accuracy by Role
Video: 05:26–07:46
Forecast accuracy isn't just a business metric — it's tied directly to credibility and career stability at every level of the sales organization.
Key challenges include:
CRO tenure has shrunk to 12–18 months in many organizations
The pressure to call a number AND hit a number is more intense than ever
Stress cascades from CROs down to VPs, frontline managers, and individual reps
The focus has shifted from calling a number to delivering on an assigned number
The Old World Order: Legacy Forecasting
Video: 07:47–10:15
Legacy forecasting was operationally heavy and manually intensive — and it worked when go-to-market motions were relatively stable.
The traditional forecasting cycle included:
Pulling CRM data and running historical conversion rate reports
Building standardized playbooks for common scenarios
Calling the number based on historical stage-to-stage analysis
Conducting post-quarter QBRs and postmortems with manual decks
Building new playbooks from QBR findings to repeat the cycle
Why the Old Way No Longer Works
Video: 10:16–15:23
The core problem: go-to-market is no longer static. The dynamics that made historical analysis reliable have fundamentally changed.
What's driving the shift:
Go-to-market motions are now changing every quarter, if not every month
App consolidation and budget rationalization are reshaping buying behavior
Churn is at historic highs, making renewals and expansions harder to predict
CFOs are scrutinizing every dollar, lengthening and complicating deal cycles
External factors — budget freezes, org changes, geopolitical situations — can kill a healthy-looking deal
Play 1: Shift Your Mindset
Video: 15:24–16:53
The first play is a mindset shift away from CRM hygiene and toward understanding the buying community and the dynamics that could stop a deal.
Key ideas include:
Legacy mindset: "If it's not in Salesforce, it doesn't exist"
Modern mindset: Focus on who is involved and what could block the deal
Asking "why change, why now, why us?" and focusing on the first why: "Can this organization actually change right now?"
Quickly disqualify deals where the org can't move — and redirect energy to better pipeline
Factors like budget shifts, leadership changes, project constraints, and geographic situations matter more than stage accuracy
Play 2: Make the Hard Calls Early
Video: 16:54–19:54
The second play shifts the postmortem mentality from post-quarter to pre-quarter — identifying risk early enough to actually do something about it.
What this looks like in practice:
Surface deal risk in week one, not week twelve
Mitigate risk before the economic buyer or CFO appears and shuts the deal down
Arm champions early with the materials and arguments they need to fight for funding
Be more skeptical of pipeline quality — don't drag unqualified deals to quarter end
If a deal won't close, move it out early to preserve time to pull something else forward
Play 3: Close the Insight-to-Action Gap
Video: 19:55–22:06
The third play addresses the lag between receiving a signal and taking a meaningful, nuanced action on it.
The problem with signals today:
Reps are inundated with alerts and red flags from multiple tools
When everything is flagged as urgent, nothing feels urgent
Generic playbooks can't account for every stakeholder dynamic and deal context
Signals without clear, specific next actions don't change behavior
What actually works:
Moving from "here's a problem" to "here's exactly what to do about it, with this stakeholder, right now"
Actions nuanced to the specific deal — not one-size-fits-all steps
Reducing the time between seeing a risk and acting on it
Demo: Modern Forecasting in Action
Video: 22:07–28:52
Chad walks through how he starts each week using People.ai — showing what a modern, data-driven forecasting workflow looks like in practice.
Headline-level risk visibility:
Three risk categories surface automatically across all pipeline (e.g., security and compliance concerns, integration concerns, ROI and financial concerns)
At the start of a quarter, $15.5M across 55 opportunities was flagged — effectively a postmortem at the beginning of the quarter, not the end
Opportunity-level drill-down:
Drill into any headline to see account-level context, next steps, and current engagement
Data is pulled automatically — no rep data entry required — showing what's actually happening versus what's being reported
Org chart and stakeholder engagement:
Visual org chart shows engagement scores by contact
Helps identify who is engaged, who is missing, and who needs to be added
Modern deals typically require at minimum a C-level contact, two VPs, and several directors
Weekly action recommendations:
Each week surfaces 2–3 specific suggested actions per deal, targeted to individual stakeholders
Shifts from serial, one-action-per-week cadences to parallel, multi-stakeholder plays
Recommendations can be delivered via Slack, email, or within CRM — meeting reps where they work
Achieving Forecast Accuracy: From 20% Down to 3%
Video: 28:53–31:21
Doing a premortem at the start of the quarter — not the end — is the single biggest driver of forecast accuracy improvement.
What's achievable:
Many companies operate at 20–12% forecast error
With this methodology, it's possible to reach 5% — and then down to 3%
How accuracy improves:
Calling out deal risks explicitly in week one
Assigning specific actions to overcome each risk with named stakeholders
Tracking whether actions were completed and adjusting the approach the following week
Moving deals out or pulling new ones forward early — before the last few weeks of the quarter
Personalizing What "Good" Looks Like
Video: 31:22–35:52
Standard performance benchmarks don't account for company context — and in today's environment, one-size-fits-all coaching is no longer effective.
Key points:
Metrics like "4x pipeline coverage" or "10 meetings per week" don't apply equally across every rep, territory, and segment
A wide performance gap exists: some reps are making President's Club while many are performing at very low bars
The goal is to define what "good" looks like for your company based on industry, personas, and deal types
Historical win/loss data reveals which activities and engagement patterns actually correlate to closed deals
Resources and Next Steps
Video: 36:09–End
Three actions to take coming out of this webinar:
Check your forecast: Do you have a data-driven view of risk by category — not just rep-reported stages — available at the start of the quarter?
Audit your signals: When did a signal from your tech stack last change what a rep did next, not just flag a problem?
Reach out to Katie or Chad via LinkedIn to continue the conversation or explore further