This webinar focuses on how to use Signals in Unified Backstory Application to move from insights to actionable steps. It addresses the common challenge of "insight overload," where sales teams have too many tools and alerts competing for attention, and shows how to identify the signals that actually matter and turn them into concrete actions. This article includes time-stamped explanations of key moments in the video so you can quickly jump to the topics most relevant to you.
The Problem: Insight Overload
Video: 03:08–05:10
Sales teams are overwhelmed with data from multiple tools, leading to paralysis instead of action.
Key challenges include:
Average sales teams use five to eight different tools (CRM, engagement platforms, conversation intelligence, forecasting, etc.)
Every tool has dashboards full of alerts, flags, and scores competing for attention
When everything is flagged as urgent, nothing feels urgent
Reps often tune out the noise and revert to gut instinct
The gap isn't the insights themselves—it's the disconnect between insight and action
The Solution: Pick Signals, Diagnose with Context, Take Action
Video: 05:11–08:02
Instead of reacting to every data point, focus on three to five key signals that correlate to revenue risk or opportunity.
The framework includes:
Identify signals that matter most (deals stalling, competitive risk, relationship gaps)
Diagnose with context—understand the "why" behind each signal
Use SalesAI to move from insight to action in minutes, not hours
Train your team by consistently connecting signals to actions, building muscle memory
Signal #1: Predicted Sales Stage
Video: 08:14–08:51
This signal uses AI to assess what stage a deal is actually in based on your stage gate framework, independent of what the rep has entered.
How it works:
AI evaluates deal activity against your stage definitions
Identifies mismatches between rep-entered stage and AI-predicted stage
Helps spot over-forecasted or under-forecasted deals
Signal #2: Deal Risk (Forecast Risk)
Video: 08:52–09:37
This signal goes beyond stage gates to layer in engagement data, conversation analysis, and technical gaps to assess overall deal risk.
What it evaluates:
Engagement levels and meeting frequency
Executive involvement
Buying process progress
Potential delays or blockers
The signal provides:
Red, yellow, or green risk rating
Ability to customize the framework based on your sales ops or enablement team's existing risk criteria
Signal #3: Account Health
Video: 09:38–10:14
This signal evaluates accounts (not just deals) using multiple data sources to provide a comprehensive health rating.
Data sources considered:
Engagement levels
Multi-threading (are you talking to multiple stakeholders?)
Executive involvement
Account scorecard data
Utilization and adoption metrics
Demo Signal: Predicted Sales Stage
Video: 11:13–15:59
Live walkthrough of the Predicted Sales Stage signal in Unified Backstory Application.
Key capabilities demonstrated:
View opportunity sheets with standard data plus AI signals
See mismatches between rep-entered stage and AI-predicted stage
Click on any value to see the AI's rationale for its assessment
View next best actions recommended by the AI
Ask follow-up questions (e.g., "Draft me an email seeking final sign-off on proposal")
Supports multiple languages—ask questions in German, get responses in German
Demo Signal: Deal Risk
Video: 16:00–18:44
Live walkthrough of the Deal Risk signal showing red, yellow, and green ratings.
Features shown:
Click into any rating to see detailed rationale
View factors like number of meetings, executive involvement, and buying process status
Customize the framework based on your organization's risk assessment criteria
Demo Signal: Stall Reason Analysis
Video: 18:45–20:03
For more mature teams, instead of just showing a risk rating, the AI can identify the specific reason a deal is stalled.
Examples of stall reasons:
Decision-making delays
Lack of engagement
Deal on track (no issues)
This provides more directly actionable insights—when you see "decision-making delays," you know exactly what type of mitigation action to take.
Demo Signal: Account Health
Video: 20:04–23:11
Live walkthrough of the Account Health signal at the account level.
Features demonstrated:
Red, yellow, green account health rating across multiple accounts
Click into any rating to see detailed rationale
Embedded links to internal resources (adoption playbooks, risk mitigation plans, LMS content)
AI surfaces relevant playbook links when certain criteria are met
Integration with CRM fields tagged by internal team members
Consideration of adoption and utilization data in health assessment
Demo Signal: Upsell Potential
Video: 23:12–24:41
For customers further along in AI maturity, this signal assesses upsell potential on accounts.
How it works:
Analyzes the nature of conversations happening on the account
Identifies discussions about specific workloads or use cases
Evaluates how far along those discussions are
Rates upsell potential accordingly
Behind the Scenes: The Task Builder
Video: 24:42–31:17
The Task Builder is where CS teams and Backstory teams create and test custom signals.
Capabilities include:
Build different signals from scratch
Test signals on specific accounts or opportunities before deploying
Configure what data sources the AI should use
Customize follow-up questions and response formats
Example walkthrough—Close Date Analyzer signal:
Purpose: Assess whether a deal will close early, on time, or delayed
Inputs: Communications, customer engagement, CRM data, stage-specific expectations
Output options: Categorical values (early/on time/delayed) or numeric estimates
Data sources: CRM data, Backstory metrics, email, and meeting summaries (chronicles)
Customization options:
Specify how the initial response should be formatted
Add conditional follow-up prompts (e.g., "If delayed, give me steps to bring it back on time")
Request specific stakeholder recommendations
Demo Signal: Account Risk with Public Company Data
Video: 31:18–33:31
This signal uses Backstory's public company news data source (8-K and 10-K reports) to identify and preempt account risk.
Risk categories it can identify:
Pricing concerns
Competitive threats
Technical gaps
Organizational changes (from earnings calls and SEC filings)
Key features:
Can return multiple risk values per account
Provides detailed breakdown of why each risk exists
Pulls from conversation transcripts, emails, and public filings
Helps you get ahead of tool consolidation or cost reduction initiatives
Best Practices for Building Signals
Video: 33:32–36:25
When building your own signals, keep these principles in mind:
Less is more—don't overload the AI with unnecessary information
Start with standard signals (predicted stage, deal risk, account health) before building advanced ones
Partner with enablement teams to surface relevant training content within signals
Use signal data to identify broader organizational needs (e.g., if pricing concerns appear across many deals, invest in pricing objection training)
Additional Example Signal: Segmentation for High-Volume Books
Video: 36:26–37:33
For teams managing high-volume books of business, signals can help with prioritization.
Example use case:
Segment accounts into Protect (invest to retain), Grow (opportunity for expansion), and Maintain (steady state)
Use segmentation to decide which accounts get regular cadence calls versus lighter touch
Helps velocity and digital teams focus time where it matters most
Resources and Next Steps
Video: 37:34–43:43
To continue learning and implementing:
Talk to your CSM about building or tweaking your own SalesAI signals
Visit the Help Center digital agent at help.backstory.ai for quick questions
If you don't have SalesAI or Unified Backstory Application yet, reach out to your AE for a demo
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