Role-aware Coach Chat, SPICED Discovery Scorecard, dashboard radar charts, adaptive AI scenarios, and performance trends
This update introduces role-aware Coach Chat and the SPICED Discovery Scorecard. Coach Chat now adapts to employees, managers, owners, admins, and superadmins. SPICED automatically scores every practice session against five discovery dimensions, your dashboard now includes radar charts and trend lines for SPICED performance, and AI-generated practice scenarios can adapt to target your weakest areas.
The Coach button in the top navigation now opens a role-aware Coach panel. The same panel is used across Oliver AI, but the title, intro message, quick actions, prompt, and data context change based on your verified role.
| Role | Coach mode | What changed |
|---|---|---|
| Employee | My Coach | Personal practice guidance, latest-call review, objection handling, and next skill focus |
| Store manager | Team Coach | Team coaching priorities, rep call review, 1:1 notes, and practice recommendations |
| Owner | Org Coach | Location comparisons, adoption risks, ROI signals, and weekly coaching priorities |
| Org admin | Admin Coach | Setup quality, permission gaps, product readiness, usage, and content gaps |
| Superadmin | Platform Coach | Customer health, trial risks, usage anomalies, and platform setup gaps |
When you open Coach Chat from a call result, the language now changes by subject. Employees see self-review wording, managers see rep-call coaching wording, and owner/admin contexts emphasize team or organization patterns.
Coach Chat uses summarized data only in this release. It can advise, draft notes, and recommend next actions, but it does not directly assign training, update goals, change permissions, or edit settings.
For details, see Coach Chat and Using the Coach Panel.
Every practice session is now automatically evaluated against the SPICED sales discovery framework. After completing a call, a new SPICED tab appears on the call results page alongside your existing Feedback, Analytics, and Objections tabs.
What SPICED measures:
The framework evaluates five dimensions of discovery conversation quality:
| Dimension | Letter | What It Measures |
|---|---|---|
| Situation | S | Did you understand the prospect's current setup, tools, team size, or business context? |
| Pain | P | Did you identify specific pain points and let the prospect articulate them in their own words? |
| Impact | I | Did you quantify the cost of the problem and connect it to a business outcome? |
| Critical Event | C | Did you uncover why the prospect is looking at this now -- a deadline, trigger, or compelling event? |
| Decision | D | Did you ask about the decision-making process, identify stakeholders, and understand the timeline? |
Each dimension is scored 0--100 based on pass/fail evaluation of specific discovery questions. The overall SPICED score is the average across all five dimensions.
How to use it:
The SPICED scorecard also appears on your Practice History detail pages for completed sessions.
For a complete guide to interpreting SPICED scores, see SPICED Discovery Scorecard.
Your personal dashboard now includes a SPICED Discovery Skills section that visualizes your performance across all practice sessions.
Radar chart:
A five-point radar chart displays your average score for each SPICED dimension. This gives you an immediate visual picture of your discovery skill profile -- where you are strong and where you have gaps. Each dimension also shows a trend arrow (up, down, or flat) based on whether your recent scores are improving compared to earlier sessions.
Trend chart:
A weekly trend line shows how your overall SPICED score has changed over time. Use the period selector (7 days, 30 days, 90 days, or all time) to zoom in on recent progress or see the big picture.
The SPICED dashboard section only appears once you have completed practice sessions with SPICED evaluation data. If you do not see it yet, complete a few sessions and the charts will appear automatically.
When managers generate AI buyer characters for practice scenarios, the system can now automatically target a rep's weakest SPICED dimension.
How it works:
After a rep has completed five or more SPICED-evaluated sessions, the system identifies their lowest-scoring dimension. When a manager generates a new AI character for that rep's scenario, the character is designed to specifically test the rep's ability in their weakest area. For example, if a rep consistently scores low on Critical Event, the generated buyer will have a complex timeline situation that requires strong discovery to uncover.
This creates a targeted practice loop: identify weakness, practice against it, improve, repeat.
For managers:
When generating an agent for a scenario, you can now optionally specify a target rep. The system will check whether that rep has enough SPICED data and, if so, inject a training focus into the AI character generation. No additional setup is required -- the adaptation happens automatically based on the rep's performance data.
If you have questions about the SPICED scorecard or suggestions for future improvements, let your organization administrator know.