Practice Overview

1,284 calls scored on 4 behavioral signals — every number traces back to call evidence

Last 90 days
All verticals
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Calls Analyzed
1,284
▲ 12% vs prior
Avg Satisfaction
81/100
▲ 3.1 pts
On-time Starts
79%
21% began late (avg 5.4 min)
At-Risk Accounts
3
low across all consultants

① Consultant Ranking

Composite score + the behavioral signal that most drags or lifts each consultant
Click a row → full "why" →

② Customer Ranking & Functional Benchmarking

Client health + how each vertical performs vs the general consultancy baseline
General vs Specialized

Customer Health (click for why)

Functional Benchmarking — Specialized vs General (76)

Specialized General baseline

Consultant Ranking

Click any consultant to see the signal-by-signal breakdown and the calls behind it

Customer Benchmarking

A client low with every consultant is a client-side factor — the evidence makes it explicit

Linking Logic — Consultant × Customer Matrix

Each cell = mean call score for that pair. Rows/columns isolate whose issue it is.

Cross-Reference Heatmap

Hover a cell for the read-out
0–55 56–70 71–84 85–100

How the linking works

Row anomaly → consultant green across the row but one red cell = that specific pairing (style/sector mismatch).
Column anomaly → a client red down the whole column = client-side factor; consultants are not penalised.
Difficulty weighting → headline scores are normalized by client difficulty, so hard accounts are rewarded.

Flagged pairings

Signal Glossary — how scores are judged

Every headline number is built from these measurable, transcript-derived signals

The 4 engagement signals

Engagement = weighted blend of these — each is auditable down to the timestamp
⏱ Punctuality

Did the call start on time? Measures scheduled-vs-actual start and no-shows / reschedules.

src: call start timestamp
🎯 On-topic focus

Did the conversation stay on the agenda, or wander into unrelated tangents? % of speaking time on-agenda.

src: topic segmentation
🔄 Reciprocity

When the consultant asked a question, did the client engage and answer — or deflect / go silent?

src: Q→A pairing
💡 Responsiveness

When the client asked a question, could the consultant answer it — or did they defer / not know?

src: Q→A pairing

What else feeds the score

🙂 Tone & sentiment

Emotional arc across the call — frustration, enthusiasm, confusion — by speaker.

✅ Value-add

Did the call produce a decision, a target metric, or a committed next step?

📈 Implementation

Did the client act on advice before the next call? Tracked across the relationship.

⚖ Difficulty

How hard is this client? Used to fairly adjust the consultant's rank.

Common Questions Asked

Clustered across all transcripts — and how often the consultant could actually answer

Frameworks Built From the Data

Patterns the engine surfaced, turned into repeatable playbooks

◆ MOCKUP V2 — illustrative data. Live data loads after approval.