Sales Calls Analytics Dashboard

Context

An AI call flow is a funnel: greeting → offer → meeting → qualification. The production export stores the calls but does not say which step the bot loses the client on. The goal is to turn the raw export into an answer in a minute: where the drop-off is, for what reasons, when to call, and what to fix.

Data & Method

What the dashboard shows

Labeling methodology

The bot script is templated, so the step a conversation reached is determined deterministically from markers in the bot’s replies:

• 1 — greeting + consent;
• 2 — offer;
• 3 — meeting;
• 4 — qualification.

The step-1 drop reason and the client reaction type are text heuristics. No LLM is used in the pipeline (everything is reproducible), but if an llm_labels.csv with phone, dt_raw, <labels> columns is placed alongside, the labels are merged automatically — reaction labeling can be strengthened via an LLM.

Data

The data/calls_demo.csv dataset is fully synthetic: generated by make_demo_data.py, deterministic on a fixed SEED, and contains no real phone numbers, names, or recordings. The numbers reproduce the shape of a typical production export (funnel, defects, hourly distribution) so the dashboard tells a realistic story on safe data.

Run

python3 -m venv .venv
.venv/bin/pip install -r requirements.txt
.venv/bin/streamlit run app.py

Impact

• A minute to an answer — which step loses the client and what to fix.
• Deterministic labeling — reproducible, no LLM in the critical path.
• A growth point — weighted contact loss and a first A/B scenario.
• Safe demo — synthetic data, free public deployment.

Documentation

• GitHub → sales-calls-dashboard