In today’s competitive logistics and e-commerce landscape, every customer interaction matters. A phone call with a client isn’t just a conversation — it’s a goldmine of information about customer intent, satisfaction, and the likelihood of an order being fulfilled.
At Boxleo Courier Company, we recently built an automated pipeline that takes Africa’s Talking call recordings, transcribes them into text, analyzes customer sentiment and intent, and even predicts the probability of order fulfillment — all without human intervention.
Here’s how we did it.
_The Problem
_We wanted to answer three key questions after every customer service call:
What exactly was said? (A reliable transcript for reference)
How did the customer feel? (Sentiment and tone)
What’s the likelihood that this order will actually be fulfilled? (Predictive scoring)
Doing this manually would be time-consuming, error-prone, and expensive. So, automation was the way to go.
_The Workflow
_Our system runs as a queued job in Laravel and follows this pipeline:
Capture the Call Recording
Africa’s Talking Voice API lets us fetch a call recording URL once the call ends. We set up a webhook that receives this URL along with call metadata.Transcribe the Audio
We pass the audio file to a Speech-to-Text engine — in our case, OpenAI Whisper API. This gives us a clean transcript of the conversation, ready for analysis. (Google Speech-to-Text or AWS Transcribe would also work here.)Analyze the Transcript
We run two layers of analysis:
Heuristic rules — Keyword matching for sentiment (positive, neutral, negative), common intents like order confirmation, cancellation, or rescheduling.
Optional GPT-powered analysis — For richer insights and context-aware scoring.
- Predict Fulfillment Probability Using detected intent and sentiment, we compute a 0–100% probability score:
Confirmed delivery? High score.
Uncertain or “call me later”? Medium.
Cancellation? Low.
We also look for keywords like “paid,” “payment,” or “sent money” to boost confidence.
Rate Customer Service Quality
By scanning for politeness markers (“thank you,” “appreciate”) and negative experiences (“rude,” “waited too long”), we assign a 1–5 star CS rating.Store & Report
All data — transcript, sentiment, score, and CS rating — is stored in our CRM, where team leads can monitor performance, flag issues, and make training decisions.
_The Tech Stack
_Laravel 10 — Orchestrates the entire workflow
Africa’s Talking Voice API — Captures call recordings
OpenAI Whisper API — Transcribes audio to text
Custom NLP logic + GPT — Sentiment and intent analysis
Redis Queue + Horizon — Handles background processing
MySQL — Stores transcripts and analysis results
_Why This Matters
_This automation isn’t just about saving time — it’s about unlocking actionable insights:
Agents can be coached based on real interactions.
High-risk orders can be flagged for follow-up.
Managers get instant dashboards of service quality.
We can quantify how call handling affects business outcomes.
Results & Next Steps
Since implementing the system:
Average follow-up time on high-risk orders dropped by 45%.
CSAT scores improved because supervisors had real examples to train from.
We started exploring speaker diarization (separating agent vs. customer speech) for more accurate scoring.
Next, we plan to integrate real-time call monitoring so supervisors can intervene during a live call if risk is detected.
Conclusion
By combining call recording, transcription, sentiment analysis, and predictive scoring, we’ve turned ordinary phone calls into a data-driven decision engine.
This isn’t limited to logistics — the same approach works for sales, support, and any business that relies on phone conversations to drive revenue.
In the age of AI, your calls are more than conversations — they’re opportunities.
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