Data analysis

Missed Call Audit

Every phone system counts missed calls. Nobody counts the people behind them. For a front office, the number that matters is how many prospective new customers called and never got through.

I built a Python audit that reads the call export plus the customer list, collapses repeat call attempts, and removes existing customers and likely vendors to report the number left.

On a sample business: 1,448 calls over 13 weeks, 150 lost new-customer inquiries, 487 voicemails that got nothing back, annualizing to 600 lost prospects and $132,600 in recoverable first-year production.

Every number and chart on this page comes from synthetic data for a fictional business and a fictional business. No real business data was used.

Answer rate by hour and weekday. Green is answered, red is missed, black is no calls. Evenings and weekends go to voicemail.
Answer rate by hour and weekday. Green is answered, red is missed, black is no calls. Evenings and weekends go to voicemail.
Where 1,448 inbound calls went. About a third reached voicemail and left no message.
Where 1,448 inbound calls went. About a third reached voicemail and left no message.
Call volume by hour against the office's published hours.
Call volume by hour against the office’s published hours.
How long a caller who left a message waited for a callback.
How long a caller who left a message waited for a callback.
Second audit, for a business already full: revenue lost to late cancellations and no-shows by weekday.
Second audit, for a business already full: revenue lost to late cancellations and no-shows by weekday.
Clients overdue for their next visit, ranked by past spend, built as a reactivation list.
Clients overdue for their next visit, ranked by past spend, built as a reactivation list.

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