Recovering Unreachable Leads by Tracing the Optimal Outreach Window
A major distance learning institution had thousands of leads marked unreachable after failed contact attempts. Sherlock analyzed outreach patterns to identify the contact windows that actually converted.
1.4K
Recovery calls/week
37s
Avg connected call
2%
Weekly lead recovery
TL;DR — What Sherlock found
- 1
A major distance learning institution had a large pool of leads marked unreachable after failed contact attempts — representing significant lost enrollment revenue with no obvious fix.
- 2
Sherlock analyzed failed outreach call patterns — time of day, attempt number, callback behavior — to identify which contact windows actually converted.
- 3
With Sherlock's timing analysis, the institution systematically re-engaged leads in high-conversion windows, recovering 2% of the previously unreachable pool every week.
The Problem
A large pool of leads was marked unreachable after failed contact attempts, representing significant lost enrollment revenue. The team had no data on why attempts were failing.
The Process
Sherlock analyzed failed outreach call patterns by time of day, day of week, number of attempts, and time since initial interest — identifying which outreach windows had the highest contact rates.
The Solution
The institution restructured its outreach sequences to prioritize high-conversion windows for previously unreachable leads, recovering 2% of the pool each week.
Results
- ✓1,400+ recovery calls connected per week
- ✓2% of unreachable leads recovered weekly
- ✓Optimal outreach windows identified per segment
Use Cases
Questions the team asked Sherlock
- SC“What time of day has the highest contact rate for unreachable leads?”
- SC“How many outreach attempts before a lead is truly unreachable?”
- SC“Show me leads that were contacted 3+ times without success”
- SC“Which days of the week have the best callback conversion rate?”
- SC“Find leads that were marked unreachable but answered on the second attempt”
Deep Dive
The full story
For a major distance learning institution, unreachable leads represent a real business problem: prospective students who expressed interest, were contacted once or twice without success, and then were quietly removed from active outreach sequences. The assumption was that these leads weren't interested — but there was no data to support or challenge that assumption.
Sherlock analyzed the full set of failed outreach call records, segmenting by time of day, day of week, number of prior attempts, and time since initial interest expression. The analysis revealed a clear pattern: leads contacted between 6 PM and 8 PM local time on weekdays had contact rates nearly three times higher than leads contacted during business hours. More importantly, leads marked 'unreachable' after two morning attempts had a significant probability of answering on a third attempt in the evening window.
The institution restructured its outreach sequences to prioritize evening contact windows for previously unreachable leads, and reduced the threshold for reattempt in the new window. Within the first month, 2% of the previously unreachable pool connected and engaged each week — representing roughly 1,400 additional conversations per week and significant recovered enrollment pipeline.
Timing intelligence is one of the most underutilized dimensions of voice AI outreach optimization. The data to identify optimal contact windows exists in every outreach program's call history — Sherlock makes that data queryable without requiring data engineering work or custom analytics pipelines.
FAQ
Frequently asked questions
How does Sherlock identify optimal outreach windows for unreachable leads?
- Sherlock analyzes all outbound call records for a lead set, segmenting by time of day, day of week, number of prior attempts, and time since lead creation. It calculates contact rates for each segment and returns a ranked breakdown — showing, for example, that leads contacted between 6 PM and 8 PM on weekdays connect at three times the rate of leads contacted during business hours. You can ask this question for any segment of your lead database from Slack.
What does 'unreachable lead analysis' mean in practice?
- Unreachable lead analysis means querying the call history of leads that have been attempted multiple times without success, looking for patterns that distinguish leads that eventually connect from those that don't. The goal is to find contact windows, attempt sequences, or lead characteristics that predict a successful connection — so that 'unreachable' leads can be reattempted with a higher probability of success rather than abandoned.
How quickly can Sherlock surface contact rate data for a lead segment?
- Contact rate analysis queries return in seconds for most datasets. You ask 'what time of day has the highest contact rate for leads in our 30-day unreachable pool' from Slack, and Sherlock returns a ranked breakdown in under 10 seconds. More complex analyses — like segmenting by lead source or geography in addition to time — add a few seconds but remain conversational.
Does Sherlock work with any telephony provider for outreach analysis?
- Yes. Sherlock connects to Twilio, VAPI, and other telephony providers for outreach analysis. As long as your outbound call records include call timestamps, lead identifiers, and disposition codes, Sherlock can analyze contact rates and timing patterns. Check the integrations page for the current list of supported providers.
How do I measure lead recovery rate over time using Sherlock?
- You can ask Sherlock 'how many previously unreachable leads connected this week' from Slack and get a rolling count. For tracking over time, Sherlock can compare week-over-week or month-over-month recovery rates for any lead segment, making it straightforward to measure the impact of outreach timing changes on recovery performance.
What is the ROI of recovering unreachable leads with timing optimization?
- ROI depends on your enrollment conversion rate and average program value. For institutions with high-value programs, recovering 2% of a large unreachable pool weekly can represent substantial additional revenue — even at a modest conversion rate from initial contact to enrollment. Sherlock surfaces the contact rate data; the revenue impact calculation uses your program's conversion economics.
Ready to stop guessing?
Let Sherlock investigate your voice calls. Find failures, cut costs, and get answers — all from Slack.