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From Spreadsheets to AI: How Moxie Pest Control Boosted Conversions 5% with Data and Call Coaching
Think pest control isn’t a digital business? Think again.
Raj Mehta, Vice President of Product and Technology at Moxie Pest Control, outlines how he turned a spreadsheet-run operation into a data-driven engine across 9,000+ daily calls.
Raj shares how consolidating fragmented systems into a data lake unlocked automation—from shrinking lead routing from 20–25 minutes to under 30 seconds—to deploying AI-powered call intelligence that scores every sales and retention conversation against a playbook. He breaks down a practical roadmap for traditional businesses: build MVPs, pilot in one branch with a trained feedback team, iterate fast, then scale.
You’ll hear how he positioned AI as a growth amplifier (not a job cutter), the difference between deterministic automation and LLM use cases, and the measurable impact: a 5% lift in conversion that compounds in a recurring-revenue model. Plus, Raj’s concise advice for leaders bringing AI into operations without breaking trust or momentum.
Timestamps
[00:45] – Guest intro: Raj Mehta, Moxie’s tech transformation, and 9,000+ daily calls
[01:20] – Starting point: 90% of ops in spreadsheets; why a data lake became the foundation
[02:45] – Automating time-to-lead: 25 minutes to <30 seconds and a 5% conversion lift
[04:27] – Roadmap design: MVPs, single-branch pilots, and scaling what works
[06:05] – Culture building: framing AI as growth and upskilling, not headcount cuts
[07:34] – Two lanes of automation: deterministic scripts vs. LLM-driven workflows
[08:45] – Call intelligence: scoring every sales/retention call and coaching at scale
[14:05] – Impact and advice: recurring revenue compounding and Raj’s playbook for getting started
Takeaways
- Build a single source of truth (data lake) to power automation and AI reliably.
- Cut time-to-lead with workflow automation and track the downstream impact on conversion.
- Pilot in one branch with a trained “feedback team,” iterate, then roll out—don’t scale too soon.
- Position AI as a growth multiplier; retain and upskill top performers to shape the culture.
- Separate deterministic automation from LLM use cases; do deep discovery with frontline teams.
- Use AI call intelligence to score every call against your playbook, surface coaching themes, and save manager time.
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By GrowthbookThink pest control isn’t a digital business? Think again.
Raj Mehta, Vice President of Product and Technology at Moxie Pest Control, outlines how he turned a spreadsheet-run operation into a data-driven engine across 9,000+ daily calls.
Raj shares how consolidating fragmented systems into a data lake unlocked automation—from shrinking lead routing from 20–25 minutes to under 30 seconds—to deploying AI-powered call intelligence that scores every sales and retention conversation against a playbook. He breaks down a practical roadmap for traditional businesses: build MVPs, pilot in one branch with a trained feedback team, iterate fast, then scale.
You’ll hear how he positioned AI as a growth amplifier (not a job cutter), the difference between deterministic automation and LLM use cases, and the measurable impact: a 5% lift in conversion that compounds in a recurring-revenue model. Plus, Raj’s concise advice for leaders bringing AI into operations without breaking trust or momentum.
Timestamps
[00:45] – Guest intro: Raj Mehta, Moxie’s tech transformation, and 9,000+ daily calls
[01:20] – Starting point: 90% of ops in spreadsheets; why a data lake became the foundation
[02:45] – Automating time-to-lead: 25 minutes to <30 seconds and a 5% conversion lift
[04:27] – Roadmap design: MVPs, single-branch pilots, and scaling what works
[06:05] – Culture building: framing AI as growth and upskilling, not headcount cuts
[07:34] – Two lanes of automation: deterministic scripts vs. LLM-driven workflows
[08:45] – Call intelligence: scoring every sales/retention call and coaching at scale
[14:05] – Impact and advice: recurring revenue compounding and Raj’s playbook for getting started
Takeaways
- Build a single source of truth (data lake) to power automation and AI reliably.
- Cut time-to-lead with workflow automation and track the downstream impact on conversion.
- Pilot in one branch with a trained “feedback team,” iterate, then roll out—don’t scale too soon.
- Position AI as a growth multiplier; retain and upskill top performers to shape the culture.
- Separate deterministic automation from LLM use cases; do deep discovery with frontline teams.
- Use AI call intelligence to score every call against your playbook, surface coaching themes, and save manager time.