The Experimentation Edge

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.

Connect with the Guest
Raj Mehta LinkedIn: https://linkedin.com/in/raj-milan-mehta
About the guest: https://www.growthbook.io/podcast/guests/raj-mehta
Episode page: https://www.growthbook.io/podcast/episode/1-2
Company Website: https://www.moxieservices.com

If 80% of A/B tests fail, how do you de-risk decisions that touch pricing, product, and brand? Aleksandra (Aleks) Bass, Chief Product & Technology Officer at Typeform, shares how her team “earns the right to A/B test” with medical-grade rigor—moving from literature reviews and user tests to simulated trials before exposing changes to customers. She details Typeform’s repositioning from “forms” to an AI engagement platform—and the pricing and packaging bet behind it: a 15% drop in new business count offset by a 32% increase in ASP and a 25% lift in annual attach.

Aleks unpacks how Typeform AI acts as a co-pilot that doubled activation and boosted one-day conversion, plus the design shifts (CTA altitude and onboarding) that increased adoption. She also reveals why they moved video features down-tier and what a head-to-head test showed: video interviewers generated 14x more words and 10x fewer skipped questions with comparable completion time.

Finally, Aleks breaks down the cultural side—eliminating “anti-knowledge,” standardizing experiment design, and creating a cross-functional review that prevents false learnings—along with how her data engineering team evaluates LLMs for quality, latency, and trust.

Timestamps
[00:45] – Rethinking experimentation: “earn the right to A/B test” with staged rigor
[03:31] – Pricing and packaging shift: from forms to flows, ASP up 32%, annual attach up 25%
[05:34] – Typeform AI as a co-pilot: doubling activation and lifting one-day conversion
[07:27] – Adoption lessons: elevating AI CTAs and reducing friction to use
[10:02] – Behind the scenes: model selection, quality bars, and why MVP can backfire in AI
[12:40] – Moving video down-tier: demand signals, cannibalization checks, and net gains
[14:52] – Video vs. standard forms: 14x more words, 10x fewer skips, similar completion time
[20:22] – Building an experimentation culture: process resistance, “anti-knowledge,” and cross-functional review
[30:16] – Leader playbook: visibility, empathy, and incentives for rigorous testing

Takeaways
- Implement a staged experimentation funnel—discovery, simulation, then customer A/B—to reduce risk.
- Use pricing experiments to trade volume for revenue quality; pair higher monthly prices with stronger annual discounts to grow annual attach.
- Treat AI as an activation lever: elevate AI-first CTAs and streamline onboarding to boost adoption.
- Add video interviewer options to increase response richness (14x more words) while keeping completion rates steady.
- Enforce experiment hygiene: change one variable at a time, randomize at the right unit (account vs. user), and run long enough for effect size.
- Purge “anti-knowledge” by standardizing design, instituting cross-functional reviews, and only codifying learnings supported by repeatable data.

Connect with the Guest
Aleks Bass LinkedIn: https://linkedin.com/in/aleksbass
About the guest: https://www.growthbook.io/podcast/guests/aleks-bass
Episode page: https://www.growthbook.io/podcast/episode/1-1
Company Website: https://www.typeform.com