Interview with Ihor Lavrenenko, SEO Manager, Pesty Marketing

This interview is with Ihor Lavrenenko, SEO Manager, Pesty Marketing.

For readers on Featured, how do you introduce yourself today and the types of companies you help grow through SEO?

I’m an SEO Manager at Pesty Marketing. I help businesses turn Google visibility into calls, form fills, and booked jobs. My focus is on practical SEO that ties directly to revenue: local visibility, conversion-focused pages, and search demand you can actually win.

Most of our work is with pest and wildlife control companies, but the same playbook fits local service businesses where customers search with urgency, such as home services, dental, legal, and more.

What pivotal decision or moment most shaped your journey into leading SEO-driven growth work?

The pivot for me was walking away from “comfortable SEO,” where you’re judged on rankings, to taking roles where I had to own the whole funnel. I started in Ukraine running a web design agency, then moved deeper into SEO through agency and freelance work. But the real shift happened when I chose tougher environments where results were obvious and unforgiving (tight budgets, imperfect products, high-pressure lead goals). That’s when I stopped thinking like an “SEO specialist” and started thinking like a growth operator who just happens to use SEO as the main channel.

That decision directly shapes how I work with clients now. I start with tracking, intent, and the SERP layout. If the money keywords are local and the Map Pack dominates, we put early effort into Google Business Profile and local signals because that’s where a huge share of clicks happens (research often cites ~42% for local queries). Then we build pages and internal links around high-intent services and measure success in calls, forms, and client business growth.

When you onboard a new account, how do you translate market analysis into a 90-day SEO and growth plan that a CFO can buy into?

We start with your unit economics (average job value, close rate, gross margin, capacity). Then, we size demand: the real keyword set in your service areas, the SERP layout (Map Pack vs. organic vs. ads), and who already owns the clicks. If the SERP is Map Pack-heavy, that’s where the first wins lie. Research often shows that most local searchers click on Map Pack results, and a huge portion of searches have local intent.

For a CFO, the 90-day plan is a “milestones + leading indicators” approach, because full SEO payback usually appears in the 6–12 month window, not week 3. So, I present:

1. Baseline (current leads, cost per lead, close rate),
2. Targets (qualified leads + revenue), and
3. Proof checkpoints every 30 days that show we’re building future pipeline.

I also highlight risks: informational clicks are getting squeezed by AI features (click-through rate drops have been measured), so we focus the first 90 days on commercial and local intent that still drives calls.

Can you walk us through a recent engagement where your SEO strategy produced clear revenue gains and the few levers that made the difference?

On Absolute Pest Control, the starting point was blunt: they were buried on pages 2–4 for the exact “money” searches that drive bookings in Texas (pest + city). The move that changed everything was building a programmatic SEO website that covered every pest they treat and every location they want to win, with a clean data model behind it. We didn’t publish a pile of near-duplicate pages; we built a scalable structure where each page answered a specific local intent: the pest problem, what it looks like in that area, what treatment involves, and how to book fast. Revenue impact was tracked from day one with call/form attribution by landing page, so we could connect rankings → visits → leads → booked jobs inside their workflow.

The levers that made the difference were pretty simple but executed hard:

• Information architecture and internal linking so Google could crawl and understand the full “pests × locations” map without wasting crawl budget,
• Template-level on-page SEO (titles, headers, FAQs, schema, and conversion blocks) that stayed consistent while still letting pages be locally relevant,
• Indexation control (sitemaps, canonicals, noindex rules where needed, and fixing anything thin before it scaled), and
• Authority and local trust signals to back the footprint (supporting content, citations where appropriate, and city-level credibility).

The result is what you’d expect when you combine coverage, structure, and trust: Absolute went from invisible to consistently #1–3 across Texas for their target keywords, and the lead flow was obvious in tracking. More qualified calls and booked jobs came from organic instead of “Where did this lead come from?” guessing.

As AI answer engines reshape discovery, what specific workflow have you adopted to test, measure, and iterate content for SGE, Perplexity, and similar surfaces?

Google’s own documents make it clear you don’t get a separate AI Overviews report in Search Console. Those impressions and clicks sit inside the regular Web report, so we build our own tracking layer per query. Then, I use the Ahrefs finding (AI Overviews correlating with a ~58% lower CTR for the top result) to justify a shift in measurement: we track citation rate (are we named/linked), lead rate (calls and forms per landing page), and assisted impact (branded search lift and return visits).

Workflow: we pull 50–200 money queries from GSC and sales calls, tag them by intent, and run a weekly sweep in Google (AI Overview present or not, who gets cited, which page type is being pulled). In parallel, we run the same prompt set in Perplexity, because it answers with cited sources from the web, and we log whether we’re cited and what page it chose. From there, updates are mechanical: add a tight 2–3 sentence answer block high on the page, build “quotable” sections (definitions, steps, comparison tables), tighten topical focus, and add structured data that matches visible content. We re-test, then keep what lifts citations and booked leads.

You’ve seen specialization lift close rates—how do you evaluate and select a niche or ICP that supports both win rate and lifetime value?

Step one is economics: LTV = average job/contract value × gross margin × expected lifespan. Then I sanity-check payback (how fast the client gets their acquisition cost back). I heavily weight retention mechanics because even a small retention lift can swing profitability. Bain/HBR cite that a 5% retention increase can raise profits by 25%–95%. Therefore, niches with recurring services, memberships, compliance-driven repeat work, or seasonal return demand score higher than one-off, low-repeat work.

Step two is market and execution fit: I validate search intent (urgent “near me” keywords versus research), SERP difficulty (who owns the top spots and why), and whether we can build a repeatable playbook (same services, same objections, same content structure). Then I run a 90-day ICP sprint: 10–20 sales calls for voice-of-customer, a small set of niche landing pages/content, and hard tracking to compare close rates, lead quality, CAC payback, and retention between niches.

I’ll greenlight the niche when we see (1) a higher close rate because the offer/message is instantly relevant, and (2) a higher LTV because retention and upsells are structurally baked in.

Reporting often decides retention—what does your “evidence a user can feel” reporting look like in practice?

My reporting is built around proof the owner can feel. Every month starts with a 60-second snapshot: calls, forms, booked jobs, revenue, plus cost per lead and payback versus last month. Then we show where it came from: top landing pages that produced leads, top keywords and locations driving those pages, and what changed (new pages shipped, GBP work, fixes). If we can’t tie work to leads, it doesn’t make the report.

The “feel it” part is visibility you notice in real life: Map Pack movement for priority cities, GBP actions (calls, direction requests), and a short “what you’ll see this month” section (new reviews coming in, more calls on specific services, and more leads from specific cities). We end with a tight next-30-day plan: 3–5 actions, expected impact, and the metric we’ll use to judge it.

If a client gives you $10k to find traction fast, where do you spend it to maximize learning and revenue signal from SEO?

If I get $10,000 to find traction fast, I will spend it where we can (1) prove lead flow and (2) learn what to scale in 30–90 days.

My $10,000 split (and why):

• $1,000 for tracking + attribution (non-negotiable): this includes call tracking, form tracking, GA4/GSC cleanup, GBP UTMs, and a simple dashboard by channel/landing page. If we can’t tie SEO work to calls, we’re guessing.
• $1,000 for Google Business Profile + local trust: GBP rebuild (categories, services, service areas, photos, Q&A), review system + response workflow, and NAP/citation cleanup on the top directories. This is the fastest path to having the “phone ring” for local intent.
• $3,000 for money pages + conversion fixes: rebuild the top 3–5 service pages and the top 2–3 priority cities (the pages that should generate revenue). This will involve a tight intent match, a strong above-the-fold offer, proof blocks, FAQs, and schema where it fits.
• $1,500 for technical blockers: address indexing/crawl fixes, sitemap/canonical issues, internal linking, and speed/Core Web Vitals basics. These are quick wins that stop leaks.
• $3,500 for authority push: focus on local links that move the needle fast (chamber/associations, local sponsorships, partner links, niche directories, local PR). This is essential for building enough trust to support the pages we just built.

What I’m watching for as “revenue signals” in the first 30 days include: GBP calls/actions trending up, more calls/forms from the rebuilt money pages, and early ranking movement on the exact high-intent terms tied to those pages. Once we see which service + city combinations convert, we will scale that pattern.

Looking 12 months out, what bet are you making about SEO’s role in growth that you’re willing to be measured against?

Over the next 12 months, SEO will continue losing “easy” informational clicks, but it will become more valuable as a pipeline channel if you focus on high-intent queries and the surfaces that still drive action (Maps, service pages, and AI citations). Pew’s analysis shows search visits with an AI summary lead to 8% clicking a traditional result versus 15% when no AI summary appears, and Ahrefs’ latest update ties AI Overviews to about a 58% CTR drop for the top result. That means “rankings-only SEO” will look worse on dashboards, even while the business can grow.

What I’m willing to be measured against: we grow qualified leads and booked jobs from organic traffic plus Maps, even if raw clicks fall. I track three scoreboards monthly:

1. Revenue events (calls/forms → booked jobs) by landing page,
2. Share of voice on the top 20–50 money keywords in each priority city (Maps plus organic), and
3. AI visibility (are we cited/linked in AI Overviews and Perplexity for those same queries).

Google confirms AI Overviews are logged inside Search Console’s Performance data, and Perplexity explicitly answers with web sources. So we treat “being a cited source” as a measurable distribution win.

Thanks for sharing your knowledge and expertise. Is there anything else you'd like to add?

I’d add a simple filter that keeps SEO honest: if it doesn’t change how the business feels, it’s not the right work. “Feels” means more booked jobs, a better job mix (higher-margin services), and steadier demand in the cities you care about.