The AI SEO expert in Lagos, Nigeria, who counts revenue, not rankings.

The situation

I'll never forget the first time I logged into this retailer's analytics. It was a major online seller of wheels and tyres in the United States, with several warehouse locations and thousands of product pages, most of them filed in the wrong part of the site.

The demand was obviously there, because people search for wheels and tyres by size, finish, vehicle and budget every single day. The business simply wasn't in position to catch it. The goal the owners and I agreed on was plain: turn a sprawling catalogue into a store that brings in buyers from coast to coast, and then keep widening the stream of buyers it can reach.

The challenge

Three problems were quietly costing sales, and each one made the others worse.

• A structure Google couldn't read came first, because the blog held product listings while product pages held random articles and the site search sent shoppers round in circles. Search engines couldn't tell which page should answer which search, so they ranked none of them well, and AI answers built on those rankings inherited the same confusion.
• Almost nobody vouched for the brand either, because its links came from a scattering of small automotive blogs that told Google almost nothing about authority in a market full of large, well-linked competitors.
• Technical drag made both problems worse, since broken links, dated metadata and slow pages tested the patience of shoppers and crawlers alike and gave even the visitors who did arrive a reason to leave.

The strategy

I ran the work in two phases, because authority spent on a broken site is authority wasted. Phase one fixed the store so that it could convert search; phase two made sure far more search reached it.

• Phase one, in 2024, rebuilt the catalogue so a shopper could move from truck tyres to SUV tyres without getting lost. I cut load times by compressing images, enabling browser caching and moving to better hosting, then added structured data for specifications, reviews and pricing so listings stood out in results.
• Phase one also matched pages to buyers rather than stuffing product pages with random terms. I mapped each high-intent phrase ("all-season truck tyres" is a good example) to the one page most likely to win it, and rewrote jargon-heavy descriptions as clear, benefit-led copy for drivers.
• To earn trust locally, I gave every warehouse an optimised Google Business Profile and asked satisfied customers for honest reviews, which built trust and local visibility at the same time.
• Phase two, from 2025 into 2026, aimed a sustained link campaign at 25 commercial pages: wheel size categories, style and finish ranges, vehicle-fit pages, the wheel and tyre package page, the financing page and the home page. Links went in on staggered windows, page by page, so I could measure each page's movement against its own starting point.

The results

Phase one paid off first, as organic traffic surged and turned straight into tyre sales: across calendar year 2024, Google Analytics recorded $1.8 million in revenue from about 4,600 purchasers.

I measured phase two in Semrush (US organic, desktop, the top 50 keywords by search volume for each page), comparing each page's position in the month before its first new link against October 2026. Across the 223 keywords that moved, the average position went from 25.5 to 10.2. Keywords in the top three rose from 15 to 85, keywords at #1 from none to 31, and every one of the 39 keywords that had been languishing between positions 51 and 100 climbed out of that band.

The page types tell the more useful story. The wheel and tyre package page went from no keywords in the top 10 to nine, putting it in front of 51,100 searches a month. The home page went from none to seven, in front of 55,400. Style and finish pages tripled their page-one search demand, from 13,840 to 44,230 a month, and even the financing page, which started with an average position of 54.4, now averages 23.3.

The biggest group held up too: eight wheel size category pages, carrying 78 of the tracked keywords, went from 41 keywords in the top 10 to 67, and the searches those rankings sit in front of rose from 34,390 to 62,080 a month. Six vehicle and brand pages moved from 14 top-10 keywords to 23. That breadth matters, because it shows the gains came from the approach rather than from one lucky page.

Why it worked

The order did the heavy lifting, because I pointed links at pages that were already fast, well structured and clearly focused on one job, so every bit of authority landed somewhere that could use it. Pointing the same links at the old, disorganised version of this site would have produced far less, because Google would still have been guessing which page deserved to rank.

The targets were commercial on purpose, since package pages, size categories and financing pages are where people go when they're ready to buy, which is why I track the monthly searches a page-one ranking sits in front of, rather than a vanity count of keywords. That figure grew 3.9 times, from 59,640 to 230,630 searches a month.

I'm careful with that number, because search demand isn't revenue and I don't report it as such. It is, however, the raw material the 2024 rebuild already proved this store can turn into money: $1.8 million of it in a single year. Wider demand reaching a store that converts is how keyword growth becomes revenue growth.

What this means for your business

Nothing about this sequence is American: fix the structure so Google and AI systems can read your site, make every important page work for one buying search, then earn the authority that pushes those pages up. The same order works whether you run an online store in Lagos, distribute across Nigeria or sell abroad, because Google rewards the same signals everywhere.

If your site has plenty of pages but few of them rank, the problem is rarely effort. It's usually that the work happened in the wrong order. In your AI SEO Growth Audit, the first thing I'll find out is which order your site needs.

The situation

The client sold high-value products online, where a single order is worth well over a thousand dollars. For the first half of 2024, Shopify's own chart shows sessions sitting close to zero, so almost nobody was finding the store. The products were good and the prices were right, but search simply wasn't bringing buyers to them.

High-ticket stores live or die on a small number of the right visitors. A store selling twenty-dollar items can survive on volume; a store selling items worth more than a thousand dollars each needs searchers who are already close to a decision, and only a few of them.

The challenge

The obvious move, chasing traffic, was the wrong one. Broad informational content would have pulled in thousands of browsers who were never going to spend this much, and the analytics would have looked busy while the bank balance stayed still.

Buyers of expensive products also research harder than anyone else. They compare models, read reviews, check specifications and, increasingly, ask AI assistants to shortlist options before they ever click through to a store. A page that answers those questions vaguely loses the sale to one that answers them precisely.

There was a measurement trap too, and you may be in it right now. Most SEO reports celebrate sessions and impressions because those numbers rise easily, which is precisely why they mislead on a high-ticket store. So I judged this project on two figures only: orders and revenue.

The strategy

I built every part of the plan around one idea: attract the searches that sit closest to a purchase, then make sure the page that wins them can close the sale.

• The plan was built for this store alone, with on-page optimisation, keyword research and targeting tuned to its products and to the way its buyers actually search, and with searches close to a purchase put ahead of searches that merely bring traffic.
• On the technical side, I made sure of fast pages, a clear site architecture and a smooth mobile experience, so Google had every reason to rank the store and buyers had no reason to leave before they'd seen the product properly.
• For the copy, I wrote product and landing pages that answer the questions serious buyers ask before they spend this much, and then move them towards checkout.
• After launch, I kept analysing, testing and reporting openly, refining what worked and cutting what didn't, because SEO is never set and forget.

The results

Shopify's report for 15 January 2024 to 13 January 2025 shows 11,778 sessions, 63 orders and $113,027.37 in total sales, at a conversion rate of 0.47%.

The shape of the curve matters as much as the totals. Sessions stayed flat until August 2024, then began climbing, held through the autumn and rose again towards the end of the year. That slow start, and the steady climb after it, is what search growth usually looks like, and it is why judging SEO on its first month is a mistake.

Divide the sales by the orders and each one is worth roughly $1,794. Divide them by the sessions and every visit to the store was worth about $9.60. Those two derived figures explain the whole result.

Sixty-three orders over a year is a little over five a month, which would barely register on a store selling cheap goods. Here, those five-odd orders a month added up to more than $113,000, because each one came from a searcher who already knew what they wanted and found a page that answered them properly.

Why it worked

A 0.47% conversion rate sounds modest until you put it next to the order value. When each sale is worth almost $1,800, a small number of searchers with real buying intent beats a large number of browsers every time. Doubling traffic with the wrong visitors would have added cost and noise; adding the right visitors added revenue.

Value per visit is the number most store owners never calculate, and it changes how you judge everything else. At roughly $9.60 a visit, a page that brings in a few hundred well-matched searchers a month is worth more than one that brings in thousands of the wrong ones, which is exactly the trade this strategy made.

It also explains why the work suits AI search. Buyers who research carefully are the ones most likely to ask an assistant for a shortlist first, and Semrush's study of more than 50,000 websites found AI referral traffic to retail sites grew 343% in 2025. It is still a sliver of total visits (0.14% across all sites in that study), but it is growing fastest exactly where considered purchases happen.

What this means for your business

If you sell something with a high ticket, from solar systems and furniture to school places or professional services in Lagos, stop measuring SEO in visitors. Work out what a visit is worth to you, then aim at the searches and AI prompts your best customers use just before they buy.

Here is the arithmetic with made-up numbers, purely to show the method. Say your average sale is ₦500,000 and one visitor in every 200 buys. Each visit to your site is then worth ₦2,500, which means a page that attracts 400 well-matched visitors a month is quietly worth ₦1 million a month to you. Once you know that figure, it becomes obvious which pages deserve the investment and which keywords are a distraction.

You can do the real version in five minutes before we talk. Pull your average order value and your conversion rate from your store's analytics for the last twelve months, multiply them together, and you have what a single visit is worth to you today. Bring that number to the audit call, because it changes which opportunities are worth chasing first.

In your AI SEO Growth Audit I'll do that arithmetic with you, and show you which buying searches you already appear for, which ones a competitor is winning, and what a single well-matched visitor is likely to be worth to your business.

The situation

Five law firms across North America: four personal injury firms in Los Angeles, Des Moines, Naperville and Youngstown, and a business law firm in Montreal. Their names stay private at their request, but the numbers don't: every one comes from the firm's own Google Business Profile data, map rank tracking or Search Console.

Several of them shared the same starting point. The Montreal firm relied entirely on referrals, with no inbound leads and no SEO at all. The Naperville firm was solely reliant on referrals too. The Los Angeles firm didn't appear at a single point on its map grid, which made these good lawyers invisible to the people searching for one.

The challenge

Legal search is brutally competitive because a single signed case can be worth more than a month of advertising, so every firm in a city is fighting for the same handful of positions. Most of that fight happens on the map: the local pack that appears above the regular results whenever someone searches for a lawyer nearby.

Referral-only firms carry a hidden risk as well. Every new client depends on someone else remembering to recommend them, which makes growth impossible to plan. And more of the research now happens inside AI tools: in a Yext survey of 3,848 adults worldwide who searched for a local business, 42.7% said they'd used an AI tool for local search in the past month. The survey is self-reported and Yext calls it directional, but the direction is clear.

The strategy

I worked the same three surfaces at once in every campaign, because a firm that wins one and ignores the others leaves calls on the table.

• On the map, I treated the Google Business Profile as a primary asset rather than an afterthought, because that is where local legal searches convert, and I tracked progress on a 49-point grid across each city instead of from a single office address.
• In organic search, the firm's own website had to back up what the profile promised, since searchers who click through from the map expect real detail before they pick up the phone.
• In AI search, I measured visibility inside Google's AI features in Search Console alongside everything else, so each firm could see whether it was part of the answer as well as part of the list.

The results

• In Los Angeles, a personal injury firm that wasn't ranking anywhere on its grid at onboarding reached an average map rank of 1.1 six months later, with first place at 45 of 49 points. Its calls from the Business Profile rose 800% when I compared the latest three months with the three before, and it logged 104K impressions in Google's AI features over the last three months.
• In Montreal, a business law firm that started on referrals alone was getting 4 leads and 3 signed cases a week after 90 days. By 180 days that had grown to 20 leads and 5 to 6 signed cases every week, with a top-three map position at 21 of 49 points (nine of them at #1) and an average rank of 3.9.
• In Des Moines, a personal injury firm took 592 calls from its Business Profile alone in the first six months, which works out at roughly 23 a week. It now holds a top-three position at 30 of 49 points with an average rank of 3.3, and earned 74.9K impressions in Google's AI features over the last three months.
• In Naperville, a personal injury firm that had lived on referrals took 162 calls straight from Google in its first six months, through a channel that simply hadn't existed for it before. Its map grid settled at an average rank of 3.8, and over the last three months it picked up 135K impressions in Google's AI features.
• In Youngstown, a personal injury firm started with an average map rank of 14.2 and only 10 of 49 points in the top three. Three months later the average was 3.9 and 19 points sat in the top three, which shows how quickly the map can respond once the foundations are right.

Why it worked

Tracking the whole city rather than one address changed the decisions. A firm can look first on the map from its own front door and be invisible two miles away, where most of its potential clients actually live. The 49-point grid showed me exactly where each firm was losing, so the work went where the calls were.

Working the three surfaces together compounded the effect. A strong Business Profile earns the click, the website earns the call, and visibility in AI features means the firm is already familiar when the searcher reaches the shortlist. Across three of these firms, Google's AI features served more than 313,000 impressions in a single quarter, which is a lot of first impressions that never needed a click.

Speed varied by market, and that is useful to know before starting. Youngstown moved from an average rank of 14.2 to 3.9 in three months, while I measured the Los Angeles and Montreal results at six months, in far larger cities where every position is harder to win. The campaigns that started from zero, with referral-only firms, showed the biggest change in the way the business actually runs: leads and calls arriving every week from people who had never heard of the firm before.

Nothing here relied on a secret; it relied on doing the unglamorous local work properly and measuring it honestly, firm by firm, against its own starting point.

What this means for your business

Swap the law firm for your clinic in Ikeja, your property agency in Lekki, your logistics company in Apapa or your hotel in Abuja and the playbook barely changes. If people search for what you do "near me" or in your part of town, the map is where most of those customers decide, and AI tools are increasingly where they start.

Markets differ, so these results don't guarantee yours. In your AI SEO Growth Audit I'll show you where you stand today on the map, in organic search and in AI answers, and what it would take to move.