Vizby · Case Study · VIGO Industries
How VIGO Grew AI Traffic 214% and AI Revenue 238% With Vizby
VIGO Industries sells bathroom and kitchen fixtures on Shopify. Vizby started work in April 2026. Eight months of attributed data, split at that date, show monthly AI sessions up 214% and monthly AI revenue up 238%. Revenue grew faster than traffic, which means the sessions Vizby won converted at least as well as the ones that came before.
January to August 2026. All figures relative to baseline.
Monthly AI sessions
3.1x the pre-engagement average
Monthly AI revenue
3.4x the pre-engagement average
Monthly AI orders
3.7x the pre-engagement average
Best collection page
its first-month AI traffic, by July
Reading the charts
Every business figure is a multiple of VIGO’s average month before Vizby started, which equals 1.0x. A value of 3.0x means three times the pre-engagement monthly average. Absolute sessions, orders and revenue belong to the client and are not published.
The Trend
AI sessions, against the pre-Vizby baseline
The shaded band is the three months before Vizby. April dips slightly, then the line climbs every single month. By August, AI sessions are running at 5.3x the pre-engagement monthly average.
The Money
Attributed AI revenue, against the pre-Vizby baseline
Monthly AI revenue runs at 3.4x the pre-engagement average across the five months since April. July coincided with a store-wide promotion and is coloured separately, but August has since overtaken it.
Before and After
Every metric against the pre-Vizby baseline
Volume tripled, orders nearly quadrupled and revenue more than tripled. Revenue per session lands at 1.08x baseline, so the channel grew without diluting the value of a session. Average order value at 0.91x is the one metric below baseline, which is what you expect when a channel widens beyond its earliest, most decided buyers.
| Month | Sessions | Orders | Revenue | AOV | Conv. | Rev / session |
|---|---|---|---|---|---|---|
| Jan | 0.79x | 0.33x | 0.33x | 0.99x | 0.42x | 0.42x |
| Feb | 0.73x | 1.00x | 0.92x | 0.92x | 1.37x | 1.26x |
| Mar | 1.48x | 1.67x | 1.75x | 1.05x | 1.13x | 1.18x |
| Before Vizby | 1.00x | 1.00x | 1.00x | 1.00x | 1.00x | 1.00x |
| Vizby starts, April | ||||||
| Apr | 1.04x | 1.00x | 0.22x | 0.22x | 0.96x | 0.21x |
| May | 2.48x | 3.33x | 2.59x | 0.78x | 1.34x | 1.05x |
| Jun | 3.03x | 3.00x | 1.62x | 0.54x | 0.99x | 0.53x |
| Julpromo | 3.90x | 4.67x | 5.40x | 1.16x | 1.20x | 1.38x |
| Aug | 5.27x | 6.67x | 7.10x | 1.06x | 1.27x | 1.35x |
| Since Vizby | 3.14x | 3.73x | 3.38x | 0.91x | 1.19x | 1.08x |
Monthly averages, not period totals, since the two periods are different lengths. Strip the July promotion out and the post-Vizby period still runs well above baseline on volume and revenue.
By Engine
Change in how often the engines name VIGO
Share of non-branded buying questions where the brand appears in the answer, expressed as the change between the first and latest test. Every engine improved, and overall the mention rate rose 94%. Starting points differed by engine, so a large percentage move can come off a small base.
- Perplexity+286%
- ChatGPT+180%
- Gemini+60%
- Claude+11%
The Compounding Page
Best-performing collection page, against its first month
One category landing page, tracked from April. By July it was running at 27x its April volume, and it is the same category where VIGO’s mention rate in the engines is strongest. Visibility work and traffic land in the same place, which is the whole thesis.
*Final month covers the 1st to the 23rd.
Getting Cited
What kind of page the engines cite
Two visibility tests eight weeks apart. What changed is the composition. In the first test the engines cited whatever category pages they happened to find, with a single article among them. In the latest test, two thirds of everything they cite is content built deliberately for those questions.
First test
Latest test
Share of all cited pages, by page type. Hub and editorial pages — content built for the question — go from 12% of citations to two thirds.
See how often AI names your brand
The same test that produced this case study runs against your store, and shows which buying questions you already win and which ones your competitors own.