Who owns the feed in ecommerce customer-service AI
Ecommerce customer-service AI, pulled in London in August 2026. Ten vendors, 90 days of their public posts, counted against one named market of 170 people.
Kustomer takes 6.6 market reactions per post, double the next vendor. Gorgias, the best known helpdesk brand in the table, takes 0.8. The account this market engages most is not a vendor at all.
The count: how this ecommerce customer-service market was built
London pull, August 2026, 90 day window.
We started with the people who reacted to or commented on the category's posts, then filtered by title. That left 264 people. The table uses the 170 most active of the 264, ranked by how often they engaged anything in the category.
Most active favours people who use LinkedIn heavily, and we say so on every page. A buyer who reads without reacting is invisible to this method.
The title filter for this category covers customer experience and support leadership. Title is a proxy for authority rather than proof of it, so we write the sampled market and not the buyers.
The attention table: market reactions per post in ecommerce customer-service AI
Sample: 170 most active of 264. Window: 90 days, August 2026. Region: London pull. Counted: reactions and comments from those 170 on each vendor's public posts.
| Vendor | Market reactions | Reactions per post |
|---|---|---|
| Kustomer | 158 | 6.6 |
| Gladly | 77 | 3.2 |
| Siena | 74 | 3.1 |
| Loop Returns | 45 | 2.0 |
| Ada | 31 | 1.3 |
| Yuma | 29 | 2.2 |
| Gorgias | 18 | 0.8 |
| parcelLab | 16 | 1.2 |
| Zowie | 12 | 0.9 |
| Narvar | 4 | 0.2 |
The two columns disagree in places, which is the point of printing both. Yuma took 29 reactions to Ada's 31 and still beats Ada on rate, 2.2 to 1.3. Fewer posts, more per post.
Siena pulls the largest raw crowd of any vendor in the category, 444 engagers before the title filter. Inside the sampled market it converts at 3.1 per post, about half Kustomer's rate. A big crowd and a well-targeted crowd are two different assets.
The invisible vendor: Gorgias at 0.8 per post
Gorgias is the best known ecommerce helpdesk brand in this table, and it takes 0.8 market reactions per post. 18 in total from the 170 across 90 days.
Nearly invisible is the accurate phrase. This is not a zero. Somebody in the market reacts, at a rate that puts the most recognised name in the category near the bottom of the list.
There is a reasonable explanation with nothing to do with the product. Gorgias talks to ecommerce operators, and this market was built from the people who engage customer-service AI vendors. When the crowd around a category is a profession, a brand aimed at store owners lands somewhere else. The number stays the number: 18 reactions from this market in 90 days.
How Kustomer wins: the profession's calendar
Kustomer's 6.6 per post is double the next vendor in the table.
Kustomer posts the profession more than it posts the product. CX Day. Certification. Awards. Community threads about the work itself. The category's own calendar, published by a vendor.
The rest of the table posts product, and the rest of the table sits at 3.2 and below. A launch asks a market to care about your roadmap. A profession post asks a market to care about the thing it already does for a living, and in this window that was worth roughly double.
The founder effect: two people out-reach the company page
Siena's company page reached 3 of the sampled market. Siena's two founders reached 40 of it.
| Person | Role | Of the 170 |
|---|---|---|
| Lisa Popovici | Co-founder, Siena | 26 |
| Melissa MacAlister | VP CS, Gladly | 20 |
| Andrei Negrau | CEO, Siena | 14 |
| Joseph Ansanelli | Co-founder, Gladly | 9 |
Same company, same category, same 90 days. The page reaches 3 and the two people reach 40. That gap holds in every market we have pulled: founders and operators out-reach their company pages by roughly 10x.
Melissa MacAlister is worth a second look. A VP of customer success out-reaches her company's co-founder inside their own category, 20 to 9. The revenue tooling market did the same thing, where an account executive outranked every CEO in the category. Seniority and reach are not the same variable.
The person this market follows: a professional association
The account engaged by most of the sampled market is not a vendor and not a creator. It is the CX Professionals Association, engaged by 50 of the 170.
50 is more than any individual in the market reached, and more than most vendor pages in the table. An institution owns the middle of this market.
For a vendor that changes where a post has to sit to be seen. The association publishes the calendar this market is already reading, and the vendor at the top of the attention table is the one publishing alongside it.
The two rooms: ecommerce-native and the profession
Of the 264 people in the sampled market, 116 are reachable in ecommerce-native rooms. 148 are reachable only in the profession's own rooms.
Those two numbers add up to the whole sampled market, so in this pull the rooms barely overlap. A vendor working the ecommerce circuit reaches 116 people. A vendor working the customer experience profession reaches 148 different ones.
This is the split we now expect to find. A channel plan built on one room misses more than half of the market it was built for, and nothing in the vendor's own engagement numbers shows the gap.
The room: CX Day and CCXP
The rooms in this market belong to the profession rather than to any vendor. CX Day is the anchor, the date the profession marks each year. CCXP is the profession's certification, and certification content is part of the mix that put Kustomer at the top of the table.
These are company level findings, derived from which events sit inside the market's most-reacted posts and from desk research on the category's calendar. We do not know who attends anything and we never claim to.
People posts beat product posts by about 2 to 1
Three posts from this window in the same category:
That is the same 2 to 1 shape we found in revenue tooling and in every market pulled so far. Four markets, one result. The people at a company and the moves they make out-pull the company's product news, inside the audience the product was built for.
Method: how these numbers were produced
Full method, including where it is weak: /data/method
One question
Which reached more of your market last quarter, your company page or the two people who founded the company?
Questions
Because this market was built from the people who engage customer-service AI vendors, and it filtered to customer experience and support leadership. Gorgias talks to ecommerce operators, who are a different crowd. The 0.8 per post is a statement about this sampled market in this window, not about the product or the company.
50 of the 170 people in the sampled market engaged that account inside the 90 day window. We rank voices by overlap with the market, not by follower count, so an institution can outrank every vendor and every individual. It is a public account measured on public posts.
In this pull the two groups add up to the full sampled market, so a plan built on ecommerce events reaches 116 people and a plan built on the profession reaches 148 different ones. Most vendors work one of them and count the result as the whole market. The useful move is knowing which half you are missing.
It is enough to compare vendors against each other in the same window, which is what the table does. It is not a population estimate for the category. We print the sample and the window on every table so you can weigh the numbers yourself.
Get this for your market
We build the named market for your category and run the same 90 day count, then hand you the table and the two rooms it splits into.