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B2B helpdesk software

AI toolsAugust 2026 edition

Which questions do you win vs lose?

Get your brand’s prompt-level results

Recommendation share

How often each brand is named, across unbranded questions and all five engines

BRANDS

SHAREShare of answers naming this brand, averaged across the five engines

ChatGPTChatGPTShare of ChatGPT answers that name this brand
ClaudeClaudeShare of Claude answers that name this brand
GeminiGeminiShare of Gemini answers that name this brand
PerplexityPerplexityShare of Perplexity answers that name this brand
Google AI OverviewsGoogle AI OverviewsShare of Google AI Overviews answers that name this brand
Freshdesk
75.9
87.0
65.7
68.5
50.0
Zendesk
56.5
85.2
75.9
47.2
25.0
Help Scout
37.0
64.8
51.9
44.4
31.5
4
Zoho Desk
36.1
57.4
34.3
46.3
25.0
5
Intercom
32.4
65.7
50.0
15.7
7.4
6
HubSpot Service Hub
32.4
37.0
40.7
15.7
23.1
7
Salesforce Service Cloud
22.2
29.6
31.5
6.5
24.1
8
Front
19.4
29.6
25.0
10.2
9.3
How we measurePowered by daydream

Every question changed this edition. The previous editions asked a set we wrote for the category; this one asks questions derived from what buyers actually searched or posted, so a month-over-month figure would compare two different questions and read as movement. Comparison returns next edition, when there are two mined runs to set against each other.

Framing sensitivity

The same question asked at each company size

Share of answers naming the brand

SMB50-person startupENTERPRISE2,000-person companyZoho Desk24.4%11.7%Zendesk67.8%80.0%Help Scout60.6%12.8%HubSpot Service Hub61.7%10.0%Salesforce Service Cloud12.2%52.2%Intercom57.8%32.2%Front45.6%6.1%Freshdesk62.2%65.6%

BRANDS

SMBShare of answers naming this brand when the question is asked at this company size50-person startup

ENTERPRISEShare of answers naming this brand when the question is asked at this company size2,000-person company

SPREADThe gap between the highest and lowest column in this row, in percentage points

HubSpot Service Hub
61.7%
10.0%
51.7%
Help Scout
60.6%
12.8%
47.8%
Salesforce Service Cloud
12.2%
52.2%
40.0%
Front
45.6%
6.1%
39.4%
Intercom
57.8%
32.2%
25.6%
Zoho Desk
24.4%
11.7%
12.8%
Zendesk
67.8%
80.0%
12.2%
Freshdesk
62.2%
65.6%
3.3%
How we measurePowered by daydream

Model divergence

The engines that name a brand least and most often, and the gap between them

BRANDS

LOWEST ENGINEThe engine that names this brand least often, and its share

HIGHEST ENGINEThe engine that names this brand most often, and its share

SPREADThe gap between the highest and lowest column in this row, in percentage points

Zendesk
25.0Google AI OverviewsAI Overviews
85.2ClaudeClaude
60.2
Intercom
7.4Google AI OverviewsAI Overviews
65.7ClaudeClaude
58.3
Freshdesk
50.0Google AI OverviewsAI Overviews
87.0ClaudeClaude
37.0
Help Scout
31.5Google AI OverviewsAI Overviews
64.8ClaudeClaude
33.3
Zoho Desk
25.0Google AI OverviewsAI Overviews
57.4ClaudeClaude
32.4
Salesforce Service Cloud
6.5PerplexityPerplexity
31.5GeminiGemini
25.0
HubSpot Service Hub
15.7PerplexityPerplexity
40.7GeminiGemini
25.0
Front
9.3Google AI OverviewsAI Overviews
29.6ClaudeClaude
20.4
How we measurePowered by daydream

Citation trail

The sites the engines linked to when they answered

SOURCES

REACHShare of all answers that cited this source at least once% of all answers

LINKSEvery link to this source, counted across all answerstotal

DEPTHLinks to this source per answer that cited itlinks/answer

featurebase.app
386
1.4
getmacha.com
565
2.4
zendesk.com
241
1.1
freshworks.com
183
1.1
eesel.ai
206
1.3
helply.com
202
1.3
reddit.com
167
1.2
salesforce.com
136
1.1
thecxlead.com
104
1.1
helpscout.com
99
1.1
front.com
92
1.0
desk365.io
147
1.7
happyfox.com
130
1.5
plain.com
150
1.8
helpdesk.com
98
1.2
intercom.com
118
1.5
pcmag.com
84
1.1
dragapp.com
129
1.7
usepylon.com
118
1.6
atlassian.com
81
1.1
How we measurePowered by daydream

Which questions do you win vs lose?

Get your brand’s prompt-level results

How we measure this3 of 12 questions published

We write a fixed set of category questions, none of which names a brand, and count how often each brand comes up. A brand is present in an answer only when one of its names literally appears in the text, because a string match cannot hallucinate.

Every question starts from one somebody already asked. Some come from search demand, where the figure is how many people typed that phrasing in a month. The rest come from a public forum post, and we link to the page it was written on. We rewrite each one into a plain question, because a search string is a fragment and a forum post is written the way people type, and neither is a fair thing to ask an engine. We change the wording and never the subject, and the original is published beside every question we disclose. The set is frozen before a single answer is collected, so every engine and every edition is asked exactly the same thing.

12questions scored
3buyer framings each
36prompts run
5engines

The 3 we publish, of 12

Chosen to span both where the questions come from and how they are shaped, so the sample describes the battery rather than one corner of it. That is 25% of the questions the ranking comes from.

  • What customer support platform should I useas asked: What customer support platform should I use?reddit.comuseCaseconsideration
  • What is the best help desk softwareas asked: best help desk software1,500/mocategoryawareness
  • What is the best support ticketing softwareas asked: best support ticketing software20/mocategoryawareness

Each is asked 3 ways

  • for a 50-person B2B startup50-person startup
  • for a 2,000-person company with a large support team2,000-person company
  • at the most affordable price for a small support teamBudget-conscious

Why the rest stays private

A published battery invites brands to write pages against the exact wording, at which point a score moves without the brand’s actual standing moving and the measurement stops describing anything. Benchmark suites keep a held-out set for the same reason. The method is public so it can be judged, a quarter of the questions are public so it can be checked, and the rest stays private so the numbers stay worth checking.

What the numbers are, and aren’t

  • The August 2026 edition is one dated run, not a rolling average.
  • Share is averaged across the engines rather than pooled, so an engine that returned fewer answers cannot look like a brand losing ground.
  • A move is marked only when a two-proportion test puts it outside what the sample size can explain. A few points between neighbouring brands is a tie.
  • Rankings reflect how often AI engines name a brand. They are not endorsements by daydream.
  • 16 further questions name competitors directly. None of them feeds the ranking, because a question that already names the contenders cannot measure who gets recommended. They do count towards the citation trail, which is measured over every answer we collected rather than the unbranded ones alone.