Review Conversation Analysis — Master Summary
Generated from 199 conversations that resulted in a 5-star review.
All times shown in both UTC and Tehran time (Asia/Tehran).
1. Root Causes of Issues
The LLM read each conversation transcript and extracted every distinct issue raised. Each issue was assigned a category and a sub-category. A single conversation can have multiple issues. % shown is share of total issues extracted, not share of conversations.
Total issues extracted: 608 across 199 conversations (avg 3.1 issues per conversation)
Category definitions
| Category |
What it covers |
| How-To / Setup |
Merchant needed guidance on configuring or using a feature — widget placement, campaign setup, email templates, etc. |
| Technical Bug |
Something wasn't working as expected — an error, broken behaviour, or incorrect result |
| Feature Request |
Merchant asked for a capability that either doesn't exist yet, or exists only on a higher plan tier than the merchant is on |
| Billing |
Questions or problems around charges, invoices, subscription plans, or pricing |
| Account / Access |
App installation, login, permissions, or account-level problems |
| Integration |
Issues connecting the app to Shopify or a third-party tool |
| Other |
Doesn't fit any of the categories above |
By category
| Category |
Count |
% |
|
| How-To / Setup |
319 |
52% |
████████░░░░░░░ |
| Technical Bug |
163 |
27% |
████░░░░░░░░░░░ |
| Feature Request |
59 |
10% |
█░░░░░░░░░░░░░░ |
| Billing |
48 |
8% |
█░░░░░░░░░░░░░░ |
| Account / Access |
11 |
2% |
░░░░░░░░░░░░░░░ |
| Integration |
8 |
1% |
░░░░░░░░░░░░░░░ |
Top 20 sub-categories
| Sub-category |
App |
Count |
% |
| How-To / Setup → Wishlist button repositioning |
Notify Me! |
7 |
1% |
| How-To / Setup → Low stock threshold configuration |
Notify Me! |
6 |
1% |
| How-To / Setup → Widget customization |
Discounty, Notify Me! |
6 |
1% |
| How-To / Setup → Pre-order button setup |
Notify Me! |
5 |
1% |
| How-To / Setup → Wishlist icon placement |
Notify Me! |
4 |
1% |
| How-To / Setup → Confirmation message styling |
Notify Me! |
4 |
1% |
| How-To / Setup → Wishlist button placement |
Notify Me! |
4 |
1% |
| How-To / Setup → Initial app setup check |
Notify Me! |
3 |
0% |
| How-To / Setup → Widget color customization |
Discounty, Notify Me! |
3 |
0% |
| How-To / Setup → Widget placement and styling |
Notify Me! |
3 |
0% |
| How-To / Setup → Email template customization |
Notify Me! |
2 |
0% |
| Feature Request → Low stock threshold configuration |
Notify Me! |
2 |
0% |
| How-To / Setup → Email notification configuration |
Notify Me! |
2 |
0% |
| How-To / Setup → Wishlist button styling and placement |
Notify Me! |
2 |
0% |
| How-To / Setup → Confirmation message customization |
Notify Me! |
2 |
0% |
| How-To / Setup → Button color customization |
Notify Me! |
2 |
0% |
| How-To / Setup → Pre-order quantity limit |
Notify Me! |
2 |
0% |
| How-To / Setup → First campaign setup |
Discounty |
2 |
0% |
| How-To / Setup → Campaign widget setup |
Discounty |
2 |
0% |
| How-To / Setup → Wishlist icon placement in header |
Notify Me! |
2 |
0% |
Resolution rate by category
'Resolved' means the specific issue was answered or fixed within the conversation — e.g. the merchant got the widget working, or the billing question was explained. A Feature Request is almost never 'resolved' in-conversation because building the feature takes time; that's expected and doesn't reflect poorly on support quality.
| Category |
Issues |
Resolved in-conversation |
Resolution rate |
| Account / Access |
11 |
9 |
82% |
| Billing |
48 |
42 |
88% |
| Feature Request |
59 |
26 |
44% |
| How-To / Setup |
319 |
285 |
89% |
| Integration |
8 |
6 |
75% |
| Technical Bug |
163 |
122 |
75% |
2. Install → First Ticket Timing
Coverage: 199/199 conversations have install timestamps. The remaining 0 are excluded from this section — install data was not recorded in Intercom for those merchants.
How counted: timestamp of app install (from Intercom company profile) subtracted from the conversation start timestamp. A value of 0s means the merchant opened a chat within seconds of installing.
| Metric |
Value |
| Conversations |
199 |
| Median |
1h 32m |
| Average |
93d 2h |
| 75th pct |
59d 1h |
| 90th pct |
284d 22h |
| Min |
0s |
| Max |
1688d 22h |
By time bucket
Groups conversations by how long the merchant had been using the app before reaching out. A value of '< 1 hour' means the merchant contacted support within the first hour of installing.
| Value |
Count |
% |
|
| < 1 hour |
96 |
48% |
███████░░░░░░░░ |
| 6 months+ |
33 |
17% |
██░░░░░░░░░░░░░ |
| 1 – 6 months |
26 |
13% |
██░░░░░░░░░░░░░ |
| 1h – 24h |
16 |
8% |
█░░░░░░░░░░░░░░ |
| 1 – 4 weeks |
16 |
8% |
█░░░░░░░░░░░░░░ |
| 1 – 7 days |
12 |
6% |
█░░░░░░░░░░░░░░ |
By app
| App |
n |
Median |
Average |
Min |
Max |
| Convi |
1 |
1m 4s |
1m 4s |
1m 4s |
1m 4s |
| Discounty |
47 |
23h 8m |
95d 2h |
0s |
826d 20h |
| Notify Me! |
135 |
34m 26s |
85d 5h |
1m 39s |
1688d 22h |
| Subi |
16 |
133d 0h |
159d 3h |
0s |
765d 3h |
3. Ticket Sources & Inboxes
Channel = how the conversation was initiated. customer_initiated = merchant opened the chat themselves; automated = a workflow or bot triggered the conversation; admin_initiated = your team proactively reached out to the merchant.
Channel
How the conversation started.
| Value |
Count |
% |
|
| customer_initiated |
190 |
95% |
██████████████░ |
| automated |
8 |
4% |
█░░░░░░░░░░░░░░ |
| admin_initiated |
1 |
1% |
░░░░░░░░░░░░░░░ |
Inbox / Team
Which Intercom inbox handled the conversation (Proactive, Chat 1, Chat 2, etc.).
| Value |
Count |
% |
|
| Proactive |
77 |
39% |
██████░░░░░░░░░ |
| Chat |
56 |
28% |
████░░░░░░░░░░░ |
| Chat 2 |
40 |
20% |
███░░░░░░░░░░░░ |
| Chat 1 |
19 |
10% |
█░░░░░░░░░░░░░░ |
| Shopify |
2 |
1% |
░░░░░░░░░░░░░░░ |
| Fin |
2 |
1% |
░░░░░░░░░░░░░░░ |
| Monitoring |
1 |
1% |
░░░░░░░░░░░░░░░ |
| Email |
1 |
1% |
░░░░░░░░░░░░░░░ |
4. Conversation Start → Human Handover
How counted: seconds between the conversation start timestamp and the first moment a human agent (not a bot) joined the conversation. Bots like Luna/Fin are excluded — only the first real human admin message counts as handover.
Coverage: 197 of 199 conversations had a measurable handoff timestamp.
| Metric |
Value |
| Conversations |
197 |
| Median |
1m 36s |
| Average |
36m 54s |
| 75th pct |
5m 49s |
| 90th pct |
18m 54s |
| Min |
1s |
| Max |
2d 0h |
By app
| App |
n |
Median |
Average |
| Convi |
1 |
1s |
1s |
| Discounty |
45 |
4m 1s |
1h 6m |
| Notify Me! |
135 |
35s |
5m 53s |
| Subi |
16 |
17m 26s |
3h 37m |
5. Human Handover → Review Ask
How counted: seconds between the first human agent joining and the moment a review was explicitly requested (detected by scanning for review request keywords in the transcript). Only conversations where a review was asked are included.
Coverage: 198 of 199 conversations had a review explicitly requested.
| Metric |
Value |
| Conversations |
198 |
| Median |
32m 22s |
| Average |
5h 16m |
| 75th pct |
1h 12m |
| 90th pct |
6h 15m |
| Min |
1s |
| Max |
10d 0h |
By app
| App |
n |
Median |
Average |
| Convi |
1 |
4h 15m |
4h 15m |
| Discounty |
46 |
26m 25s |
4h 0m |
| Notify Me! |
135 |
34m 50s |
4h 4m |
| Subi |
16 |
42m 1s |
19h 2m |
6. Geography
How counted: country is taken from the Intercom contact's location field (city/region/country pulled from the contact profile). Where the contact location was missing, the company's country code from Intercom was used as a fallback. % is share of all 199 conversations.
Top countries (by review volume)
| Country |
Count |
% |
|
| United States |
71 |
36% |
█████░░░░░░░░░░ |
| United Kingdom |
16 |
8% |
█░░░░░░░░░░░░░░ |
| Australia |
14 |
7% |
█░░░░░░░░░░░░░░ |
| Canada |
11 |
6% |
█░░░░░░░░░░░░░░ |
| India |
11 |
6% |
█░░░░░░░░░░░░░░ |
| Germany |
8 |
4% |
█░░░░░░░░░░░░░░ |
| France |
7 |
4% |
█░░░░░░░░░░░░░░ |
| Italy |
5 |
3% |
░░░░░░░░░░░░░░░ |
| Netherlands |
5 |
3% |
░░░░░░░░░░░░░░░ |
| United Arab Emirates |
4 |
2% |
░░░░░░░░░░░░░░░ |
| Denmark |
4 |
2% |
░░░░░░░░░░░░░░░ |
| Spain |
4 |
2% |
░░░░░░░░░░░░░░░ |
| New Zealand |
4 |
2% |
░░░░░░░░░░░░░░░ |
| Japan |
4 |
2% |
░░░░░░░░░░░░░░░ |
| Mexico |
3 |
2% |
░░░░░░░░░░░░░░░ |
| South Africa |
3 |
2% |
░░░░░░░░░░░░░░░ |
| Singapore |
3 |
2% |
░░░░░░░░░░░░░░░ |
| Portugal |
2 |
1% |
░░░░░░░░░░░░░░░ |
| Sweden |
2 |
1% |
░░░░░░░░░░░░░░░ |
| Austria |
2 |
1% |
░░░░░░░░░░░░░░░ |
By region
| Region |
Count |
% |
|
| United States |
69 |
35% |
█████░░░░░░░░░░ |
| UK |
16 |
8% |
█░░░░░░░░░░░░░░ |
| Australia |
15 |
8% |
█░░░░░░░░░░░░░░ |
| Canada |
11 |
6% |
█░░░░░░░░░░░░░░ |
| India |
11 |
6% |
█░░░░░░░░░░░░░░ |
| France |
8 |
4% |
█░░░░░░░░░░░░░░ |
| Germany |
8 |
4% |
█░░░░░░░░░░░░░░ |
| Spain |
5 |
3% |
░░░░░░░░░░░░░░░ |
| Italy |
5 |
3% |
░░░░░░░░░░░░░░░ |
| Netherlands |
5 |
3% |
░░░░░░░░░░░░░░░ |
| UAE |
4 |
2% |
░░░░░░░░░░░░░░░ |
| Denmark |
4 |
2% |
░░░░░░░░░░░░░░░ |
| New Zealand |
4 |
2% |
░░░░░░░░░░░░░░░ |
| Japan |
4 |
2% |
░░░░░░░░░░░░░░░ |
| Mexico |
3 |
2% |
░░░░░░░░░░░░░░░ |
7. Merchant Profile — Who Leaves Reviews?
How counted: Shopify plan and app plan are pulled directly from the Intercom company profile (populated by Shopify/app integration). Store age = days between when the company was first created in Intercom and the conversation date. StoreLeads data (avg rating, review count) is partial — only 79/199 stores had this populated in Intercom.
Shopify plan
| Value |
Count |
% |
|
| Basic |
99 |
50% |
███████░░░░░░░░ |
| Grow |
52 |
26% |
████░░░░░░░░░░░ |
| Advanced |
19 |
10% |
█░░░░░░░░░░░░░░ |
| Plus |
10 |
5% |
█░░░░░░░░░░░░░░ |
| Plus Trial |
1 |
1% |
░░░░░░░░░░░░░░░ |
App plan by app
Discounty (41 with plan data)
| Value |
Count |
% |
|
| Essential |
22 |
54% |
████████░░░░░░░ |
| Starter |
7 |
17% |
███░░░░░░░░░░░░ |
| Discounty |
6 |
15% |
██░░░░░░░░░░░░░ |
| Lite |
4 |
10% |
█░░░░░░░░░░░░░░ |
| Ultimate |
2 |
5% |
█░░░░░░░░░░░░░░ |
Notify Me! (124 with plan data)
| Value |
Count |
% |
|
| Lite |
62 |
50% |
████████░░░░░░░ |
| Starter |
29 |
23% |
████░░░░░░░░░░░ |
| Standard |
19 |
15% |
██░░░░░░░░░░░░░ |
| Kickstart |
7 |
6% |
█░░░░░░░░░░░░░░ |
| Rocket |
5 |
4% |
█░░░░░░░░░░░░░░ |
| Pro |
1 |
1% |
░░░░░░░░░░░░░░░ |
| Essentials |
1 |
1% |
░░░░░░░░░░░░░░░ |
Subi (15 with plan data)
| Value |
Count |
% |
|
| Subi |
5 |
33% |
█████░░░░░░░░░░ |
| Growth |
5 |
33% |
█████░░░░░░░░░░ |
| Free |
3 |
20% |
███░░░░░░░░░░░░ |
| Subi Plus |
1 |
7% |
█░░░░░░░░░░░░░░ |
| Starter |
1 |
7% |
█░░░░░░░░░░░░░░ |
| Metric |
Value |
| Conversations |
181 |
| Median |
573 days |
| Average |
999 days |
| 75th pct |
1564 days |
| 90th pct |
2692 days |
| Min |
1 days |
| Max |
4360 days |
Existing app store rating (StoreLeads — 79/199 stores)
-
Average existing rating: 4.75 / 5
-
Median existing rating: 5.00 / 5
-
Average existing review count: 3
-
Median existing review count: 2
How counted: the merchant's email domain was classified automatically. personal_business = custom domain (e.g. name@mybrand.com); personal_free = Gmail, Hotmail, Yahoo etc.; team_mailbox = generic role address (info@, support@, hello@, etc.); system = automated/noreply address; unknown = couldn't be classified.
| Email Type |
Count |
% |
|
| team_mailbox |
71 |
36% |
█████░░░░░░░░░░ |
| personal_free |
59 |
30% |
████░░░░░░░░░░░ |
| personal_business |
57 |
29% |
████░░░░░░░░░░░ |
| unknown |
9 |
5% |
█░░░░░░░░░░░░░░ |
| system |
3 |
2% |
░░░░░░░░░░░░░░░ |
Email type by app
| App |
personal_business |
personal_free |
team_mailbox |
system |
internal |
| Convi |
0 (0%) |
1 (100%) |
0 (0%) |
0 (0%) |
0 (0%) |
| Discounty |
12 (26%) |
16 (34%) |
15 (32%) |
1 (2%) |
0 (0%) |
| Notify Me! |
41 (30%) |
37 (27%) |
49 (36%) |
2 (1%) |
0 (0%) |
| Subi |
4 (25%) |
5 (31%) |
7 (44%) |
0 (0%) |
0 (0%) |
9. Agent Count
How counted: number of unique human agents (admins) who sent at least one message in the conversation, extracted from the conversation parts. Bots (Luna, Fin) are excluded. An agent who only left an internal note is still counted.
| Agents |
Count |
% |
|
| 1 |
78 |
39% |
██████░░░░░░░░░ |
| 2 |
66 |
33% |
█████░░░░░░░░░░ |
| 3 |
31 |
16% |
██░░░░░░░░░░░░░ |
| 5 |
9 |
5% |
█░░░░░░░░░░░░░░ |
| 4 |
9 |
5% |
█░░░░░░░░░░░░░░ |
| 6 |
3 |
2% |
░░░░░░░░░░░░░░░ |
| 8 |
2 |
1% |
░░░░░░░░░░░░░░░ |
| 11 |
1 |
1% |
░░░░░░░░░░░░░░░ |
By bucket
| Bucket |
Count |
% |
|
| 1 |
78 |
39% |
██████░░░░░░░░░ |
| 2 |
66 |
33% |
█████░░░░░░░░░░ |
| 3 |
31 |
16% |
██░░░░░░░░░░░░░ |
| 3+ |
24 |
12% |
██░░░░░░░░░░░░░ |
Agent count by app
| App |
1 agent |
2 agents |
3+ agents |
| Convi |
1 (100%) |
0 (0%) |
0 (0%) |
| Discounty |
21 (45%) |
15 (32%) |
11 (23%) |
| Notify Me! |
43 (32%) |
50 (37%) |
42 (31%) |
| Subi |
13 (81%) |
1 (6%) |
2 (12%) |
10. Fin AI Involvement
How counted: Fin participated = Intercom recorded that the Fin AI bot was active in the conversation (from the fin_participated flag in the Intercom API response). Fin role (attempted / escalated / none) was determined by the LLM reading the transcript. Intercom resolution state is the official Fin outcome as reported by Intercom.
|
Count |
% |
| Fin participated |
87 |
44% |
| No Fin |
112 |
56% |
Fin role breakdown
| Role |
Count |
% |
|
| attempted |
93 |
47% |
███████░░░░░░░░ |
| none |
59 |
30% |
████░░░░░░░░░░░ |
| escalated |
47 |
24% |
████░░░░░░░░░░░ |
| State |
Count |
% |
|
| Escalated |
82 |
41% |
██████░░░░░░░░░ |
| Assumed Resolution |
2 |
1% |
░░░░░░░░░░░░░░░ |
| Confirmed Resolution |
1 |
1% |
░░░░░░░░░░░░░░░ |
| Negative feedback |
1 |
1% |
░░░░░░░░░░░░░░░ |
Fin by app
| App |
Total |
Fin participated |
% |
| Convi |
1 |
0 |
0% |
| Discounty |
47 |
32 |
68% |
| Notify Me! |
135 |
42 |
31% |
| Subi |
16 |
13 |
81% |
11. When Do Conversations Happen? (Tehran Time)
How counted: conversation start timestamp (UTC) converted to Asia/Tehran timezone (IRST UTC+3:30 in winter, IRDT UTC+4:30 in summer — DST-aware). Day of week and hour extracted from the Tehran-local datetime.
Day of week (Tehran)
| Day |
Count |
% |
|
| Wednesday |
42 |
21% |
███░░░░░░░░░░░░ |
| Monday |
35 |
18% |
███░░░░░░░░░░░░ |
| Tuesday |
34 |
17% |
███░░░░░░░░░░░░ |
| Thursday |
33 |
17% |
██░░░░░░░░░░░░░ |
| Friday |
24 |
12% |
██░░░░░░░░░░░░░ |
| Sunday |
16 |
8% |
█░░░░░░░░░░░░░░ |
| Saturday |
15 |
8% |
█░░░░░░░░░░░░░░ |
Hour of day (Tehran, 24h)
| Hour (TEH) |
Count |
% |
Bar |
| 00:00 |
5 |
3% |
█░░░░░░░░░░░░░░░░░░░ |
| 01:00 |
7 |
4% |
█░░░░░░░░░░░░░░░░░░░ |
| 02:00 |
1 |
1% |
░░░░░░░░░░░░░░░░░░░░ |
| 03:00 |
8 |
4% |
█░░░░░░░░░░░░░░░░░░░ |
| 04:00 |
5 |
3% |
█░░░░░░░░░░░░░░░░░░░ |
| 05:00 |
4 |
2% |
░░░░░░░░░░░░░░░░░░░░ |
| 06:00 |
9 |
5% |
█░░░░░░░░░░░░░░░░░░░ |
| 07:00 |
8 |
4% |
█░░░░░░░░░░░░░░░░░░░ |
| 08:00 |
11 |
6% |
█░░░░░░░░░░░░░░░░░░░ |
| 09:00 |
3 |
2% |
░░░░░░░░░░░░░░░░░░░░ |
| 10:00 |
6 |
3% |
█░░░░░░░░░░░░░░░░░░░ |
| 11:00 |
3 |
2% |
░░░░░░░░░░░░░░░░░░░░ |
| 12:00 |
13 |
7% |
█░░░░░░░░░░░░░░░░░░░ |
| 13:00 |
12 |
6% |
█░░░░░░░░░░░░░░░░░░░ |
| 14:00 |
12 |
6% |
█░░░░░░░░░░░░░░░░░░░ |
| 15:00 |
9 |
5% |
█░░░░░░░░░░░░░░░░░░░ |
| 16:00 |
10 |
5% |
█░░░░░░░░░░░░░░░░░░░ |
| 17:00 |
10 |
5% |
█░░░░░░░░░░░░░░░░░░░ |
| 18:00 |
4 |
2% |
░░░░░░░░░░░░░░░░░░░░ |
| 19:00 |
15 |
8% |
██░░░░░░░░░░░░░░░░░░ |
| 20:00 |
10 |
5% |
█░░░░░░░░░░░░░░░░░░░ |
| 21:00 |
10 |
5% |
█░░░░░░░░░░░░░░░░░░░ |
| 22:00 |
12 |
6% |
█░░░░░░░░░░░░░░░░░░░ |
| 23:00 |
12 |
6% |
█░░░░░░░░░░░░░░░░░░░ |
Peak hour (Tehran): 19:00 — 15 conversations (8%)
Day × hour heatmap (Tehran) — conversation count
| Day |
00-03 |
04-07 |
08-11 |
12-15 |
16-19 |
20-23 |
| Monday |
2 |
5 |
2 |
9 |
3 |
14 |
| Tuesday |
5 |
9 |
6 |
7 |
2 |
5 |
| Wednesday |
2 |
3 |
7 |
12 |
12 |
6 |
| Thursday |
6 |
5 |
6 |
5 |
6 |
5 |
| Friday |
3 |
3 |
2 |
6 |
7 |
3 |
| Saturday |
3 |
· |
· |
3 |
4 |
5 |
| Sunday |
· |
1 |
· |
4 |
5 |
6 |
12. Additional Signals
Conversation sentiment
The LLM assessed the merchant's overall tone throughout the conversation — separate from the review rating. Since all conversations in this dataset led to a 5-star review, you'd expect mostly positive, but a small number show negative/neutral sentiment mid-conversation before being resolved.
| Sentiment |
Count |
% |
|
| positive |
97 |
49% |
███████░░░░░░░░ |
| very_positive |
90 |
45% |
███████░░░░░░░░ |
| neutral |
7 |
4% |
█░░░░░░░░░░░░░░ |
| negative |
4 |
2% |
░░░░░░░░░░░░░░░ |
| very_negative |
1 |
1% |
░░░░░░░░░░░░░░░ |
Conversation resolution status
Whether the LLM assessed the conversation as resolved by the end. 'Partially resolved' means the main issue was addressed but something remained open.
| Status |
Count |
% |
|
| resolved |
136 |
68% |
██████████░░░░░ |
| partially_resolved |
61 |
31% |
█████░░░░░░░░░░ |
| unresolved |
1 |
1% |
░░░░░░░░░░░░░░░ |
| unclear |
1 |
1% |
░░░░░░░░░░░░░░░ |
SLA compliance
| SLA Status |
Count |
% |
|
| hit |
72 |
36% |
█████░░░░░░░░░░ |
| missed |
14 |
7% |
█░░░░░░░░░░░░░░ |
| canceled |
2 |
1% |
░░░░░░░░░░░░░░░ |
| active |
1 |
1% |
░░░░░░░░░░░░░░░ |
Conversation language
| Language |
Count |
% |
|
| English |
182 |
91% |
██████████████░ |
| French |
5 |
3% |
░░░░░░░░░░░░░░░ |
| Italian |
3 |
2% |
░░░░░░░░░░░░░░░ |
| Spanish |
3 |
2% |
░░░░░░░░░░░░░░░ |
| Simplified Chinese |
2 |
1% |
░░░░░░░░░░░░░░░ |
| Portuguese |
1 |
1% |
░░░░░░░░░░░░░░░ |
| German |
1 |
1% |
░░░░░░░░░░░░░░░ |
| Japanese |
1 |
1% |
░░░░░░░░░░░░░░░ |
| Hebrew |
1 |
1% |
░░░░░░░░░░░░░░░ |
Refund discussed
| Refund discussed |
Count |
% |
|
| no |
189 |
95% |
██████████████░ |
| yes |
10 |
5% |
█░░░░░░░░░░░░░░ |
Conversation reopens
- Conversations with at least 1 reopen: 80 (40%)
| Reopens |
Count |
% |
|
| 0 |
119 |
60% |
█████████░░░░░░ |
| 1 |
47 |
24% |
████░░░░░░░░░░░ |
| 2 |
13 |
7% |
█░░░░░░░░░░░░░░ |
| 4 |
11 |
6% |
█░░░░░░░░░░░░░░ |
| 3 |
7 |
4% |
█░░░░░░░░░░░░░░ |
| 5 |
2 |
1% |
░░░░░░░░░░░░░░░ |
Other apps mentioned
Apps (of any kind — competitors or integrations) that merchants mentioned by name in their conversation.
| App |
Count |
% |
|
| Klaviyo |
4 |
29% |
████░░░░░░░░░░░ |
| Fast Bundle |
2 |
14% |
██░░░░░░░░░░░░░ |
| Mailchimp |
1 |
7% |
█░░░░░░░░░░░░░░ |
| WooCommerce |
1 |
7% |
█░░░░░░░░░░░░░░ |
| MailChimp |
1 |
7% |
█░░░░░░░░░░░░░░ |
| Boost.ai |
1 |
7% |
█░░░░░░░░░░░░░░ |
| Smile.io |
1 |
7% |
█░░░░░░░░░░░░░░ |
| ConvertKit |
1 |
7% |
█░░░░░░░░░░░░░░ |
| Wishlist Hero |
1 |
7% |
█░░░░░░░░░░░░░░ |
| Appikon |
1 |
7% |
█░░░░░░░░░░░░░░ |
Product blockers / missing features
Total blocker mentions: 225 across 147 conversations
| Blocker |
Count |
| Backend bug preventing scheduled campaign price modifications from applying on time |
1 |
| AI bot unable to handle urgent technical escalations, causing significant delays in reaching a human agent |
1 |
| Third-party app conflict overwriting Discounty price and compare-at price fields after campaign activation |
1 |
| Discounty cannot lock prices in Shopify admin to prevent other apps from overwriting them |
1 |
| Discount code expired before merchant had time to upgrade, requiring manual reactivation by the agent |
1 |
| No bulk method to assign specific products to a pre-order offer or manage exclusions in bulk |
1 |
| Pre-order availability date format is fixed and cannot be customized (e.g., year cannot be omitted) |
1 |
| Checkout payment method line cannot be hidden through the app due to Shopify compliance requirements |
1 |
| SMS reminder template contains literal \n characters instead of proper line breaks |
1 |
| Migrated subscription contracts inherited $0 shipping fee from prior app, causing incorrect free shipping on renewals |
1 |
| Reinstalling the app automatically reactivated and charged subscription contracts without merchant action, contradicting agent assurances |
1 |
| No bulk-delete option for contracts — only cancellation is available, leaving residual data in the app |
1 |
| Contracts tab inaccessible without selecting a paid/free plan, preventing merchant from self-managing cancellations immediately after reinstall |
1 |
| Custom Shopify theme prevented standard app embed from rendering the wishlist button without manual code intervention |
1 |
| Wishlist button on homepage/collection featured products requires a paid Rocket plan upgrade |
1 |
Conversation length (messages)
| Metric |
Value |
| Conversations |
199 |
| Median |
47 messages |
| Average |
54 messages |
| 75th pct |
66 messages |
| 90th pct |
101 messages |
| Min |
8 messages |
| Max |
170 messages |
Review text length (characters)
-
Reviews with no written text (stars only): 43 (22%)
-
Among reviews with text — median length: 108 chars, avg: 150 chars
Merchant explicitly agreed to leave a review
| Agreed |
Count |
% |
|
| yes |
179 |
90% |
█████████████░░ |
| not_explicitly |
18 |
9% |
█░░░░░░░░░░░░░░ |
| no |
2 |
1% |
░░░░░░░░░░░░░░░ |
13. Review Origins
Of 200 five-star reviews collected, 199 were traced back to a support conversation in Intercom. 1 review was submitted organically — no prior conversation was found.
|
Count |
% |
| Originated from a support conversation |
199 |
99.5% |
| Submitted without a conversation |
1 |
0.5% |
| Total |
200 |
100% |
14. Issue Patterns in Conversation-Linked Reviews
Each conversation had its issues extracted by the LLM. The tables below cross-tabulate those issues against app, channel, install age, and each other to surface recurring patterns.
Issue category by app
| Category |
Convi |
Discounty |
Notify Me! |
Subi |
Total |
| Account / Access |
0 |
3 |
8 |
0 |
11 |
| Billing |
0 |
8 |
34 |
6 |
48 |
| Feature Request |
0 |
18 |
37 |
4 |
59 |
| How-To / Setup |
1 |
52 |
246 |
20 |
319 |
| Integration |
0 |
0 |
8 |
0 |
8 |
| Technical Bug |
1 |
38 |
107 |
17 |
163 |
| Total |
2 |
119 |
440 |
47 |
608 |
Top sub-categories with resolution rate
| Sub-category |
Count |
Resolved |
Rate |
| How-To / Setup → Wishlist button repositioning |
7 |
7 |
100% |
| How-To / Setup → Low stock threshold configuration |
6 |
6 |
100% |
| How-To / Setup → Widget customization |
6 |
6 |
100% |
| How-To / Setup → Pre-order button setup |
5 |
5 |
100% |
| How-To / Setup → Wishlist icon placement |
4 |
4 |
100% |
| How-To / Setup → Confirmation message styling |
4 |
4 |
100% |
| How-To / Setup → Wishlist button placement |
4 |
4 |
100% |
| How-To / Setup → Initial app setup check |
3 |
3 |
100% |
| How-To / Setup → Widget color customization |
3 |
3 |
100% |
| How-To / Setup → Widget placement and styling |
3 |
3 |
100% |
| How-To / Setup → Email template customization |
2 |
1 |
50% |
| Feature Request → Low stock threshold configuration |
2 |
2 |
100% |
| How-To / Setup → Email notification configuration |
2 |
1 |
50% |
| How-To / Setup → Wishlist button styling and placement |
2 |
2 |
100% |
| How-To / Setup → Confirmation message customization |
2 |
2 |
100% |
| How-To / Setup → Button color customization |
2 |
2 |
100% |
| How-To / Setup → Pre-order quantity limit |
2 |
2 |
100% |
| How-To / Setup → First campaign setup |
2 |
2 |
100% |
| How-To / Setup → Campaign widget setup |
2 |
2 |
100% |
| How-To / Setup → Wishlist icon placement in header |
2 |
2 |
100% |
Issue category by conversation channel
| Category |
admin_initiated |
automated |
customer_initiated |
| Account / Access |
· |
1 |
10 |
| Billing |
1 |
3 |
44 |
| Feature Request |
· |
2 |
57 |
| How-To / Setup |
· |
17 |
302 |
| Integration |
· |
· |
8 |
| Technical Bug |
· |
14 |
149 |
Issue category by install age at time of conversation
Reveals whether issue type correlates with how long the merchant has been using the app.
| Category |
< 1 hour |
1h – 24h |
1 – 7 days |
1 – 4 weeks |
1 – 6 months |
6 months+ |
unknown |
| Account / Access |
9 |
1 |
· |
1 |
· |
· |
· |
| Billing |
23 |
1 |
3 |
3 |
6 |
12 |
· |
| Feature Request |
30 |
3 |
5 |
5 |
9 |
7 |
· |
| How-To / Setup |
180 |
22 |
15 |
24 |
27 |
51 |
· |
| Integration |
7 |
· |
· |
· |
· |
1 |
· |
| Technical Bug |
70 |
13 |
10 |
18 |
20 |
32 |
· |
Co-occurring issue categories (within the same conversation)
When a conversation raised multiple issues, which categories appeared together most often?
139 conversations had issues in more than one category.
| Category A |
Category B |
Co-occurrences |
| How-To / Setup |
Technical Bug |
103 |
| Feature Request |
How-To / Setup |
48 |
| Feature Request |
Technical Bug |
38 |
| Billing |
How-To / Setup |
32 |
| Billing |
Technical Bug |
21 |
| How-To / Setup |
Integration |
8 |
| Billing |
Feature Request |
7 |
| Integration |
Technical Bug |
7 |
| Account / Access |
How-To / Setup |
7 |
| Account / Access |
Technical Bug |
7 |
15. Shop Uniqueness & Cross-App Overlap
How counted: shops are identified by Shopify domain. A domain appearing across multiple apps means that merchant uses and reviewed more than one of your apps.
-
Total unique shops: 199 (= 199 conversations — every review came from a different merchant)
-
Shops with 2+ review conversations (same or different app): 0
-
Shops that reviewed more than one app: 0
All 199 reviews came from 199 fully distinct shops. No merchant appears more than once in this dataset — meaning there is no cross-app overlap and no shop submitted multiple reviews across the period covered. Each review represents a unique merchant relationship.