Every customer conversation, turned into retail signals
Support chats, return notes, and store calls carry the truth about your products and your service. Semarize reads those conversations across every channel and returns structured, SKU-level fields your CX, merchandising, and lifecycle teams can act on.
Kit
Retail CX Kit
Structured signals from a customer conversation
Output
{
"return_reason": "runs small",
"quality_issue": true,
"resolution_quality": 82,
"cancellation_intent": true,
"upsell_accepted": true
}
The problem
Your customers tell you everything, and almost none of it gets recorded
Shoppers talk to you across chat, email, phone, and the store, and they explain exactly what is wrong and what they want. The signal lands as free text, disposition codes, and gut feel. Across a busy omnichannel operation it never adds up to something product or CX can query, and the patterns that matter get buried under volume.
Returns reasons stay as free text
A return code says nothing about why the jacket came back. The real reason sits in a sentence the customer typed, unread and unstructured, so merchandising never sees the SKU-level pattern.
Quality slips exactly at peak
Service is easy in January and hard in December, which is when manual QA sampling falls furthest behind. The interactions you most need to check are the ones nobody reviews.
Save and upsell moments pass by
A cancellation chat is also a save, and a sizing question is also an upsell, but only if someone catches it live. Most of these moments are gone before the monthly report is written.
Channels never speak the same language
Chat, phone, email, and store conversations each get logged differently. With no shared schema, you cannot compare experience or product feedback across the channels a single customer actually uses.
Retail examples
Customer conversations, across every channel
Run the same Kit across support, chat, email, and store conversations, then send the structured fields to the systems your retail teams already work in.
01 / Product insight
Turn returns and complaints into product fixes
Customers explain exactly what went wrong - sizing, quality, delivery, the listing - in their own words. Semarize turns those returns and support conversations into structured, SKU-level fields, so merchandising and product see the patterns instead of a pile of free-text reasons.
JKT-204
Quilted jacket
142
returns / 30d
02 / Service quality
Hold service quality through peak volume
Quality is easy in January and hard in December, exactly when sampling falls behind. Semarize scores resolution, policy adherence, and empathy on every interaction across the spike, by channel, so CX leaders can see where peak pressure is breaking the experience.
Service quality vs volume
Holding through peak
03 / Lifecycle
Catch the save and upsell moments in live chats
A cancellation chat or a sizing question is also a save or an upsell, if anyone catches it. Semarize spots loyalty signals, save-offer outcomes, and upsell acceptance as they happen, so lifecycle and commerce teams can act on the moment instead of the monthly report.
save_offer_accepted
false
upsell_accepted
true
Acted on in the chat, not the monthly report
How it works
How Semarize turns retail conversations into structured signals
Connect the chats, calls, emails, and tickets you already capture. A reusable Kit evaluates the retail signals that matter and returns typed fields ready for merchandising, CX, and lifecycle systems.
- Step 1
Conversation captured
Support chats, calls, emails, return notes, and store transcripts are sent through the API.
- Step 2
Retail CX Kit runs
Bricks evaluate product, service-quality, and lifecycle signals against your policies and SKUs.
- Step 3
Signals extracted
Return reasons, quality scores, save outcomes, and upsell flags come back in the same schema each run.
- Step 4
Evidence attached
Each important field carries quote-level evidence and confidence, grounded in the conversation it came from.
- Step 5
Systems updated
Typed fields feed merchandising dashboards, CX QA, the warehouse, and lifecycle automation.
Example Bricks
The retail signals Semarize returns
Each Brick evaluates one field, score, flag, or extracted value with confidence and evidence. Bundle Bricks into reusable Kits for product insight, service quality, and lifecycle work, and ground them in your own catalogue and policies with Knowledge grounding.
Product insight
Classify why an item came back, in the customer's own terms.
Pull the product or SKU the conversation is actually about.
Flag when the customer reports a fault, defect, or build problem.
Service quality
Score how well the agent resolved the customer's issue, 0 to 100.
Check the agent applied the right returns or refund policy.
Rate the tone and acknowledgement the customer received.
Lifecycle
Detect when a customer signals they want to cancel or churn.
Record whether a retention offer was taken on the conversation.
Capture when the customer accepts a size, bundle, or add-on.
Example output
Structured signals, not summaries
The same schema comes back every run: typed values, confidence, and evidence spans tied to the conversation they came from. One chat becomes fields merchandising, CX, and lifecycle can query side by side.
Retail CX Kit returned
System-ready fields for merchandising, CX QA, and lifecycle automation
- Return reason
- runs small
- Sku
- JKT-204
- Quality issue
- true
- Resolution quality
- 82
- Upsell accepted
- true
- Save offer accepted
- false
{
"kit": "retail-cx",
"channel": "chat",
"sku": "JKT-204",
"return_reason": "runs small",
"quality_issue": true,
"resolution_quality": 82,
"policy_followed": true,
"empathy_score": 74,
"peak_load": true,
"cancellation_intent": true,
"save_offer_accepted": false,
"upsell_accepted": true,
"repeat_purchase_intent": true,
"evidence": "the jacket fit too tight across the shoulders"
}The difference
Why a summary is not enough for retail
A summary helps someone read one chat after the fact. Merchandising, CX, and lifecycle teams need fields they can query, compare, and route across every conversation, in every channel.
Conversation summaries
Structured retail signals
FAQ
Retail conversation intelligence, answered
How Semarize evaluates retail and e-commerce conversations and returns structured fields for your systems.
Keep exploring
Where retail signals go next
The same structured outputs can support adjacent teams, workflows, and product layers.
Score service quality and policy adherence across every interaction.
Track save outcomes, loyalty, and repeat-purchase signals over the lifecycle.
Feed SKU-level return and quality fields into the warehouse and models.
Turn agent behaviour on chats and calls into coaching signals.
Define the typed retail checks Semarize evaluates on each conversation.
Run Kits against conversations and receive structured JSON back.
Get started
See your conversations as retail signals
Build a Retail CX Kit, run it against the chats and calls you already capture, and get structured, SKU-level fields back through the API.
No card required. Start testing in minutes.