Every order question resolved before it becomes a ticket

Where is my order, can I return it, does it come in blue. Spark answers with live order and stock data, and issues refunds and exchanges inside the rules you set.

The atrium of a bright shopping mall, with shopfronts on several floors

78%

of contacts resolved without an agent

−41%

cost per contact in the first quarter

0.6s

median reply on chat

4 wks

from contract to live traffic

78%

78%

of contacts resolved without an agent

−41%

−41%

cost per contact in the first quarter

0.6s

0.6s

median reply on chat

4 wks

4 wks

from contract to live traffic

The conversations that fill an e-commerce queue

Seven intents make up almost every e-commerce contact. Each one maps to a playbook that ships with the commerce pack, and you can change any of them.

Intent

Share of contacts

Resolved

Playbook

  • Where is my order

    “It said Tuesday and it is Thursday”

    34%

    92%

    92%

    Order status

  • Returns and exchanges

    “Can I swap this for a medium?”

    21%

    81%

    81%

    Returns

  • Product questions

    “Is the jacket waterproof or just resistant?”

    15%

    88%

    88%

    Catalogue answers

  • Delivery changes

    “Can you leave it with a neighbour?”

    11%

    74%

    74%

    Delivery update

  • Payments and promo codes

    “My discount code did not apply”

    9%

    69%

    69%

    Payment check

  • Account and loyalty

    “Where did my points go?”

    6%

    77%

    77%

    Account lookup

  • Everything else

    “Complaints, press, the unexpected”

    4%

    To a person

    To a person

    Hand-off

Spark writes the reply. The playbook moves the money.

Returns run you can read. Spark understands the customer and writes in your voice, but whether a refund is allowed is decided by a rule, every time.

  • Return windows, final-sale items and refund limits set per market

  • Exchanges checked against live stock before they are offered

  • Labels created with your carrier, refund released on first scan

  • Anything outside policy handed to a person with the case attached

A woman packing an order into a cardboard box beside a rail of colourful shirts

The jacket is a bit small. Can I swap it for a medium?

Exchange placed

Inside the window, medium in stock

Black Friday without the hold queue

Contacts tripled across peak week at one fashion retailer. The agent took the extra volume and the support team stayed the size it was in October.

19,600contacts on Cyber Monday, 82% resolved by the agent

  • All contacts
  • Handed to a person
Contacts per day across peak week
DayContactsHanded to a person
Nov 216,2001,400
Nov 225,8001,300
Nov 237,1001,600
Nov 248,4001,800
Nov 259,9002,100
Nov 2611,6002,300
Nov 27, Black Friday18,9003,400
Nov 2816,2003,000
Nov 2914,1002,700
Nov 30, Cyber Monday19,6003,600
Dec 115,3002,900
Dec 212,2002,400
Dec 310,1002,100
Dec 48,7001,900

Connected to the systems that hold the answers

The commerce pack comes with connectors for the usual stack. Each one lists exactly what the agent can read and what it is allowed to change.

System

Reads

Writes

Storefront

Catalogue, prices, stock by size

Nothing

Order management

Orders, fulfilment, tracking

Delivery notes, exchanges

Payments

Charges and refund status

Refunds inside policy

Returns portal

Open returns and labels

New returns, labels

Loyalty

Points, tier, history

Goodwill points, capped

Helpdesk

Past tickets

Hand-offs with a summary

Portrait of Priya Raman

parcelry

“We expected Spark to deflect the easy tickets. It ended up closing refunds and delivery changes end to end, and our team finally has time for the conversations that need a person.”

Priya Raman

VP Customer Operations, Parcelry

84%

resolved end to end

Questions commerce teams ask

Which commerce platforms does Spark connect to?

Can the agent issue refunds on its own?

How does it handle peak season?

Does it answer product questions from our catalogue?

Put Spark on your busiest queue

Send us a month of transcripts and we will show you which conversations an agent could have finished, and how.

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