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Commerce

Use case: E-Commerce

Answer buyers with the order already in front of you

Order number, value, status and delivery date sit on the conversation, so any agent can pick up a thread without asking the customer to repeat themselves.

In e-commerce on Telegram, the order, the objection and the delivery chase happen in one thread. Without structure the thread is all you have, and finding a reference means scrolling weeks of history.

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Entergram's own measurements: product usage data and our in-app customer survey. Not independently audited.

The operating loop

How E-Commerce run marketing, sales and service on Telegram

Capture
  1. One shared storefront account

    Several agents answer from the single identity the customer already knows, instead of a bot or a rotating cast of personal accounts.

  2. All accounts in one inbox

    Storefront, supplier and courier accounts read from one filterable table.

  3. Tag by channel and campaign

    Workspace-shared tags record where a buyer came from, so attribution survives the first reply.

  4. Import the customer list

    Imported columns land as CRM fields, usable as filters and merge tags for later sends.

  1. Get every buyer question into one queue with an owner.

  2. Turn a question into an order without losing the context.

  3. Bring buyers back and staff the queue properly.

How Entergram helps

See how Entergram works as a Telegram CRM

The challenge

Buyer DMs, supplier threads and courier chases all arrive in the same place, at volume, and none of them carry the order they refer to. A customer asks where their parcel is and the agent has to scroll for the reference, or ask them to repeat it. Two agents answer the same person because nobody owns the thread, and nobody can tell which orders are still waiting on dispatch.

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How Entergram helps

Entergram attaches the commercial record to the conversation. Order number, value, status and delivery date become custom fields, so the inbox filters down to everything awaiting dispatch or overdue. The storefront account is shared across the team with real ownership on each chat, so two agents never collide. Problem orders escalate into SLA tickets, and broadcasts announce restocks or delays to a filtered segment from the same real account the buyer already knows.

Capabilities

What E-Commerce actually use

Shared account with ownership · Custom columns for order data · Tickets with SLA and Kanban · Broadcasts from the real account · Messaging analytics

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  • Shared account with ownershipA team shares one Telegram account and the CRM layer assigns who owns which conversation, which is what stops collisions on a busy storefront.
  • Custom columns for order dataText, number, date, multiselect and checkbox fields. Imported list columns become fields too.
  • Tickets with SLA and KanbanTicket custom fields cover category, issue type and resolution, with a board view of what is moving.
  • Broadcasts from the real accountServer-side proxied jobs, personalized per recipient, so a restock notice comes from the shop the buyer knows.
  • Messaging analyticsMessages sent and received, response times, and busiest hours and days on a 7x24 view.

Setup

How it works

Share the storefront account · Set up order fields · Assign ownership on each thread · Escalate problems into tickets · Broadcast and review

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01 Share the storefront account

Connect the shop's Telegram account and share it with the workspace so the whole team answers from one identity.

02 Set up order fields

Order number, value, status and delivery date as custom columns on every buyer chat.

03 Assign ownership on each thread

One agent owns a conversation, so nobody doubles up and nothing sits unclaimed.

04 Escalate problems into tickets

Returns and disputes get an SLA, an owner and a resolution field instead of sliding down the inbox.

05 Broadcast and review

Announce restocks to a filtered segment, then read the heatmap to staff the next week's cover.

Integrations

Keep the rest of your stack in sync

Telegram stays the place the conversation happens, while the systems your team already uses stay up to date.

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  • Make.com: a scenario triggered by your store can update the tags on the buyer's chat (for example shipped or delayed), so the agent sees order state without opening the store.
  • CSV and raw analytics export: take open-order fields and response times into the operations sheet the team already runs.
  • Entergram MCP: auto-populate order fields from the store backend and open a ticket when a delivery passes its promised date.

FAQ

Questions from E-Commerce

Can several agents answer the same e-commerce Telegram account?
Yes. An account can be shared with the workspace so the whole team replies from one identity, which is the usual storefront setup.
Does the buyer know there is a CRM behind it?
No. Entergram is a layer on top of Telegram. The buyer sees an ordinary conversation with the shop account, exactly as before.
Can order data flow in from our store?
Yes, through the API, through Make.com, or through the MCP, which can auto-populate CRM fields on the conversation.
How is this different from dropshipping?
Dropshipping puts far more weight on supplier threads and delivery-chase volume, because fulfilment is somebody else's. Both run on the same workspace, and the dedicated page covers the supplier side in depth.
Can we message buyers who never wrote first?
You can message contacts you already have, resolved by username or phone number. Entergram does not build that list or harvest group members.
Can a restock notice go only to buyers who asked about that product?
Yes. Record product interest or customer tier as a multiselect field, or bring it in as a column on an imported list. Filter to that value and broadcast to the result, personalized per recipient with merge tags. Replies come back into the Entergram inbox against each buyer's record, so a restock notice turns into tracked conversations.
Can we get our messaging numbers out of Entergram?
Yes. Messaging analytics (messages sent and received, response times, busiest hours and days) download as raw data, so they can go into the operations sheet or a weekly report. The same event data reaches an AI client through the MCP, which can answer questions no built-in report covers, such as response time per agent account.

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