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Scenarios

Everyday retail problems, worked through.

Each scenario below is an illustrative example built from situations Indian stores face every week. They show how DragBizz handles the problem step by step. They are not customer testimonials.

Illustrative scenarioGrocery & kirana

A shelf of honey jars close to expiry, caught weeks early.

Before
Four dozen jars sit on a back shelf with under three weeks of shelf life left. Nobody notices until the monthly count, when half the window to sell them has gone.
With DragBizz
Batch expiry dates are tracked from the purchase entry. The AI Co-Partner flags the batch early and suggests a small discount so the stock moves before it expires.
Explore AI Co-Partner

How it plays out

  1. Purchase

    Batch and expiry date recorded on the purchase bill

  2. 3 weeks out

    Expiry alert raised with a suggested discount

  3. Same day

    Owner approves the offer at the counter

  4. Before expiry

    Batch sold through; nothing written off

Scenario library

More situations, step by step.

Filter by the kind of store you run. Every scenario links to the part of DragBizz that handles it.

Filter by industry
  • Illustrative scenario

    Pharmacy

    Batch-level expiry tracking across thousands of medicines.

    Before: Staff check shelf dates by hand across thousands of SKUs. Syrups and antibiotics expire unnoticed, creating write-offs and compliance risk.

    1. Receiving

      Each batch scanned with its expiry date

    2. Daily

      Expiring-soon list ready when the store opens

    3. Billing

      Oldest batch picked first automatically

    4. Reorder

      Purchase suggestion held until old stock clears

    • No manual date checks
    • Batch-level records
    • FIFO enforced at billing
    Explore Inventory
  • Illustrative scenario

    Fashion

    One stock count across the counter and online orders.

    Before: A boutique sells in store, on its own website and on a marketplace, updating each by hand. Selling a size that has already gone is a weekly problem.

    1. 10:02

      Last medium sold at the counter

    2. 10:02

      Stock updated across every connected channel

    3. 10:05

      Online listing shows the size as sold out

    4. Dispatch

      Customer gets a WhatsApp shipping update

    • One stock record
    • Fewer oversold orders
    • Automatic order updates
    Explore Online selling
  • Illustrative scenario

    Distribution

    A seasonal demand spike, planned for before it arrives.

    Before: A distributor runs short of fast-moving biscuits every school-reopening season. The spike is predictable, but reorders only happen after retailers call about stockouts.

    1. 4 weeks out

      Seasonal trend spotted in last year's sales

    2. 3 weeks out

      Draft purchase order prepared

    3. Approval

      Owner adjusts quantities and sends to supplier

    4. Season

      Shelves stocked before demand peaks

    • Earlier reorders
    • Draft POs, owner decides
    • Fewer stockout calls
    Explore AI Co-Partner
  • Illustrative scenario

    Electronics

    GST returns prepared from live billing data.

    Before: An electronics store's accountant spends a full day each period matching purchase invoices, input tax credit and sales before filing returns.

    1. Every sale

      GST captured correctly on the invoice

    2. Every purchase

      Input tax credit recorded with the bill

    3. Month end

      Return summaries generated from live data

    4. Filing

      Accountant reviews and submits

    • Returns from live data
    • Less manual matching
    • Accountant stays in control
    Explore GST & Compliance
  • Illustrative scenario

    Distribution

    Quiet customers found and invited back before a festival.

    Before: A wholesaler has retailers who haven't ordered in two months, but no simple way to see who they are or reach them all at once.

    1. Weekly

      Customers inactive for 60 days are listed

    2. Festival

      Restock offer drafted for the list

    3. Send

      Campaign sent on WhatsApp

    4. Follow-up

      Replies and orders tracked against the campaign

    • At-risk customers surfaced
    • One-step campaigns
    • Replies tracked
    Explore WhatsApp CRM