SalesUPRazorpay Cross-Border
Data EngineeringOutboundRevOps & Analytics

Mining Public Trade Data to Find Cross-Border Payment Buyers

Razorpay's cross-border payments team needed Indian exporters who already bill international clients.

SalesUP mined public GST invoice data to find them, including businesses using a competing provider, and ran outbound on those lists.

Razorpay Cross-Border case study
The transformation

From a clear constraint to a measurable result

  1. 01

    Starting point

    Standard B2B databases cannot tell you which Indian businesses earn in foreign currency.

  2. 02

    First move

    SalesUP mined public GST invoice data to find businesses already paying a competing provider.

  3. 03

    Verified outcome

    747Meetings set

    747 meetings set at a 74% show rate, with displacement lists converting at 2.8x.

Key numbers

Meetings set
747
Show rate
74%
Meeting rate on competitor displacement lists
2.8x

About Razorpay Cross-Border

Razorpay Cross-Border

Payments infrastructure that helps Indian businesses get paid from anywhere

Razorpay's cross-border product lets Indian businesses collect international payments. Its buyers are exporters, agencies and service firms with meaningful monthly overseas revenue.

The business challenge

  • Databases miss foreign revenue

    Standard B2B databases cannot tell you who earns in foreign currency.

  • A rising qualification bar

    Billing is per SQL, and the qualifying floor rose from $5k to $10k monthly volume mid-engagement.

  • Volume and intent together

    Pipeline had to be volume-rich and high-intent at the same time.

The engagement

How the work unfolded

Each step in the order it was built and run.

  1. 01

    Mined GST invoice data

    Competitor's GST B2B data, 35,418 invoices across 6,955 buyers (Apr-Jun 2026), became a displacement list.

  2. 02

    Export data crossover

    Matched export and GST records to find active exporters.

  3. 03

    Outbound and qualification

    Calls, follow-ups and qualification against Razorpay's volume criteria.

  4. 04

    RevOps and client base

    Funnel reporting, no-show tracking, deal-size pricing tiers; re-engage and expand Razorpay's existing customers.

How SalesUP ran the Razorpay Cross-Border program
The strategy

The channels behind the result

  • Data engineering

    Exporters from trade data

    GST invoice data from a competing provider became a list of businesses already paying for cross-border payments.

    Invoices mined
    35,418
    Buyer businesses
    6,955
  • Outbound

    Qualified against volume

    SDR calls and qualification against Razorpay's monthly volume floor.

    Meetings set
    747
    Show rate
    74%
  • Client base

    Existing customers, re-engaged

    Separate programs re-engaged and expanded Razorpay's existing customer accounts.

    Meetings handed over
    185
    Meetings completed
    90

The outcome

Meeting rate by sourceMeetings set as a share of contacts worked
  • Competitor displacement list6.4%

    78 contacts · 5 meetings set

  • Export + GST crossover2.0%

    702 contacts · 14 meetings set

  • All sourcesBaseline2.3%

    32,819 contacts · 747 meetings set

Why it matters

From March to September 2026, SalesUP worked 32,819 contacts, set 747 meetings and completed 416 at a 74% show rate.

Competitor displacement lists converted at 2.8x the meeting rate of all sources combined.

The math

Meetings set
747
Show rate
74%
Meeting rate on competitor displacement lists
2.8x

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