ONDC Onboarding Readiness of Small Sellers in Bengaluru: Costs, Catalogue Effort and Order Conversion

The Open Network for Digital Commerce (ONDC), promoted by the Department for Promotion of Industry and Internal Trade (DPIIT), is India's attempt to make online selling work like UPI: instead of joining one large marketplace, a seller signs up with any seller-side app and becomes visible to buyers on every connected buyer-side app. Bengaluru was one of the first cities where ONDC opened to the public in 2022, and the network is often presented as a way for kirana stores, bakeries and small manufacturers to sell online without high marketplace commissions.

What small sellers actually experience is less documented. Joining requires choosing a seller app, completing registration and documents, building a catalogue with photos, prices and stock, and handling delivery, returns and settlement. This B.Com survey project measures how ready small Bengaluru sellers are to onboard, what it costs them in money and time, and whether listings turn into orders.

The study surveys about 80 small sellers (half already on ONDC through a seller app, half not), compares three seller apps (coded A, B and C) on onboarding steps, fees and support, and builds an Excel per-order cost model that compares ONDC with walk-in and phone or WhatsApp orders. Chi-square and Mann-Whitney tests examine which seller characteristics relate to adoption and to continued activity. The report follows the BCU B.Com SEP 5.6 Survey Project format.

Abstract

This study examines the readiness of small sellers in Bengaluru to join the Open Network for Digital Commerce and the costs and outcomes they experience. A survey of about 80 sellers, half already active on ONDC, measures awareness, onboarding effort, catalogue preparation time, fees, orders received and problems faced. Three seller-side apps are compared on onboarding requirements and charges. A per-order cost model compares ONDC orders with offline channels, and chi-square and Mann-Whitney tests identify factors associated with adoption and continued use.

Introduction

E-commerce in India is dominated by a few large platforms, and small sellers often find commissions, advertising costs and platform rules difficult. ONDC was set up as a not-for-profit company to create an open, interoperable network where buyer apps and seller apps connect through common protocols. Policymakers present it as a route for MSMEs and neighbourhood stores to reach online customers. For a B.Com student, it is a live example of digital public infrastructure applied to commerce.

Research gap

Reports on ONDC focus on order volumes, participating apps and policy goals. There is little ground-level evidence on what onboarding costs a small seller in time and money, how many listed sellers actually receive orders, and why some sellers stop using the network. A seller survey with a simple cost model addresses this at a scale suitable for a semester project.

Proposed study

• Measure awareness and readiness among small sellers.
• Document onboarding steps, documents and catalogue effort.
• Compare costs per order across channels.
• Test associations between seller characteristics and adoption.
• Suggest practical support measures for sellers, seller apps and trade associations.

Feasibility

• Data: face-to-face survey in four localities; sellers on ONDC identified through local trader associations and seller-app listings.
• Tools: Excel and SPSS from the college lab.
• Ethics: consent; no GSTIN, PAN, bank details or customer data recorded; seller apps coded to avoid endorsing or criticising specific companies.

ONDC promises small sellers lower-cost access to online buyers, but adoption among neighbourhood businesses appears uneven, and many sellers who sign up receive few orders or stop updating their listings. Sellers face choices about which seller app to use, what documents and registrations they need, how much time to spend on catalogue photos and stock updates, and how fees, delivery charges and returns affect their margin on each order. Without ground-level evidence, trade associations and seller apps cannot design the right support. The study addresses the question: how ready are small sellers in Bengaluru to onboard to ONDC, what does onboarding and selling cost them per order, and which seller characteristics are associated with adoption and continued activity?

The study covers small retail and manufacturing sellers (kirana and provision stores, bakeries and sweet shops, apparel and handicraft sellers) in four Bengaluru localities. It covers seller-side experience only, not buyer behaviour or the technical protocol. Order and cost data are self-reported for the last three months. Seller apps are coded and the study does not rank or recommend any company.

Research design

Descriptive survey research with a comparative document analysis of seller apps and a simple cost model. Secondary data from ONDC's public website and DPIIT releases, and GST provisions for supplies through e-commerce operators (including relaxations for small intra-state suppliers, checked on the date of study).

Sample

• Population: small sellers in Jayanagar, Malleshwaram, Rajajinagar and Indiranagar.
• Groups: 40 sellers active on ONDC (identified through trader associations and seller-app listings) and 40 not on ONDC (systematically selected along the same streets: every third shop of the same business types).
• Total n ≈ 80, giving adequate expected counts for 2 × 3 chi-square tables.

Questionnaire

Pilot: 8 sellers; revise wording and timing (under 15 minutes).

Seller-app comparison

Three seller apps coded A, B, C: sign-up steps, documents asked, onboarding fee, commission or subscription, logistics options, settlement cycle, language support, help channels; compiled from public information and walk-throughs with participating sellers.

Cost model (Excel)

Per order: seller-app fee or commission + payment charges + delivery charge borne by seller + packaging + allocated catalogue and update time (hours × owner's hourly value) + expected cost of returns and cancellations; compared with walk-in and WhatsApp or phone orders for the same average basket.

Hypotheses

Timeline (10 weeks)