SME LENDING & FINANCE

MSME Finance Broker Loan Advisory & Competing Lender Recommendation Survey

Map how MSME owners evaluate broker advisory quality, compare competing lender recommendations, and choose final loan products, so you can sharpen acquisition targeting, fix conversion drop-offs, and benchmark your positioning against competing lenders.

Pan-India sample
MSME owners (Finance Decision-Makers)
15-20 min
Talk to a Survey Consultant
Broker influence & conversion gapsIdentify where broker recommendations accelerate or stall final lender selection decisions.
Lender ranking & trade-offsBenchmark competing lenders across rate sensitivity, approval speed, and MSME segment preference.
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CONTEXT & RELEVANCE

Why run this survey now

Most finance brokers don't lose MSME clients purely on loan rates. They lose them due to misread credit needs, lender-fit mismatches, opaque fee structures, slow turnaround expectations, and competing lender positioning gaps, none of which fully show up in disbursement reports or broker CRM logs.

If you are...

  • Bank competing against NBFC speed
  • NBFC repositioning on credit flexibility
  • Fintech lender scaling MSME reach
  • Credit or product head, MSME segment
  • Distribution or channel growth lead

You're likely facing...

  • Lender-fit confusion: bank vs NBFC
  • Drop-offs at documentation or appraisal
  • Banks: stable but slow perception
  • NBFCs: fast but costly perception
  • Renewal attrition, broker switching risk

This will help answer...

  • Broker recommendation drivers beyond rate
  • Funnel drop-off stage, by lender type
  • MSME segment preference, bank vs NBFC
  • Fee, tenure, and pricing tension
  • Renewal triggers and switching signals

RESEARCH THEMES

What This Survey Investigates

Eight interconnected research themes that map the complete MSME borrower journey from lender discovery to post-disbursement loyalty.

TENETS 01

Discovery & Trust

  • Broker discovery channels, referral triggers
  • First-contact credibility signals
TENETS 02

Preference Drivers

  • Lender shortlisting criteria, product fit
  • Broker recommendation rationale
TENETS 03

Product & Servicing

  • Loan product mix, ticket size range
  • Post-sanction servicing touchpoints
TENETS 04

Journey Friction

  • Drop-offs across application stages
  • Documentation delays, KYC bottlenecks
TENETS 05

Pricing & WTP

  • Broker fee structures, commission transparency
  • Willingness to pay for faster processing
TENETS 06

Usage & Stickiness

  • Repeat engagement frequency, loan cycles
  • Broker retention triggers, switching intent
TENETS 07

Trust & Credibility

  • Regulatory compliance signals, licensing checks
  • Lender relationship depth, panel strength
TENETS 08

Competitive Positioning

  • Competing broker awareness, switching triggers
  • Fintech DSA versus traditional broker trade-offs

SAMPLING STRATEGY

Tell us about your ideal sample

Help us understand your target respondent profile. Select what applies, we'll design the optimal sample plan based on your inputs.

Sample size
How many respondents do you need?
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Target audience
Who should we survey?
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Region
Which regions should we cover?
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Segments
How should we slice the data?
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Discuss sample plan

METHODOLOGY

Survey approach

For the MSME Finance Broker Loan Advisory & Competing Lender Recommendation Survey, we recommend a quant-first design with flexible data-collection modes to balance reach, depth, and verification.

PRIMARY
Online web surveySelf-administered survey shared via email / panels to capture structured responses at scale.
Best for
1
Ranking competing lender recommendations by broker segment.
2
Measuring loan advisory frequency and ticket-size patterns.
3
Comparing broker behavior across MSME size and sector.
Deliverables
Lender preference ranking
Advisory gap matrix
Segment comparison cuts
OPTIONAL
CATI (phone survey)Interviewer-led telephone interviews to reach owners who are harder to get online.
Best for
1
Micro-enterprise brokers with low digital engagement.
2
Quick coverage across Tier 2 and Tier 3 towns.
Deliverables
Broker reach coverage
Call-log diagnostics
SELECTIVE
Face-to-faceOn-ground surveys or interviews in key industrial clusters or high-value cohorts.
Best for
1
High-volume brokers handling large MSME loan tickets.
2
Clusters where lender relationships need direct verification.
Deliverables
Cluster insights
Broker journey maps
OPTIONAL
FGDs
Deliverables
Themes and quotes
Proposition feedback
OPTIONAL
Mixed surveysAny 4-mode combo Online + CATI + F2F + FGDs to maximise reach and representation. Mode-specific quotas and weighting for clean comparisons.
Deliverables
Unified dataset
Mode-adjusted analytics
Our Recommendation
Start with: Online web survey as the core quant layer, supported by CATI to capture brokers in low-digital Tier 2 and Tier 3 markets.
Consider adding: F2F interviews for high-ticket broker cohorts in key MSME lending clusters, with a focused FGD layer to sharpen lender messaging and advisory positioning.

EXECUTION PROCESS

How we execute

A proven 9-step process from scoping to delivery, designed to ensure quality, speed, and actionable insights.

Define the decision frame

Confirm objectives, target cohorts, geographies, and reporting cuts

Step 01

Define the decision frame

Design the instrument

Build workstream modules mapped to outputs (drivers, friction, pricing, retention, trust)

Step 02

Design the instrument

Lock the questionnaire

Review wording, sequencing, LOI, and competitive context; approve final version

Step 03

Lock the questionnaire

Pilot and calibrate

Test comprehension and ease quality; refine quotas and remove friction where needed

Step 04

Pilot and calibrate

Run fieldwork

Execute collection with active quota management and feasibility controls

Step 05

Run fieldwork

Assure quality

Dedupe, attention checks, speed/consistency rules, removals with audit trail

Step 06

Assure quality

Prepare the dataset

Clean data and deliver codebook/variable definitions

Step 07

Prepare the dataset

Analyse and synthesise

Driver ranking, leakage diagnostics, pricing bands, segment insights

Step 08

Analyse and synthesise

Deliver and align

Executive deck (optional dashboard) and leadership readout with recommendations

Step 09

Deliver and align

COMMERCIAL TERMS

Request a Commercial Proposal

Pricing depends on cohort, geography, sample size, approach, LOI, and deliverables. Configure below for an indicative estimate.

Select Sample Size

100

Geography

  • India
  • APAC (Singapore, Vietnam, Philippines, Indonesia, Australia, NZ, Japan, Thailand)
  • Middle East (UAE, KSA, Qatar, Bahrain, Oman, Kuwait)
  • North America (US, Canada)
  • Europe
  • Africa (South Africa, Kenya, Nigeria, Egypt, Algeria)
  • LATAM (Brazil, Mexico)

Select Mode of Survey

  • Online
  • CATI
  • Online FGD (5 people per FGD)
  • F2F

Length of the Interview

  • Select
  • 0-15
  • 16-20
  • 21-30
  • 31-45
  • 46-60
  • Custom
Indicative Estimate
  • Indian Rupee (INR)
  • United Arab Emirates Dirham (AED)
  • Afghan Afghani (AFN)
  • Albanian Lek (ALL)
  • Armenian Dram (AMD)
  • Netherlands Antillean Guilder (ANG)
  • Angolan Kwanza (AOA)
  • Argentine Peso (ARS)
  • Australian Dollar (AUD)
  • Aruban Florin (AWG)
  • Azerbaijani Manat (AZN)
  • Bosnia-Herzegovina Convertible Mark (BAM)
  • Barbadian Dollar (BBD)
  • Bangladeshi Taka (BDT)
  • Bulgarian Lev (BGN)
  • Bahraini Dinar (BHD)
  • Burundian Franc (BIF)
  • Bermudian Dollar (BMD)
  • Brunei Dollar (BND)
  • Bolivian Boliviano (BOB)
  • Brazilian Real (BRL)
  • Bahamian Dollar (BSD)
  • Bhutanese Ngultrum (BTN)
  • Botswana Pula (BWP)
  • Belarusian Ruble (BYN)
  • Belize Dollar (BZD)
  • Canadian Dollar (CAD)
  • Congolese Franc (CDF)
  • Swiss Franc (CHF)
  • Chilean Peso (CLP)
  • Chinese Yuan (CNY)
  • Colombian Peso (COP)
  • Costa Rican Colón (CRC)
  • Cuban Peso (CUP)
  • Cape Verdean Escudo (CVE)
  • Czech Koruna (CZK)
  • Djiboutian Franc (DJF)
  • Danish Krone (DKK)
  • Dominican Peso (DOP)
  • Algerian Dinar (DZD)
  • Egyptian Pound (EGP)
  • Eritrean Nakfa (ERN)
  • Ethiopian Birr (ETB)
  • Euro (EUR)
  • Fijian Dollar (FJD)
  • Falkland Islands Pound (FKP)
  • British Pound (GBP)
  • Georgian Lari (GEL)
  • Ghanaian Cedi (GHS)
  • Gibraltar Pound (GIP)
  • Gambian Dalasi (GMD)
  • Guinean Franc (GNF)
  • Guatemalan Quetzal (GTQ)
  • Guyanese Dollar (GYD)
  • Hong Kong Dollar (HKD)
  • Honduran Lempira (HNL)
  • Croatian Kuna (HRK)
  • Haitian Gourde (HTG)
  • Hungarian Forint (HUF)
  • Indonesian Rupiah (IDR)
  • Israeli New Shekel (ILS)
  • Iraqi Dinar (IQD)
  • Iranian Rial (IRR)
  • Icelandic Króna (ISK)
  • Jamaican Dollar (JMD)
  • Jordanian Dinar (JOD)
  • Japanese Yen (JPY)
  • Kenyan Shilling (KES)
  • Kyrgyzstani Som (KGS)
  • Cambodian Riel (KHR)
  • Comorian Franc (KMF)
  • South Korean Won (KRW)
  • Kuwaiti Dinar (KWD)
  • Cayman Islands Dollar (KYD)
  • Kazakhstani Tenge (KZT)
  • Lao Kip (LAK)
  • Lebanese Pound (LBP)
  • Sri Lankan Rupee (LKR)
  • Liberian Dollar (LRD)
  • Lesotho Loti (LSL)
  • Libyan Dinar (LYD)
  • Moroccan Dirham (MAD)
  • Moldovan Leu (MDL)
  • Malagasy Ariary (MGA)
  • Macedonian Denar (MKD)
  • Burmese Kyat (MMK)
  • Mongolian Tögrög (MNT)
  • Macanese Pataca (MOP)
  • Mauritian Rupee (MUR)
  • Maldivian Rufiyaa (MVR)
  • Malawian Kwacha (MWK)
  • Mexican Peso (MXN)
  • Malaysian Ringgit (MYR)
  • Mozambican Metical (MZN)
  • Namibian Dollar (NAD)
  • Nigerian Naira (NGN)
  • Nicaraguan Córdoba (NIO)
  • Norwegian Krone (NOK)
  • Nepalese Rupee (NPR)
  • New Zealand Dollar (NZD)
  • Omani Rial (OMR)
  • Panamanian Balboa (PAB)
  • Peruvian Sol (PEN)
  • Papua New Guinean Kina (PGK)
  • Philippine Peso (PHP)
  • Pakistani Rupee (PKR)
  • Polish Złoty (PLN)
  • Paraguayan Guaraní (PYG)
  • Qatari Riyal (QAR)
  • Romanian Leu (RON)
  • Serbian Dinar (RSD)
  • Russian Ruble (RUB)
  • Rwandan Franc (RWF)
  • Saudi Riyal (SAR)
  • Solomon Islands Dollar (SBD)
  • Seychellois Rupee (SCR)
  • Sudanese Pound (SDG)
  • Swedish Krona (SEK)
  • Singapore Dollar (SGD)
  • Saint Helena Pound (SHP)
  • Sierra Leonean Leone (SLL)
  • Somali Shilling (SOS)
  • Surinamese Dollar (SRD)
  • São Tomé and Príncipe Dobra (STD)
  • Syrian Pound (SYP)
  • Swazi Lilangeni (SZL)
  • Thai Baht (THB)
  • Tajikistani Somoni (TJS)
  • Turkmenistani Manat (TMT)
  • Tunisian Dinar (TND)
  • Tongan Paʻanga (TOP)
  • Turkish Lira (TRY)
  • Trinidad and Tobago Dollar (TTD)
  • New Taiwan Dollar (TWD)
  • Tanzanian Shilling (TZS)
  • Ukrainian Hryvnia (UAH)
  • Ugandan Shilling (UGX)
  • United States Dollar (USD)
  • Uruguayan Peso (UYU)
  • Uzbekistani Som (UZS)
  • Vietnamese Đồng (VND)
  • Vanuatu Vatu (VUV)
  • Samoan Tālā (WST)
  • Central African CFA Franc (XAF)
  • East Caribbean Dollar (XCD)
  • West African CFA franc (XOF)
  • CFP Franc (XPF)
  • Yemeni Rial (YER)
  • South African Rand (ZAR)
  • Zambian Kwacha (ZMW)
  • Zimbabwean Dollar (ZWL)

$0.00

+ applicable taxes

Proposal turnaround typically 24–48 hours

Note: Estimate is indicative only. Final pricing is subject to scope finalization after discovery call.

REFERENCE CASELETS

Reference

Real-world examples of survey work in the MSME lending and broker advisory space.

CASELET 1

MSME borrower segmentation & lender shortlisting behaviour (India)

CASELET 2

Broker channel trust & competing lender recommendation drivers (West India)

MSME borrower segmentation & lender shortlisting behaviour (India)

OBJECTIVE

A digital-first NBFC needed to map how micro-enterprise owners and small business proprietors build their lender shortlists, and which product attributes and broker touchpoints drive final selection versus abandonment at the application stage.

WHAT WE DID

Ran a structured quant survey across 320 MSME borrowers in 6 cities, capturing lender shortlist composition, broker influence weight, documentation burden perception, and turnaround time tolerance by loan ticket size and business vintage.

DELIVERED

A borrower segmentation framework by decision style, a lender attribute priority map by ticket size, and a ranked friction list identifying the 4 stages where broker-referred applications stall before disbursement.
CASELET 1

MSME borrower segmentation & lender shortlisting behaviour (India)

CASELET 2

Broker channel trust & competing lender recommendation drivers (West India)

MSME borrower segmentation & lender shortlisting behaviour (India)

OBJECTIVE

A digital-first NBFC needed to map how micro-enterprise owners and small business proprietors build their lender shortlists, and which product attributes and broker touchpoints drive final selection versus abandonment at the application stage.

WHAT WE DID

Ran a structured quant survey across 320 MSME borrowers in 6 cities, capturing lender shortlist composition, broker influence weight, documentation burden perception, and turnaround time tolerance by loan ticket size and business vintage.

DELIVERED

A borrower segmentation framework by decision style, a lender attribute priority map by ticket size, and a ranked friction list identifying the 4 stages where broker-referred applications stall before disbursement.

FREQUENTLY ASKED QUESTIONS

Common Questions

Answers to frequently asked questions about this survey mandate.

What decisions will this survey enable?

Who is the buyer vs who are the respondents?

Can we see differences between micro-enterprise borrowers, small business borrowers and medium-enterprise borrowers?

How will you measure broker recommendation acceptance beyond simple ratings?

Will the survey map the full broker-assisted loan journey and drop-offs?

Can this survey inform product and pricing strategy?

How will findings improve our broker channel conversion and lender placement rates?

Still have questions?

Schedule a discovery call to discuss your specific needs and get a custom quote.

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