SME LENDING & FINANCE

Business Loan vs Overdraft Facility Preference Survey

Map how small business owners and finance decision-makers evaluate, compare, and choose between term loans and overdraft facilities across cost, flexibility, and repayment structure, so you can sharpen acquisition targeting, refine product positioning, and convert high-intent borrowers faster.

Pan-India sample
SMEs (Owners/Finance Decision-Makers)
15-20 min
Talk to a Survey Consultant
Facility selection frictionIdentify where borrowers stall, switch preference, or abandon the credit application.
Pricing sensitivity & trade-offsBenchmark interest rate thresholds, fee tolerance, and repayment flexibility across segments.
TRUSTED BY LEADING BRANDS
Brand 0Brand 1Brand 2Brand 3Brand 4Brand 5Brand 6Brand 7Brand 8Brand 9Brand 10Brand 11Brand 12Brand 13Brand 14Brand 15Brand 16Brand 17Brand 18Brand 19Brand 20Brand 21Brand 22Brand 23Brand 24Brand 25Brand 26Brand 27Brand 28Brand 29Brand 30Brand 31

CONTEXT & RELEVANCE

Why run this survey now

Most lenders don't lose SME borrowers purely on interest rates. They lose them due to product misfit, opaque fee structures, rigid collateral norms, slow credit decisions, and poor renewal communication, none of which fully show up in loan origination reports or portfolio dashboards.

If you are...

  • Bank competing against fintech lenders
  • NBFC positioning on speed and flexibility
  • Credit or product head, SME lending
  • Distribution or growth head, business loans
  • Lender expanding into underserved SME segments

You're likely facing...

  • SME fit confusion: loan vs overdraft
  • Drop-offs at documentation or credit stage
  • Banks: stable but slow perception
  • NBFCs: fast but costly perception
  • Renewal gaps and mid-cycle switching

This will help answer...

  • Preference drivers beyond rate
  • Funnel drop-off by product type
  • Bank vs NBFC segment preference
  • Fee, tenure, and limit sensitivity
  • Renewal triggers and switch intent

RESEARCH THEMES

What This Survey Investigates

Eight interconnected research themes that map the complete borrower journey from initial credit need to facility renewal.

TENETS 01

Discovery & Awareness

  • First lender touchpoint, channel
  • Product awareness, business loan vs OD
TENETS 02

Preference Drivers

  • Repayment flexibility, drawdown control
  • Loan tenure vs revolving credit preference
TENETS 03

Product & Servicing

  • Sanctioned limit, drawdown frequency
  • RM support, digital self-service
TENETS 04

Journey Friction

  • Documentation burden, KYC turnaround
  • Drop-offs across application stages
TENETS 05

Pricing & WTP

  • Effective interest rate sensitivity
  • Processing fee, prepayment charge tolerance
TENETS 06

Usage & Stickiness

  • Utilisation rate, limit adequacy
  • Renewal intent, top-up behaviour
TENETS 07

Trust & Credibility

  • Lender reputation, peer referral weight
  • Transparency in terms, hidden charge concerns
TENETS 08

Competitive Positioning

  • PSB vs private bank vs NBFC preference
  • Fintech lender consideration, switching barriers

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?
Not Selected
Target audience
Who should we survey?
Not Selected
Region
Which regions should we cover?
Not Selected
Segments
How should we slice the data?
Not Selected
Discuss sample plan

METHODOLOGY

Survey approach

For the Business Loan vs Overdraft Facility Preference 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
Measuring business loan vs overdraft preference and usage
2
Ranking drivers of facility selection by borrower segment
3
Comparing segments by business size, sector, and tenure
Deliverables
Driver ranking
Preference gap matrix
Segment profiles
OPTIONAL
CATI (phone survey)Interviewer-led telephone interviews to reach owners who are harder to get online.
Best for
1
Micro and small business owners with low digital comfort
2
Quick coverage across Tier 2 and Tier 3 lending markets
Deliverables
Representative borrower coverage
Call-log diagnostics
SELECTIVE
Face-to-faceOn-ground surveys or interviews in key industrial clusters or high-value cohorts.
Best for
1
High-ticket borrowers in sensitive credit decision segments
2
Contextual mapping of local lender-borrower ecosystems
Deliverables
Cluster insights
Rich journey maps
OPTIONAL
FGDs
Deliverables
Themes and quotes
Concept 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 business owners with low digital access across Tier 2 and Tier 3 markets.
Consider adding: F2F interviews for high-ticket borrower clusters and a focused FGD layer to pressure-test facility positioning and refine communication angles.

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 business lending and credit products space.

CASELET 1

Working capital product positioning & segment fit (India)

CASELET 2

Credit line renewal intent & lender switching triggers (West India)

Working capital product positioning & segment fit (India)

OBJECTIVE

A digital-first NBFC needed to isolate how micro-enterprise owners and small business proprietors evaluate working capital products , specifically which product attributes drove shortlisting and which triggered drop-off before application.

WHAT WE DID

Ran a structured quant survey across 420 respondents in 6 cities, capturing product attribute rankings, shortlisting triggers, fee sensitivity, collateral tolerance, and stated reasons for abandoning a product inquiry before submission.

DELIVERED

A segment-level preference map by business type, a ranked friction list at each shortlisting stage, and a set of message territories tied to the top three product attributes that drove final selection decisions.
CASELET 1

Working capital product positioning & segment fit (India)

CASELET 2

Credit line renewal intent & lender switching triggers (West India)

Working capital product positioning & segment fit (India)

OBJECTIVE

A digital-first NBFC needed to isolate how micro-enterprise owners and small business proprietors evaluate working capital products , specifically which product attributes drove shortlisting and which triggered drop-off before application.

WHAT WE DID

Ran a structured quant survey across 420 respondents in 6 cities, capturing product attribute rankings, shortlisting triggers, fee sensitivity, collateral tolerance, and stated reasons for abandoning a product inquiry before submission.

DELIVERED

A segment-level preference map by business type, a ranked friction list at each shortlisting stage, and a set of message territories tied to the top three product attributes that drove final selection decisions.

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 business loan users, overdraft facility users and mixed credit users?

How will you measure credit facility preference beyond simple ratings?

Will the survey map the full credit facility selection journey and drop-offs?

Can this survey inform product and pricing strategy?

How will findings improve our credit product acquisition and retention messaging?

Still have questions?

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

Book a Discovery Call