WEALTH & DIGITAL ADVISORY

Retail Investor Unmet Advisory & Digital Wealth Platform Expectation Survey

Retail investors evaluate advisory quality, compare platform features, and weigh fee structures against perceived value, so you can sharpen acquisition messaging, fix retention gaps, and benchmark your platform positioning against unmet expectations.

Pan-India sample
Retail investors (Active Platform Users)
15-20 min
Talk to a Survey Consultant
Advisory friction & drop-offsIdentify where retail investors disengage, switch platforms, or reject advisory prompts.
Feature gaps & pricing toleranceDiagnose unmet feature expectations and quantify willingness-to-pay across investor segments.
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CONTEXT & RELEVANCE

Why run this survey now

Most wealth platforms don't lose retail investors purely on fee structures. They lose them due to generic advice delivery, mismatched risk profiling, low digital trust, feature-to-need misalignment, and unmet onboarding expectations, none of which fully show up in platform engagement metrics or AUM attrition reports.

If you are...

  • Digital wealth platform product head

  • Robo-advisory vs full-service competitor

  • Retail brokerage distribution leader

  • Wealth-tech revenue and pricing lead

  • Investment platform strategy director

You're likely facing...

  • Advisory fit gap: digital vs human

  • Onboarding drop-off: KYC/risk stage

  • Robo = cheap/impersonal perception

  • Fee opacity vs value tension

  • Portfolio review frequency mismatch

This will help answer...

  • Advisory preference drivers by segment

  • Platform drop-off stage and trigger

  • Digital vs hybrid investor segments

  • Fee sensitivity and value thresholds

  • Switching triggers and retention levers

RESEARCH THEMES

What This Survey Investigates

Eight interconnected research themes that map the complete retail investor journey from first account opening to portfolio advocacy.

TENETS 01

Discovery & Onboarding

  • First platform touchpoint, referral source
  • KYC friction, account activation drop-off
TENETS 02

Advisory Expectations

  • Human RM vs. robo-advisory preference
  • Advice frequency, personalisation gap
TENETS 03

Product & Coverage

  • Asset class breadth, instrument availability
  • Unmet demand for alternative products
TENETS 04

Platform Friction

  • Transaction failure, execution delay points
  • Navigation drop-offs, feature discoverability
TENETS 05

Fee Sensitivity

  • Brokerage, advisory fee tolerance thresholds
  • Willingness to pay for premium tiers
TENETS 06

Engagement & Retention

  • Login frequency, active feature usage
  • Notification relevance, re-engagement triggers
TENETS 07

Trust & Credibility

  • SEBI registration, grievance redressal confidence
  • Data security perception, fund safety signals
TENETS 08

Switching & Loyalty

  • Multi-platform usage, primary broker share
  • Switch triggers, consolidation intent
Request a scope validation call

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?
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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 Retail Investor Unmet Advisory and Digital Wealth Platform Expectation 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 unmet advisory needs by investor segment
2
Benchmarking platform feature satisfaction scores
3
Comparing digital engagement by portfolio size
Deliverables
Feature gap matrix
Advisory need rankings
Segment preference map
OPTIONAL
CATI (phone survey)Interviewer-led telephone interviews to reach owners who are harder to get online.
Best for
1
Older retail investors with low platform adoption
2
Tier 2 and Tier 3 city investor coverage
Deliverables
Geographic coverage data
Low-digital segment profile
SELECTIVE
Face-to-faceOn-ground surveys or interviews in key industrial clusters or high-value cohorts.
Best for
1
High-net-worth investors requiring sensitive advisory probing
2
Validating platform switching triggers in person
Deliverables
HNI advisory maps
Switching trigger profiles
OPTIONAL
FGDs
Deliverables
Verbatim themes
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, targeting retail investors across digital wealth platforms and investment apps, supported by CATI for low-digital and Tier 2 or Tier 3 city segments.
Consider adding: F2F for high-net-worth and active trader cohorts requiring deeper advisory probing, and a focused FGD layer to pressure-test platform feature concepts and advisory messaging before finalising recommendations.
Confirm approach
Request timeline

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 retail wealth and advisory space.

CASELET 1

Robo-advisory adoption barriers & feature preference mapping (India)

CASELET 2

Human advisor vs. platform messaging territories for mass-affluent segments (India)

Robo-advisory adoption barriers & feature preference mapping (India)

OBJECTIVE

A digital-first wealth platform needed to isolate why first-time equity investors and passive SIP holders stalled before activating robo-advisory features, and which onboarding steps and trust signals drove or blocked feature adoption.

WHAT WE DID

Ran a structured quant survey across 600 retail investors in 8 cities, capturing feature awareness, activation drop-off points, perceived risk of automation, and ranked preference scores for advisory formats across three investor tenure cohorts.

DELIVERED

A feature preference map by investor cohort, a ranked friction list across 6 onboarding stages, and a set of trust-signal levers tied to specific activation barriers for the product and growth teams.
Talk to Survey Consultant
CASELET 1

Robo-advisory adoption barriers & feature preference mapping (India)

CASELET 2

Human advisor vs. platform messaging territories for mass-affluent segments (India)

Robo-advisory adoption barriers & feature preference mapping (India)

OBJECTIVE

A digital-first wealth platform needed to isolate why first-time equity investors and passive SIP holders stalled before activating robo-advisory features, and which onboarding steps and trust signals drove or blocked feature adoption.

WHAT WE DID

Ran a structured quant survey across 600 retail investors in 8 cities, capturing feature awareness, activation drop-off points, perceived risk of automation, and ranked preference scores for advisory formats across three investor tenure cohorts.

DELIVERED

A feature preference map by investor cohort, a ranked friction list across 6 onboarding stages, and a set of trust-signal levers tied to specific activation barriers for the product and growth teams.

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 self-directed investors, hybrid advisory users and full-service advisory clients?

How will you measure platform and advisory preference beyond simple ratings?

Will the survey map the full digital wealth onboarding and engagement journey and drop-offs?

Can this survey inform product and pricing strategy?

How will findings improve our platform acquisition and retention conversion rates?

Still have questions?

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

Book a Discovery Call