Investment Advisory & Robo-Advisory Usage Survey
Retail and high-net-worth investors evaluate, compare, and choose between human advisors and robo-advisory platforms on trust, fee transparency, and portfolio control, so you can sharpen acquisition targeting, refine pricing tiers, and fix retention gaps.
CONTEXT & RELEVANCE
Why run this survey now
Most wealth platforms don't lose advisory clients purely on returns. They lose them due to misaligned risk profiling, opaque fee structures, low digital trust, poor goal-mapping, and weak hybrid model clarity, none of which fully show up in AUM dashboards or platform engagement metrics.
If you are...
- Robo-advisory platform scaling AUM
- Wealth management product head
- Digital brokerage vs traditional RIA
- Revenue head, fee model review
- Strategy lead, hybrid advisory build
You're likely facing...
- Robo vs human trust gap
- Fee opacity: flat vs AUM-based
- Drop-offs: onboarding / risk stage
- Digital-only = impersonal perception
- Switching triggers: underperformance vs service
This will help answer...
- Advisory preference drivers by segment
- Onboarding drop-off stage
- Robo vs hybrid adoption split
- Fee sensitivity and structure preference
- Retention risk and switching triggers
RESEARCH THEMES
What This Survey Investigates
Eight interconnected research themes that map the complete investor advisory journey from initial discovery to long-term portfolio commitment.
Discovery & Awareness
- First advisory channel contacted
- Robo-advisory awareness triggers
Preference Drivers
- Human vs. algorithm preference signals
- Portfolio customisation expectations
Onboarding & KYC
- Digital KYC completion rates
- Risk profiling friction points
Journey Friction
- Drop-off stages across advisory funnel
- Switching barriers between providers
Pricing & WTP
- Fee model tolerance by AUM band
- Performance fee vs. flat fee preference
Usage & Stickiness
- Login frequency and portfolio review cadence
- Feature adoption beyond core investing
Trust & Compliance
- Regulatory registration as trust signal
- Data privacy and algorithm transparency
Competitive Positioning
- Primary platform held vs. considered
- Switching intent and retention triggers
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.
METHODOLOGY
Survey approach
For the Investment Advisory and Robo-Advisory Usage Survey, we recommend a quant-first design with flexible data-collection modes to balance reach, depth, and verification.
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
Define the decision frame
Design the instrument
Build workstream modules mapped to outputs (drivers, friction, pricing, retention, trust)
Design the instrument
Lock the questionnaire
Review wording, sequencing, LOI, and competitive context; approve final version
Lock the questionnaire
Pilot and calibrate
Test comprehension and ease quality; refine quotas and remove friction where needed
Pilot and calibrate
Run fieldwork
Execute collection with active quota management and feasibility controls
Run fieldwork
Assure quality
Dedupe, attention checks, speed/consistency rules, removals with audit trail
Assure quality
Prepare the dataset
Clean data and deliver codebook/variable definitions
Prepare the dataset
Analyse and synthesise
Driver ranking, leakage diagnostics, pricing bands, segment insights
Analyse and synthesise
Deliver and align
Executive deck (optional dashboard) and leadership readout with recommendations
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
Geography
Select Mode of Survey
Length of the Interview
$0.00
+ applicable taxes
Cost Breakdown
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 investment advisory and wealth management space.
Digital wealth platform adoption & channel preference mapping (India)
OBJECTIVE
WHAT WE DID
DELIVERED
Digital wealth platform adoption & channel preference mapping (India)
Advisor trust & switching intent among retail mutual fund investors (West India)
Digital wealth platform adoption & channel preference mapping (India)
OBJECTIVE
A digital-first wealth platform needed to identify how mass-affluent investors and HNI segments choose between app-based advisory and human-assisted channels , and which friction points stall first-time portfolio commitment.
WHAT WE DID
Ran a structured quant survey across 600 respondents in 8 cities, capturing channel shortlisting triggers , trust signals by platform type , fee sensitivity thresholds , and portfolio review frequency by investor segment and age cohort.
DELIVERED
A channel preference map by investor segment, a ranked friction list at onboarding and first-transaction stages, and a fee sensitivity corridor segmented by portfolio size and prior advisory relationship type.
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 robo-only, advisor-only and hybrid advisory users?
How will you measure advisory channel preference beyond simple ratings?
Will the survey map the full investor advisory journey and drop-offs?
Can this survey inform product and pricing strategy?
How will findings improve our client acquisition and retention strategy?
Still have questions?
Schedule a discovery call to discuss your specific needs and get a custom quote.