Auxeira Startup Success Engine: A Randomized Controlled Trial to Reduce Startup Failure Rates

Last registered on August 08, 2025
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Pre-Trial

Trial Information

General Information

Title
Auxeira Startup Success Engine: A Randomized Controlled Trial to Reduce Startup Failure Rates
RCT ID
AEARCTR-0016444
Initial registration date
August 04, 2025

Initial registration date is when the trial was registered.

It corresponds to when the registration was submitted to the Registry to be reviewed for publication.

First published
August 08, 2025, 6:45 AM EDT

First published corresponds to when the trial was first made public on the Registry after being reviewed.

Locations

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Primary Investigator

Name
Emmanuel Luthuli
Affiliation
Auxeira
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Other Primary Investigator(s)

Additional Trial Information

Status
In development
Start date
2025-09-01
End date
2026-12-15
Keywords
Behavior, Finance & Microfinance, Governance
Additional Keywords
Behavioral, economics-driven
JEL code(s)
Secondary IDs
0
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
The global startup failure rate of 90% within three years, costing over $1 trillion annually, highlights systemic inefficiencies in early-stage venture development. The Auxeira Startup Success Engine (SSE) is an AI-blockchain platform delivering dynamic, verifiable incentives to enhance startup success across Market Access, Management, Funding, and Operations. Using AI-driven analytics and blockchain-secured transparency, Auxeira SSE integrates with tools like Stripe and QuickBooks to provide real-time performance insights, targeting a 50% reduction in failure rates and a 20% increase in investor IRR by 2030. A Randomized Controlled Trial (RCT) with 2,000 early-stage startups (1,000 treatment, 1,000 control) will validate its impact on a Sustainable Success Index (SSI), measuring product-market fit, operational efficiency, and investor readiness. Designed for global scalability, especially in emerging markets, Auxeira SSE aims to unlock $500 billion in economic value. We seek collaborators and feedback to refine this transformative platform. Keywords: Startup success, AI, blockchain, RCT, venture capital, entrepreneurship, impact investing.
External Link(s)

Registration Citation

Citation
Luthuli, Emmanuel. 2025. "Auxeira Startup Success Engine: A Randomized Controlled Trial to Reduce Startup Failure Rates." AEA RCT Registry. August 08. https://doi.org/10.1257/rct.16444-1.0
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
This intervention evaluates the effect of the Auxeira platform—an AI- and blockchain-powered system that delivers real-time, milestone-based incentives to startups in emerging markets. Participating startups will be onboarded to the Auxeira platform, which autonomously tracks key performance indicators (KPIs) and issues rewards (e.g., cloud credits, legal tools) upon verified progress.

Startups are randomized into control and treatment groups. The treatment group gains full access to Auxeira’s automated monitoring, reporting, and incentive distribution system. The control group receives light-touch support and no milestone-based rewards.

The goal is to assess whether the platform improves startup survival rates, operational KPIs, and investor confidence relative to traditional support models.
Intervention Start Date
2025-12-01
Intervention End Date
2026-03-31

Primary Outcomes

Primary Outcomes (end points)
Survival rate at 12 months, validated KPI improvement, and investor interest (measured via VC match rates).
Startup Survival Rate at 12 Months: Whether a startup remains operational one year after onboarding.
Improvement in Operational KPIs: Measurable improvements in key business performance metrics, such as revenue, customer growth, or engagement.
Investor Interest: Measured by investor follow-ups or match confirmations initiated after visibility on the Auxeira platform.
Primary Outcomes (explanation)
Survival Rate: Binary outcome (1 = still operational after 12 months). Confirmed via API integration (e.g., payment platforms, public website, CRM activity).
Operational KPIs: Startups set 3–5 key milestones at onboarding. These are auto-monitored via integrated APIs (e.g., revenue via Stripe, user activity via GA4). Metrics normalized across sectors.
Investor Interest: Measured by VC outreach, match rate on Auxeira’s partner dashboard, or signed term sheets where disclosed.

Secondary Outcomes

Secondary Outcomes (end points)
Founder Engagement: Continued active use of the platform, measured via logins, updates, or KPI inputs.
Access to Resources: Uptake of rewards (e.g., AWS credits, legal tools).
Network Effects: Referrals and collaboration within the Auxeira startup community.
Secondary Outcomes (explanation)
Engagement: Daily/weekly/monthly active usage by founders or team members. Dropout is defined as >30 days of inactivity.
Resource Use: Proportion of startups redeeming incentives within 90 days of issuance.
Network Effects: Referrals made through Auxeira’s built-in recommendation engine; co-applications for funding or accelerators.

Experimental Design

Experimental Design
This is a randomized controlled trial (RCT) involving 2,000 early-stage startups from 10 countries. After baseline data collection, startups are randomly assigned to treatment or control groups. The treatment group receives access to the Auxeira platform with real-time monitoring and automated incentives; the control group receives basic educational materials only. Outcomes are measured 6 and 12 months post-onboarding.
Experimental Design Details
Not available
Randomization Method
Randomization Method: Stratified randomization by country and sector using Qualtrics + blockchain time stamping.
Randomization Unit
The randomization unit is the startup organization.
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
0
Sample size: planned number of observations
2,000 startups (randomized 1:1 to treatment and control groups)
Sample size (or number of clusters) by treatment arms
Treatment Arm (Auxeira access): 1,000
Control Arm (light-touch support): 1,000
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Power analysis was conducted assuming: α = 0.05 Power = 0.80 Binary outcome (e.g., 12-month survival rate) With N = 2,000 (1,000 per group), the study is powered to detect a minimum detectable effect (MDE) of 8 percentage points in startup survival (e.g., 60% in control vs. 68% in treatment). For continuous KPI outcomes, assuming SD = 1 (standardized), MDE = 0.2 SD.
Supporting Documents and Materials

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IRB

Institutional Review Boards (IRBs)

IRB Name
IRB Approval Date
IRB Approval Number
Analysis Plan

Analysis Plan Documents

Pre Analysis Plan

MD5: 5d4f9cb27e72d714caf05ad6567208da

SHA1: 2ea20a429709b71b8a37498cd7efd24ee4db166c

Uploaded At: August 04, 2025