Data Methodology

Get actionable financial advisor benchmarks from hundreds of thousands of anonymized client conversations. SOC 2 compliant with full PII removal.

Quick Facts

Source

Hundreds of thousands of anonymized and aggregated advisor-client transcripts.

PII status

All personally identifiable information is fully removed.

Coverage caveat

Results are illustrative and may not mirror your own book of business.

How we keep data anonymous and safe to share

  1. 1

    Transcript ingestion

    • Raw text arrives from meeting platforms.
    • Transcripts are processed for generating AI outputs for Jump users.
  2. 2

    PII Sweep

    • An NLP pipeline identifies and removes tokens that could identify individuals or firms (names, emails, phone numbers, account numbers, locations).
    • Eliminates every direct link to any individual.
  3. 3

    Fresh IDs

    • Original meeting and advisor IDs are cryptographically hashed using one-way functions
    • Each transcript receives a deterministic anonymized identifier, which prevents direct identification while enabling aggregate analysis.
  4. 4

    Aggregated Storage

    • Sanitized transcripts are securely stored in a data lake containing only unidentifiable text, providing a secure environment for analytics.
  5. 5

    Insight Generation

    • Metrics, dashboards, and benchmarks are derived from the sanitized data lake.
    • Underlying text is never exposed or resold, ensuring outputs remain aggregated and privacy-preserving.

What the Data is Used For

We use anonymized transcripts solely to create aggregated statistics, trend dashboards, and benchmark reports to help firms understand client engagement patterns.

What the Data is Never Used For

  • Reidentification Attempts: Our system is designed with technical and administrative safeguards to prevent anonymized data from being linked back to individuals or specific meetings.
  • Selling, Renting, or Sharing: We do not sell, rent, or share personal data as those terms are defined under applicable privacy laws including CCPA/CPRA.
  • AI/ML Training: Jump uses anonymized data solely for descriptive statistics, benchmarking, and basic analytical modeling to improve our services. We do not use personal data to train large-scale AI models or machine learning systems for external purposes.

Data Sources and Limitations

Insights are derived from hundreds of thousands of anonymized Jump Advisor AI advisor‑client conversations (Jan 2024 to present). All PII is removed before analysis; no individual advisor or client can be identified. Figures are aggregated, rounded and will update as new transcripts are processed. Correlation ≠ causation and results may not match your own book of business. Content is for illustration only and is not investment, legal or tax advice.

Security and Compliance Snapshot

  • AES-256 encryption at rest; TLS 1.3 encryption in transit
  • Annual SOC 2 Type II audits
  • Layered safeguards: technical, administrative, and physical controls
  • Prompt breach notifications to customers

About the Jump Advisor Insights Team

The Jump Insights Team is responsible for:

Designing and validating anonymization safeguards
Defining statistical methodologies and quality thresholds
Interpreting data patterns to produce clear, actionable reports for advisors and firms

Team Lead

Liam Hanlon

Head of Insights at Jump

LinkedIn: Liam Hanlon
For collaboration inquiries: liam.hanlon@jumpapp.com

Opt-Out Option

Participation is entirely optional, and no personal or company-identifying information is ever shared. If you prefer not to contribute, account owners can manage this setting anytime under the Compliance tab. Opting out means your firm won’t receive access to benchmarking insights within your account.