The metrics, scorecards, and benchmarks that help RIA leaders drive advisor performance and firm growth.

Advisor productivity analytics is the practice of measuring advisor performance across a consistent set of metrics — AUM growth, revenue contribution, client retention, meeting activity, and pipeline conversion — and using that data to coach advisors, benchmark performance, and drive firm-wide growth decisions.

Why Most Firms Can't Measure Advisor Productivity

Ask a managing partner which of their advisors is the most productive, and most will give you a gut answer. Ask them to back it up with data, and the room goes quiet. Not because the data doesn't exist — it does — but because it lives in four different systems that were never designed to talk to each other.

The Data Is Scattered Across Every System You Own

CRM systems like Salesforce, Redtail, or Wealthbox hold client relationship data: who an advisor works with, what activities they've logged, and what's in the pipeline. But CRM data alone tells you nothing about AUM or revenue — those numbers live in your portfolio management system (Orion, Black Diamond, Tamarac) and your billing platform. Meeting frequency might be tracked in the CRM, or it might live in a calendar integration that nobody has properly connected. Client satisfaction data lives in a survey tool that stands entirely alone.

The result is that no single system has the complete picture of an advisor's performance. To measure AUM per advisor, you need data from the portfolio system. To measure revenue per advisor, you need billing. To measure meeting frequency, you need CRM activity logs or calendar data. To measure client retention, you need client records reconciled across CRM and the custodian. There is no system that connects all of these by default.

Manual Assembly Is Slow, Expensive, and Stale

Most firms that do track advisor performance do it the hard way: an analyst exports data from each system at month-end, assembles a spreadsheet, reconciles discrepancies manually, and distributes a PDF report that leadership reviews in a meeting two weeks later. By the time that report lands in front of the people who need it, the data is three to four weeks old.

Beyond the staleness problem, the process is fragile. One person usually owns the spreadsheet. If they leave, the institutional knowledge of how to build it goes with them. If one source system changes its export format, the whole thing breaks. And because the process is painful, it tends to happen quarterly at best — leaving leadership flying blind for months at a time.

The Consequence: Gut-Feel Management

Without reliable, timely data, managing partner conversations about advisor performance default to impressions. Which advisors seem busy? Who attends every event? Who's complained about their comp recently? These proxies are unreliable. High-activity advisors are not always high-revenue advisors. The quiet one in the corner might be quietly managing $200M in complex client relationships. Without data, you cannot know — and you cannot coach what you cannot measure.

The 10 Metrics That Matter

Not every number you can measure is worth tracking. These ten metrics give leadership a complete view of advisor performance — combining financial output, client health, and activity signals — without drowning advisors in vanity metrics.

01 — AUM Per Advisor

What it measures

Total assets under management attributed to an advisor divided by the number of active advisors. Reveals the financial scale each advisor carries and how efficiently the firm's asset base is distributed across the team.

02 — Revenue Per Advisor

What it measures

Total fee revenue attributable to an advisor's book of business. Revenue per advisor differs from AUM per advisor when advisors carry different fee schedules or serve different client tiers — a critical distinction for comp modeling.

03 — Net New Assets

What it measures

Inflows minus outflows for an advisor's book over a period. The cleanest measure of growth productivity — it strips out market performance and isolates how much new capital an advisor actually brought in or retained.

04 — Client Retention Rate

What it measures

Percentage of clients (and AUM) retained over a trailing twelve-month period. Retention is the foundation of a compounding business — losing clients destroys years of growth. Low retention in an advisor's book signals a relationship problem that must be addressed.

05 — Clients Per Advisor

What it measures

Total active client relationships per advisor. This metric benchmarks advisor capacity and service model sustainability. Too many clients signals potential service degradation; too few may indicate underutilization or segmentation issues.

06 — Revenue Per Client

What it measures

Average revenue generated per client relationship. Reveals pricing efficiency and client mix quality. An advisor with high AUM but low revenue per client may be underpricing or over-serving smaller accounts that dilute margin.

07 — Meeting Frequency

What it measures

Average number of client meetings per relationship per year. A leading indicator of retention risk — clients who are not being met with regularly are more likely to leave. Also a proxy for advisor engagement and proactive relationship management.

08 — Pipeline Conversion Rate

What it measures

Percentage of prospects in the pipeline who convert to clients within a defined period. Differentiates advisors who are effective closers from those who generate activity without results — and identifies coaching opportunities in the sales process.

09 — Client Satisfaction / NPS

What it measures

Net Promoter Score or equivalent satisfaction metric at the advisor level. Low NPS scores are a leading indicator of future attrition and referral drought. High NPS advisors are your most likely source of organic growth through client referrals.

10 — Advisor Tenure & Growth Trajectory

What it measures

AUM and revenue growth rate over the advisor's tenure at the firm. Helps identify advisors who are growing rapidly and may be ready for more responsibility, as well as veterans whose books have plateaued and may need a different support structure.

How to Calculate Each Metric

Most of these metrics require data from multiple systems joined on an advisor identifier. AUM per advisor requires pulling total client AUM from your portfolio system and joining it to an advisor roster. Revenue per advisor requires pulling fee data from billing and attributing it to the responsible advisor. Net new assets requires beginning and ending AUM minus market appreciation — a calculation that requires both portfolio data and a benchmark return assumption. Clients per advisor requires a clean client-to-advisor mapping from your CRM, reconciled against the custodian to exclude inactive accounts.

Meeting frequency is deceptively complex: it requires activity data from the CRM (logged meetings, calls) or calendar data from an integrated scheduling tool, filtered to client-facing meetings and attributed to the correct advisor-client pair. Without clean data pipelines, this metric alone can take an analyst days to reconstruct manually.

Building Advisor Scorecards

A scorecard is not a spreadsheet of every metric you can measure. It is a curated view of the metrics that matter most for your firm's strategy, presented in a format that enables fast, consistent evaluation of every advisor against the same standard.

What a Good Scorecard Looks Like

Effective advisor scorecards are one page or one screen. They show each advisor's performance on the core metrics alongside a firm average and a peer benchmark — so the reader immediately understands context, not just a raw number. An advisor with $95M AUM per advisor looks different if the firm average is $80M versus $140M. A 92% retention rate looks different if the industry benchmark is 95%.

Good scorecards also distinguish between lagging indicators (AUM, revenue — outputs of past work) and leading indicators (meeting frequency, pipeline activity — predictors of future results). Leadership that only looks at lagging indicators discovers problems too late. Leading indicators give you a warning system before retention or revenue suffers.

How to Weight Metrics

Weighting depends on your firm's strategic priorities. Growth-focused firms should weight net new assets and pipeline conversion rate heavily — these directly measure an advisor's ability to expand the firm. Retention-focused firms should weight client retention rate and NPS more heavily, since losing a client is more expensive than acquiring one. Efficiency-focused firms may prioritize revenue per client and clients per advisor to ensure the service model is sustainable and profitable at scale.

A reasonable starting framework for most mid-market RIAs: AUM growth (25%), client retention (20%), revenue per client (20%), meeting frequency (15%), and net new assets (20%). Revisit the weighting annually as the firm's strategy evolves. When you change the weighting, communicate the change to advisors before it takes effect — surprises in performance reviews damage trust.

Benchmarking: Internal and External

The most useful benchmarks are internal. Comparing an advisor to peers within the firm on the same metrics, at similar tenure and service tier, surfaces meaningful signal about relative performance. An advisor who has been at the firm for eight years managing high-net-worth clients should be benchmarked against other experienced HNW advisors — not against a first-year associate building their book.

External benchmarks — from sources like the InvestmentNews benchmarking study, Schwab's RIA Benchmarking Study, or Fidelity's RIA Benchmarking — provide industry context but must be used carefully. Different firms define these metrics differently, which makes raw comparisons unreliable. Use external benchmarks to establish broad ranges (top quartile vs. median) rather than precise targets.

Review Cadence: Monthly vs. Quarterly

Activity-based metrics — meetings held, pipeline movement, new prospects added — should be reviewed monthly. These metrics move quickly and benefit from timely feedback. If an advisor hasn't held a client meeting in six weeks, leadership wants to know now, not at the end of the quarter.

Financial metrics — AUM, revenue, retention rate — move more slowly and are most meaningful in quarterly and annual reviews. Reviewing AUM performance monthly creates noise from market movements and short-term fluctuations that obscure real trends. Quarterly reviews allow market effects to smooth out and surface the advisor-driven signal more clearly.

The Data Challenge Behind Every Metric

Understanding what each metric requires — in terms of data sources, joins, and transformations — is essential for anyone trying to build advisor analytics in practice. Here is where each metric's data lives and what it takes to assemble it.

AUM Per Advisor

• Data Source(s): Portfolio management system, advisor roster

• Key Complexity: Requires clean advisor-to-account mapping; household vs. account level matters

Revenue Per Advisor

• Data Source(s): Billing platform, advisor roster

• Key Complexity: Split revenue between co-advisors; handle tiered fee schedules

Net New Assets

• Data Source(s): Portfolio system (beginning/ending AUM, market return)

• Key Complexity: Must isolate market appreciation from actual flows; requires benchmark data

Client Retention Rate

• Data Source(s): CRM, custodian, billing

• Key Complexity: Must define "lost client" consistently; partial departures complicate calculation

Clients Per Advisor

• Data Source(s): CRM, custodian

• Key Complexity: CRM and custodian counts often diverge; requires reconciliation logic

Revenue Per Client

• Data Source(s): Billing, CRM

• Key Complexity: Household vs. individual counting affects the denominator significantly

Meeting Frequency

• Data Source(s): CRM activity log, calendar integration

• Key Complexity: Requires filtering to client-facing meetings; many firms don't log calls consistently

Pipeline Conversion

• Data Source(s): CRM pipeline

• Key Complexity: Depends on consistent CRM stage usage; requires historical stage tracking

NPS / Satisfaction

• Data Source(s): Survey tool (Delighted, Qualtrics, SurveyMonkey)

• Key Complexity: Response rates vary; matching survey responses to advisor-client pairs requires data joining

Tenure Trajectory

• Data Source(s): Portfolio system (historical AUM), HR system (start date)

• Key Complexity: Historical AUM data may not be available in all portfolio systems

Without a unified data layer connecting these sources, building each metric requires a custom export and a manual join — every single time. That is why most firms that attempt advisor analytics without a data platform end up with a monthly spreadsheet exercise that consumes two to three days of analyst time and is stale before anyone reads it.

How a Data Platform Enables Advisor Analytics

A wealth management data platform solves the data assembly problem by connecting all of your systems — CRM, portfolio management, billing, calendar, custodian — into a single, normalized data warehouse. Once the data is unified, advisor analytics becomes a query rather than a project.

Connect Everything Into One Warehouse

The foundation is a complete set of data integrations. Your CRM feeds client counts, activity logs, and pipeline data. Your portfolio system feeds AUM and account balances. Your billing platform feeds revenue and fee data. Calendar and email integrations feed meeting activity. Custodian connections provide the authoritative account and asset data needed to reconcile discrepancies between CRM and portfolio system records.

A purpose-built platform like Milemarker arrives with pre-built connectors for the most common wealth management systems — Salesforce, Redtail, Wealthbox, Orion, Black Diamond, Tamarac, Schwab, Fidelity, Pershing, and more — so your team is not building integrations from scratch. The platform handles extraction, transformation, and loading into a clean, analytics-ready data model with advisor identifiers consistently mapped across all sources.

Dashboards That Update Automatically

With a unified data warehouse, advisor scorecards are not a monthly manual exercise — they are a dashboard that refreshes automatically as new data arrives. Leadership can open the scorecard on any day of the month and see current performance, not last month's numbers. Trend lines update in real time. Alerts can be configured to surface when an advisor's meeting frequency drops below a threshold or when a client hasn't been contacted in 90 days.

Drill Into Any Advisor's Performance in Seconds

Self-service analytics change how leadership conversations happen. Instead of waiting for a monthly report, a managing partner can open a dashboard, click on any advisor's name, and immediately see their full performance profile: AUM trajectory, revenue trend, client retention history, meeting log, pipeline status, and NPS score — all in a single view. Questions that previously required a two-day analyst request can be answered in thirty seconds.

Use Cases Beyond Scorecards

Once advisor performance data is unified and accessible, firms discover that the same data infrastructure supports decisions far beyond the monthly scorecard review.

Recruiting ROI

When you hire an advisor — particularly an experienced hire bringing a book of business — the business case typically includes revenue and AUM growth projections that justify the cost of the move. A data platform lets you track whether those projections materialized. Did the hired advisor actually bring the clients they projected? Is their net new asset generation meeting the targets that justified their signing bonus and guaranteed comp? Without data, this analysis happens in someone's head at the one-year mark. With data, it happens automatically and continuously.

Succession Planning

Succession risk is a real and underappreciated threat to RIA enterprise value. An advisor who manages 80% of a firm's highest-value clients represents enormous concentration risk. Analytics can surface which advisors' books are most concentrated in clients with the lowest meeting frequency and the oldest average client age — a combination that predicts near-term attrition. Succession planning begins with visibility into where the risk lives before it becomes a crisis.

Compensation Modeling

Compensation plans are among the most consequential strategic decisions a firm makes. Change the comp plan and you change advisor behavior. But most firms model compensation changes using last year's spreadsheet data and guesswork. A unified advisor analytics platform lets leadership model proposed comp changes against actual advisor performance data — projecting the revenue impact, the advisor behavior changes, and the retention risk before the new plan goes into effect.

M&A Integration

When acquiring another RIA, leadership needs to quickly understand how the incoming advisor team compares to the existing team on consistent metrics. A data platform enables an apples-to-apples comparison: AUM per advisor, revenue per client, retention rate, and meeting frequency for acquired advisors versus your existing advisors. This comparison accelerates integration decisions, surfaces where acquired advisors need support or coaching, and helps set realistic post-close revenue projections.

Manual vs. Automated Advisor Analytics

The difference between a manual scorecard process and an automated one is not just efficiency — it changes what decisions are possible and when they can be made.

Before — Manual Process

Monthly export from CRM, portfolio system, and billing — separately

2-3 days of analyst time to assemble and reconcile the spreadsheet

Report distributed 2-3 weeks after month-end — data already stale

No drill-down — questions require new analyst requests

Process breaks when one system changes its export format

Institutional knowledge lives in one person's head

After — Automated Platform

All systems connected once — data flows automatically every day

Dashboard refreshes automatically — no analyst time required

Real-time data available to leadership on any device, any day

Self-service drill-down — answer any question in seconds

Automated alerts when leading indicators drop below thresholds

Logic lives in the platform — survives personnel changes

Frequently Asked Questions

What is advisor productivity analytics?

Advisor productivity analytics is the practice of measuring, tracking, and benchmarking advisor performance across metrics like AUM per advisor, revenue per advisor, client retention rate, meeting frequency, and pipeline conversion. It gives RIA leadership a data-driven view of how each advisor contributes to firm growth — and where coaching or support would have the most impact.

Why is it hard to measure advisor productivity at most RIAs?

Advisor productivity data is scattered across multiple systems: CRM holds activity and client counts, the portfolio management system holds AUM, billing holds revenue, and the calendar holds meeting data. No single system has the complete picture. Without a unified data layer connecting all of these sources, building even basic scorecards requires manual exports, spreadsheet assembly, and hours of reconciliation every month.

What is a good AUM per advisor benchmark for RIAs?

Industry benchmarks vary by firm size and model. Top-quartile RIAs typically see $150M to $250M or more in AUM per advisor. Mid-market firms often range from $80M to $150M. The more important comparison is internal: how does each advisor's AUM trend over time, and how do they compare to peers within your firm at similar tenure and service tiers?

What should an advisor scorecard include?

An effective advisor scorecard typically includes AUM per advisor, revenue per advisor, net new assets, client retention rate, clients per advisor, meeting frequency, pipeline conversion rate, and client satisfaction (NPS). Weight the metrics to reflect your firm's strategy — a growth-focused firm may weight net new assets heavily, while a retention-focused firm may prioritize NPS and meeting frequency.

How often should advisor scorecards be reviewed?

Monthly scorecards work best for activity-based metrics like meetings held and pipeline movement, where timely feedback enables quick course correction. Quarterly reviews are appropriate for AUM and revenue metrics, which move more slowly and reflect longer-term trends. Annual reviews should incorporate tenure trajectory and year-over-year growth rate comparisons. When dashboards are automated, leadership can monitor in real time and escalate reviews as needed.

Can advisor analytics help with succession planning?

Yes. Succession planning analytics examine which advisors' books are concentrated in older or high-net-worth clients with low engagement frequency — a risk indicator for attrition. Analytics can surface books with declining meeting frequency, falling NPS, or flat AUM growth year over year, allowing leadership to proactively address transition risk before clients leave the firm.

How does advisor analytics apply to M&A integration?

When acquiring a firm, advisor analytics lets you compare incoming advisors to your existing team on consistent metrics — AUM per advisor, revenue per client, retention rate, and meeting activity. This comparison quickly surfaces where acquired advisors over- or under-perform relative to your firm's norms, informs integration priorities, and helps set realistic revenue expectations post-close.

What technology is needed to build advisor scorecards?

Building automated advisor scorecards requires connecting your CRM (for client counts and activity), portfolio management system (for AUM), billing platform (for revenue), and calendar or email system (for meeting data) into a single data warehouse. From there, a business intelligence layer — such as Tableau, Power BI, or a purpose-built wealth analytics tool — can render the dashboards. A data platform like Milemarker handles the integrations, normalization, and data modeling so your team can focus on analysis rather than data assembly.

Related guides

Part of the Analytics & BI series:

• RIA Growth Analytics: Data-Driven Strategies for Advisory Firm Growth

• Claude for RIA Operations: AI-Powered Answers for COOs, Ops Managers & Firm Leadership

• Wealth Management Reporting Automation: From Manual Exports to Real-Time Dashboards

• Client Segmentation for RIAs: Data-Driven Service Tiers & Growth Strategies

• Business Intelligence for Wealth Management: Dashboards, Analytics & Self-Service Reporting

• Natural Language Analytics for Wealth Management: Ask Your Data Anything