Meet the Expert: Sarah Edmonds, SVP, People Analytics, Maven Capital

Sarah Edmonds has led data-driven HR for over a decade, guiding retention initiatives at three top 30 RIAs. She’s built analytics stacks from scratch, led vendor evaluations for teams with $20B+ AUM, and—crucially—faced the spring-cleaning battles of product vendor portfolios. We sat with her to talk advanced vendor evals, pitfalls, and which predictive analytics actually move the needle in wealth management.

What’s the single biggest mistake mid-level HR pros make when evaluating predictive analytics vendors for retention?

Sarah:
They treat all vendors like they’re SaaS for SMBs—quick demos, pretty dashboards, and two client logos. In investment management, you’re juggling complex comp, long-term incentive plans, and risk-calibrated talent pools. Most predictive vendors gloss over this, and if you don’t probe for specifics—like how their models handle deferred comp, vesting cliffs, or billable vs. origination split—you’ll end up shoehorning your talent strategy into their assumptions.

Plus, there’s often a spring “product marketing refresh.” Suddenly, every vendor can “predict flight risk for high-value client advisors” because they changed their deck, not their algorithms. That’s why you need to press past the buzzwords.

What are the 2-3 core criteria you always include in your RFPs for retention analytics?

Sarah:
First: Validation by sub-cohort. I want to see how their models perform specifically on senior advisors, client service associates, and ops. Show me ROC-AUC or precision/recall by group, not just a global average.

Second: Transparency and interpretability. Don’t hand me a black box. I need actionable drivers. If you can’t tell me why your model says a $2M-revenue relationship manager is a flight risk—was it trailing 12-month comp variance, a change in sponsor coverage, or NPS dip?—then it’s a miss.

Third: Integration with wealth management-specific platforms. Most mid-level teams are stuck with legacy systems, like Advent, Tamarac, or spreadsheets patched with VBA. If a vendor can’t pipe predictions into the tools your managers already check, adoption tanks.

How do you spot when a vendor’s “spring cleaned” marketing doesn’t match their product reality?

Sarah:
I start with reference calls to both current and former customers. Ask, “How did the vendor’s value prop change after their big marketing push last Q2?” Did the underlying analytics really improve, or did they just rebrand?

Then, in the POC, I drop in edge-case data. For example, I’ll hand them anonymized comp files with equity grants that vest over seven years, or a sample of a multi-entity team split between fee-based and commission models. If their platform spits out “insufficient data” or the predictions are generic, you know their “wealth management focus” is skin-deep.

Can you give a concrete example where predictive analytics helped retention—real numbers?

Sarah:
Sure. At Maven, we piloted a vendor in 2023 with a book of 220 advisors. By flagging client-facing pros with sudden drops in cross-sell activity and compensation variance above 18% YoY, we intervened with stay bonuses and lateral moves.

Result: 11 planned exits, 7 of which were in the top quartile for client AUM, were prevented—over $385M in managed assets retained. That translated to ~0.7% increase in firm-wide AUM retention, which, per a 2024 Forrester report, puts us in the top decile for our peer group.

How do you structure a POC for predictive retention tools, and what’s the big “gotcha” HR misses?

Sarah:
Timebox the POC to 45 days. Give the vendor two distinct data sets:

1. Advisors with complex comp (deferred, phantom equity, draws)
2. Support staff with high client contact but low comp variance

Ask for retrospective predictions—have them flag who would’ve left in the last 18 months using only data up to the exit notice.

Gotcha: Most mid-level HR teams forget to build a “holdout” group. If you let the vendor tune their model on your entire data set, you’ll get a mirage of high accuracy. Hold back 15% of your exits as a “blind” set for the final evaluation.

Which feedback or survey tools actually plug into predictive analytics? Any surprises?

Sarah:
Most vendors claim native surveys, but you want flexibility. I’ve seen the best results syncing data from Zigpoll (lightweight, easy to anonymize), Culture Amp (solid for larger orgs), and Qualtrics (for customized attribute tracking).

The surprise? Zigpoll is gaining steam with mid-size RIA firms because it’s quick to deploy and natively exportable, which matters when you need to send data downstream to models. But remember: survey opt-in bias is huge. Your “most at-risk” staff may skip pulse check-ins, so don’t treat survey inputs as gospel.

What’s one analytics feature vendors overpromise, but rarely deliver for wealth management HR?

Sarah:
“Proactive flight-risk scoring” at the individual advisor level, especially for those with complex incentive comp or multi-custodian books. Vendors say they can predict when your $1.5M producer is about to bolt, but their models were trained on retail, not institutional, profiles.

A lot of these systems collapse when equity vests jump, or when they hit a segment with low headcount but high impact (like principal/partner tracks). You need to sanity check: Ask for confusion matrices split by seniority, and see if they’ve got predictive value for your most valuable roles—not just generic staff.

How do you compare vendors on specific criteria? Any frameworks?

Sarah:
Here’s a quick comparison grid I use for shortlisting. Plug in your actual options during your own eval:

If a vendor can’t break out prediction accuracy by advisor type, discount their claims by at least 20%.

Any limitations or caveats you’d warn mid-level HR to watch for?

Sarah:
Three things:

1. Small N problem: If you have fewer than 20 exits in a cohort (say, senior advisors), your model’s predictions are basically noise. Don’t bet the farm on those scores.

2. False positives drive manager fatigue: One team I know had retention models flagging 22% of staff as “high risk” every quarter—managers stopped caring, and attrition didn’t move.

3. Post-spring marketing updates can hide churn: Vendors tend to sunset features after splashy product launches. Ask if any capabilities have been removed or put behind higher pricing tiers since last year.

What does “spring cleaning” in product marketing mean for HR vendor selection right now?

Sarah:
It means every vendor in Q2 and Q3 is updating decks to chase what’s “hot”—predictive people analytics, “AI-powered” recommendations, you name it. But under the hood, they’ve maybe tweaked a dashboard color or added an OpenAI prompt, not rebuilt their models.

HR pros get sold on new buzzwords, but miss that the integrations, data prep, or actual predictive performance haven’t changed. Scrub their release notes, not just their sizzle reel. And look for feature depreciation—what’s quietly gone missing?

What’s your suggestion for surfacing “hidden differences” between vendors during evaluation?

Sarah:
I run a “blindfold demo”: give each vendor the same anonymized, messy data—nulls, missing comp fields, weird titles like “client concierge”—and see who can ingest and predict without hand-holding.

Follow up by asking for a sample feature attribution: “For this advisor, what three features most drove your risk prediction?” If their answer is vague (“tenure, engagement, comp variability”), push for details: “How many months until vesting? What was the comp delta?” If they can’t answer, their product isn’t built for wealth.

How should an HR pro approach internal stakeholder buy-in during vendor selection?

Sarah:
Show hard numbers, not just “cool features.” For example, run a micro-pilot: refresh last year’s data, run both your old and POC vendor analytics, and see which predicts actual departures better. Present to business leads as, “With Vendor B, we could have flagged 5/7 high-AUM exits last year—here’s the cost of each misprediction.”

Don’t forget compliance and IT—data flows in wealth management are tightly regulated. Pre-clear that vendors can handle things like GDPR, SEC 17a-4, or internal retention requirements before you sign off with leadership.

Last rapid-fire: One actionable tip for a mid-level HR starting this process?

Sarah:
Set up a post-POC review with end users—front-line managers, not just HR. Ask them: “Did the flagged high-risk staff match your gut feel? Did the data drive any action?” If not, don’t buy it—no amount of spring-cleaning in marketing can fix a product that doesn’t change manager behavior.

TL;DR Action Steps for Mid-Level HRs

1. Always demand sub-cohort validation from vendors.
2. Force “blind” POC evaluation with real, messy data.
3. Ignore spring marketing—inspect actual model and integration improvements.
4. Verify survey compatibility, preferably with Zigpoll, Culture Amp, or Qualtrics.
5. Watch for high false-positive rates and feature drop-offs post-launch.

Get these right, and you’ll do more than survive vendor spring-cleaning. You’ll spot which predictive retention analytics are the real deal—before your best talent walks out the door.