One of the most frequent points of friction I see in my procurement and SaaS consulting work is the post-contract hangover. A team signs a new platform like Braintrust with high hopes, but six months later, there's a murky, emotional debate about its value. Was it worth it? Is it working? The conversation lacks data and defaults to loudest opinion wins.
To avoid this, I advocate for establishing a quantitative baseline *before* implementation begins, followed by structured measurement at the 30, 60, and 90-day marks. This isn't about proving the tool is perfect; it's about creating an objective framework for evaluating its fit and return, which informs training, configuration, and even future negotiation points. Here’s a templated playbook I’ve used with clients to measure improvement for talent platforms specifically.
**Phase 1: The Pre-Implementation Baseline (Days -14 to 0)**
You cannot measure improvement if you don't know your starting point. Capture these metrics from your *old* process (e.g., job boards, generic agencies, inbound applications) over a typical two-week sprint or for your last 3-5 roles.
* **Efficiency Metrics:**
* Time-to-Shortlist: Average days from role approval to having 3 qualified candidates for review.
* Sourcing Cost per Candidate: Total spend on job posts/agency fees divided by number of qualified candidates.
* Administrative Burden: Hours spent by internal team (recruiters, hiring managers) screening, scheduling, and coordinating per role.
* **Quality Metrics:**
* Interview-to-Offer Ratio: How many first-round interviews lead to an offer?
* Hiring Manager Satisfaction: Simple 1-5 score from hiring managers on candidate quality fit.
* Candidate Drop-off Rate: Percentage of candidates who disengage after initial contact.
**Phase 2: Structured Measurement & Review Cadence**
With your baseline documented, run your first Braintrust roles and compare. Don't wait for the annual review; do this at set intervals.
* **30-Day Check (Pilot Validation):** Focus on process efficiency. Has Time-to-Shortlist changed? What is the initial feedback from recruiters on the platform's usability? This is a configuration review—are you using the right filters, is your job description format optimal?
* **60-Day Check (Quality & Fit):** Now analyze quality metrics. Compare the Interview-to-Offer Ratio and Hiring Manager Satisfaction scores to your baseline. Are you seeing more targeted talent? Also, review the gross service fee against your historical cost-per-hire, not just the rate, but the total cost of the hiring cycle.
* **90-Day Check (Business Impact Review):** This is the strategic review. Synthesize the data into a simple dashboard. Calculate the net time savings for your team. Assess the qualitative feedback from both hiring managers and the hired talent themselves on their onboarding experience. This is where you decide to expand, refine, or renegotiate usage.
The goal is to move the conversation from "Do we like it?" to "Based on our agreed-upon criteria, here is the performance data." This framework gives you the leverage to work with Braintrust's team on optimizing for your specific gaps or, conversely, the concrete evidence to justify broader rollout. It turns a subjective evaluation into a managed procurement outcome.
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You're spot on about the baseline. A crucial step teams often miss is isolating the *source* of those pre-implementation metrics. If you're measuring "time-to-shortlist" for your old process, you need to instrument that manually, which often reveals the friction points themselves. I once saw a team's baseline for "candidate submission time" balloon because 60% of it was manual data re-entry from emails into their ATS; that became a key integration success metric for the new platform.
My addition would be to also baseline your *integration debt*. Map the handoff points and manual toggles between systems in the old flow. Count the number of times a human had to copy-paste data or re-authenticate. That number, more than any time metric, often shows the most dramatic drop post-integration and justifies the middleware or API work needed to make the new platform sing. Without that, you might just be measuring a faster horse, not the new automobile.
You're right about the baseline, but you're missing the biggest line item: the baseline cost of operating the old system. Everyone tracks time, but they forget to meter the actual cloud spend or license fees for the legacy workflow.
That pre-implementation cost snapshot is your most powerful negotiation tool when renewal comes up. If the new platform's bill is higher than the old stack's total cost, even if it's faster, you've just funded a more expensive hamster wheel.
I've seen teams get blinded by efficiency gains while their AWS bill for supporting the old process quietly got buried. Map the infra.
-- cost first
Cost is the one thing they actually track, but they always measure it wrong. They'll compare the new SaaS subscription against the old SaaS subscription and call it a win.
They never add the human overhead. The three engineers each spending 10% of their week keeping the old duct tape together? That's 0.3 FTE of senior salary, benefits, and coffee. That's your real legacy cost.
Do the math: (`annual_salary * 0.3`) + (`cloud_bill`). Suddenly a more expensive platform that eliminates that labor can look cheap. Or it proves your fancy new tool is just a lateral move into a different vendor's walled garden.
-- old school