How to Measure Valley's ROI and Performance

Comprehensive Valley measurement requires tracking metrics across activity, engagement, pipeline, and revenue.

Activity Metrics (Input Measures):

Prospects contacted monthly: How many qualified prospects received outreach?

Benchmark: 100-200 per seat monthly

Signals captured: Profile views, post engagers, website visitors, etc.

Benchmark: 150-300 signals per seat monthly

Message approval rate: Percentage of AI-generated messages approved without editing

Benchmark: 80%+ after 30-day training

Campaign velocity: How quickly prospects move through sequences?

Benchmark: First message within 24 hours of signal capture

Engagement Metrics (Response Measures):

Connection acceptance rate: Percentage of connection requests accepted

Benchmark: 30-40%

Response rate: Percentage of messaged prospects who reply

Benchmark: 6-10% overall, 25-35% positive among responders

Reply quality: Positive vs. neutral vs. negative sentiment

Benchmark: 60%+ positive among replies

Conversation depth: Multi-message exchanges vs. single replies

Benchmark: 40%+ multi-message threads

Conversion Metrics (Pipeline Measures):

Meeting booking rate: Percentage of prospects who schedule meetings

Benchmark: 2-4% of all contacted, 25-35% of positive responders

Qualified opportunity rate: Percentage of meetings that become real opportunities

Benchmark: 60-70%

Pipeline value per prospect: Average pipeline generated per contacted prospect

Benchmark: $1,000-$2,000 per prospect at $25,000 average deal size

Time to meeting: Days from first contact to scheduled meeting

Benchmark: 7-14 days

Revenue Metrics (Outcome Measures):

Pipeline generated: Total dollar value of opportunities sourced by Valley

Benchmark: $60,000-$100,000 per seat monthly

Win rate: Percentage of Valley-sourced opportunities that close

Benchmark: 25-35% (may exceed other channels due to warm introduction)

Revenue per seat: Closed revenue attributable to each Valley seat

Benchmark: $180,000-$360,000 annually per seat

Customer acquisition cost: Total Valley cost ÷ customers acquired

Benchmark: 50-70% lower than cold outbound

Efficiency Metrics (Resource Utilization):

Time saved on research: Hours per week not spent on manual prospecting

Benchmark: 15+ hours per seat weekly

Cost per qualified meeting: Valley cost ÷ meetings booked

Benchmark: $50-$100 (vs. $200-$400 for cold methods)

Outreach capacity: Prospects contacted per hour of human effort

Benchmark: 50-100 prospects per hour with Valley vs. 2-3 manually

Rep productivity: Pipeline per rep vs. previous methods

Benchmark: 2-3x improvement

How to Set Up Proper Attribution for Valley's Pipeline Contribution?

Attribution determines which marketing and sales activities receive credit for pipeline and revenue. Proper Valley attribution ensures its impact is visible and valued.

First-Touch Attribution Model:

Valley gets credit when it generated the initial contact with a prospect who had no prior relationship with your company.

Qualifying criteria: Prospect discovered through Valley signal (not on prior target lists), First conversation initiated through Valley outreach, No prior inbound activity from prospect, No prior sales outreach attempts.

Last-Touch Attribution Model:

Valley gets credit when it drove the final action before opportunity creation.

Qualifying criteria: Prospect was known but dormant, Valley re-engagement triggered response, Meeting booked through Valley sequence, Opportunity created from Valley conversation.

Multi-Touch Attribution Model:

Valley receives partial credit alongside other touchpoints.

Example journey: Prospect attended webinar (Inbound gets 25% credit), Prospect visited website (Website gets 25% credit), Prospect viewed profile (Valley gets 25% credit), Sales rep called (Sales gets 25% credit).

Valley's contribution is recognized without claiming full credit.

Position-Based (U-Shaped) Attribution:

First and last touches get more credit than middle touches.

Example: Prospect engaged with LinkedIn post (Valley: 40%), Visited website twice (Website: 10%), Downloaded case study (Content: 10%), Booked meeting through Valley (Valley: 40%).

Valley receives 80% total credit for bookend contributions.

Time-Decay Attribution:

More recent touches receive higher credit.

If prospect engaged with Valley 90 days ago then recently again, recent engagement gets majority credit recognizing recency impact.

Implementation in CRM:

Tag all Valley-sourced contacts with "Valley" campaign attribution, Create Valley-specific opportunity source category, Use opportunity fields to track: First touch source (Valley vs. other), Last touch source, All touches in prospect journey, Pipeline velocity by source.

Generate reports filtering by Valley attribution to measure contribution.

How to Track Valley Performance Over Time and Identify Trends?

Point-in-time metrics provide snapshots. Trend analysis reveals whether performance is improving, declining, or stable—informing strategic adjustments.

Month-Over-Month Trend Tracking:

Create dashboards showing: Response rates by month (improving or declining?), Meeting volume by month (growing or shrinking?), Pipeline generation trends (accelerating or flattening?), Message quality scores over time (AI learning or degrading?).

Expected Trend Patterns:

Months 1-2: Ramping performance as AI trains and team learns, Response rates 50-70% of steady-state, Meeting volume below target.

Months 3-6: Approaching steady-state performance, Response rates stabilizing, Meeting volume hitting targets, Some volatility as team optimizes.

Months 7+: Mature performance with consistent results, Continuous improvement from ongoing optimization, Seasonal variations become visible.

Trend Analysis Questions:

Declining response rates: Has ICP definition drifted too broad?, Is message personalization degrading?, Are we exhausting our addressable market?, Has LinkedIn changed policies affecting delivery?

Declining meeting bookings: Are qualification criteria too loose?, Is response management slow (prospects cooling off?), Are calendars not optimized?, Have win rates changed for a reason?

Stagnant pipeline: Has market cooled for our category?, Are we targeting wrong segments?, Is competition intensifying?, Should we adjust value proposition?

Cohort Analysis:

Track prospects by signup month cohort: January prospects: X% response rate, Y% meeting rate, $Z pipeline, February prospects: Compare to January, March prospects: Compare to February.

Cohort analysis reveals whether recent changes improved or hurt performance.

A/B Test Tracking:

When testing messaging, signals, or approaches: Track control group performance, Track test group performance, Calculate statistical significance, Implement winners, iterate losers.

Continuous testing drives incremental performance improvements.

► Check Out More of Valley's Incredible Outreach: A compilation of real time messages and responses!