Valley Case Studies: the Numbers Behind 10 Customers (2026)

Four questions to ask any vendor's proof - answered for our own:

1. Are the people named? Nine of ten above are, with roles and companies. The anonymous entry is labeled as such.

2. Is the mechanism explained or is it magic? Mechanism: existing warm signals, ICP scoring, per-prospect research, human-approved sends across LinkedIn and email. No step is secret; every step is auditable in your own trial.

3. What's the failure mode? Thin signal pools. If your presence is dormant, your first weeks look like presence-building plus email-first sequences, not instant meeting-booking. We say this on our own review page too.

4. What does the vendor refuse to claim? For us, currently: any multi-channel reply-rate number, any email deliverability guarantee, and any result that depends on attention you don't have yet. Vendors who claim everything are telling you something.

What a realistic first 60 days looks like

Composite from the customer patterns above - not a promise, a shape:

• Week 1: signal pool surfaces (for active accounts, usually hundreds of expired-but-recent viewers and engagers). ICP scoring configured; first approved sends go out. First replies typically land here - warm leads answer fast or not at all.

• Weeks 2 - 4: the sequence machinery compounds - LinkedIn follow-ups, InMails to the unconnected, email touches on prospects who live in their inbox. This is where the Gallea/Linarca-style early numbers (14 meetings) came from.

• Weeks 4 - 8: the flywheel stabilizes: your content generates signals → signals feed sequences → conversations feed the calendar → visible activity generates more signals. Klaar's 8 meetings across two months is the honest steady-state picture for a focused, smaller-pool motion; ThinkFish's 400/month is what the same machine looks like industrialized across 50 seats.

The variable you control most directly: presence. Two posts a week and a coherent profile can double the input side of this system for free.

Where these numbers come from (methodology, briefly)

Three data layers feed this page, and they're worth distinguishing because they carry different weight. First, the customer case studies - the table above - are self-reported outcomes from named customers, published with their consent on our case-studies page. They're the strongest form (names attached, checkable) and the narrowest (ten companies isn't a distribution). Second, the platform aggregates - the 7.3M-prospect analysis, the 2.3×/2.6× lifts - come from campaign-level data across Valley's customer base in 2025, comparing warm signal-based campaigns against cold campaigns run through the same system. Same tooling, same sending infrastructure, different targeting input: that isolation is what makes the comparison meaningful. Third, the operating receipts - the 8,415-message week, the 2,851-demo year - are Valley's own outbound, published by the founder as it happened. Use each layer for what it is: the case studies prove it works for real companies; the aggregates prove the pattern isn't survivorship; the receipts prove we run our own machine.

What we deliberately don't do on this page: mix eras (every number is LinkedIn-era, stated above), quote percentages without their denominators' context, or launder projections into results. If a number here ever fails your own diligence, we want to know more than you do.

The segment-by-segment read

If you're a founder: benchmark against SaanSerif and Linarca, not ThinkFish. A one-person motion with real presence lands 10 - 20 meetings in a good month at a 20%+ reply rate - life-changing for an early company, and achievable inside the first 30 days because the signal pool already exists. Your leverage: you are the content engine; every post feeds the machine.

If you run a sales team: benchmark against Gallea, Klaar, and the anonymous Director of Sales. The pattern is multiplication, not replacement - research and first touches automated across both channels, reps concentrated on live conversations. The 6× output number is the ceiling case; 2 - 3× is the conservative planning number.

If you run an agency: benchmark against WeLaunch, Buttered Toast, and ThinkFish. Per-client orbits compound, reporting gets cleaner (meetings, not activity), and the seat economics scale linearly while the operational load doesn't. ThinkFish's 50-seat, ~400-meeting month is what the industrialized version looks like.

If you're enterprise-motion: benchmark against Bolt.new and Smallest.ai. Fewer, larger meetings; the signal layer matters more at enterprise because committee buyers research quietly - profile views from a buying committee are one of the highest-intent signals that exist, and almost nobody works them.

► From reading to results: start your free 7-day Valley trial. Meetings, not impressions, are the test.

FAQ

Where can I read Valley's case studies? joinvalley.co/casestudies - ten customers with names, roles, and metrics; the table above reproduces them verbatim.

Who uses Valley? Founders (SaanSerif, Linarca), sales teams from startup through enterprise motion (Bolt.new, Smallest.ai, Klaar, Gallea), and agencies running it across client accounts (WeLaunch, Buttered Toast, ThinkFish at 50 seats).

What reply rate should I actually expect? Somewhere in the 15 - 45% band on warm LinkedIn signals, position depending on your presence and ICP tightness - Linarca's 22% is an honest midpoint. Cold outreach benchmarks sit at 1 - 3%. For the new multi-channel motion: no published number yet, on purpose.

Do these numbers include the email channel? No - every published number is from the LinkedIn-era motion. Email shipped in August 2026; combined-motion data will be added when the sample is meaningful. Distrust any weeks-old feature with precise benchmark claims, ours included.

Are these results guaranteed? No - and distrust any outbound vendor who implies theirs are. The unrestricted 7-day trial exists so your own signal pool, not our case studies, makes the argument.

What's the biggest predictor of results? Input volume of warm signals: profile views, post engagement, site traffic. Active-presence accounts land high in the band; dormant accounts should expect a slower build (and should weight the presence-building playbook as heavily as the tool).

How fast do results typically show up? Warm leads answer fast or not at all - first replies usually land inside week one, first meetings in weeks one to three (Gallea: 14 meetings in 15 days; Linarca: 14 in month one). The compounding effects - flywheel between content, signals, and conversations - stabilize around weeks four to eight.

Can agencies use these numbers in client pitches? The aggregate stats and this page, yes - they're public. Specific customer names and metrics belong to those customers; cite the page rather than repackaging their numbers as your own results, and build your own per-client baseline in the first 30 days, which is the number a client will actually hold you to.

Related: Valley AI review - honest · Valley pricing explained · Warm outbound, explained · LinkedIn reply-rate benchmarks