How Does Valley's AI Personalization Work for LinkedIn Outreach?

Valley replicates your writing style through the Studio section where you input examples of past messages, emails, or content you've written. The AI analyzes your word choice, slang, acronyms, sentence structure, grammar patterns, and tone preferences.

Valley also allows you to specify do's and don'ts, essentially giving the AI a sticky note of rules about how you communicate, similar to instructions you'd give a human BDR.

The analysis goes beyond surface-level pattern matching. Valley examines how you construct arguments, do you lead with problems or solutions?

How do you transition between topics?

Do you use questions to engage, or statements to inform?

How formal or casual is your language?

Do you use industry jargon comfortably, or avoid it?

How do you create urgency without being pushy?

For example, if your writing samples consistently use short, punchy sentences with occasional fragments for emphasis, Valley replicates this rhythm.

If you frequently use rhetorical questions to engage readers, Valley incorporates this technique.

If you have a habit of using specific phrases like "here's the thing" or "the reality is," Valley integrates these verbal signatures that make your messages recognizably yours.

Can Valley handle multiple different writing styles for different audiences?

Yes. Valley's writing style section allows you to create different personas and approaches. You can set up separate writing styles for engineering leaders, product leaders, HR leaders, or salespeople.

Each style can have unique structural elements, tone preferences, and research priorities. When creating campaigns, you select which writing style to apply, ensuring your messaging matches the recipient's context.

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

This multi-style capability enables sophisticated audience segmentation.

Your engineering leader style might emphasize technical specifications, include more jargon, reference open-source projects or GitHub contributions, and focus on implementation details.

Your C-suite style might strip out technical details, emphasize ROI and business impact, reference board-level concerns, and include more strategic framing.

The ability to switch between styles within a single Valley workspace prevents the need for multiple tools or accounts.

You might run three simultaneous campaigns, one targeting CTOs with technical messaging, one targeting CFOs with financial impact messaging, and one targeting COOs with operational efficiency messaging; all using different writing styles but managed from the same dashboard.

How does Valley determine which research details to include in each message?

Valley triangulates three elements: your value propositions, the pain points you solve, and the prospect's specific situation. The AI stack-ranks research findings based on relevance to what you're selling and what would capture the prospect's attention most effectively.

For example, if you're selling to financial services and a prospect's bank just released quarterly earnings, Valley prioritizes that information over less timely details.

The triangulation process works like a three-dimensional filter.

First dimension: Does this research point relate to what we're selling?

Second dimension: Does it connect to a pain point we solve?

Third dimension: Is it timely, specific, and meaningful to this individual prospect?

Research details that score high across all three dimensions get prioritized for inclusion in the initial message.

Consider selling marketing automation software to a marketing director.

Valley's research reveals:

(1) the prospect recently posted about struggling with lead attribution,

(2) their company just raised Series B funding,

(3) they attended MarketingProfs conference last month,

(4) their company website shows they're hiring three marketing roles,

(5) they previously worked at a competitor using your software.

Valley's stack-ranking identifies that items 1, 4, and 5 directly relate to your value proposition (attribution tracking, team scaling, and proven track record), while items 2 and 3, though interesting, are less directly relevant.

Does Valley's AI improve over time as I give it feedback?

Valley learns marginally from your message edits and approvals, understanding your preferences inconsistently. The more reliable training method is explicitly updating your writing style settings in the Studio section. When you see messages you don't like, add those patterns to your "don'ts" list to codify preferences permanently. Valley is developing autonomous learning from rejections, but currently requires manual refinement.

The current feedback mechanism works like this: when you edit a message before approving (for example, consistently shortening Valley's drafts or removing certain phrases), Valley's models observe these patterns and attempt to adjust future messages accordingly. However, this learning isn't guaranteed—Valley might adapt for some messages while reverting to old patterns in others.

The more effective approach treats your writing style configuration as a living document. After running campaigns for a week, review the messages Valley generated and identify patterns you consistently edit. Add these as explicit rules: "Keep messages under 150 words," "Never use the phrase 'I hope this finds you well,'" "Always include a specific question in the closing," etc. These codified rules ensure 100% consistency versus relying on Valley to infer your preferences.