Message Personalization Strategy

Why Valley's Personalization is Different

Most outreach tools do mail merge: "Hi {firstName}, we work with companies like {company}..." It's lazy, obvious, and ignored.

Valley's approach is different. Every message is genuinely unique because Valley's AI models analyze extensive data about each prospect - company info, role details, recent activity, industry context, and more. Not a template with a name swapped in. A message that references something specific about THEM.

What Valley Researches

Valley's research agents pull data across several categories before writing each message:

Professional Context

Current role and title, company tenure, career trajectory, skills and expertise, previous companies, education background, professional certifications and achievements.

Company Context

Company size and stage, industry and vertical, recent funding or growth, hiring patterns, tech stack signals, competitive landscape, recent news or announcements.

Activity Signals

Recent LinkedIn posts and engagement, content topics they care about, professional interests, thought leadership themes.

Timing Signals

Recent job changes, promotions, company funding rounds, new product launches, hiring sprees, industry shifts.

The more data available, the more specific the personalization. This is why warm list prospects (who have recent LinkedIn activity) get better personalization than cold list prospects with minimal public data.

What Makes Personalization Good vs. Creepy

There's a line between smart research and creepy surveillance.

Good Personalization (Do This)

Reference things the prospect has publicly shared on LinkedIn:

• "I saw your post about scaling sales teams. We help companies do exactly that."

• "Looks like you just got promoted to VP of Sales. Congrats - that growth usually comes with ops challenges."

• "Your company just closed Series B. We usually chat with newly-funded teams about scaling operations."

All of these reference public information. It shows research in a professional, transparent way.

Creepy Personalization (Never Do This)

Reference personal details or information from outside their professional footprint:

• "I see you're into rock climbing based on your photo." (personal, not professional)

• "I noticed you visited my website last Tuesday." (tracking their behavior)

• "You live in San Francisco based on your posts." (inferring location)

• "I saw you checked my profile 3 times this month." (implying surveillance)

How to Improve Personalization Quality

Valley's AI is only as good as the inputs you give it. Here are the levers:

1. Writing Style

If your writing style sounds corporate and robotic, messages will sound robotic even with personalization.

Bad input: "We leverage cutting-edge AI solutions to synergize your revenue operations and drive exponential growth..." Result: AI personalizes it, but it still sounds like a corporate memo.

Good input: "I help sales teams skip the data entry nonsense and actually close more deals." Result: AI keeps it natural and human when it personalizes.

Write your DOs and DON'Ts like you talk. Casual, specific, no jargon.

2. Product Description

The better you describe what you do in Studio, the better Valley connects it to prospect pain points.

Vague: "We're an AI platform for sales teams." Result: AI can't find specific angles. Generic messages.

Specific: "We automate research and personalization for LinkedIn outreach. Prospects get messages that reference their recent posts, hiring challenges, or company news instead of generic templates." Result: AI sees the value prop and connects it to signals in the prospect data.

3. ICP Definition

Tight ICP = tight personalization. Broad ICP = generic messages.

Broad: "Any B2B SaaS company with a sales team." Result: AI tries to personalize for a million different use cases.

Tight: "Series A-C SaaS companies in HR tech, $5M-50M ARR, in hypergrowth and struggling with hiring quality." Result: AI knows exactly who you're targeting and finds sharper hooks.

4. Warm Signals

Cold lists get good personalization. Warm lists get great personalization.

Why? Warm signal prospects have recent LinkedIn activity that Valley can reference. Profile viewers showed interest in you. Post engagers care about a specific topic. This gives Valley more context to work with.

Warm lists get 4x the reply rate of cold outreach. The personalization quality difference is a big reason why.

5. Research Agents

Make sure "Include research details for context" is enabled on your research agents. This is the single biggest factor in personalization quality. Without it, Valley writes messages based on basic profile data. With it, Valley weaves in company news, role context, and activity signals.

Before and After

Generic Cold Message

"Hi John, we help companies like Acme increase revenue and improve their sales processes. Our platform has helped hundreds of companies close more deals faster. Interested in learning more?"

This could be sent to anyone. No proof of research. Lists features, not value.

Valley Personalized Message

"Hi John, saw your post about scaling sales teams without doubling headcount. Same challenge we had before building our tool. Took us 8 months of trial and error to figure it out. Now we help other sales teams skip that timeline. Curious if it's on your radar?"

References his specific post. Shows real research. Connects the solution to his exact pain point. Sounds like a human wrote it.

When Personalization Breaks

ICP Too Broad

AI can't find tight hooks because your product could apply to anyone. Messages feel generic despite personalization. Fix: Narrow your ICP in Studio. Be specific about titles, company size, industry, and stage.

Product Description Too Vague

AI doesn't understand your specific value prop. Can't connect your tool to their pain points. Fix: Rewrite your product description with specific outcomes, concrete numbers, and clear use cases.

Cold Lists with No Activity Data

Prospects have minimal public LinkedIn activity. AI has limited context beyond name/title/company. Fix: Prioritize warm lists where prospects have recent activity. Use cold lists as supplementary volume, not your primary source.

Writing Style Too Corporate

Even with personalization, every message sounds like a marketing email. Fix: Rewrite your Writing Style DOs and DON'Ts to be conversational. Add DON'Ts like "no corporate jargon," "no buzzwords like synergy or leverage." Add DOs like "write like you're texting a colleague."

Personalization Quality Checklist

• Writing Style configured with natural, conversational tone?

• Product description specific with concrete outcomes?

• ICP tight enough to describe your ideal customer in 30 seconds?

• Warm lists set up and feeding campaigns?

• Research agents enabled with "Include research details" on?

• DOs and DON'Ts specific enough to guide Valley's tone?

If any of these are missing, fix them before expecting great personalization. Valley's AI amplifies what you give it - specific inputs produce specific, compelling messages.