Situations Beat Markets: Why Demographics Don't Buy

By Mitchell Keller, Founder & CEO, LeadGrow. Managed 3,626+ cold email campaigns. 6.74% average reply rate. 2,230+ meetings booked in 2025.

Targeting "CEOs at SaaS companies" is lazy

I see this every week. A founder comes to us, shows their prospect list, and it's some version of "VP of Sales at B2B SaaS companies, 50 to 200 employees."

That's not targeting. That's a census.

You just described 40,000 people. Maybe 200 of them need what you sell right now. The other 39,800 are going to ignore your email, mark it as spam, or reply with "not interested" because you caught them at the wrong time with the wrong message.

We've managed 3,626+ cold email campaigns. The single biggest variable that determines whether a campaign works or doesn't is targeting. Not copy. Not subject lines. Not sending infrastructure. Targeting.

And the targeting that works isn't demographic. It's situational.

What "situation-based targeting" actually means

A situation is the inferred context behind a set of observable signals. It's not what someone looks like on paper. It's what they're going through right now.

Think about it this way. Two companies both have 15 employees and sell B2B SaaS. Same industry, same size, same titles. But one just raised a Series A and is hiring 3 SDRs. The other has been flat for two years with no job postings.

Same demographics. Completely different situations. One is in growth mode with pressure to build pipeline. The other might be in maintenance mode or quietly struggling. The email that works for Company A will get ignored by Company B.

Demographics tell you who someone is. Situations tell you what they need.

One signal is noise. Combinations are gold.

The mistake most teams make is treating a single signal as enough. "They just raised funding. Let's email them." That's better than pure demographics, but it's still surface level.

Funding alone doesn't tell you much. They could be a 200 person company that raised a $100M Series D. That funding isn't going to create the same urgency as a 12 person company that just raised a $3M seed round.

Signal combinations are where the magic happens. Here are real examples from our campaigns:

Signal combination: 15 employees + no leadership hires in 6 months

Inferred situation: Founder is still doing everything. Wearing the sales hat, the ops hat, the hiring hat. Stretched thin.

Frame: "You're probably still running sales yourself. Here's how similar founders got 3 to 5 meetings per week on their calendar without hiring an SDR."

Signal combination: Recent funding + hiring SDRs + no outbound infrastructure

Inferred situation: Growth pressure. Board expects pipeline numbers. They're building the team but don't have the systems yet.

Frame: "You're hiring SDRs but they won't have a playbook. We can have outbound running before they start."

Signal combination: Competitor's customer + recent negative G2 reviews of competitor

Inferred situation: Unhappy with current vendor. Open to alternatives but not actively searching yet.

Frame: "Noticed you're using [Competitor]. A lot of teams have been switching because of [specific issue from G2]. Curious if that's been your experience."

Signal combination: Attending industry event + posted about growth goals on LinkedIn

Inferred situation: Actively investing in learning and networking. Receptive to new solutions. Planning for growth.

Frame: "Saw you'll be at [Event]. Most attendees we talk to are trying to solve [specific problem]. We just helped [similar company] with that."

Each of these uses two or three signals to infer a specific moment in time. That's what makes the email relevant. Not the personalization. Not the clever subject line. The diagnosis.

The data: 21.7% vs 2.9%

We track reply rates across every campaign we run. When we break them down by targeting specificity, the pattern is obvious.

Here's what our data shows across 3,626+ campaigns:

Targeting Type	Average Reply Rate	Example
Hyper-specific (situation-based)	21.7%	NJ Coordinators at specific company type with specific pain
Industry-specific	7.5%	EdTech companies with 50+ employees
Segment-specific	6.1%	SaaS companies in growth stage
Broad	2.9%	B2B companies, 50 to 500 employees
Generic	0.4%	All companies in a geography

The 21.7% campaign (Diversus NJ Coordinators) targeted a hyper-specific situation. Not just "coordinators" but coordinators in a specific state, at a specific type of organization, experiencing a specific regulatory change that made the product urgent.

Same product. Same company sending the emails. The only difference was how precisely we defined the situation.

The buckets framework

Here's how we actually operationalize this. We call it the buckets framework.

Take your total addressable market. Let's say you sell to marketing agencies. There might be 50,000 of them in North America. If you send the same email to all 50,000, you'll get that 2.9% reply rate (if you're lucky).

Instead, break them into buckets based on situations:

Bucket 1: Agencies that just lost a major client (signal: leadership changes, layoffs, reduced social activity)

Frame: "When a big client leaves, pipeline becomes urgent. Here's how agencies like yours filled that gap in 30 days."

Bucket 2: Agencies hiring their first sales role (signal: SDR/BDR job posting, no previous sales hires)

Frame: "You're building a sales function from scratch. Most agencies get outbound running before the hire starts so they have pipeline day one."

Bucket 3: Agencies expanding into a new vertical (signal: new case studies, new industry mentions on website, hiring for specific expertise)

Frame: "Noticed you just published a fintech case study. We help agencies break into new verticals by getting them meetings with decision makers in 60 days."

Bucket 4: Agencies whose founder is still selling (signal: small team, no sales title on LinkedIn, founder posting about growth)

Frame: "You're probably still the one on sales calls. We can get meetings on your calendar so you can focus on delivery."

Same vertical. Four completely different situations. Four completely different messages. Each one lands because it describes what that specific group is going through right now.

Why "personalization" misses the point

Most agencies and SDR teams think personalization means mentioning someone's company name, referencing a LinkedIn post, or congratulating them on a recent hire.

That's not personalization. That's decoration.

Real personalization is diagnosis. It's showing the prospect you understand their situation before they've told you about it. When you email someone and say "Noticed you're hiring SDRs but don't have outbound infrastructure yet," you're not personalizing. You're diagnosing.

The difference matters because decoration gets a polite "thanks, not interested." Diagnosis gets "how did you know? Let's talk."

We call this the technician approach. A maintenance man says "I can fix anything." A technician walks through your house and says "The problem isn't your toilet. It's the pressure valve. Does your shower run scalding when you flush?" The technician gets hired because they proved they understand the problem before pitching the solution.

Your cold email should do the same thing. We call this approach diagnosis over personalization, and it consistently outperforms variable-swap personalization by 5x or more.

How to build situation-based prospect lists

Here's the practical workflow we use across 3,626+ campaigns:

Step 1: Define 3 to 5 situations where your product is urgent

Not "who could use this" but "who needs this right now." Complete this sentence for each: "When a company is experiencing [situation], our product becomes urgent because [reason]."

Step 2: Identify the signal combinations that indicate each situation

For each situation, find 2 to 3 observable signals. These could be hiring data, funding events, tech stack changes, leadership transitions, regulatory shifts, competitive moves, or content engagement patterns. Our situation mining signals guide covers 20+ signal combinations with the exact situations they reveal.

Step 3: Source the signals from data providers

Apollo for firmographic data and contact info. LinkedIn Sales Navigator for org chart and job changes. Trigify for audience engagement signals. Crunchbase and Lead Magic for funding data. BuiltWith for tech stack. Clay for enrichment and combining data sources.

Step 4: Build separate lists for each situation bucket

Don't mix buckets. Each situation gets its own list, its own messaging, its own campaign. When you combine them, you dilute the specificity that makes this work.

Step 5: Write messaging that diagnoses the situation

The first line of your email should describe their situation, not your product. If the prospect reads the first line and thinks "that's exactly what's happening," you've earned the rest of the email.

The signal hierarchy

Not all signals are equal. Here's how we rank them based on what we've seen across thousands of campaigns:

Tier 1 (strongest): Event attendance, content engagement, community participation

These are active behaviors. Someone attending a conference on sales automation is actively thinking about that problem. That's a strong signal.

Tier 2: Hiring signals + tech stack changes

Hiring an SDR means they're building pipeline capability. Adding a new CRM means they're investing in sales infrastructure. These are investment signals.

Tier 3: Company news, press mentions, funding

These are context signals. They tell you something about the company's trajectory but don't necessarily indicate immediate need.

Tier 4: Firmographic data (size, industry, geography)

This is the baseline. Necessary for qualification but not sufficient for targeting. Everyone has access to this data, so it creates zero differentiation.

The campaigns that hit 15%+ reply rates are the ones that combine Tier 1 or Tier 2 signals with Tier 4 qualification. They find the right companies (Tier 4) that are in the right moment (Tier 1/2).

Common mistakes with situation-based targeting

Mistake 1: Treating a single signal as a situation

"They raised funding" is a signal, not a situation. "They raised a seed round, have 10 employees, and just posted a sales role" is a situation. The combination creates context. One data point doesn't.

Mistake 2: Making the situations too broad

"Companies that are growing" describes half of all B2B companies. That's not a situation, it's a category. Get specific. "Companies that grew from 10 to 30 employees in the last 6 months and are hiring their first VP of Sales" is a situation.

Mistake 3: Not separating situations into different campaigns

If you have three situations, you need three campaigns with three different messages. Mixing them into one campaign with one message defeats the purpose. The specificity is the product.

Mistake 4: Ignoring the frame

Same offer, different situation, different frame. A founder who's stretched thin needs to hear "get your time back." A company with a new SDR team needs to hear "give your reps a playbook." The product might be identical but the frame changes everything.

Situations beat markets because timing beats targeting

The 30/67/3 rule says at any given time, about 3% of your market is actively looking for a solution, 30% have the problem but aren't searching, and 67% don't have the problem right now.

Demographic targeting sends the same message to all three groups. Situation-based targeting identifies the 3% who are ready and the 30% who have latent pain, then sends each group a different message matched to where they are.

That's why the numbers are so different. 21.7% vs 2.9% isn't because one email was better written. It's because one email was sent to people in the right situation with the right frame.

We've booked 2,230+ meetings in 2025 using this approach. Not because we write better emails than everyone else. Because we're better at finding people who are already in a moment where our clients' products make sense.

Fix the targeting first. Everything else gets easier after that. Once you've found the right situations, our B2B list building guide walks through the full funnel from 100K companies to 5K contactable targets.