The easier something becomes, the less effective it is. At least, that’s the case with cold email outreach. Ever since 2010, revenue operations (RevOps) leaders have been forced to keep upping the difficulty level in order to maintain conversion rates. In this collab with Roman Hipp of BetterContact, we’ll take a look at the history of cold outreach — and what RevOps specialists will be doing to improve conversion rates through 2030.
I. The Apollo & ZoomInfo Era (2010 - 2016)
From 2010 to 2016, Apollo and ZoomInfo ran the show. It was an exciting time — spam filters were simplistic and risk-takers often got high rewards. But on the other hand, Apollo and ZoomInfo were both static databases, and they were rarely updated. That led to a lot of low-quality targeting.
Simplistic Segmentation
During this period, segmentation was driven by simple industry classifications, such as SIC/NACE codes (the US/European system for classifying industries). This made it easier for sales teams to target specific sectors but lacked the sophistication of modern segmentation methods.
Basic Personalization
“Personalization” emerged during this era, but it was extremely basic. Do you remember these openers?
- “I took a look at [website] and found…”
- “Since you’re in [industry] you might face this challenge…”
- “Businesses in your area are struggling with…”
It was better than nothing, but not by much.
Email Guessing
Data quality was often poor, so RevOps leads relied on standard corporate email formats (e.g., firstname.lastname@company.com, firstinitiallastname@domain.com) to guess email addresses.
Some would send emails to multiple guessed addresses, hoping one would land in the correct inbox. This was inefficient and often led to high bounce rates.
Many companies had catch-all email addresses where all incorrect addresses were still delivered to the main domain, masking the problem of bounced emails.
Eventually, RevOps started monitoring bounce rates and removing invalid addresses from their lists after sending. But that reactive approach came with its own problems, including:
- High bounce rates, harming sender reputations.
- Spam complaints and blacklisting risks.
- Wasted time and resources on bad data.
Early email verification methods were rudimentary. For example, revenue leaders used Excel to clean email lists manually, removing formatting errors and checking typos. Some used WHOIS or DNS lookups to conduct basic domain validation as well.
Before Mailgun, RevOps also used SMTP pings. However:
- Not all servers responded to pings (especially major providers like Gmail).
- Many servers began introducing anti-spam measures, like rate-limiting or rejecting pings, to combat abuse.
- Results were often unreliable.
Minimal Spam Filtering
Everyone remembers what spam was like in the early 2010’s — virtually infinite. A host of issues contributed to an incredibly annoying status quo:
Primitive Spam Filters
- Lax Filtering Mechanisms: Early email systems didn't have robust spam filters. Blacklisting systems like Spamhaus were still less than sophisticated, and email deliverability algorithms were still evolving.
- Open SMTP Standards: Email protocols like SMTP (Simple Mail Transfer Protocol) allowed messages to be sent with minimal checks. The lack of domain reputation tracking made it harder for servers to identify and block mass emails.
Little Legal Oversight
- GDPR and CCPA Absence: Before regulations like GDPR (Europe) and CCPA (California) became prominent, there was less emphasis on user consent and data protection. RevOps had free rein to scrape and use data for cold outreach.
- Email Marketing Loopholes: Anti-spam laws like CAN-SPAM (in the U.S.) focused more on allowing opt-out mechanisms rather than prohibiting unsolicited emails outright, making cold email campaigns legally viable.
Unsaturated Tactics
- Low User Awareness: People were less aware of spam tactics and didn't have tools like Google Workspace's advanced spam detection to filter messages effectively.
- High Engagement Rates: Fewer cold email campaigns meant recipients were more likely to open and respond, improving conversion rates.
- No Sophisticated Tools Required: Mass emailing could be set up with basic tools like email clients and SMTP servers. Today’s emphasis on verified domains, SPF/DKIM/DMARC records, and email warming wasn't as prevalent.
The Wild West of Cold Outreach
All in all, this was a period characterized by aggressive, unchecked cold outreach. Sales teams could scale quickly but with diminishing returns as inboxes filled with irrelevant, low-quality messages.
II. The Crunchbase & BuiltWith Era (2016–2020)
In the second phase of cold email outreach, people got a lot more serious. Email verification tools became commonplace, as did specialized databases. Yet without insight into the impact of email campaigns, personalization and relevance were still a long way off.
Specialized databases
During this period, data providers like Crunchbase and BuiltWith began to specialize in offering databases tailored for specific use cases and industries. This allowed sales and marketing teams to be more targeted and specific in their outreach, moving away from broad segmentation to more focused verticals. That being said, contact-level databases were still rare.
Basic email verification
In 2013, Mailgun introduced its inaugural email validation API, kicking off a rush to create sophisticated algorithm finders. However, many problems remained. For instance, an algorithm might try:
- alex.smith@amazon.com
- a.smith@amazon.com
- alexander.s@amazon.com
…and so on. But an algorithm will never guess unusual patterns like:
- alex7@amazon.com
- alex-hr@amazon.com
At the end of the day, algorithm finders were pretty limited. Because the algorithms the email finders use were so similar, RevOps often paid for the same email twice — and it was still invalid.
That’s why revenue leaders brought databases in the mix. Yet even today, databases are hard to find on Clay’s provider list.
Plus, 26% of the time, emails don’t actually belong to the person RevOps leads want to reach. Here’s why:
If a company uses a common email pattern (like first initial + last name), so let's say j.fischer(at)amazon(dot)com, and you’re looking for a John Fischer, you could easily end up with a valid email — but it belongs to Jason Fischer, not John Fischer. If John Fischer joined the company later than Jason, his email might be: jo.fischer(at)amazon(dot)com.
How many of these cases do you think are out there?
Email warmup
Thanks to a combination of market forces and regulatory pressures, email warm up also became necessary during the mid-2010s. Here’s a brief timeline —
- Rise of Advanced Spam Filters (Mid-2010s):
- ESPs started using machine learning and advanced algorithms to detect spam based on sender reputation, email content, and engagement rates.
- Cold emails sent from new or dormant domains were more likely to be flagged as spam, prompting the need for email warming.
- Proliferation of Cold Emailing Tools (2015-2018):
- As automated cold emailing tools (e.g., Mailshake, Lemlist, Outreach) became popular, many inexperienced senders abused these tools, leading to widespread spam complaints.
- ESPs tightened their policies to identify and block accounts with poor sending practices, making email warming crucial.
- GDPR (2018) and CCPA (2020):
- The introduction of strict data protection laws pushed cold emailers to focus on compliance and quality.
- Email warming became a best practice to ensure campaigns were effective while remaining within the legal gray areas of cold outreach.
Weak spam filters
While spam filters were starting to become more sophisticated, they remained relatively weak during this period. As a result, warmed-up domains were often enough to get emails through, enabling high-volume outreach strategies to continue working, though with decreasing returns.
Blind decision makers
Despite the improvements in targeting and deliverability, decision-makers were still largely "blind" as to the source and quality of outreach. This era marked a shift towards more strategic targeting, but personalization and genuine relevance were often still lacking, leading to a sense of detachment from the recipients.
III. The Clay and OpenAI Era (2020 - 2024)
The ubiquity of AI in the early 2020s may have made email content more personal, but it also made it harder for recipients to figure out if someone had actually invested in them. Personalization for the sake of personalization quickly delivered diminishing returns. And though new tools connected to third-party data sources and extended them with AI enrichment, issues with data accuracy substantially diminished the impact of email outreach.
Custom, low-accuracy AI-powered enrichment:
With the rise of AI-powered tools, data enrichment became more customizable, but accuracy still lagged. On the one hand, AI could generate context-aware email copy at scale by using information from enriched data, making outreach feel human and tailored even for thousands of contacts. This was a huge step up from the Crunchbase / BuiltWith era.
Yet on the other hand, AI tools sometimes created noise instead of providing the clarity needed for effective outreach. AI began to enable basic personalization in outreach, such as custom subject lines and content. However, these efforts often lacked genuine relevance to the recipient, relying on superficial data points rather than deep understanding of the individual's needs or interests. This led to a higher volume of outreach, but with little increased effectiveness.
Complex email verification and warm-up
As spam filters grew more sophisticated, complex email verification and domain warm-up strategies became even more critical. Enter catch-all verifiers. By the mid-2020s, sophisticated catch-all verifiers increased total addressable market (TAM) by up to 10-15% by safely validating about 50% of catch-all emails.
Skyrocketing decision-maker blindness
Ironically, despite the increasing sophistication of AI tools, decision-makers became more immune to outreach efforts. With higher volumes of content hitting their inboxes and a growing reliance on AI-driven, generic messaging, they became more disengaged, making it harder for any message to stand out. The "blindness" of decision-makers reached new heights as the noise from over-automated outreach drowned out the signal.
III. The Inbound-Led Outbound Based on Forecasts (2025 - 2030)
What's next? Marrying personalization with accuracy and relevant timing by using proprietary research agents. In the years to come, not only will AI tools become more autonomous — they will help RevOps leaders use their company’s past pipeline to drive new outbound campaigns. Soon, RevOps teams will be using forecasts to estimate the value of inbound leads, unlocking truly efficient outbound ops.
Inbound-led outbound
To maintain a competitive edge in a world awash with superficial personalization, revenue leaders need to establish trust and provide value upfront through content. Once they’ve positioned themselves as thought leaders, they can target the leads/content consumers via outbound campaigns.
Benefits of inbound-led outbound
- Precision Targeting: Outbound campaigns focus on prospects who resemble inbound leads, significantly improving engagement rates.
- Relevance at Scale: AI-generated messaging draws from inbound behavior to personalize outreach while maintaining efficiency.
- Custom Forecasts: Using autonomous forecasting platforms like AlgOps, RevOps leads can estimate the value of inbound leads to prioritize their outbound efforts.
Waterfall enrichments will be standard
As we approach 2030, it will no longer be enough to have just one data provider. Increasing globalization means that outbound has to crisscross multiple countries. Since every data provider has unique geographical strengths and weaknesses, RevOps leaders need to diversify their contact data sourcing — especially for mobile phone numbers.
- In fact, it’s estimated that just 25-40% of mobile numbers are valid. That means that to reach 1,000 prospects, you need to find 2,500 leads!
- Spending hours to build lists you can’t even use is simply unsustainable.
Waterfall enrichment will become the new standard because it cuts out waste. Here’s how:
- Aggregates 20+ premium providers (e.g., PDL, RocketReach).
- Runs optimized provider sequencing to find the best matches.
- Validates all results through multiple scoring layers.
- You’re only charged for valid data.
The bottom line? When everyone uses AI, only those who apply it strategically will win.
What Outreach May Look Like Soon
In 2030, RevOps teams could be finding new ways to bring value to potential customers. Imagine the following scenario:
You’re providing partnership management services and you want to time your outreach perfectly so you can win trust and convert your leads into paying customers. Using an autonomous forecasting platform like AlgOps, you quickly set up an account prioritization tool to alert you when any of the companies you’re monitoring start growing fast. Your account prioritization tool tracks custom signals like hiring announcements, fundraising, and LinkedIn posts over time. Then, it alerts you when your lead is likely to need your services.
Now, you can reach out with a powerful message:
“If you keep growing at this pace for three months, you’ll be underwater. We can help — let’s talk about how we can get ahead of the curve.”
That’s a future worth building.
Take the Next Step in Your Cold Outreach Journey with AlgOps
As the history of cold outreach shows, you have to up the difficulty level to maintain a competitive edge. Relying on static lists and single data sources just won’t cut it in the era of AI. To make the right contact at the right time with the right message, you need to leverage inbound data and incorporate custom forecasts. That way, you can make every message count.
Ready to take the next step in your RevOps journey? Schedule a demo with AlgOps today and keep your outreach accurate, relevant, and impactful.
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