How We Built a $2M Pipeline for an Analytics Startup in 90 Days
This is a story about patience, iteration, and compounding. It starts with $2M in qualified pipeline during the first 90 days of a cold email engagement. It ends, two years later, with $26M in pipeline generated in a single year. The client is Elly Analytics, now known as Plurio, and the co-founder we work with is Kirill Kasimskiy.
I am going to walk through the strategic decisions, the infrastructure, the data methodology, and the early experiments that laid the foundation for what became one of our longest and most successful client relationships. If you are evaluating whether cold email can work for a technical B2B product, or if you are considering hiring a cold email agency, this case study will give you a realistic picture of what the first 90 days look like and why the real payoff comes later.
For a broader overview of how cold email works at a foundational level, start with our complete guide to cold email in 2026.
The Challenge: A Powerful Product Without a Predictable Pipeline
Elly Analytics builds analytics solutions for businesses that need to make better decisions from their data. The product was strong. Companies that adopted it saw measurable results. But the gap between having a great product and filling a sales pipeline is wider than most founders expect.
Before working with us, Elly's outbound was inconsistent. The founding team had tried cold email on a small scale, but without proper infrastructure, verified data, or a systematic approach to segmentation, the results were sporadic. Some weeks produced a few conversations. Most weeks produced nothing.
Kirill and his team knew cold email could work for their market. They had a B2B product with strong deal sizes, a large enough total addressable market, and clear buyer personas. What they did not have was the operational engine to run it properly.
That is what we set out to build.
The Strategy: Pipeline Value Over Meeting Volume
Before writing a single email, we aligned with the Elly Analytics team on a critical principle. We were not optimizing for meeting count. We were optimizing for pipeline value.
This distinction changes everything downstream: which segments you prioritize, which titles you target, how you write your messaging, and how you evaluate campaign performance. A campaign that books 50 meetings with junior managers might look impressive, but if the average deal size is $5K, you have generated $250K at best. A campaign that books 20 meetings with VP-level decision-makers, where deal sizes average $100K or more, produces a fundamentally different result.
For Elly Analytics, we focused on organizations with complex data environments and real budgets for analytics infrastructure. Fewer, better conversations beat high-volume, low-quality meetings every time.
If you are weighing whether to build this kind of operation in-house or work with an agency, our comparison of cold email agency vs. in-house breaks down the tradeoffs in detail.
Phase 1: ICP Definition and Segmentation
The first two weeks of every engagement are about getting the targeting right. For Elly Analytics, this meant going deeper than "companies that need analytics."
We worked closely with Kirill and his team to map out the segments where Elly's product had the highest demonstrable impact and the largest potential deal sizes. We looked at their existing customer base, analyzed which types of companies had the shortest sales cycles and highest contract values, and used that data to define our initial target segments.
We identified multiple vertical segments to test in parallel. Each segment had different pain points, different buying triggers, and different language around their analytics challenges. A financial services company thinks about data differently than a logistics company. The messaging has to reflect that.
Within each segment, we mapped the decision-maker titles most likely to own the analytics budget. We layered in firmographic criteria: company size, revenue thresholds, technology signals, and recent indicators of growth or investment. We ranked each vertical by TAM size and estimated average deal value. Segments with the largest TAM and highest deal potential got the most sending volume. Smaller or more speculative segments received less volume but were still tested, because early data would inform future decisions.
In the first 90 days, concentration beats diversification.
Phase 2: Data and Infrastructure
This is the operational foundation that everything else depends on. If you want to understand how cold email agency pricing reflects the complexity of this work, it starts here.
Multi-Source Data Pipeline
We built Elly's contact database from multiple sources. No single data provider covers the entire market accurately. Our approach combined Apollo for initial contact sourcing (roughly 25-45% of contacts), custom web scraping for signals that do not exist in traditional databases (another 25-45%), and Outscraper for supplementary business data (10-20%). The principle is always the same: cross-reference multiple sources to build the most accurate picture possible.
Email Verification
Every email address went through LeadMagic verification before entering any campaign. Unverified lists produce high bounce rates, which damage domain reputation, which kills deliverability. We removed all catch-all and unverifiable addresses. The list got smaller. The quality got significantly better.
AI-Powered Enrichment
Raw contact data tells you who someone is. Enrichment tells you why they should care. We used Claygent with custom AI prompts to research each account and identify high-value signals: recent technology investments, organizational changes, growth indicators, and pain points specific to their vertical. This enrichment data fed directly into our copy, enabling opening lines and value propositions that felt researched rather than templated.
Infrastructure at Scale
We set up over 100 dedicated sending domains and more than 200 sending accounts. Each domain went through full DNS authentication (SPF, DKIM, DMARC) and a multi-week warmup process before any cold email was sent. This distribution of volume across many domains and inboxes is what keeps deliverability high over months and years.
Spintax Variations
Every message template was built with Spintax, producing between 10,000 and 50,000 unique variations per campaign. No two emails were identical. Email providers use pattern detection to flag bulk sending. When every message has slightly different phrasing and word choices, each email looks like a one-to-one conversation.
Phase 3: Campaign Execution
With infrastructure warmed and data verified, we launched campaigns across multiple segments simultaneously. This parallel approach is critical because you need comparative data fast. Running one segment at a time means waiting months before you know which audience responds best. Running several in parallel gives you that signal within weeks.
Each vertical segment received its own dedicated campaigns with tailored messaging. The product was the same. The way we talked about it was different for every audience. An analytics pitch to a retail operations leader emphasizes inventory optimization. The same product pitched to a financial services executive emphasizes compliance reporting and risk modeling. Same underlying value, completely different framing.
Within each segment, we A/B tested multiple messaging angles and tracked performance at the variant level, not just the campaign level. We monitored results weekly. Underperformers were paused or restructured. Winners received more volume and additional testing. This is the operational rhythm: launch, measure, learn, adjust. Every week.
By the end of the first month, patterns emerge. Certain segments respond at higher rates. Certain title clusters convert from reply to meeting more consistently. Those patterns become the basis for every decision that follows.
Early Results: The First 90 Days
In the first 90 days, the Elly Analytics campaigns generated over $2M in qualified pipeline.
That number did not come from a single breakout campaign or a lucky week. It came from systematic execution across multiple segments, continuous optimization, and the compounding effect of a multi-touch sequence reaching thousands of verified contacts over 12 weeks.
We identified the highest-performing segments. Not every vertical responded equally. Some had strong engagement but modest deal sizes. Others had lower reply rates but produced conversations with buyers who controlled six-figure budgets. That distinction between segments that generate activity and segments that generate pipeline is everything.
We established baseline benchmarks. Open rates, reply rates, meeting conversion rates, pipeline value per meeting. These benchmarks became the foundation for scaling in months four through twenty-four.
We learned what did not work. Some messaging hypotheses were wrong. A failed experiment in month one saves you from repeating that mistake for the next 23 months.
The $2M in 90 days was a strong start. But honest assessment: the first 90 days were primarily about learning. The big results came from what we did with those lessons.
The Compounding Effect: From $2M to $26M
Most companies evaluate cold email in 90-day windows. They run a campaign, measure the results, and decide whether to continue. That approach misses the most powerful dynamic in outbound: compounding.
Every campaign we ran for Elly Analytics made the next one smarter. The data from the first 90 days did not just produce pipeline. It produced knowledge. We knew which ICPs converted at the highest rates. We knew which objections came up most frequently and could address them proactively in our messaging. We knew which deal sizes were realistic for each segment and could target accordingly.
Refining ICP Based on Closed-Deal Data
After the first few months, deals from early campaigns started closing. That closed-deal data is gold. It tells you not just who took a meeting, but who actually became a paying customer. We fed that information back into our targeting, tightening the ICP based on who converted all the way through the funnel, not just who opened an email. That shift from optimizing for replies to optimizing for revenue only happens when you have enough closed-deal data to see the full picture.
Expanding Into Adjacent Segments
With the core segments validated, we expanded into adjacent markets. Each new segment benefited from everything we had learned. The infrastructure was mature. The data pipeline was proven. The copy frameworks had been battle-tested. A segment that might have taken six weeks to produce results in month one could produce results in three weeks by month eight. That accumulated intelligence is the compounding asset that most companies never build because they stop too early.
Countless Experiments Over Two Years
Over the course of our engagement with Elly Analytics, we ran countless experiments. Different segments, different messaging angles, different data sources, different sequence structures. Each experiment generated data. Each data point informed the next decision. This iterative loop, running continuously over two years, is what transformed a $2M first-quarter result into something much larger.
Critically, the team at Elly Analytics did not treat this as a set-it-and-forget-it channel. Kirill and his team provided feedback from prospect calls, shared insights about which deals were progressing and why, and collaborated with us on which new segments to test. The hundreds of calls they had with prospects generated qualitative data that no dashboard could capture, and we used that intelligence to sharpen every subsequent campaign.
The Full-Scale Result
In 2025 alone, the second full year of our partnership, Elly Analytics generated over $26M in pipeline through cold email outreach. They booked hundreds of calls with prospects and closed multiple large deals across various segments.
The underlying product did not change. The market did not suddenly become easier. What changed was the depth of our knowledge about which buyers convert, which messages resonate, and which segments produce outsized returns. That knowledge, accumulated through two years of disciplined experimentation, is what made the scale possible.
Here is what Kirill Kasimskiy, co-founder of Elly Analytics, said about the partnership:
"We've worked with Alchemail for over two years. We ran countless experiments, targeted various segments, did hundreds of calls with prospects and closed large deals. Last year alone we generated over $26M in pipeline through cold email outreach."
That quote reflects something important. This was not a vendor relationship where we sent reports and they checked a box. It was a genuine partnership built on shared experimentation, honest assessment of what was and was not working, and a mutual commitment to long-term results over short-term metrics.
Key Takeaways
After two years and $26M in pipeline, here are the lessons that matter most from the Elly Analytics engagement.
Pipeline Value Matters More Than Meeting Count
If we had optimized for meeting volume, the pipeline numbers would have been a fraction of what they became. A single meeting with the right buyer is worth more than ten meetings with people who will never close.
Segmentation Is Everything
The segments that performed best were not the ones we would have guessed on day one. Only by running parallel campaigns and measuring results at the segment level could we identify the real opportunity. Same product, different messaging per segment.
The Compounding Effect Is Real
The first 90 days produced $2M. That knowledge, compounded over two years, produced $26M in a single year. Early campaigns are not just pipeline generators. They are learning engines. The agency that ran your campaign in month one knows your market ten times better by month twelve.
Long-Term Relationships Outperform Short-Term Engagements
The outsized returns come from sustained partnerships where the agency accumulates deep knowledge of your market and your buyers. That institutional knowledge cannot be rebuilt from scratch with a new agency every six months. If you want to understand what makes an agency relationship work long-term, our guide on how to hire a cold email agency covers the key factors.
Data-Driven Iteration Beats Spray and Pray
At every stage, decisions were driven by data. Which segments to prioritize, which messaging to scale, which experiments to run next. We never relied on assumptions when we had evidence. That discipline, applied consistently over two years, is what produced the results.
Conclusion
The Elly Analytics engagement started with a clear objective: build a predictable, scalable pipeline for a powerful analytics product that was not reaching enough of the right buyers. In the first 90 days, we built over $2M in qualified pipeline. Over two years of partnership, that foundation compounded into $26M in pipeline in 2025 alone, with hundreds of prospect calls and multiple large deals closed.
The relationship that starts with $2M in 90 days can compound to $26M in a year. But only if you build it on the right foundation: verified data from multiple sources, robust infrastructure at scale, disciplined segmentation, and a commitment to iteration over time.
If you want to explore whether cold email can build this kind of pipeline for your business, book a call with me directly.