Is it crowded in here?

The number of agencies that offer B2B cold email services has grown tremendously in a short period of time, and for good reasons:

  • It can take as little as 30-60 days to close 6-figures or more of work

  • A team of 1-5 people can easily manage a small portfolio of 10-20 clients

  • Getting started can be done with little upfront cost or effort

With such a low barrier to entry, it’s easy for many agencies to get started. But when the workload exceeds their capacity, they begin to stagnate, struggle, or fail.


One key differentiator that separates winning cold email agencies from the crowd is how well they productize their service offering so they can scale up and grow.

In this post, I’ll share how to productize one of the most important areas of cold email: optimization testing.

By building a repeatable, scalable set of processes for driving predictable client outcomes, your agency will be better positioned to scale.

[Make sure to check out the Productizing Lead Generation Content Bundle on the website to help take your agency’s cold email services to the next level.]

Why Does Optimization Testing Matter for B2B Cold Email?

How Do You Get Started with Cold Email Optimization?

How Do You Design the Testing Process?

How Do You Measure Success in Cold Email Optimization?

What Are Some Best Practices for Cold Email Optimization?


Why Does Optimization Testing Matter for B2B Cold Email?

It doesn’t take significant effort to make a repeatable process for client onboarding, email creation, and campaign management.

Create. Launch. Manage. Repeat.

In fact, there are tons of great cold email guides and worksheets that anyone can find online and use to create a workable process for cold email services.

Optimization, however, is very different.

It’s a process that demands disciplined testing, consistency, and knowledge about how to improve results for a client’s particular situation.

With the right process for optimizing cold emails, your agency can build a repeatable, scalable solution for consistently delivering results while providing unique advantages that can help you differentiate from the crowd:

  • Faster Improvements – With a systematic way to test new ideas and identify the best path to success, your team will be able to drive better outcomes faster.

  • Higher Performance Potential – Even the best cold email formula or sequence in the world will eventually become obsolete and need tune-ups. By optimizing, you unlock a higher performance level that most agencies can’t realistically achieve without it.

  • Deeper Insights – Research and client insights don’t compare to the real data you get on the front lines of a cold email lead generation campaign. A repeatable optimization process also makes information gathering and interpretation easier.

The Results

Continuously testing your email sequence in a targeted and systematic way can unlock growth potential that is hard to achieve without a repeatable and scalable process:

  • Higher Retention Rates

  • Higher Customer Lifetime Value

  • Larger Deal Sizes

By learning how to include optimization testing as a part of your cold email services, you’ll find the ability to recruit, hand off accounts to new team members, and scale your business easier.

How Do You Get Started with Cold Email Optimization?

Productization is about creating a system that can generate consistent, predictable outcomes while still being repeatable and scalable.

Because optimization can be difficult, it’s important to make this area of your services understandable and easy enough to hand-off to new team members.

Simplicity is your friend here.

To create an optimization process that is effective and repeatable, it’s important to know where to start, how to design it, and what to focus on throughout the journey.

Getting Started

At the beginning of your journey with a client, you’ll usually start with a simple multi-step cold email sequence that only contains a single version of each email for every step.

The first step of creating an optimization process begins with a review of your client campaigns: analyze the current status, review up-to-date market research, and identify areas where your team could potentially improve performance.

From there, your team can use this information to decide on what you want to achieve for your clients.

Set Goals

The purpose of using an optimization process in your service offering is to help you achieve the expected outcomes you framed when you first sold clients the deal.

Some clients are probably doing great and only need a tune-up using optimization. Others might be on the brink of a cancellation notice and need to be fixed soon.

Each client's situation varies.

Because of this, it’s important to have a concrete long-term goal for each client on how team members can use optimization to reach positive outcomes.

Some examples might be:

  • Achieve an average of 5 meetings generated per week for a client that has asked for 20 meetings/month

  • Improve the number of total qualified prospects generated by 30% for a client who wants to 3x their sales pipeline regardless of buying stage

  • Change the direction of a campaign positively for a client that might not be starting off on the right track

This long-term goal acts as a compass to guide the direction of your efforts and keep your team aligned with the needs of the client.

Identify Target Metrics

From there, your team can break these longer-term goals down into the different metrics you need to improve in order to get there.

This gives your team the exact Key Performance Indicators (KPI) you should focus on improving each month to achieve your long-term goal.

From the previous examples, we could say:

  • To generate 5 meetings per week, we need to increase the number of prospects that respond positively to the campaign

  • To help maximize the growth of their sales pipeline, we need to maximize open rates and generate more clicks and replies

  • To turn this client’s campaign around, we need to improve engagement with more clicks and replies from prospects who open our emails

These target metrics provide visibility that your team can use to decide on the type of optimization you need and where in the sequence you need to focus each month.

Create a Monthly Goal

It’s best to set up monthly milestones so your team can track their progress towards the end goal you’re trying to accomplish.

These milestones act like a monthly cycle that focuses on specific smaller goals that can be achieved with 2-4 optimization tests.

As an example, take a sequence that isn’t getting a target audience engaged. The best metric to start with would be the open rate.

The month would then be focused on testing different themes, approaches, and keywords in the subject line and pre-text to drive open rates by X%.

Once the month finishes, your team would then reflect on your progress and create a new monthly milestone so you consistently get closer to your clients’ long-term goals.

By using this monthly cycle, you’ll keep your optimization efforts aligned and make it easier to review the status and progress of each client.

How Do You Design the Testing Process?

So far, you’ve set long-term goals, identified target metrics, and created milestones for each client. The next step is to decide how you want to design the optimization test.

This testing process will be the repeatable system your team uses every 1-2 weeks to make improvements towards your monthly goals and ultimately your long-term goals.

While there are several different approaches to cold email optimization, some more advanced than others, I’ll be focused on the 5 steps to designing a simple A/B test.

Step 1 – Choose a Focus

What you optimize in the 1st optimization test will be completely different from the 5th and 10th test. Your team will need to adjust how you optimize depending on how each client campaign is doing and the goals you have.

While not every journey will be the same for each client, a campaign will likely go through these 5 predictable steps as you progress:

  1. Initial Optimization – This is what you use when you have limited data and insights to use. When you’re first starting out, it’s better to focus on testing themes, approaches and angles so you can identify initial insights, spot potential areas of weakness, and dial in on the right messaging and language.

  1. Open Rate Optimization – After identifying some topics and approaches that work, the next step in optimization is usually increasing open rates. High open rates are very important for testing narrow metrics like a prospect’s actions and sentiment, which require enough email opens to make the testing statistically significant.

  1. Optimizing for Prospect Actions – Once you’ve optimized for open rates, your team can narrow down to optimizing for prospect actions like clicks and replies. This involves testing value propositions, call-to-actions, and different positioning to identify the best way to get prospects to take action on your sequence.

  1. Sentiment Optimization – This type of optimization is focused on improving how prospects respond to emails. Instead of trying to get any reply, your team will be focused on decreasing the number of uninterested or negative responses and increasing the number of positive responses like meetings or info requests.

  1. Continuous Improvement – After several months of testing, you will begin to see marginally smaller results and will want to shift your focus to sustainability. This stage of optimization involves rapid testing to make small, targeted changes in an attempt to continue improving the sequence on an ongoing basis.

By tailoring your optimization tests for a particular focus, your team can hone in on what works and create a predictable process that leads to high-performing cold emails.

Step 2: Review & Hypothesize

For each email step being optimized, review previous outcomes, quantitative data, and current campaign insights. This will be used to create a hypothesis for an A/B test.

 A hypothesis is a claim that a certain change will generate a certain result. It can be as simple or specific as your team needs. Here are a few examples of different hypotheses you might find yourself using in a cold email optimization test:

  • If we use X language instead of Y language in the subject line, we will increase the open rate by Z%

  • If we take a more formal approach to the introduction in the pre-text, we will improve the number of positive sentiment responses by X%

  • If we use CTA 1 instead of CTA 2, we will increase the open-to-reply rate by X%

Keep in mind that these hypotheses should be aligned with the target metrics you’re after and the long-term goal you have for each client.

Step 3: Test Variations

Use these hypotheses to test your changes. You’ll want to set up two variations of each email, with one of the variations containing changes based on your hypothesis and the other variation containing the original email.

With two variations for each step of the sequence, you now have everything you need to do a round of optimization.

From here, test your hypotheses by sending emails to prospects until you have enough data to make comparisons and conclusions. Through these results, you’ll either validate or invalidate your hypotheses, which provides new data and learnings for the next test.

Step 4: Analyze

Once your test is complete, review the performance of each variation to see how your hypotheses stacked up.

You’ll want to use both the quantitative data coming from Quickmail and the qualitative insights your team has gathered from the campaign inbox to ensure you have a good idea of how each email performed.

Once you’ve finished reviewing the outcomes, insights, and data, you can come to a conclusion about each of your hypotheses and whether or not they achieved the results you expected.

These tests will achieve two goals: it delivers clear insights you can use for future tests and shows you what works with cold email.

Step 5: Evaluate

Tests are hard to make successful in isolation. The end of your optimization process should always conclude with a reflection of how your test impacted your target metrics and your long-term goals.

Once you’ve finished the round of optimization, analyze the outcomes of your testing efforts and reflect on the core learnings you gathered for future testing.

With this 5-step optimization test, your team will be able to continuously form new hypotheses, test, and gain insights every 1-2 weeks to help you achieve success for your clients.

How Do You Measure Success in Cold Email Optimization?

If you don’t know how to achieve your goals with optimization, your team will struggle to know what to optimize, when to optimize, and how to optimize it on a monthly basis.

Clarity is crucial.

Fortunately, cold email marketing offers 4 different layers of measurable information that can be used to achieve specific goals with your optimization testing.

Layer 1 – Engagement

Engagement is the first step of the optimization process because a prospect’s interest begins with surfing through piles of emails, viewing yours, and deciding to take a look.

Engagement is measured through the open rate and is affected by the name, what’s in the subject line, and the first 35 – 140 characters of your email, depending on the email service.

For open rates, averages across the web range from 30% to 50%. However, with optimization, agencies can realistically reach 70%-75%+ open rates.

A few caveats:

  1. Keep it Aligned – While it’s important to focus on the areas that motivate an open, be careful not to lose track of how that open will align with the rest of the email.

  1. Pay Attention to Sentiment – Be careful not to get lost in the numbers. You won’t see good outcomes with high open rates driven by messaging that causes negative responses or misunderstanding.

Layer 2 – Actions

Once a prospect opens the email, their expectations rest upon the content in your email. The perfect cold email will lead to a positive next step after the prospect opens it. It’s all about seeking specific actions based on your client’s particular goals.

Prospect actions are much more difficult to optimize than opens and include several factors that need to be taken into account. Here are just a few examples:

  • Email Theme – The specific topic, pain point, or opportunity behind your email, i.e. growing sales, eliminating data decay, automating data entry

  • Email Angle – Your approach to trying to start a conversation on that topic, i.e. survey, customer discovery, article, recent events, networking/connections, etc.

  • Alignment – How well the first 35-140 characters of your email aligns with the rest of the email once they’ve read/scanned it

  • Value Proposition – The vital introduction of how your company impacts the topic of the email in a way that’s relevant to prospects

  • Next Step Incentives – The offer of insider knowledge, resources, expertise, free services, etc. to incentivize the conversation to move onto the next step

  • Call-to-Action (CTA) – The single (or several) asks that are requesting the prospect to commit time and effort to move forward with a next step action

Because so many variables affect a prospect’s decision to take action, it’s important to use metrics that can be sensitive to small changes.

However, much of the content out there on cold email marketing still focuses on more traditional, “raw” metrics for measuring prospect actions:

  • Click-Through Rates (CTR)

  • Response Rates (RR)

There are a few problems with using these as the only metrics in optimization:

  1. Dulls Sensitivity to Change – Because these metrics include unnecessary noise from bounces and prospects that didn’t open your email, smaller and more targeted changes will be harder to differentiate.

  1. Requires More Data – Because tests are harder to differentiate, a basic 2-email A/B test with an average 30%-50% open rate realistically needs 360-600 prospects to provide enough data for comparison, which is still only a sample size of 90 opens per email.

What I prefer is measuring actions from only the prospects who’ve opened the email and can take an action, giving you a clearer look at what the body of your email is actually doing:

  • Open-to-Click Rate (OTC Rate) 

  • Open-to-Response Rate (OTR Rate) 

  • Open-to-Ignore Rate (OTI Rate) 

These metrics dive down into the specific actions prospects take after opening your email, which is exactly what you need to test changes in the body of the email.

A few caveats:

  1. Remember the Outcome – Taking action is the focus here, but not all action is good action, especially when it leads to unsubscribes and wasted time for the client. See what outcomes look like as you’re comparing the results.

  1. Lower Open Rates = Less Data – When using metrics like open-to-click and open-to-reply, the data you need to make conclusions will depend on the open rate. A lower open rate makes it harder to optimize for prospect actions.

  1. Be Conservative – With so many variables affecting the data and the lack of large sample sizes, be careful what assumptions and conclusions you make without having the right amount of data to reinforce it.

Layer 3 – Sentiment

A prospect taking action is one thing.

How a prospect responds to your email is entirely different.

How prospects respond can offer unique qualitative information that can be used to gather deeper insights, maximize interest, and minimize negative outcomes.

Sentiment metrics look at the actual interest level and intent behind a prospect’s response. I break it down into 3 categories:

  • Positive Sentiment – The percentage of responses showing interest in you, either by agreeing to your requested next step, offering their own, or offering to help by referring you up/down.

  • Neutral Sentiment – The percentage of responses not immediately taking a next step with you, which can range anywhere from “no thanks” and “maybe later this quarter” to your standard sales objections.

  • Negative Sentiment – The percentage of responses either requesting an unsubscribe/Do Not Contact or responding in an unprofessional manner.

These metrics are tied to the same variables as layer 2 and can even be measured as open-to-positive reply, open-to-neutral reply, and open-to-negative reply. The only difference is that sentiment is optimizing for the best type of action.

A few caveats:

  • Lower Stats, Better Results – It’s not always a bad thing to see lower open, click, or reply rates if it leads to more productive conversations and more valuable outcomes for the client.

  • Quantitative is Secondary Here – Use the quantitative data as an aid, but emphasize qualitative insights more than the numbers when optimizing for sentiment.

  • You Can’t Be Perfect – You’ll never not piss people off or get 50% responses from bottom-funnel prospects, so don’t overoptimize for diminishing marginal gains.

Layer 4 – Outcomes

It’s critical to measure how your service actually contributes to a client’s sales pipeline. Are qualified opportunities closing? What are the outcomes of the meetings you generate for clients?

To help measure outcomes, your team can categorize responses from your campaign by different next steps to get an idea of what’s being handed off. Here are some examples:

  • Meetings

  • Information Requests

  • Qualifying Questions

  • Referrals

By collaborating with a client to get access to their CRM, you can gain even better transparency into the impact of your campaign and optimization tests.

What Are Some Best Practices for Cold Email Optimization?

We’ve just looked at why we optimize, how to get started in creating a repeatable, scalable process, and a look at how we measure success.

As a final note, I wanted to share a few insights for email optimization that can help your team better plan for success.

Tip #1 – Heed Statistical Significance

Make sure the data that you’re using in testing is statistically significant enough to make conclusions. While a sample size of 30 is the absolute minimum, I’d recommend making conclusions after 60 or more.

Tip #2 – Optimization Testing Frequency

The frequency in which you cycle through an optimization round depends on several things, including sending volume, the cadence between emails, and the number of variations in play. Keep this in mind as you design your sequence and process.

Tip #3 – Quantitative Data Isn’t Enough

I’d recommend your team not make decisions based on numbers alone. It’s a good best practice to get up-to-date on industry changes, qualitative campaign insights, and new client learnings to help keep your optimization relevant and aligned with your client.

Tip #4 – Be Wary of Data Quality

Test with data you can depend on when making hypotheses and decisions. Up-to-date and accurate lead lists are important in making tests more meaningful. Without good data quality, tests can become ineffective.

Tip #5 – Keep Hypotheses Aligned

It’s easy to make a hypothesis for testing. However, if it’s not aligned with your goals, it can leave you lost and going in the wrong direction. Make your hypotheses targeted to what you’re trying to improve and use them to build upon future tests.

Tip #6 – Goal Setting

While you’ll have goals for each optimization test you perform, it’s best to also have longer-term goals that span further than 1 test so your team can focus on optimizing the campaign as a whole and meeting client expectations.

Tip #7 – Always Learn

Even with limited data, it’s important to try your best to always take away something from each optimization test. Knowledge is easy to gather and valuable for cold email services when you can generate learnings consistently.

Tip #8 – Larger Trends

Pay attention to how all of your client campaigns change over time as you optimize. This can help unearth larger, more general market shifts in how email marketing is evolving.

Tip #9 – Repeatable Process

Optimization needs a concrete process in place in order to do it successfully and replicate that success across clients. Without a good process, you’ll find it will be difficult to scale and onboard new team members to do cold email optimization effectively.

Tip #10 – Targeting is Key

Without an accurate audience to base your findings and conclusions on, testing can easily become obsolete or ineffective. Make sure your team is using targeted lead lists that contain relevant, qualified prospects with the right roles and company firmographic data.


The hardest hurdle for an agency doing cold email is that first hire, hand-off, or extra client that exceeds the team’s capacity.

By productizing your services with the worksheets and processes mentioned in this post, your team will have an easier time leveling up while having a solid, repeatable process to improve performance and keep existing clients happy.

Check out our Productizing Lead Generation Content Bundle to help your team get started in creating a repeatable system for driving success and growing your business.