The road to a 20% email response rate at Reach (1/5)

Building in Public
September 30, 2022
The road to a 20% email response rate at Reach (1/5)

Last week we finished up our 3 part series focusing on tools that make up our tech stack to facilitate and optimize our sales processes. Now that we’re at a relatively stable place as a sales organization (while constantly iterating, adapting, and reacting), we want to look back at how we got started and how we ultimately arrived at this point where we’re getting ~20% response rate to our cold emails in only 2 months. The next few posts will focus on what we tried, what worked and what didn’t.

Getting Started

The first thing we did was set up our domain and email accounts. It almost seems too boring to mention, but the reality is that properly setting up the “backend” of your email/domain is a key piece to ensure that your emails actually get delivered to the recipients. Before doing anything we added the DNS records to our domain that are most important to ensuring deliverability. These records are DMARC, DKIM and SPF.

The next thing we did was warm up our domain. Going from 0–60 with email outreach using a brand new domain is a great way to hurt the deliverability of your emails and ruin the credibility of your domain. If you have always worked at established (e.g. not brand new) companies then you’ve probably never had to deal with this. Warming up a domain means sending and receiving emails to give your domain credibility. Sending many emails without getting many responses can make your domain seem spammy.

The tendency is to set up the email accounts and start blasting emails. We’ve learned from past experience that it’s better to give yourself a couple of weeks of warm up to avoid deliverability issues. Afterall, all the work we do would be wasted if the emails never arrive. There are plenty of services to choose from that will warm up your domain for you. They will connect to your account and will send emails from your account to others who are warming up as well and they will respond to you, and vice versa — all automatically. The volume and the ratio of sends to replies is what will help establish credibility. We used GWarm for this.

First Targets + Messaging

While our domain was warming up we started crafting email sequences for the buyer personas we planned on targeting. We already discussed the assumptions behind these personas, but once we identified SDR Managers, VP of Sales and CEO as our first personas, we needed to craft messaging that would be relevant for each role. The bulk of the messaging is largely the same but minor tweaks allow us to recognize their differences.

We set up our sequences within MixMax and Apollo. 3 stages for each persona. The first message would include a hyper personalized icebreaker written by Reach, followed by the same short pitch, segmented by persona. The second message is a reminder and offer to add value while the third message is a break up message and attempt to get introduced to someone else on the team.

Our goal was simply to test our initial assumptions and then go where the data took us. For every change we made, whether it was an update to the email subject line or something within the body of the email itself, we would make a new sequence. Doing so helped us keep track of the impact of each change, for better or worse.

Getting Leads

We started by using Apollo to create our lead lists. One person on the team owned this process to ensure that we didn’t accidentally reach out to the same people or create duplicate lists. Lists were generated using various filters; buyer persona, sector, company size, geography. Each list corresponded to a specific persona. That way we could easily add the list to the relevant sequence. Apollo lists come with the work email for each lead. We learned the hard way that there is a pretty significant bounce rate with these emails and it’s worth using another external tool to verify each email before sending.

Blast Off

With our lead lists divided, we would personalize them using Reach in batches of 25 or 50. The efficiency gain compared to manually personalizing each icebreaker was easily 20x+. Each list would be personalized in bulk and therefore easy for us to add at once to the relevant sequence.

Once our sequences were activated we would continue adding leads to them while monitoring their performance. We would check that the open rate was always above 50%. This meant that our deliverability was healthy. And over time we would monitor the response rate we were getting for each sequence. A higher response rate is what we’re after — but it’s important to remember that not all responses are positive. It’s possible that your sequence is getting a lot of responses from people replying “no thanks”. Just looking at response rate could lead us astray. Therefore we monitor the response rate but also whether the responses are positive or negative.

Our first message

Here’s the email we started with. Needless to say it has come a long way since this first iteration, but we always try to apply basic principles when coming up with email copy. It should be short, personalized, relevant and add value to the recipient.

Email Subject: Make sales personal again

Hey {{first name}},

{{Reach Message}}

I’m the co-founder of Reach, a tool that increases the response rate of your cold email outreach. It achieves this by hyper-personalizing each message you send to your leads with information taken from Linkedin, company website, news, blogs, etc.

You can expect a 30%+ increase in response rate. Since prospects are increasingly ignoring generic sales messages, how is your team currently personalizing outbound at {{company Name}}?

Best,

Elad

The email is short and it’s relevant to these personas in so far as they are interested in improving their email response rate and therefore increase the number of calls they book. But admittedly we struggled a bit to show value and build trust.

In our next post we’ll continue looking at our journey to a 20% response rate. We’ll look at some of the specific tweaks we made to our sequences and how we got smarter about our buyer personas based on the data.

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