How to get people to open your emails

We’ve aggregated the world’s best growth marketers into one community. Twice a month, we ask them to share their most effective growth tactics, and we compile them into this Growth Report.

This is how you’re going stay up-to-date on growth marketing tactics — with advice you can’t get elsewhere.

Our community consists of 600 startup founders paired with VP’s of growth from later-stage companies. We have 300 YC founders plus senior marketers from companies including Medium, Docker, Invision, Intuit, Pinterest, Discord, Webflow, Lambda School, Perfect Keto, Typeform, Modern Fertility, Segment, Udemy, Puma, Cameo, and Ritual.

You can participate in our community by joining Demand Curve’s marketing webinars, Slack group, or marketing training program. See past growth reports here and here.

Without further ado, onto the advice.


[Update: This section’s previous email advice was wrong or outdated, so we’re replacing it with accurate email wisdom courtesy of the experts at Women of Email, including inspiration from the advice of Skyler Holobach, Nout Boctor-Smith, Stephanie Griffith, Laura Atkins, and others. Specifically, we’re rewriting this section to bust myths about email marketing, some of which were in this section’s previous version:]
  • Even if you execute an email campaign perfectly, it’s not realistic to judge the campaign’s success relative to an open rate of 100%. First, it’s technically infeasible to reach 100%. Second, open rate isn’t the ultimate email metric to focus on.
  • In terms of the technical infeasibility of reaching 100%, email open-tracking mechanisms aren’t flawless: Some prematurely load tracking pixels before the recipient opens their email, and sometimes the pixel is never loaded at all. More importantly, some of your recipients simply don’t care to read your email — and can’t be bothered to unsubscribe. That means some portion of your list is always going to be “unopened.” Consider that there’s always a distribution of engagement/interest among any audience.
  • Further, many companies see averaged tracked open rates of around 20% (depending on the campaign’s audience, subject, content, and other factors). So, don’t necessarily consider your campaign a failure if it’s not clocking an open rate much higher than that. Instead, get into the habit of comparing yourself to averages: What is the average open rate of your past email campaigns to the same audience? And what’s the typical open rate of a similar campaign sent by other companies in your industry? Your goal is to improve that number over time. Run tests in service of sending increasingly valuable emails to an increasingly higher quality, opted-in audience who genuinely want the content you’re emailing.
  • More importantly, keep in mind that open rates aren’t your ultimate metric. That belongs to engagement: are your recipients clicking the links in your email as intended, are they regularly opening your emails over time, and are they not unsubscribing above the average rate in your industry/campaign type? Keep your eye on why you’re sending emails in the first place: Providing value and increasing engagement — not getting people to open your email, which can be more a measure of how clickbaity your subject line is. That’s short-sighted.
  • The practice of avoiding “spam keywords” (e.g. “free,” “money,” “loan”) in your email’s subject and body in pursuit of avoiding going into spam folders is an outdated email tactic. Modern spam filters are sophisticated enough to not bury emails based on word inclusion if those words are otherwise in the context of a non-spammy, legitimate email campaign sent to an opted-in audience with a history of engagement. Follow email best practices; don’t over-optimize for hacking the system. The system exists for a reason.
  • And that brings us to the ultimate point of this section: Focus your email optimization energy on building a high-engagement, opted-in audience who enjoys your consistently valuable content.

You have more SEO-worthy content than you realize.

Based on insights from Nima Gardideh of Pearmill

Your product documentation can double as SEO content. In fact, many things on your site can — beyond your blog. Consider this:

  • When people Google for technical help and come across your product documentation, they learn how your product perfectly solves their problems. Problem-oriented traffic such as this often converts the best.
  • The implication is that you shouldn’t overlook content marketing best practices across your documentation:
    • Research the best keywords to use in your pages’ titles and headers.
    • Insert a table of contents at the top of every page.
    • Have an introductory paragraph explaining what the reader will get from reading the page.
    • Prominently link to your product for those visitors who are interested in buying.

Should you run Bing Ads if you have Google Ads working?

From Neal O’Grady of Demand Curve.

I tested Bing on many clients for whom Google Ads worked. With Bing, however, volume is very low for the same terms. I find conversion rate is also lower (CPC’s were lower, however, so it can net out okay). Rarely was Bing a significant revenue driver, BUT you can have Bing Ads auto-sync with Google Ads. So it’s set-it-and-forget-it. Might as well test it and see.

Also, keep in mind the ‘average user’ on Bing skews older and less technically sophisticated. If that aligns with your target demographic, great.

Jeremy Gurewitz of Imperfect Produce: “I agree with Neal, with one caveat. I’ve had nice success on competitor brand bidding on Bing.”

Is it worth it to up Multi-Touch Attribution logic for your ads?

Based on insights from Sam Ross of Kozu Labs.

  • If you’re running several marketing efforts concurrently, it can be tempting to try to measure exactly where conversions are coming from using Multi-Touch Attribution (MTA). The problem is that it’s extremely difficult to reliably measure, and is often a waste of time.
  • Facebook has best-in-class cross-device/browser data for measuring other channels. You couldn’t get that data on your own. Yet even they couldn’t build a reliable model to help Airbnb’s attribution efforts when partnering with them. Further, as you shift the distribution of your ad channel spend as time goes on, your model becomes less useful because it was trained on historical data.
  • Instead, consider doing lots of one-offs, incremental tests. Apply linear multipliers and use common sense estimates. Then, pipe everything into a database and write simple, editable attribution logic (ie. discount conversions from search campaigns with “brand” in title by 8x) into summary tables for your dashboards.