Case Study: How Live Polls Increased Click Rate by 53%

As a jeweler, my client Kesslers Diamonds generally focuses their marketing on gift-giving holidays throughout the year. (I’m talking about you, Valentine’s Day!) After Mother’s Day and before the end-of-year holidays, the summer season is fairly quiet, without any major gifting occasions beyond a birthday or an engagement. But this year was different. We wanted to take advantage of no holidays to discuss and find ways we could learn more about our customers while increasing click rate with some A/B email testing!

With personalization being a huge puzzle piece of effective email marketing, we knew summer was the time to learn more about our audience outside of purchase history. Research shows that 80% of customers are more likely to purchase from a brand that provides personalized experiences. ⁽¹⁾

But how do we learn more about our customers’ preferences without sending them running in the opposite direction and instead engage them so they’re open to sharing? We found an answer: live polls with Litmus Personalize!

Investing in Litmus Personalize

Together, Kesslers and ByLaurenJean invested in Litmus Personalize (LP) to help us focus on delivering a relevant experience to every contact on the email list—wherever and whenever they open emails. (When we pulled the trigger on this, I was as giddy as my kids are before eating s’mores!)

Before live polls, we used LP features such as countdown timers, social signals, the Instagram live feed feature, and a store locator to enhance our audience’s experience. Our email service provider (ESP, i.e., Mailchimp) doesn’t have these features. We were excited to try live polls because we wanted to test click-rate performance. Plus, gain information for future campaigns and segmentation. 

Starting in June, we began the Diamond Cut Showdown. Think of a typical tournament bracket, but putting one diamond cut against another (e.g., radiant vs. cushion cut).

As much as we wanted to email the entire list to have as many contacts as possible provide their favorite, we knew an A/B email test was the best thing in the long run.

The A/B Test Details

We hypothesized the email with the call to action (CTA) to vote (email B) would have a higher click rate than the control (email A), which invited customers to learn more about the two different cuts and browse the site.

Email A: The Control – CTA: Browse “Diamond Cut” 

Email B: The Test – CTA: Vote “Diamond Cut” + “Which Cut is Your Favorite?” with a live poll percentage

The first A/B test was sent to the entire active email list. Three days later, we sent the exact same email. But with a different subject line to those who did not click the first email. 

First Send: Entire active email list – 50/50 split

• Subject Line: Diamond Sparkle Showdown 🥊
• Pre-Header: Cushion and Radiant cut diamonds are unique and beautiful!

Second Send: Did not click the first email

• Subject Line: Are You Team Radiant or Cushion?
• Pre-Header: Each diamond cut is uniquely different!

*Those who were sent email A the first time also received email A the second time. Same with B. Both emails had the same subject lines and pre-headers.

The A/B Test Results

First Send:

Email B’s click rate was 53% better* than email A’s.

Second Send:

Email B’s click rate was 126% better* than email A’s!

*We calculated results based on the # of sends / # of clicks (i.e., 1000 unique sends / 90 unique clicks), not actual click rate percentages. Both A/B tests were statistically significant.

We performed additional A/B tests with live polls. They all had similar results—the live poll always outperformed! Because of the consistent performance of live polls, we have been able to roll out the showdown across the entire email list.

Each vote tags contacts with their diamond cut preferences. These tags will help create dynamic emails to showcase each user’s preference when multiple diamond shapes are applicable. 

We can’t wait to see what kind of results those emails produce! Sign up for my email list to be notified about additional case studies and A/B testing results.

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