Author: Matt

Email segmentation is going to be a sexy topic in 2024. But perhaps you don’t believe me yet.

Email segmentation helps you sort your subscribers into different groups. For e-commerce this is critical. Not all contacts are at the same part of the journey with your brand, and not all benefit from the same messages.

Sending poorly targeted emails does considerable harm to your ability to contact your customers in the future. Doubly so as inbox providers like Google and Yahoo clamp down on spam to reduce the amount of noise in people’s inboxes. An unopened and unclicked email is a signal to Google that you send bad emails. Don’t train Google’s spam filters this way.

Send the right email to the right person.

Email is not dead. Email has been the biggest driver of growth and revenue for most businesses. It is the audience that you have the most direct control over. Unlike social media followers, email is less likely to be blocked due to closed accounts or algorithm changes.

Email continues to grow. Here’s some stats:

Segmented emails drive 30% more opens and 50% higher CTRs than unsegmented ones

Email drives an impressive ROI of $36 for every $1 invested

81% of B2B marketers say their most used form of content marketing is email newsletters

there are 4 billion daily email users globally, expected to climb to 4.6 billion by 2025

38% of brands are increasing their email budget, just 10% are cutting.

Today, marketers use AI for emails to help in the writing of copy. Crafting a better subject line is something AI is great at. Great copy can help engage more readers to open and click. But it’s only part of the equation.

Using AI to segment your contacts opens many new possibilities. Advanced segmentation is hard data analysis and as a result mostly a tactic used only by sophisticated marketing teams. AI democratizes this kind of analysis. It helps even smaller brands focus their marketing at the people most likely to appreciate it.

AI has the ability to take what would be complex software logic and turns it into the business question.

Given a customer profile you can ask questions:

has this customer churned?

does this customer like to use coupons / are they price insensitive?

do they live in the North East?

is this customer a VIP?

Each of these questions could be answered with various logical checks – written in code, or implemented in spreadsheets. AI models like GPT-4 can answer if provided with english.

It won’t be long before solutions like this are scaled up and available to email marketers for defining segments.

If this is something you find intriguing lets connect – I’m looking for beta testers for case studies. Let me segment your customers for you! I’m accepting 5 test clients to run this system with and prove it out.

How much AI is too much AI? When have you gone too far?

This is a story of how I used AI on Twitter and what it taught me about posting AI generated content.

In April of 2023, AI generated images were just getting good enough to blur the line of easily identifiable as AI generated. The quality passed a threshold to be more useful.

I had the idea of using this new capability to re-invigorate a neglected Twitter account. The plan was to post 3x a day, using AI to help write and provide images for the posts. It would take less time, and I’d batch up and schedule a week of content at a time. 7 days times 3 per day = 21 posts created every Monday morning. I’d try to keep the time commitment for this below 2 hours per week.

To be fully transparent – I used my personal account to comment about how each post was created, and share more information about the performance of this test.

So what happened?

This took what was an account that reached zero people, and instead we reached over 300k people with our brand. Considering the only cost was my time it seems like a decent return.

However, you’ll immediately notice that big spike. This was a learning moment. Twitter’s algorithm is not geared towards discovery like TikTok – it’s not easy to ‘go viral’ with a post unless you have a big follower count.

You can see that the posts from April 1st up to mid April received just a trickle of engagement.

While the increase in posts saw an increase in impressions as a result (and no complaints) our organic reach was limited by our follower count. Around April 20 the strategy shifted.

Instead of posting to the public feed, I’d spend most of the time going after trending topics and conversations. Jump into the replies with a relevant on-brand take that contributes some fun into the conversations that were already happening.

Lesson: Use social media to be social.

Tapping into the attention that already exists proved to be a 10x multiplier. In contrast, yelling into the void and expecting someone to hear is not a great strategy.

The great thing is that AI enabled a much broader set of conversations than I could have handled on my own, and add a comment or graphic that was more on point and stood out. I could jump from a reply about what happened at the Oscars and then right into a science question before dropping some heat on a live play-by-play of the NBA game.

AI can fill in the knowledge gaps about who the actor is, what position the player has or parsing some research paper while also mixing in jokes and adding creative flair. It’s super-human, and a great example of how a person with AI can do better than either on their own.

Using an LLM to ask “What is an on-brand response to this tweet: XXXX” was all that was needed to get the inspiration for a quick reply. And if it made sense – “Write the description of an image to go along with that reply” which could be converted into an image in a minute or two.

With this kind of workflow, I could write more timely, more engaged and more relevant replies. Instead of 200 impressions on a post, some of those replies got 10,000+ impressions by grabbing the attention of bigger accounts.

This is how I used AI to wield social media with super-human skill.

If you liked this story – please share it with your social media manager.