8 Ways to Leverage Data Analytics for Smarter Email Campaign Strategies
By modern standards, email feels like an ancient technology. And yet it remains a go-to channel for digital marketing, with 82% of marketers saying they incorporate email campaigns into their strategies, and an even higher percentage (89%) naming email as their first-choice channel for generating leads.
The reasons why email is the great survivor of digital marketing are easy to appreciate. Pairing massive reach (4.5 billion email users worldwide and counting) with low cost, email still manages to beat the likes of social media and search marketing on both conversion rates and ROI.
But the most impressive thing of all about email marketing is that it appears to be getting more effective. And that’s because, old technology though it may be, email marketing has moved with the times. It’s easy to assume that email campaigns are all about volume and playing the percentages. Bombard enough inboxes, and you’ll get results.
Yet the truth is, that modern email marketing strategy espouses the exact opposite of such a crude and scattergun approach. It’s all about precision and targeting, understanding your audience(s), and hitting them with the right message at the right time.
To put it another way, email marketing is increasingly effective because it leverages data so effectively – data about who recipients are, what they like, what they do, and also data about which campaigns (or which aspects of them) work, and why.
So how exactly can you harness the power of data to take email marketing for your business to another level? Well, you could contact a leading UK email marketing agency like Key Element and we’ll give you our precision-targeted insight into how to make data and email supersize your marketing.
Or, you could read our breakdown of the eight best ways to achieve smarter, higher-impact email campaigns…
Audience Segmentation
One of the oldest tricks for using data in email marketing is to divide contact lists into distinct groups based on information you hold about them. This could be demographic data such as age, location, gender etc, it could be details about purchase history, or it could be metrics used to gauge engagement levels (we’ll consider this below as its own category of email analytics). Segmenting audiences like this allows you to tailor and target content for different groups. It’s the first step away from the scattergun approach and towards acknowledging that relevance and differentiation are key to boosting open and click rates.
Personalisation
The next step beyond tailoring campaigns to different segments is using data to personalise emails at a more individual level. The classic example is to use mail merge features to insert a recipient’s name or other simple contact details into the email’s subject line or body content. However modern data analytics can power a much more sophisticated approach to personalisation. Dynamic content involves changing what a recipient receives based on data held about them, typically behavioural data gathered from web tracking (or interaction with previous emails). A good example is tailoring product recommendations in a promotional email to an individual’s purchase and browsing history. The broader idea is to deliver content that aligns with subscriber interests and past interactions in order to foster stronger connections and improve conversion rates.
Behaviour-Triggered Emails
As well as informing content personalisation, data from behaviour tracking can be used to trigger automated email sequences. On one level, this is about making life easier for email marketers. So, when a new subscriber signs up to your lead capture form, for example, pretty much every email marketer around will have this simple action trigger a welcome email. But it’s also about getting messages out ‘in the moment’, capitalising on interest while it is there. An example would be sending out a promotional email for a particular product straight after someone has been browsing that particular page.
Behavioural triggering can be more sophisticated than a single action, too. You might track data to build up a picture of a contact’s level of engagement (again, see below) – number of visits to your website, number of emails opened or links clicked – and trigger emails when they reach (or fall below) a certain ‘score’. This approach goes hand in hand with customer journey planning, and changing the way you approach interactions depending on where a customer is in their journey. It is also great for re-engagement activity, identifying and trying to tempt back customers who have fallen off the radar.
A/B Testing
A/B or split testing involves conducting experiments with different versions of the same asset (an email campaign, web page, app etc) to see which one works best. In email marketing, this typically involves sending different versions of an email to samples of your intended audience, and then measuring things like open and click rates. The one that scores the highest you can presume will perform the best when used in a ‘live’ campaign. You can A/B test whole email emails, or you can compare specific parts like subject lines, images, calls-to-action etc.
Send Time Optimisation
A/B testing is one common example of how performance analytics can be used to improve email marketing outcomes. Another is comparing the same kinds of metrics (open rates, click rates etc) with the time different emails are sent. These analyses often show that emails sent at certain times perform better, presumably because they happen to coincide with when a larger percentage of your audience is ‘active’ in their inbox. Scheduling sending during these peak times is therefore a simple way to get more eyes on your emails, and drive up engagement.
Email Frequency Management
As well as when to send emails to get optimal results, another question data analytics can answer is how often to send. Again, far from sending out as many emails as you can as often as you can, there comes a point where email frequency becomes counterproductive. People begin to get tired of or actively annoyed by receiving emails from the same source too often. They’ll either start ignoring your emails. Or, worst-case scenario, flag them as spam. Frequency tolerance tends to vary from person to person. But by mapping open and click rates against frequency rates, you can identify different ‘peak frequencies’ (i.e. optimum balance of frequency versus opens/clicks) for different groups. And then use these insights to create segments.
Engagement Tracking
The different ways people respond to your emails – open them, click links, follow CTAs, ignore them, send them to spam – can all be summed up under the blanket term engagement. High open and click rates equals high engagement. Lots of unopened emails and spam complaints, you’ve got an engagement problem. In many ways, engagement tracking underpins the entire data-led approach to email marketing. It’s critical to the kind of performance analytics that drives A/B testing, send time optimisation and frequency management. But as we’ve touched on a couple of times already, you can also collate all the different performance metrics into an engagement ‘score’ that evaluates how responsive (or otherwise) an individual is to your emails. This data can then be used to make decisions on who to focus your attention on to get the best results. But also on how to differentiate your approach to different people. Who’s heavily engaged and ready for the big sell? Who needs a bit more gentle nurturing? Who you might be best saying goodbye to, but is worth one last re-engagement effort?
Deliverability Optimisation
We’ve talked about low engagement and contacts getting so fed up with receiving your emails that they flag you as spam. Deliverability is a different issue for email marketers to navigate. Deliverability is all about whether your emails land in someone’s inbox at all. Or whether they ‘bounce’ (don’t make it into the recipient email box full stop) or get diverted by spam filters into other folders (spam, promotional, social etc) on the way.
Measuring bounce rates and using tools like spam filter tests provide email marketers with crucial data about deliverability. High bounce rates and/or high ‘spamminess’ scores tell you that your emails aren’t even going to be seen by the number of contacts you’d like. That undercuts your performance metrics before you even get to opens and clicks. So, this kind of data tells you that you need to look urgently at the composition and content of your emails. Or address issues like a poor sender reputation, or poor list hygiene (i.e. not keeping your list clean of old, broken or fake email addresses, which causes high bounce rates and makes it more likely that email client filters will flag your emails as spam).
So now you’ve read our take on the best ways to leverage data analytics in email marketing, why don’t you take the next step to secure the rewards of smarter email campaigns? Get in touch with our London-based email marketing specialists, and find out how to unlock the power of data today.
Frequently Asked Questions
How can data analytics help reduce email unsubscribe rates?
By tracking user engagement, businesses can identify patterns that lead to unsubscribes, such as sending too many emails, irrelevant content, or poor timing. By adjusting frequency, segmenting lists, and personalising emails based on user behaviour, marketers can reduce unsubscribes and retain more engaged subscribers.
What are the key metrics to track in email marketing analytics?
The most important email marketing metrics include:
- Open Rate – Percentage of recipients who open your email
- Click-Through Rate (CTR) – Percentage of recipients who click on links in the email
- Conversion Rate – Percentage of recipients who complete a desired action (e.g., purchase, sign-up)
- Bounce Rate – Percentage of emails that fail to be delivered
- Spam Complaint Rate – Percentage of recipients who mark your emails as spam
- Unsubscribe Rate – Percentage of users opting out of future emails
Tracking these metrics helps fine-tune email campaigns for better performance.
What is the best way to re-engage inactive subscribers?
To re-engage inactive subscribers, businesses can use:
- Reactivation email campaigns with exclusive offers or incentives
- Personalised emails reminding them of their past interactions
- Survey emails to understand why they disengaged
- Automated win-back sequences that gradually encourage them to return
If there is no response after multiple attempts, it’s best to remove inactive contacts to maintain a healthy sender reputation.
Why do emails sometimes land in the spam folder, and how can this be prevented?
Emails may land in spam due to:
- Poor sender reputation
- Spam trigger words in subject lines (e.g., “Free,” “Act Now”)
- High complaint rates
- Lack of authentication (SPF, DKIM, DMARC)
- Sending to unverified or inactive email lists
To prevent this, marketers should follow email best practices, use spam-check tools, and send relevant, engaging content.
What are some best practices for writing compelling subject lines?
To increase open rates, subject lines should be:
- Short and to the point (under 50 characters)
- Personalised (e.g., using the recipient’s name)
- Actionable and intriguing (e.g., “Don’t Miss Out on This Deal!”)
- Avoid spammy words (e.g., “100% Free,” “You’ve Won!”)
- A/B tested to determine which ones perform best
How does customer journey mapping improve email marketing?
Customer journey mapping helps marketers understand how customers interact with a brand at different stages (awareness, consideration, purchase, retention). By aligning email campaigns to these stages, businesses can deliver the right content at the right time, improving engagement and conversions.
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Dominique Childs
