Data-driven email marketing without a headache, using customer data to the fullest

Data is an inexhaustible source of insights and tips for successful email marketing. It helps shape topics, occasions, approaches to audiences, and much more. According to statistics, approximately 30% of marketers say data helps them determine the most effective marketing strategies. However, you can’t just start creating data-driven emails. There are several challenges you may face. But our expert, Brian Riback, can help you overcome them.

The main challenges in data-driven marketing and how to overcome them

Using data in your email marketing is not rocket science, but it’s not a walk in the park either. Putting your marketing on track to use data often comes with hurdles. We’ll discuss the most problematic ones and ways you can make your life as an email marketer easier.

Lack of operational readiness 

Data-driven email marketing relies entirely on data. While it’s tempting to simply grab the best tool on the market and start using it, messy or insufficient data will only hinder your efforts. The main obstacle to using data effectively and the tools that work with it is simply a lack of preparation and overall readiness for data-driven email marketing.

I see this with most of the companies and projects I interact with. They want to use tools like journey builders, automation, and advanced segmentation, but their data isn’t ready, or the tools aren’t set up optimally. There’s a lot of backend work that needs to be done first. The challenge, especially for small-to-midsized businesses (or SMBs), is that they often lack the financial or human resources to handle this work. As a result, it becomes difficult to fully operationalize the tools and technologies that are available. They try to implement them but end up doing so in a very limited capacity because the system isn’t configured properly. Despite all the great tools and innovations, if you don’t start with clean data and a properly configured system, it becomes a real challenge to leverage those tools effectively.

Brian Riback,

СRM and retention expert.

First things first. To set your email marketing on a data-driven path, you need to gather a lot of data. The more diverse the data is, the easier it will be to make predictions and ideations for your next moves in email marketing.

This is the data you should gather:

• demographic data on your audience, like name, age, gender, location, and language (may also include income level, occupation, and so on, if you want a deeper understanding of your audience);
• psychographic data like interests, lifestyle, personality traits, values, and motivations to make assumptions about how your marketing and services can benefit your audience;
• performance and deliverability data to have a clearer picture of how your email marketing performs now and what tweaks should be made (bounce rate, spam rate, sender reputation, deliverability rate);
• engagement data to better understand how your audience interacts with your email marketing efforts (open rate, click-through/click-to-open rate, reading time, unsubscribe rate, revenue per email);
• device and platform data to help you shape email designs for each device (device type, browser, email client, platform-specific open rates);
• zero-party data that your audience shares voluntarily and gives you insights into what they really like or dislike in your email marketing.

This information looks quite extensive and seems impossible to gather. We’re not saying that all of this data needs to be 100% complete, but having a specific database for each item will bring you closer to a smooth entry into data-driven email marketing.

You have a variety of ways to collect this data:

• opt-in forms are the easiest way to start your data-gathering journey, as you can use them to ask only for necessary information and gradually add more and more questions that bring you more awareness of your audience’s traits, interests, needs, and more;
• email marketing platforms like Klaviyo, Hubspot, Mailchimp, and others come with tools for gathering and analyzing data like open rates, click-throughs, bounces, unsubscribes, and basic engagement rates, so use this to your advantage and gather historical data;
• survey and feedback tools like Typeform, SurveyMonkey, and Hotjar allow you to gather feedback by simply asking your audience to receive straight answers, without digging into data, or if you want a simpler option, you can do surveys through your emails as well.

These tools and approaches can help you gather the starting pool of data you’ll need to start shifting to data-driven email marketing.

Staff training takes time and effort

The technology has evolved faster than most teams have learned how to use it.

Jasper van Laethem,

Sr. Email Marketing Strategist and Co-Founder of The Future Funnel.

New technologies and pipelines require training staff on new tools, and marketing is no exception. In 2025, 43% of surveyed companies purchased new technologies and software and had to train their employees to use them. On average, companies spend 13% of their annual budget on training staff to master new strategies, tools, and other technologies.

Training your team how to use new tools is always a challenge, but there are always things you can do to make it easier:

• shape a role-based approach, where each team member receives new things to learn that they actually need, making the whole training process more tailored and less stressful;
• combine different types of training like eLearning, guided walkthroughs, live sessions, microlearning, and quick, mobile-friendly training to keep things fresh and engaging for the learners;
• make the whole training process interactive with scenarios, quizzes, practice tasks, and exercises in which your team can actually practice things they learn right away, and help them see the practical application of the new skills they’ll need;
• benefit from flexible and accessible learning that can provide your team with a convenient process that can fit into their busy lives and, as a result, bring more fun into learning;
• share experiences across team members and units by conducting meetings and talking sessions where every team member can share their experiences of the whole learning process and the subject they’re learning and help each other have a better grasp on what they’re learning, as teamwork makes the dream work.

Training your staff to use new tools when migrating or starting a business is incredibly important. But if you don’t have the necessary resources at present, there’s an alternative approach:

I’m not a fan of training because studies show that people are really good at their day-to-day work, but when they have to step away from that routine to focus on training, they don’t perform well. To address this, instead of forcing people who lack proficiency with the tool to undergo extensive training, I built custom dashboards outside of Salesforce CRM. These dashboards used simple web forms that allowed users to handle customer service inquiries in a familiar, straightforward way, which is similar to using 'Contact Us' forms on websites. The beauty of this approach was that it didn’t require complex training; it was a simple solution that connected to the back end of the CRM.

Brian Riback,

СRM and retention expert.

Relying on AI as a “one-click miracle”

AI is conquering the minds and workflows of many companies, no matter their size, their industry, or what they do.

Currently, 87% of organizations use AI for email marketing, but there are many pitfalls to using it:

• inaccurate or overconfident information;
• lack of human touch from AI responses and results;
• biased responses depending on the models and the data they previously trained on;
• and many more.

All this leaves us with the understanding that AI isn’t a magic wand that will send all email marketing metrics to the moon in a few clicks, and relying 100% on AI results isn’t a good idea. To implement AI effectively, it’s first necessary to understand why you’re implementing it.

You have to know how to operationalize it. AI will provide information, but if you don’t properly train it, it only knows what you’ve told it.

Brian Riback,

СRM and retention expert.

You can use AI to

• segment the database by various criteria;
• analyze the data and structure it in a convenient format; and
• create rough drafts of email content (ideas, themes, subject lines, etc.).

But the most important thing remains: before AI’s potential can be realized, it needs to be trained on a wealth of data so that it becomes useful specifically for you, your business, and your needs.

Training AI with data specific to your products, services, and brand voice is extremely important. From a marketing perspective, I don’t use AI extensively yet. To me, it’s more of a time-saver for tasks like analysis and reporting rather than a full-fledged marketing department. Some might say it could replace a whole team, but I don’t agree. I don’t think AI tools are equipped with enough training or data to fully take over right now.

That said, there are some great AI tools, such as Einstein, which learn based on the data in the Salesforce Marketing Cloud. However, they still rely on campaign results and historical information to function properly. Take send-time optimization, for example. If AI doesn’t know how a person interacts with content, it won’t be able to predict the optimal send time.

Brian Riback,

СRM and retention expert.

To train AI models to comply with your goals and needs in your email marketing workflows, you should apply these tips:

• use relevant and fresh data with the “quality over quantity” trait for better and more accurate AI predictions;
• segment your audience and data before feeding the data to the AI in order to make it see dependencies and patterns;
• train AI on behavior and not assumptions so it can better understand what marketers actually did (and what clear outcome they had), not what marketers think works;
• retrain your AI models on new data, as the whole industry is changing, so must your AI to keep helping you effectively.

And the most important rule is to keep email marketers in the loop throughout the whole process of using AI in email marketing. Like follows:

1. Your AI model suggests a new strategy, analysis results, etc.
2. The marketer reviews it, adjusts it (if needed), and approves it.
3. Your email marketing systems log in the results of AI suggestions in action.
4. The marketer feeds this new data to the AI.

The whole loop repeats itself as long as it benefits your email marketing.

Neglecting marketing personalization

Personalization of marketing campaigns works wonders, as statistics show that 9 out of 10 marketers see positive changes in metrics after implementing it.

And for good reason, as personalization blurs the line between you and the client and helps build lasting relationships that benefit everyone.

Personalization brings:

• emotional connection by demonstrating that you understand your audience’s problems and are striving to solve them;
• increased engagement with your content, as people are always looking for interesting offers and discounts;
• simplified audience retention, because you provide what they need.

However, not everyone reaps these benefits, as they neglect personalization (either accidentally or intentionally). Overall, 85% of brands believe they provide personalized content, but only 60% of their audiences actually agree.

Personalization and dynamic content are king in email marketing because they create a connection between the brand and the customer. Customers expect brands to know their needs, so providing value is key. Small and medium-sized companies should definitely learn from their data to understand what’s popular and leverage it to enhance the user experience. The more zero- and first-party data you can collect along the way, the better, and then use that with site statistics to guide your strategy. It doesn’t have to be complicated. It just has to be data-driven.

Brian Riback,

СRM and retention expert.

As you can see, a lack of data that defines your audience’s characteristics, habits, and attitudes means your shots at perfect personalization miss more frequently. You don’t have a proper base on which you can build assumptions that help you see what your audience really wants and needs.

However, it’s important to remember that data in personalization also goes hand in hand with how you use it, namely, with what techniques, as we may call them:

• personalized subject lines and addressing by name require you to know your audience’s names and their previous actions to make your email approaches relevant;
• personalized recommendations can’t work without data about your audience’s interests, frequent purchases, and favorite products;
• abandoned cart emails require you to act fast and have data on average abandonment time, as well as all of the above, to make a perfect offer for your audience to finish the purchase;
• personalized imagery is based on gender, location, and other data that can give you a hint on what your audience will be pleased to see in terms of design, product photos, and more;
• loyalty programs require you to know the exact time and day your most loyal customers joined your ecosystem to give them a worthy “Thank you” present to retain them and to help your loyal buyers feel unique.

And don’t be afraid to start small. Initially, your personalization may be limited to your audience’s geographic location, which will result in sending messages at a time that’s convenient for that segment. But gradually, as you gather more information about your audience, you’ll begin to deepen your personalization approaches, making them more diverse and engaging.

We’ve covered how to approach personalization and hyper-personalization in great detail in a dedicated article, which will help you become a master of personalized marketing.

You might also like

Personalization in email marketing

Important note: Personalization is closely related to the collection and storage of your audience’s data. For your efforts to remain ethical and legal, it’s crucial to adhere to these rules when collecting and storing information:

1. Collect and store only the information necessary for personalization, and do not store it longer than necessary.
2. Provide your audience with a simple and transparent opt-out option for those who do not want to receive personalized content.
3. Regular audits are essential to verify data integrity, security, and compliance with customer data storage regulations.
4. Respect your audience’s choice and their right to privacy and data control.

Wrapping up

A data-driven approach is a cornerstone of successful email marketing, as data brings you valuable insights into trends and audience behavior, and it provides a source for ideation. However, stepping onto a data-driven path is a challenge in itself, and you need to be prepared. The most important part is having enough data at the start to move forward and adopt AI tools for better predictions or to use hyper-personalization to make your emails more personal for each of your subscribers.

Once you gather enough high-quality historical data, you can apply it to your data-driven email marketing, scaling this approach and overcoming the challenges we discussed above. We hope this article will help you make an easy entry into data-driven email marketing and boost your overall marketing efforts.