How AI Enhances Data Counts: Behind the Scenes with Emailmovers
Is the value in the cream, or in the milk that makes it possible?
In traditional thinking, cream is the luxury - the sought-after prize skimmed from the top. But what if the real value isn’t the cream at all? What if the magic lies in the milk beneath it?
Our email data is like cream. Focused, refined and often the first thing people see. It’s what we’re known for - easy to showcase, bottle and explain. It commands attention and rightly so.
But here’s the twist: that product is just the visible result of a much deeper process.
The Process Behind Our Email Data
The pioneering methods we've developed over the past 20 years form the very foundation of Emailmovers - they underpin everything we do. There's undeniable value for businesses looking to buy an email database. Yet over time, we’ve come to realise something even more powerful: the real value doesn’t lie just in the cream that rises - it’s in the milk that makes it possible.
It’s the process behind the product that carries businesses forward. Without it, there’s nothing to rise to the top.
At the core of our email data is a wealth of experience that goes into crafting high-performing marketing lists. The infrastructure behind it holds immense value - the moving parts, the expertise and the behind-the-scenes work that make effective email marketing possible.
Think of it as the pasteurisation of milk - the essential process that makes the cream possible. This ensures everything is safe from the start and helps our clients simplify the complexities of email marketing in an ever-evolving world of legislation and compliance.
Here at Emailmovers, we carry out extensive work on data - from mapping and standardisation to thorough cleansing and compliance - to ensure our data is the crème de la crème among data owners and brokers. It’s clean, safe and ready to use.
And now, AI is helping us make that process even smarter.
How AI Helps Us Run More Effective Data Counts
AI has become a powerful tool for enhancing the way we approach data counts. It allows us to work against data sets in ways we may not have previously considered - helping us uncover more opportunities and extract greater value.
Whether it’s expanding job title criteria, asking specific targeting questions, or demystifying SIC codes and industries, AI enhances both the speed and precision of our work.
Here are some practical examples of how AI supports the process:
1. Expanding Job Title Targeting
Suppose we’re trying to reach senior decision-makers in UK businesses. While “Managing Director” or “MD” might be the most obvious search terms, they represent just a fraction of the relevant titles. Many other roles carry similar levels of seniority that are often overlooked in manual counts.
With AI, we can broaden our targeting by using a prompt such as:
“List other job titles similar to Managing Director in UK businesses.”
This generates a much wider set of relevant titles - such as CEO, Owner, Founder, Executive Director and more - enabling us to identify a greater number of valuable prospects and maximise the volume of the count.
We can take this a step further by asking AI to provide variations and abbreviations of these titles. This might return results like MD, Chief Executive Officer, Co-Founder, Co-Owner and other relevant equivalents we may not have considered.
2. Job Title Components: Title vs Function vs Level
Understanding the components that make up a job title is essential for effective data targeting and segmentation. Typically, a job title can be broken down into three distinct elements:
- The Title Itself – e.g., “Operations Manager”
- The Function – “Operations”
- The Seniority Level – “Manager”
By recognising and leveraging these components, we can fine-tune our approach to targeting. Depending on the objective of a campaign or dataset, we may choose to filter contacts based on just one of these dimensions (e.g., all roles in “Operations”), or a combination (e.g., “Manager” level contacts in “Operations”).
Being deliberate about which criteria we apply enables us to achieve greater precision, reduce irrelevant records and enhance the overall quality and relevance of the data we extract.
3. Translating Job Titles for Global Targeting
If we are looking to target global data, particularly in countries where English is not the primary language, it may be beneficial to incorporate the local language into our search criteria.
While many professionals around the world list their job titles in English—especially in international organisations—this is not always the case.
Take France as an example. If our goal is to identify Managing Directors in the French market, limiting our search to the English term “Managing Director” will likely return only a portion of the relevant contacts. However, if we expand our criteria to include the French equivalents — “Directeur général” (masculine) and “Directrice générale” (feminine) — we stand a much better chance of capturing a larger and more accurate dataset.
In short, leveraging local-language job titles alongside their English counterparts can help maximise reach and relevance when you buy an email database for international campaigns.
4. Refining Business Size Segmentation
If we want to focus on small to medium-sized businesses (SMBs), AI can help define and classify them based on measurable criteria rather than just assumptions.
Instead of manually cross-referencing size brackets, we can ask AI:
"What are the typical revenue and staff size ranges for small and medium businesses?"
This provides a consistent framework for identifying the right targets based on employee count, revenue, or both - removing guesswork and ensuring more accurate segmentation.
5. Understanding Industry Classifications
AI can also assist in interpreting complex industry classifications like SIC codes. These codes can often be broad, unclear and difficult to interpret.
A simple AI prompt such as:
“Explain what businesses fall under SIC code 62020” or “What industries are covered by SIC section G?”
can instantly break down the complex classifications into clear, plain-language descriptions. This gives us a clearer understanding of what’s actually included within a code, helping us make more informed decisions about whether it aligns with our target market.
6. Postcode Targeting Made Easy
AI can dramatically reduce the time it takes to identify and list postcodes within a specific area. What would traditionally take significant time and manual effort can now be achieved almost instantly with a well-crafted prompt.
For example:
“List all postcode districts within a 20-mile radius of Leeds.”
This not only saves time but also ensures greater accuracy and consistency when defining geographic targeting for counts.
Learnings: From Cream to Milk to AI-Powered Insight
These examples highlight just a few of the many ways we can utilise AI to improve the effectiveness and efficiency of our data counts.
From broadening targeting criteria and refining segmentation to simplifying industry classifications and accelerating geographic filtering, AI helps us approach counts with greater speed, accuracy and insight.
At Emailmovers, we believe the real value isn’t just in the cream - the visible product - but in the milk: the proven process, enhanced now by AI, that allows us to deliver the best email data in the industry.
As the technology continues to evolve, so too will the opportunities to enhance our targeting strategies and unlock even more value from the data at our fingertips.
Author - Xavi Gledhill - Data Project Controller, Emailmovers
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