Data-driven personalisation: How to use analytics to create campaigns that convert

07/09/2023A 4 minute read by Dale Higginbottom
Data-driven personalisation

With GA4 in full swing and artificial intelligence being one of the most talked about trends in digital marketing right now, we wanted to share some important ways to utilise machine learning and data for personalisation in your marketing strategy. Personalising your campaign messaging is one of the key focuses of digital marketing today in order to convert customers. While developing a good SEO strategy or paid campaign may drive traffic to your site, personalisation will help convert that traffic by addressing each customer’s wants and needs.

Personalised messaging is not a new concept, however, is fast becoming vital to gain loyal customers online. A 2022 study revealed that 62% of online consumers would lose their brand loyalty if it delivered a non-personalised experience. Customers need to feel like their buying experience is focused on their specific requirements. As a result, marketers are finding new and innovative ways to use data to drive personalised customer experiences.

Some of our recommended analytics tools available for gathering customer data include:

  1. Google Analytics – Used by the majority of our clients at Evolved, the tool has recently undergone a complete overhaul and the primary version is now Google Analytics 4. It is a free web analytics tool that provides insights into your website traffic, including demographics, behaviour, and conversions.
  2. HotJar – This tool helps you visualise how your users interact with your website or App. It offers features such as heat maps, scroll maps, and user recordings.
  3. Microsoft Clarity – Similar to HotJar, Clarity allows you to quickly piece together what users are doing on your website/App through session recordings and heatmaps, and seamlessly connects to Google Analytics. This powerful tool is completely free and is a good place to start with behavioural analytics.
  4. Convert – A comprehensive A/B testing tool that allows the team here to experiment with ideas well before a client needs to push them live on the website – risk mitigation at its best. The data we gather from these experiments, or A/B tests, can provide us with some of the richest insight into behaviour and channel experiences. The best part is, if the test wins, this can go live on the client site with little, to no fuss.

Depending on your business goals, the data you collect will change, however, a good place to start is by looking at product popularity, purchasing habits, and website engagement. These areas should allow you to see what products to focus on, individual customers’ behaviour, and what on your website makes them engage. For example, are they reading a blog post, saving a product or claiming a promotion?

In order to create a personalised marketing campaign, there are a few steps you can take with the data you have gathered.


The process of segmentation in GA4 audiences revolves around categorising customers into distinct groups based on their online activities. Each customer is assigned a profile, enabling marketers to gain insights into their identity, preferences, and behavioural patterns. This segmentation process encompasses various factors, including demographics, purchase history, interests, and habits. These insights serve as the foundation for creating marketing campaigns infused with machine learning elements.

By leveraging predictive audiences in GA4, brands can develop targeted campaigns that resonate with these segmented groups. These personalised campaigns are strategically positioned to capture the attention of each group, drawing on their unique characteristics. Segmentation not only informs campaign creation but also plays a pivotal role in optimising marketing efforts, spanning from search campaigns to content creation.

Furthermore, this approach proves invaluable when assessing campaign effectiveness, providing insights into how similar customers respond to marketing initiatives and guiding any necessary adjustments.

Track customer behaviour

Analysing customer behaviour can help marketers understand the best way to meet their demands. For instance, tracking what pages they visit on your website, what products they view, and what actions they take (such as adding items to their cart or making a purchase). This data can be used to create personalised recommendations for products or services that the customer is likely to be interested in.

Watching the way your customers move around your website or interact with your brand can determine how you engage with them through marketing efforts. You know if they are frequently engaging on Instagram, this could be a good platform for targeted advertising.

Tracking buying behaviour also allows you to quickly discover any issues they are having with your brand. If they keep leaving a particular page, then you can examine any errors on that page. Being reactive and responsive to your customer’s actions will influence brand loyalty.

Customised content

Once you have enough data to analyse your customer’s needs, you can create customised content across a range of channels that will interest your audience. This content will aim to answer your customer’s pain points, attract them to your products or provide them with helpful information. Generating this content, not only addresses your target audience but benefits your SEO efforts and provides you with marketing materials to share across multiple platforms.

By developing personalised content, you can align your business goals to your strategy. The deep and meaningful connection you can make with your audience through different content techniques will ultimately drive better results for your brand and have a positive impact on your bottom line.

The rise of video marketing has been an essential part of content strategies over the past couple of years, with further development expected throughout 2024. Reportedly, 91% of businesses use video as a marketing tool. By analysing data gathered from video performance and personalising your messaging based on findings, your customised content can be taken to the next level. A good example of this for an e-commerce brand is to take a segmented customer group that responds well to promotions and target them on social media with a video explaining your latest promotional offer. This humanises your brand and demonstrates you are responding to their needs.

Real-time data

Use real-time data to drive your personalised marketing efforts immediately, while you’re still at the forefront of your customer’s mind. This involves using data that is collected in real-time, such as the customer’s current location or the weather forecast. This data can be used to create personalised marketing messages that are timely and relevant to the customer’s needs.

Real-time data allows you to be reactive to your customer’s requirements or problems before they have to come to you. It also helps you introduce yourself to new customers at the time they most need your product. This method of marketing can be invaluable when building a loyal customer base.

Personalising your marketing campaigns is essential for success in today’s digital age. By using data and machine learning, you can create personalised messages and experiences that resonate with your customers and drive results.

By considering the actions above, you can create personalised messaging that will help you reach your target audience, increase engagement, and drive sales.

Personalising your marketing campaigns is an ongoing process. As you learn more about your customers, you can continue to refine your campaigns to deliver the best possible results.

If you’d like to learn more about utilising data and building relevant online campaigns, get in touch

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