In today's rapidly evolving business landscape, a thorough understanding of customers is becoming crucial. While traditional segmentation methods have been successful in the past, the advent of artificial intelligence (AI) has ushered in a new era where marketing success relies on micro-segments. Old segmentation methods are becoming obsolete in a world where demographics and interests are very diverse and dispersed, especially among younger generations. Companies can leverage AI to segment their customers with exceptional precision and efficiency, leading to better business outcomes.

In this blog post, I will highlight the benefits of using AI for customer segmentation and how you can leverage them to improve your marketing campaigns.

Micro-segmentation as an effective alternative to demographic segmentation

Micro-segmentation’s ability to accurately target and personalise offers has made it essential in marketing. By focusing on customers’ behavioural and psychographic characteristics, companies can optimise their advertising campaigns, increasing efficiency and return on investment. It also enables them to offer products personalized to individual preferences and boost customer satisfaction and loyalty.

On the other hand, traditional macro-segmentation based on demographics, while still useful, often falls short of modern marketing needs. Demographic data such as age, gender or education do not capture the complexity of modern consumer behaviour and can lead to overgeneralisation. Additionally, the collection of demographic data faces limitations raised by e-privacy regulations, such as GDPR, which restrict how and what type of data can be collected.

Using first-party data for micro-segmentation

The most effective way to start your segmentation effort is by collecting first-party data, including website behaviour, content preferences, past purchases and geolocation. This data is then analysed using a data management platform such as iPROM Private DMP to identify key patterns and common characteristics of different customer segments, leading to clear and precise customer groups based on their specific needs, preferences and behaviours.

This process lays the foundations for effective customer segmentation and target audience identification for more precise and effective marketing communication. We can also use artificial intelligence to process the collected data in the DMP and develop look-alike models that identify potential customers who are behaviourally similar to existing customers or share their interests. By matching this data with the behavioural patterns of digital media users, we can extend our target audience to the whole population and reach new potential customers with our advertising messages.

Technical details of micro-segmentation

AI significantly accelerates and improves the accuracy of identifying consumer needs and interests to drive personalised marketing approaches based on micro-segmentation. AI uses machine learning and deep learning to identify subtle and complex patterns within large datasets. For example, AI can identify groups of consumers with similar shopping habits who may differ in their geographical location and may be completely new or unrelated to a brand. Consumer behaviour is changing rapidly and individualism is a cornerstone for younger generations, the ability to micro-segment and personalise appeals is key for successful marketing activities.

Less media waste

Using AI-powered micro-segmentation, you can better understand customer preferences. By focusing your marketing efforts on audiences with a higher likelihood of conversion, you reduce media waste and increase your return on investment.

Additionally, AI enables highly effective A/B testing of your ads. In practice, this means that AI does the work of identifying which ad variations perform best in terms of budget and ad creative combinations. This allows you to reallocate ad spend to the most effective ads and optimise your overall ad spend.

Using AU to design dynamic ad creatives accelerates the creation process and generates personalised ads in real time based on user data.

Example of micro data segmentation with iPROM Private DMP

iPROM Private DMP is an advanced solution that leverages first-party data to improve digital advertising strategies, demonstrated by real-world examples. For instance, SKB Bank has significantly improved its advertising results with iPROM Private DMP, as shown by a 72% increase in direct response rates and a 25% reduction in cost per user. The platform uses AI to analyse data and develop lookalike models to target new customers who match the behaviour of existing customers.

Similarly, Lisca used first-party data and modelling using advanced algorithms to develop a strategy for identifying and targeting new potential customers who shared similar interests and behaviours with existing customers. This strategy led to an eight-fold increase in online sales of swimwear in the Croatian market and provided Lisca with valuable insights into their target audience.

Innovation and business growth

AI-driven customer micro-segmentation can uncover new segments or your true target audiences. Through deep insights into your customers’ preferences, you can also identify their unmet needs and even discover new opportunities for product or service development. In this way, companies not only respond to current market needs but proactively shape future market strategies, fostering sustainable growth and innovation.