Your Guide to Advanced Segmentation in Tech


Guide to Leveraging Data Analytics for Advanced Segmentation in Tech

Gone are the days of one-size-fits-all marketing. Today's consumers have instant access to every tech product and service on the planet. So, to thrive in this hyper-competitive industry, tech marketers must redefine how they find and speak to their potential customers by making interactions utterly unique and conversion-focused. 

Advanced segmentation is a vital first step in helping tech brands personalise customer journeys and bolster the success of their marketing efforts. 

This article will explore how the latest data analytics tools can help you seamlessly incorporate advanced market segmentation into your marketing strategies and tailor every customer interaction. Read on to learn more. 

What is advanced segmentation?

Customer segmentation is the practice of splitting audiences into clearly defined groups based on shared characteristics such as gender, age, and location. Advanced market segmentation goes beyond these traditional categories, deploying data analytics to provide more insights into customer behaviours and characteristics such as customer lifetime value, churn risk and satisfaction levels. 

For example, advanced segmentation can be used to develop targeted email campaigns based on purchase history or deliver product recommendations based on a user's previous site interactions. 

Within the tech sector specifically, research by Foundry revealed that 96% of IT decision-makers are interested in marketing content that reflects the needs of their industry, existing IT infrastructure and organisation size. However, only around two-thirds of tech marketers polled in the same survey say they successfully create this type of highly-tailored content. 

Therefore, for larger tech organisations operating in an extensive and diverse marketplace, investing time and effort in creating highly segmented audience groups can help you boost your bottom line. A recent survey found that 80% of companies that use market segmentation report increased sales, in some instances boosting conversion rates by as much as 50%. 

Four characteristics of tech brands embracing advanced audience segmentation


Tech offerings are not bound by geography or conventional logistics cycles, so if you have a great solution, you can instantly sell to all corners of the world. This means that tech brands must capitalise on their unique differentiators to stand out from the thousands of competitors offering similar products and services. 

Advanced audience segmentation enables tech brands to create marketing messages that are unique to their rivals, helping brands stay top-of-mind and lifelong favourites amongst their audience base. 


Tech brands often have complicated product offerings packed with functionalities that would be too extensive and overwhelming to convey in a 30-second elevator pitch. This means that marketing teams have to pick and choose how they communicate solution features and benefits to ensure they're capturing the attention of the customers they're targeting. 

Advanced marketing segmentation gives marketers more groups and data points to consider when crafting their campaigns. The result: they can more accurately and concisely relay the message that will be the most convincing to individual customers. 

Moreover, advanced segmentation simplifies resource allocation. Armed with more granular customer insights, marketing teams can pool resources into high-converting audience segments and minimise wasted spend on ineffective messages and channels. 


Consumer needs are incredibly diverse in the tech industry. So, of course, not all customers will fit nicely into basic audience segments centred around occupation, age, etc. 

To use an illustrative example, a customer who may have previously slotted comfortably into your 'busy data analyst' category may have become frustrated with your latest software update. From their perspective, your UI changes have eliminated the keyboard shortcuts their team relies on heavily. Although highly skilled at their role, the data analyst must prioritise their team's needs, so they may look elsewhere for a more user-friendly solution. 

Developing empathy for your customers and understanding how their needs may change is essential in gaining their long-term trust in your brand. Advanced segmentation dives deeper than demographic categories, uncovering scenarios that zoom in on differing psychological cues to help you win and retain more customers. 


The tech industry is continually evolving with technologies like generative AI and Robotic Process Automation (RPA), rewriting the rules on how humans and machines interact on a daily basis. 

To keep up with the ever-accelerating pace of change, tech marketers need to be early adopters and embrace cutting-edge tools so they can respond to changing customer demands and outpace their equally forward-thinking competitors. 

Advanced segmentation tools harness generative AI and automation to generate highly-personalised customer journeys. With each detailed audience segment delivering a wide range of data points, RPA tools can be deployed to anticipate site visitor needs and automatically alter text and imagery to make the content more compelling.

For example, suppose a Chief Data Officer visits your landing page after clicking through a LinkedIn ad. In that case, training an RPA to recognise their previous interaction history and automatically display a relevant white paper (rather than beginner user tutorials) creates a more engaging experience completely responsive to their needs. 

The tech marketer's advanced market segmentation toolkit

While advanced segmentation delivers a wealth of benefits, tech brands still need to make sure that they are optimising the segment creation process. Embrace tools that unite your customer data points across systems, facilitate real-time learning capabilities, and give you the power to customise segments on the fly.  

Here are some essential technologies for your advanced segmentation toolkit: 

Customer Data Platform (CDP)

CDPs like Oracle Unity Customer Data Platform and Adobe Experience Platform leverage AI and machine learning algorithms to consolidate customers' data across all customer journey touchpoints. 

These tools give you a 360-degree view of your customers in real-time and apply data governance protocols to protect their data privacy. Unified CDPs help you unleash the full potential of advanced segmentation by ensuring that all your customer data is accessible in the moment. 

Predictive Analytics

Advanced data analytics platforms utilise AI and statistical modelling techniques to identify trends and patterns in customer data, enabling tech marketers to segment customers into highly targeted groups. 

For instance, solutions like Microsoft Power BI offer the following predictive analytics capabilities: 

- Propensity modelling: This data analytics technique examines customer interaction data (such as past purchases and site interactions) and predicts a customer's lifetime value (CLV), empowering you to allocate more resources to high-value customers. 

- Basket analysis: Basket analysis examines data from items/solutions most frequently sold together, giving marketers clues on what to upsell and cross-sell to customers. 

- Churn risk assessment: Predictive analytics solutions can monitor real-time online activity data and assign customers an accurate churn risk score. This feature empowers marketers with the data insights they need to intervene (through targeted ads, personalised offers, and preemptive outreach communications) to prevent customers from dropping out of the sales funnel.

- Sentiment analysis: Advanced data analytics tools incorporate natural language processing (NLP), social media and customer feedback data to measure customer sentiment. This predictive analytics model helps marketers tailor their campaigns in alignment with their customers' emotions, gauging things like an individual's level of frustration within a helpdesk chat or their excitement about an up-and-coming productivity tool. 

- Customisation: The most powerful predictive analytic solutions enable marketers to customise models to help them further segment customers into unique categories. For example, a large tech brand could create a bespoke predictive analytic model that tracks software data usage and feature adoption. Evaluating these signals in real-time will tell marketers which customers would be first to jump on their new productivity tool release and, therefore, may be worthy of a special offer. 


Hyper-personalisation tools combine AI/ML-enhanced Dynamic Content Optimisation (DCO) and Omnichannel Marketing Automation (OMA) capabilities to deliver your advanced segmentation insights. 

Solutions like Dynamic Yield go further with marketing personalisation by offering additional predictive insights on customer intent to transform interactions across all touchpoints in real-time. 

For instance, hyper-personalisation tools can act as an automated concierge that knows your customers' preferences and can deliver the ideal conversion path before they even ask. 

With these data analytics tools and capabilities, tech enterprises can unlock new revenue opportunities from their existing customer databases while reducing the time to insight to enhance ROI. 

CopyHouse: Tech marketing content specialists that can tailor your brand's story

We're an award-winning content marketing agency with a wealth of experience working with global tech brands. Our expert copywriters, marketing strategists and designers are dedicated to telling your brand story in the most targeted and impactful way possible to help you gain more leads and conversions. 

From social media posts to infographics and eBooks, we put your customers' needs first, crafting your marketing messages to meet their highly-specific needs in the moment. So, if you want to learn more about CopyHouse's services, get in touch for a free consultation now. 

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