Why technographic segmentation is important

Consciously or unconsciously - we divide people into groups all day long: those from work; those with the same interests; those from the same neighborhood; those who have the same style; those who fit in with me and those who don't.

Why do we do this? We want to feel understood. It's exactly the same with products: We buy things that suit us, that appeal to our attitude toward life. And with employers or customers: We go to companies where people tick like we do - that's how we make sure we'll feel good in the long run.

So if you want to attract and retain customers or recruit employees, you have to offer what people are looking for, where they are looking for it, and in a way that makes them feel good. Complicated is out - and nowadays, for many, just 3 more clicks or creating a customer account in addition to downloading an app is "too complicated" - the user experience suffers when things get complicated. Result: The customer cancels the purchase or the candidate does not send her application after all.

As always, the magic word here is: Personalization. According to Zendesk's 2022 Customer Experience Trends Report, 90 percent of consumers spend more with companies that personalize their customer service.

Today, it is no longer enough to know the customers and candidates demographically and psychologically well. If you want to reach them, you have to know their technology habits and segment them accordingly.

Technographic segmentation using the example of Michael and Thomas

Michael, 35 years old, industrial clerk, married, lives in rented accommodation, has a positive outlook on the future, earns 65,000 euros gross per year, enjoys playing soccer and is interested in music. Michael owns a MacBook, an Ipad, an Iphone, a smart TV (with Apple TV) and a game console. He stores his data in the cloud. Spotify is on all his devices. He does online banking through the app, reads Zeit and Spiegel in the app, checks Tripadvisor before traveling, books online at Booking.com, and buys from Amazon. From time to time, he checks Instagram and subscribes to the premium version of YouTube. If there's a tech news, Michael knows about it early and is inclined to buy or try it out. He is a fan of Chat GPT and experiments around with it enthusiastically.
Michael's innovation type is "early adopter".

Thomas, 35 years old, industrial clerk, married, lives in rented accommodation, has a positive outlook on the future, earns 70,000 euros gross per year, enjoys playing soccer and is interested in music. Thomas owns a laptop, a Samsung smartphone, a vinyl record player, a smart TV and a game console. For his backup, he has a large external hard drive. He likes to listen to the radio. Thomas does online banking only on his PC because he finds mobile banking via app insecure. He reads Zeit and Spiegel in the app, but also likes to buy the paper version. Thomas finds out about travel destinations from friends or via Google, only books flights directly on the airline's site and hotels directly on the hotel's site. He buys in the store or online directly on the supplier's site. Instagram and the like don't interest him, and he uses Youtube with advertising. If there is a technical news, Thomas only gets it from more tech-interested friends or from the press when everyone is already talking about it. He only uses it when it is already firmly established or can no longer be avoided. He has heard of Chat GPT, but has not yet visited the site. Thomas' innovation type is "latecomer".

Michael and Thomas have a lot in common, are perhaps even colleagues or neighbors. Nevertheless, the "classic" segmentation methods are not enough to make both of them happy as customers, not even if they want to buy the same thing.

The segmentation technique of the 21st century

To get Michael and Thomas both where they need to be, technographic segmentation, a marketing technique that uses technographic data to understand the digital habits of ideal customers or ideal candidates, takes hold. Customers or candidates are thus segmented based on the tools and technologies they use, e.g. devices, operating systems, web browsers and software, but also social media usage, cloud usage and app preferences, e-commerce activities, etc. In addition, with technographic segmentation you get important insights into how customers or candidates use their technical tools, with what regularity they renew their devices and what they will use in the future.

It doesn't matter to a company whether it wants to attract candidates, sell new technologies, or attract customers for other products. If you know your customers' technology preferences, you can segment your target audience in detail to create an ideal customer profile. Based on this, they can then create tailored content and relevant marketing messages to improve the customer experience: For example, for Thomas, a radio ad that emphasizes the security aspects of the banking app. Or for Michael, a job application app based on AI.
Via technographic segmentation, it is also possible to draw conclusions about potential customers' planned technology spending and usage. By targeting potential customers with their preferred technology, you can engage your customers at every stage of the customer journey and generate high-quality leads. The same is true for application processes: If you want to attract candidates, you need to provide the right application tools (increasingly landing pages or apps) and place job ads where candidates see them - more and more often on TikTok, Instagram & Co.

Technographic segmentation not only facilitates customer discovery, but also customer retention. Our data-driven personas naturally include technographic segmentation. That's because: your teams can use it to communicate more specifically with customers and propose solutions to their problems. From there, you can also tap into new niches and develop new products, especially in B2B software-as-a-service. More on this in a separate article.