Psychometric segmentation is subjective in its nature and takes into account, personality traits, hobbies, life goals, values, lifestyle and psychological influences.

There are several techniques that are employed to gather this information from surveys to interviews, to customer data and associated memberships. Psychographic segmentation allows organisations to position their products so that compatible customers can “discover” them. It’s how brands find the right customer match based on customer attitudes and lifestyles.


Where behavioural segmentation looks at how a segment interacts with your brand, psychographic segmentation looks at how they interact with the wider world. Understanding the motives, needs and desires of your customers can be incredibly useful in order to execute emotive marketing.  Lifestyle, attitude, emotions and preferences are crucial factors in analysing how consumers allocate their money.

Psychographic data is qualitative in nature, but with the amount of information that users share on social media, organisations can also understand at a broader level the interests, opinions, values, hobbies and lifestyle choices, etc of different segments.


Most of the research methods used to collect psychographic data are qualitative in nature, and this means that typically a small number of respondents will represent a larger group. Time is limited in interviews, surveys can only have so many questions and so the quality of responses must be called into question. Psychographics can easily be misunderstood or incorrectly applied, and it is also important to consider that while every business will consider the buying journey a consumer takes, the consumers themselves may never have considered it at such a granular level.

Patterns can change rapidly meaning that Psychographic Segmentation often fails to deliver ROI due to the time needed to conduct research, the cost in doing so, and the recommendations brought forth. While psychographic data enables rich consumers personas, often they aren’t well-differentiated on category behaviour, and this is why they are best utilised when combines with Needs Segmentation.

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