Result 10: Creating customer segments

As part of the Result 10 Customer Research, we created customer segments based on the main telephone survey of 1,500 New Zealand residents. Segments are distinct customer groups created by grouping customers based on their characteristics. This segmentation was behavioural and attitudinal, and each customer fits into one and only one group. Segments are different from personas in that segments are not fictional representations of customers, but rather actual representative groups.

In total, there are seven segments that capture the range of customers:

  • ‘Anxious’ (13% of customers) — experienced a high number of pain points and a high number of transactions. They were nervous when dealing with government and more likely to be receiving government assistance.
  • ‘Frustrated’ (12% of customers) — experienced a high number of pain points and a high number of transactions, but were more digitally confident than the ‘Anxious’ segment and felt in control of their lives. They were often busy establishing careers, had young children and disliked needing to provide the same information multiple times.
  • ‘Young’ (16% of customers) — likely to be unmarried and under 30, this segment were digitally savvy and more likely to have started a new job or had a baby. They were the exception to the rule that the more you deal with government the more pain points you experience.
  • ‘Mainstream’ (14% of customers) — experienced fewer pain points, because they used fewer services. They are more likely to be 45-59, digitally confident, employed and on good salaries.
  • ‘Unconnected’ (14% of customers) — preferred dealing with government in person or over the phone, as many didn’t use the internet. They were likely to be older (60+) and dealing with a disability.
  • ‘Savvy’ (17% of customers) — digitally competent and confident dealing with government. They were likely to be employed, on good salaries and have few interactions with government.
  • ‘Conservative’ (14% of customers) — confident dealing with government, but preferred to do transactions in person, rather than online.

How the segments were created

The survey was designed to capture people’s attitudes and behaviours as well as patterns of service use. For example, we asked questions such as if they felt nervous when dealing with government, or spent a lot of time checking they were doing the right thing. We asked them about their (generic) channel preferences and if they felt they were able to find information quickly.

How we settled on these segments

The raw data presented us with numerous options in terms of how we could cut the segments. The rule with segments is that each respondent could be included in only one group; this is one of the big things that differentiates them from personas.

Given out interest in the shift to digital services, we settled on segments cut by the number of pain points experienced, the number of transactions and digital aptitude. This seemed useful since one of our main research findings was the more you dealt with government, the more pain points you were likely to have experienced.

Segments are often cut by income, ethnicity or other demographics, such as age. For us, a demographic segmentation would not have given us particularly distinct segments as this is not what determined most respondents experience of government. Income especially provided little guidance.

In the end, we did overlay demographics on the segments, although they do not form the basis of it and this is how we know, for example, that those in the ‘Unconnected’ segment were more likely to be aged over 60.

Why we created segments

So why did we create segments? In 2012, R10 created a set of eight personas that you can see in the back of the Result 10 Blueprint. These were created after a series of in-depth interviews with users of government services and represent the types of customers we have in government.

The problem we faced when socialising these personas was that people often confused them with segments, deducting that eight personas meant each persona represented 12.5% of the population, which was not the case.

The segments instead give public servants and designers a better idea of who will be using their services and who to design for.

So if we did it again what would we have done differently?

Initial response from colleagues was mixed. Many thought the segments were good, but did not go far enough. They wanted segments that cut across agency boundaries more intentionally and showed us who our common customer groups were. We agree that this is something that’s missing. The research showed us that most people use a medium of three services per year, but we know very little about peoples’ end-to-end use of government services through their lifetime.

Unfortunately we don’t have all the necessary raw data to create these common customer groups and it would have been out of scope to collect it. However, it’s something we would like to explore. We’d really like to hear from you if you’ve attempted to create all of government segments or if you have the data to do so (or what data you think we’d need).

2 comments

  1. Comment #1. Lana Gibson:

    Hi Corinne,

    Thanks for sharing your user insight – every bit helps to build the jigsaw puzzle showing what people need from government services.

    I’m working on something similar, creating segments in Google Analytics to better understand audience groups. For example for Govt.nz a significant part of our audience comes from overseas, from people who are interested in immigrating to NZ. We can look at their behaviour on the site by filtering reports to show only those visits that come from outside NZ, arrive via Google searching for ‘nz immigration’ etc.

    It would be awesome to attach some tangible online behaviours to these segments, so that we can find out how they interact with our websites, and what they need from us.

    Cheers, Lana

  2. Comment #2. Corinne Cordes

    Hi Lana,

    That sounds really interesting – it would be great to discuss it further.

    Corinne.

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