29th March, 2018

Prioritising beneficiaries through segmentation

Welcome to the third and final blog in our series on prioritising for non-profits. In the first two blogs we looked at: 1) how to focus resources for projects and services, and 2) how to make better decisions about the projects and services you want to develop. In this blog we’ll be discussing how to use segmentation to prioritise who you support.

Prioritising is essential as demand for charitable services increases. According to a 2016 Local Giving survey, 73% of respondents reported an increase in demand for their services and 78% predict further increases in demand over the coming year.

As much as you may want to, you can’t help everyone, which means you have to focus and prioritise your resources. But how do you decide who you should prioritise or target your service to? To do this you’ll need to delve into your data to identify people who most need your help.

Why segment data?

Segmentation can help you to prioritise and better meet the needs of a particular beneficiary group, in order to focus resources where they can make the biggest difference. The process of segmentation is to identify groups of people from within your wider beneficiary group or target market, who have a common characteristic or need. It’s a concept taken from marketing, where Kotler suggested using the process to identify target markets.

Segmentation can be applied to both beneficiary groups and donors. For now, we’ll focus on beneficiaries. The aim is to identify sub-groups of people who are most in need, or likely to benefit most from your services, so you can target resources for maximum impact.

Four key ways to segment your data

Assuming you have good quantitative data on your beneficiaries, outputs and outcomes, you first need to gather your data on:

  • Who you work with
  • The various ways that you support them
  • The difference that this makes

This information could come from case records or surveys, or a combination of the two.

Next, you need to identify how you want to segment your data. The segments must be relevant to your project or service. The most common bases for segmenting markets are:

  • Geographic: e.g. country, region or local authority, urban/rural classification, income or Index of Multiple Deprivation (IMD) decile, etc.
  • Demographic: e.g. age, gender, ethnicity, religion, family type, employment status, education, income, disability, etc.
  • Psychographic: e.g. the lifestyle, opinions, social or personality characteristics that might drive motivation
  • Behavioural: e.g. how often they use your service, the benefits they are seeking from it

The category of your segmentation isn’t important; what is important is deciding how and why you want to segment data.

How to segment your beneficiary data

To avoid getting lost in data, start developing some hypotheses about the factors that might affect how beneficiaries use the service, the difference it makes to them and why.

For example, you might want to start with a set of questions such as:

  • At which age do people most benefit from our service?
  • At what stage in someone’s disability would they most benefit from this service?
  • Does gender influence outcomes achieved?
  • Is there a difference in how different BME groups access and benefit from the service?

Unless you run a very big project, the number of people that you are comparing might be low, in which case you might question how reliable the findings are. For example, can they be replicated throughout the wider community/society?  Try to draw on external evidence to compare and strengthen your findings.

The analysis can be done in Excel, using filters, sums or pivot tables.  The data you look at depends on your hypotheses.  Let’s take the first question as an example – ‘at which age do people most benefit from our service’?  You might firstly want to great categories of people’s ages if this has not already been done in your data (e.g. under 16, 16-25, 26-35 etc.).  Then create a pivot table that compares the distance travelled score for the age groups.

You then need to look for clear correlations that might indicate a link between age and outcomes.  Follow this process for all your hypotheses.

Segmenting to target services

The findings from your analysis should lead to strategic questions about if and how you should target your services to one specific beneficiary group, based on the resources available.

For example, if resources are particularly tight, you might decide to focus your resources on the age group that benefits the most.  Or if you are looking to expand your service, you might decide to consult with people that benefit the least, to find out what they really need to benefit from your service.

Of course, there are lots of factors at play when making decisions about how to focus your resources, but your data should certainly be one of them. 

Get in touch if you’d like some help making sense of, or strengthening your data.


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