The recipe for attention-grabbing messages: TURF analysis

The recipe for attention-grabbing messages: TURF analysis

This week's blog looks a technique to identify the most focused and coherent messages for your organisation.
Cian Murphy

We know that when talking to the public, we have at best a few seconds of someone’s attention before they switch off and stop paying attention. Choosing what messages to bundle together about your work when talking to prospective supporters is not easy under this pressure. Going for too narrow an approach risks creating an incredibly specific proposition that is unlikely to find broad appeal with your target audience. A campaign on a niche topic broadcast to a general public audience is not going to lead to many sign ups.

On the other hand, a scattergun approach that aims to have something for everyone is likely to make your brand seem unfocused and even incoherent. Think of the international development sector’s Enough Food for Everyone IF… campaign from a few years back. While the campaign represented an ambitious platform of more than 200 organisations across the UK, it seemed to be designed to have a little bit of everyone’s priorities in there, and lacked a forceful overall message.

So how do you go about designing campaigns that both appeal to a broad swathe of your potential audience, while remaining coherent and focused? Luckily, there is a simple market research analysis tool designed to do just this, known as TURF (Total Unduplicated Reach and Frequency) analysis. The analysis technique takes a list of potential items (say 15 messages that you could put out there about your charity) and returns the optimal basket (in this case perhaps the best five messages to put out as a combination). It chooses these items (i.e. the best five messages) by looking at two things, as the name suggests – reach, and frequency.

Reach is the proportion of the target audience who would find at least one of the basket of messages appealing. For example, to stick with the international development sector, consider a basket of messages containing work on gender equality overseas, campaigning on social justice, and human rights. This campaign is most likely to reach a small subset of the population, with very universalist, right-based values. Consider also a basket of messages which includes helping farmers to produce more food, tackling governmental corruption overseas, as well as gender equality. This second basket is likely to have a much broader appeal – after all there is probably at least one thing in there that appeals to almost everyone. In other words, we would say the second campaign has higher reach.

On the other hand, we also need to take into account that while the first basket of messages targets a smaller audience, your average member of that audience is probably likely to be very enthusiastic about all of the different messages, not just one of them. The same cannot be said of the second campaign – while it has higher reach, your average member of its audience probably only likes one or two of the messages, not all of them. In a TURF context, this means it has lower frequency.

The trick is to find a way to balance these two, sometimes conflicting, priorities. TURF automates this process, and allows you to simply identify the best basket of messages to run with. In fact, at nfpSynergy, we can create simple dashboard tools for you to explore the data yourself and see which of your messages work best with which of your target audiences.

Of course, while very powerful when used to identify the best set of messages to use with a given audience, this is not the only area that TURF can help you. Traditionally, it is also used to identify the best basket of media channels to advertise in to best reach your audience, and could also be used to work out the best mix of fundraising channels and methods to offer potential supporters. In fact, any time you need to pick out the best mix of options from a list of potentials, TURF can be a lifesaver. It’s also a simple technique that, unlike other operations such as segmentations or conjoint analyses, does not need reams and reams of additional original research – it is quite possible that you will be able to apply this technique to a survey question you have already asked.

If you’d like to find out more about how you can use TURF to help you make better decisions, get in touch with your nfpSynergy account manager, or Cian at

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