How do I appeal to the largest number of consumers? (TURF analysis)

  1. How do I appeal to the largest number of consumers? (TURF analysis)
  2. How do I prioritise marketing messages or product attributes? (Max Diff)
  3. How do I find out what people value in my (new) product / service? (Conjoint)
  4. How do I identify what drives a desired behaviour or outcome? (Key driver analysis)
  5. How do I know what to prioritise to meet strategic goals? (Gap analysis)
  6. How do I build consumer loyalty? (Consumer journey mapping)
  7. How do I use behavioural science to improve my research? (Cognitive biases)
  8. How do I live without you? (LeAnn Rimes)
  9. How do I know how many people will buy my product at a given price? (Van Westendorp’s price sensitivity meter)
  10. How do I assess the impact of my advertising? (Ad effectiveness)
  11. How do I turn data into clear findings (Data visualisation)
  12. How do I tap into the unconscious perceptions that influence decision-making? (Implicit response testing)
  13. How do I reduce a large amount of data into something more meaningful? (Factor analysis)
  14. How do I group people together based on shared characteristics? (Segmentation) Scheduled for 14/05/2021
  15. How do I forecast market share at a given price point? (Brand price trade off) Scheduled for 21/05/2021
  16. How do I account for cultural differences when surveying across markets? (ANOVA) Scheduled for 28/05/2021
  17. How do I judge brand performance relative to competitors (Correspondence analysis / brand mapping) Scheduled for 04/06/2021

Welcome to the first in a series of blog posts focusing on business questions you might have, and how research can help to answer them with a variety of exciting techniques.

Over the coming weeks we’ll tackle questions such as:

  • How do I find out what people value in my (new) product / service? (Conjoint)
  • How do I know what to prioritise to meet strategic goals? (Gap analysis)
  • How do I build consumer loyalty? (Consumer journey mapping)
  • …and many more!

But enough about what’s to come, today we start by asking: How do I appeal to the largest number of consumers?

What does my target audience want?

At first glance, it seems pretty obvious that if you want to know which products your target audience is most interested in, or which marketing messages / user benefits best resonate, you can simply ask them in a survey.

This is absolutely true, and a great starting point (naturally we’re big fans of a survey!), but it might not always give you the full picture or make best use of resources – what if the same people are interested in multiple products, or motivated by multiple messages / benefits? Is it more efficient to overserve these people, or should you create a strategy that reaches a higher proportion of your target audience?

If you’re seeking to appeal to the largest number of consumers, we’ll start with the survey data captured to assess interest or preference, and then conduct total unduplicated reach and frequency analysis, known as TURF analysis for short.

How can TURF analysis help?

We’re primarily interested in the ‘total unduplicated reach’ part of the analysis, as it tells us which combinations (of products, or marketing messages, or user benefits etc.) appeal to the largest number of unique consumers.

We start with the product or message that has the highest individual score, and everyone who is counted there is not included in the next most popular product or message. This process continues for as many products / messages as it is helpful to layer together for analysis, showing you how many unique consumers can be reached with different combinations of products or messages, and proportionally what each additional product or message adds.

Comparing stated preference with TURF analysis

Let’s say we have five marketing messages that our target audience found appealing, imaginatively labelled message A – E. The survey data suggests that the best messages to focus on are messages A, D and C, because more people like these messages

When we run TURF analysis, we see that the best three messages to focus on are actually messages A, B and C – because it turns out a lot of the people who like message D also like message A.

This means that although 30% find message D appealing, incrementally it only reaches a small proportion (6.9%) of our target audience after messages A, B and C. It might therefore be better to focus on delivering fewer messages with higher reach than trying to squeeze an extra one in.

Of course, TURF is much more useful if you’re testing more than five messages / products / attributes, but hopefully the example above helps bring it to life!

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