67% of women over 45 say they enjoy celebrating Christmas with their families, while 95% of turkeys would rather spend the festive season getting away from it all!
Do you believe those statements or do you think we made them up? It is not unreasonable to assume that any claim is based on some form of research and that this research was specifically designed to investigate and substantiate the claim in question.
Claims can be the key to a successful product, making it stand above its competitors. Claims can also validate a premium pricing strategy. However, to do this, claims made in advertisements or on-pack need robust data to support the specific statement made. Sensory Claims are very common and can relate to specific sensory attributes or to how much people like the product.
Why should we be wary of claims?
Because you can be challenged about what you are stating. In the USA, claim disputes are very common – there are a number of companies who specifically work on disputing claims. This is likely to happen more and more in the UK. The more compelling or aggressive your claim the more likely it is that it will be challenged.
There are different ways of classifying claims:
Non-comparative claims. These are the least contentious claims, such as ‘crunchy’ or ‘fresh’. They are often descriptive, using sensory information to make the descriptor. They are unlikely to be challenged, providing your competitors don’t think your claim is unreasonable.
Comparative claims. These are usually made in relation to a market leader and can be classified as either a claim of parity or of superiority. Each can relate to preference (hedonic) or a specific sensory attribute. Examples include "... preferred to the brand leader ..." or "... no other crisp is crisper". These sorts of claims may well be challenged so you must carry out robust testing in order to support them.
What is Good Data?
There is no hard and fast rule about what constitutes good data and there is no standard test that you can carry out to ensure your testing is robust. However, data needs to be numerical: qualitative data is not suitable.
It is all about being reasonable. So it’s about designing a study that collects data from a suitable group of people in a scientifically robust manner. For example, you should use data from people who typically use the product. If you’re making a claim about children’s preferences, you must use children in you study. Depending on the product and the scope of your claim, you may need to consider regional and demographic effects.
You also need to have enough people in your study to have a statistically valid result. A minimum of 100 people in your sample cell is recommended.. To prove a claim of parity you may need a larger sample size. Because statistically we cannot ever prove that two samples are the same, we have to define limits within which we will accept they are the same.
The technique that you use needs to be appropriate for the products, the claim and the population being tested. Examples are hedonic scales, agree/disagree scales for consumer testing, difference and similarity testing for claims such as ‘tastes the same as’ and descriptive profiling for claims relating to the strength of specific flavours or textural impressions. Agree/disagree type scales are especially valuable in claims research because they allow you to make statements such as "X% of people surveyed agreed that .."
Who Enforces Claims?
In the UK, the Advertising Standards Association (ASA) and Trading Standards Institute are mainly responsible. Some industry bodies such as the Cosmetics, Toiletry and Perfumery Association (CTPA) provide guidelines to members to help them ensure that adverts are true and not misleading.
Some terms such as ‘fresh’ and ‘natural’, when applied to food products, must show specific routes to their ingredients and the composition of the product.