Consumer surveys reduce package redesign risk

Assuming the number of alternative new designs has been narrowed to a handful (preferably through a screening survey) package-testing surveys can be used to obtain deeper insights. You may not even be aware of some of the possibilities. Examples include search-time testing, stand out, recognition, tachistoscope evaluation, element identification (‘click on image’ questions) and positioning impact tests. Further, driver analysis might be helpful to determine how each element impacts on purchase intent.

Whenever an existing product’s packaging is redesigned search times are potentially impacted; at least in the short term. The more radical the change, the more likely it is that the average search time for your product on shelf will be negatively impacted. The immediate consequence is, that while consumers who have a strong preference for your brand may be willing to seek out the product again, this is less likely for those with only mild interest in your brand. If it is a crowded repertoire market, this problem is even more pronounced, because so called “100% loyals” make up a small proportion of the customer base.

How can market research help? Search times will eventually improve as consumers learn to identify your package again. But ideally it would be best to select the new design with the shortest search time, so that the short-term sales impact is minimised. Once search times improve, effective redesigns should result in increased sales, while bad redesigns may have the opposite effect. That is why it is important to research the impact on purchase intent in the same survey (discussed later).

With a new design it would be expected that your package may stand out on shelf less effectively than before. Familiar packages tend to do better in stand out testing. Stand out testing is typically conducted during a survey. Assessing the impact on stand provides allows you to identify which of the new alternatives is least likely to reduce stand out.

Another important area to test in packaging research is brand recognition. Are consumers able to still recognise the package design at a distance, even when text is not really visible? What percentage will confuse your new package with competitors – in effect helping competitors win accidental sales? Which of the new designs you are considering is correctly identified as belonging to your brand?

Consumers tend to glance far more briefly – than we might hope – for at shelves. When consumers are close to the shelf, it is still possible that they will miss your package on a cluttered shelf. A tachistoscope test is a way of seeing what information comes across in a fraction of a second. Traditionally this might have required consumers to visit a special venue for testing, using a specially adapted projector and screen. Today, this can be done online. Key questions may include: Do they recognise the product category? Are consumers seeing your brand during the brief exposure?

When you change a design, you need to ask “What elements play the key role in package recognition?”. Is it the colouring – is it especially unique? Is it the shape of the label, it is imagery in the background or perhaps font and text? While managers may be able to guess at this, it is always wise to see how far you can go with changes before consumers are confused.

This can either be gauged indirectly using experimental cells using the previously mentioned methods, or more cheaply by asking consumers directly to indicate which parts of a new pack are most familiar. You can also identify negative changes that consumers don’t like.

Measuring before and after positioning impact of package design – relative to competitors – might also be an important element of your packaging survey; if anything more than a slight design refresh is intended. Something as minor as a font change can have an impact on positioning. When meauring positioning you evaluate a variety of attributes; such as quality perceptions, perceptions of value and symbolic benefits.

By exposing consumers to the new packages during the survey and asking them to rate each design on various aspects, along with purchase intent ratings, an understanding can be gained of what impact each new element might have on purchase intent (this is often referred to as driver analysis). In this way you can screen out any negative changes that may negatively affect trial purchases of your new package.

It is important to know how to analyse this data, as you can easily reach the wrong conclusion when using sophisticated statistical models if assumptions are not checked and violations corrected.

Do you have new package designs that you wish to evaluate? Make sure you understand where you are in the process: