All consumer product companies are intimately familiar with conducting trials to understand consumer needs and preferences, test out ideas, validate the effectiveness of their potential new products, and better understand their products’ acceptance in the market.

The success of these trials depends heavily on recruiting trial participants who represent the target end users, and the accuracy of data collected from these participants. For many trials, participants are tasked with using the product at home for several weeks, and then reporting usage information and subjective feedback to the trial coordinator, perhaps returning a diary of their time with the product, and reactions to different aspects of the product experience and design.

But, it is difficult, if not impossible, to ensure the accuracy of the trial data. Despite being given instructions on how to use the product, participants still might not use it as expected.

For example, participants might forget about the trial for a short while and then make up results to cover this up, or might not think consciously about how they are actually using the product. Trial coordinators can certainly detect when data has been omitted, but otherwise might not identify inaccurate or incomplete data.

It seems, accordingly, that there is an opportunity and a need for capturing smarter data from consumer trials. What if, for example, your product could have eyes and ears that sense the truth?

Of course, you don’t want the participant to feel like they’re being watched because they might modify their behavior as a result, but imagine if you could unobtrusively and securely capture true product usage...

Let’s review an example to illustrate the type of information your product could sense. Imagine you can integrate a sensor to detect movement, temperature and fluid flow in a bottle of day cream used in a consumer trial. What could your day cream tell you?

The day cream could track its usage in the participant’s home. It could tell you, for example, if it is stored in a location that is too warm or cool, and if it is stored in the correct orientation.

The day cream could tell you if it is used at the correct times, and how often it is used. It could also tell you about the amount dispensed for each use, or even it was shaken first.

There are several advantages to this approach. First, you can identify and filter out data associated with incorrect use. For example, you could choose to discard data from participants who are not using the product as intended, or who stopped using it altogether. This ensures that the data collected is accurate and eliminates the need for trial coordinators to manually review the data to look for outliers or omissions. Moreover, because you have more confidence in the data collected, this could mean a smaller sample size for the trial, ultimately resulting in lower costs and quicker completion times.

Next, you can automate the collection and filtering of data. This can reduce, at least in part, the burden of data capture from trial participants, who might no longer need to manually record and report usage information to the trial coordinators.

The longer-term advantages of having this information are even greater. If a product can tell you more about actual use, then it can help to identify opportunities for design iterations and improvements. Let’s imagine that you observed some interesting patterns in the data collected from participants in the day cream trial…

If the sensors revealed that many participants stored the bottle on its side, rather than upright, this might have implications for the size of the bottle, which could be too tall for conventional medicine cabinets and shelves.

If the sensors also told you that most participants did not fully close the bottle cap after use, perhaps this speaks to the difficulty in closing the cap, or a feature of the design that makes the cap appear closed when it is still partially open.

Of course, the manufacturer would need to confirm the root causes of these data patterns by conducting follow-up research. However, this data has invaluable potential in terms of predicting actual product use. If in fact, the sensors point to design features that are hindering effective use of the product, the manufacturer can take advantage of opportunities to improve the design before the product is launched.

Clearly, incorporating sensors into products will come at a cost and there needs to be infrastructure in place for collecting and analyzing the sensor data. However, the cost and size of the required sensor components has significantly reduced over time, as the power of the processors that can be used has increased. So, although it won’t be free, it is a real possibility –  you can now unlock the benefits of having eyes and ears to uncover the truth associated with consumer trial product use.

Principal Human Factors Engineer, Cambridge Consultants