People should be able to confidently share their data and be rewarded for it. Sounds simple doesn’t it? After all, this is the supposed template on which the market research industry is based. We need consumer input in order to do our jobs and that means creating an environment in which they will actually share this input.
In reality, this premise is not so simple to implement. As an industry, we face ongoing, tightly woven challenges surrounding trust, transparency, privacy and compensation. Consumer privacy legislation is pushing us to find new ways of doing things. Our value propositions as research companies must shift to encompass these challenges. In fact, we’ve completed two studies that clearly illustrate that it is well past time for the insights industry to have frank conversations about new actions that will impact trust, data quality and consumer participation.
Data privacy: the first step in building trust
How do people really feel about data privacy? In Q1 of 2021, we completed an exploration into consumer concerns surrounding the collection and use of personal data, as well as the impact of these concerns on future planned behaviors. What we found wasn’t too surprising: businesses still are not getting it right when it comes to consumer privacy.
With a full 82% of our respondents (3,200 individuals in the U.K. and U.S.) indicating some level of concern surrounding these issues, it is critical for organizations that use data to understand this sentiment. People are more closely scrutinizing how their data is collected and used, and this has implications across the business ecosystem.
If you want to understand how awareness and behaviors are changing, just look at the growth of privacy-first consumer products like the Brave browser and DuckDuck Go search. Just one year ago, Brave had 3 million users. Today it has more than 20 million. People are actively seeking out these types of solutions.
For our industry, understanding this movement is crucial because, after all, we depend on consumers sharing data in order to do business. Yet, our study showed that half of individuals have concerns about their privacy when participating in an online survey. If they don’t trust you, nearly one-third will refuse to participate in the research at all and 38% will limit the information they provide.
This is just the tip of the iceberg when it comes to declining participation and trust. One thing is abundantly clear: as people’s concerns surrounding their own data privacy rises, their willingness to share information decreases, which directly impacts data-driven decision making.
The picture isn’t completely bleak though. Our data showed that:
- 65% of individuals surveyed indicate they’d be willing to share more data if they felt an organization was being more transparent in how they were using their data.
- 57% said they’d share more data if they felt their privacy was being protected
- 72% said they would be likely to share more data if they were being paid fairly for it. Consumers are increasingly understanding that their data has value and are growing weary of giving it away for free.
At the heart of all these points is the importance of building trust. But we have to be willing to take the steps to get us there. One way to achieve this, as we will see in our next study, is by deploying key trust principles, which not only can lead to greater participation, but also result in greater data quality.
Key trust principles for better data quality
The other study we’ll discuss here was a research-on-research exploration of data quality, which has been an interminable topic in our industry for quite a few years. Poor experiences and distrust somehow continue to persist, negatively impacting overall quality. Our results showed that trust built over time with consistent positive experiences actually ends up encouraging respondent engagement and having a measurable impact on outcomes.
For this RoR project, we examined the effect of trust principles such as data sovereignty, privacy by design, fair reward and transparency on data quality. With a side-by-side comparison between data collection using these principles and traditional data sources, we were able to track a number of quality metrics and set out a guideline for defining quality, trust and future research.
We revealed that the foundation for data quality is a focus on consumer data privacy and control, user experience, compensation, transparency, accountability and more. Respondents who were operating in a trusted environment outperformed those in traditional data collection settings across a number of dimensions. The principles used in creating this trusted environment include things like:
- Data control: Providing users’ greater custody of their data and helping them build their data asset. In this scenario, if an individual provides false data they only hurt themselves. We are starting to see solutions that are moving away from massive databases and are instead providing tools for individuals to harness their own data. This means true portability and control over access.
- Data privacy: Going above and beyond to protect users’ privacy. These means exceeding “privacy by compliance” and focusing instead on “privacy by design.” We see this in action when we look at companies like Apple that are integrating privacy into their product design, or products like the secure browsers mentioned above in which the user and ad model is being rewritten.
- Transparency: Being as transparent as possible, and fostering accountability, at each and every step. This can be as simple as just stating your intentions and what people can expect, using simple language. You’d be amazed how much people are willing to participate. This transcends all aspects of our lives, not just research participation. Tell them really how long it will take, how much they will earn, and, if necessary, why exactly they may have been disqualified from participating. This is the foundation of building trust.
- User experience: Creating the very best experience possible for participants. This is not about what you can get out of a respondent, or how many more questions you can squeeze out in the survey. It’s about ensuring the user has a positive experience from A to Z. This may require new skill sets, but once you fix the poor experience, everything downstream becomes easier.
- Fair incentives: Paying users fairly – something which demonstrates that you are taking their feedback seriously. Our research has found that appropriate rewards are a key motivator in information sharing, and can also contribute to the all-important goal of building trust. People will feel more valued.
I’m the first to admit that there isn’t a silver bullet here. Each of these principles are much more powerful when assembled cumulatively. So it’s not about just having a prettier survey or a shorter survey. It’s about doing many things together, and doing them well. These principles can be used as a scorecard on a monthly basis where you ask yourself things like: How well am I doing on each of these? How do these principles enter my mode of thinking and the decisions I make? This level of awareness and action can take us, as an industry, in the right direction when it comes to trust and data quality.
The reward takes us beyond survey data
While much of the above discussion has largely focused on surveys, what’s important is that these concepts translate to other types of data. Today, we increasingly demand more data from individuals and new forms of data with varying levels of sensitivity. This may include Amazon purchases, what they watch, their browsing history, and even health related data. This behavioral data has traditionally been difficult to access, but a trusted ecosystem can change all that.
As we seek holistic audience insights, we must more heavily rely on multiple data streams. Reported data from surveys married with passive data that shows what people are actually doing, buying and watching can create a powerful insights recipe. As you increase trust, you are also increasing the willingness of individuals to share more types of data, and as we’ve demonstrated, improves the quality of the data, and inturn, the value of the data for industries and individuals alike.
