Follow these steps to ensure your surveys meet a high standard and see bolstered user engagement
Those in the marketing research industry are in a unique position to gain an intimate understanding of consumers, their behaviors and opinions. Developing trust with survey research participants, through transparency and accountability, is key to the future success of this industry. One way to accomplish this is through the use of blockchain and crypto-technologies.
Blockchain helps solve persistent data privacy issues and removes security concerns of centralized databases (where survey participants have traditionally been found). It also predicates that transparency is one of its core values. Unsurprisingly, this approach has another beneficial side effect: better user experiences.
My company, Measure Protocol, recently ran a pilot project that showed some interesting findings, among which was surprisingly high—and fast—user engagement in survey projects. In fact, 92 percent of those who started a data job completed it. Another indicator of a good experience is a quick response rate—44 percent of completed jobs were responded to within 15 minutes of notification during our pilot.
Why were these numbers so high? We focused heavily on user experience. The transparency that surrounds the blockchain means that you can’t get away with a bad experience, because the users have more visibility into the process. For every survey data job, participants had a voice in the quality through an Uber-like rating system that is exposed at various points.
A transparent blockchain means that one can write unalterable information to it and, conceivably, people could interrogate it to view this information. By adopting blockchain and its underlying philosophy, a number of transparency and privacy-related directives need to be clearly established. Interrogating the blockchain isn’t an easy undertaking for the average person, so we’ve chosen to build in other ways to expose the information and make it clearly available at the application level.
Here are some of the basics to this approach.
Transactional Activity Data
Supply-chain and network activity is often a black hole with little to no transparency. By adopting a blockchain-transparency philosophy, general network activity is made transparent to the users of the system. This includes the number of data requests, offers, accepts, terminations, number of surveys taken, payment and ratings. This level of transparency provides the opportunity for analysis to understand the drivers of success and data quality. From here, this data can also be used in a number of ways to encourage positive behaviors across the ecosystem as outlined.
Give users an instant opportunity to rate the quality of each survey or data job. Their ratings are written to the blockchain, become part of the reputation of the survey provider and shared, so other users will know up front how other users are rating that particular survey. If ratings are low, they may choose not to participate.
From a researcher side, if a survey is launched and the first wave of respondents all give it one star, that’s an immediate indicator that something is wrong with the experience. You can halt the survey immediately, send an alert to the provider and let them know that this survey isn’t meeting quality standards for your network. This kind of fast feedback loop can help manage reputations of research firms and prevent more users from punishing experiences.
Some mystery exists today surrounding what people get paid for taking a specific survey. Most of the market research industry doesn’t want to publicize that they are paying just a few cents for a respondent to spend 25 minutes on a survey.
In my company’s app, we have chosen to publish this information at the beginning of a job—incentive level, estimated time to complete, etc.—which holds everyone accountable. No survey provider wants to be at the bottom tier of compensation, so this transparency starts to push us toward a fairer compensation model.
This may not have as much to do with user experience, but it is important to note that ratings can work both ways in this ecosystem. Respondents are also rated, this information is saved to the blockchain and is made available to research firms. Building algorithms around the characteristics of a “good respondent” exposes their behaviors, such as average response rates to surveys, completion rates, number of surveys taken recently and more. Researchers can then target quality, higher-scoring respondents if they want feedback from those with better reputations.
This type of environment starts setting a different stage for incentives and motivations for survey participation—users are fully aware of “what they’re getting into” before they begin. This transparency also puts pressure on the survey providers to only run high-quality surveys. So, while all the data is transparently stored on the blockchain, it becomes truly powerful when you start to unpack it for use in some of the tasks outlined above. After all, better user experiences mean better insights on which businesses can base important decisions. This is one way to achieve that goal.