The pillars of data quality

Data-driven decisions are essential in today’s fast-paced market. That being said, data-driven decision-making must be based on high-quality data. At Vypr, we employ robust data quality methodology so you can base important decisions on valid data at every stage of the product development process.

We have several quality checks embedded in our end-to-end processes to ensure data validity. These checks include fraud detection, response integrity and confidentiality validation. We run these checks weekly and monthly to flag bad data, and we permanently remove consumers from the panel who fail to meet our stringent standards. This decreases the chances of unreliable data or other data quality issues appearing.

1. Engaged community

Make informed decisions based on customer data from real people who give real feedback. We have a proprietary panel that enables us to control the sample design and monitor the integrity of our panel, ensuring data reliability that is up to our high standards.

Our intuitive, consumer-facing app allows us to launch concise, fast-response steers (questions) designed to increase engagement and retention. Our high engagement rates, paired with our ongoing removal of consumers who do not follow our data integrity protocols, allow us to maximise the data accuracy of the responses.

2. Reliable responses

Vypr implements a number of data reliability assessment strategies to eliminate poor-quality data. We recruit new community members from pre-vetted sources that have already conducted consumer profiling and validation checks. We further validate member authenticity through our own comprehensive validation process.

We monitor open responses and flag and resolve invalid data. Consistent data is one of the major data quality metrics, so we look for consistency in responses and take action where necessary to mitigate data reliability issues. We make use of integrity steers and steer violation tracking to identify and weed out bad actors within our panel, thus maintaining data reliability.

3. Representativeness

Data representativeness ensures you can explore results at a granular level. It also means that once hard-to-reach audiences are now accessible through our tailored insights platform.

We manage the composition of our community to ensure the sample is representative of the population in key geo-demographic criteria, including age, gender and region.

We consider these criteria along with buying behaviour and consumer attitudes. This means you can use our platform to target demographic profiles with both combinations and weighting.

4. Up-to-date research protocols and methodology

Vypr is your gateway to understanding how and why people make purchasing decisions. We apply the latest understanding of behavioural science to our quantitative and qualitative research methodology.

Using a steer character limit, we help you to create clear and concise steer questions that garner accurate responses. Our steers encourage quick and instinctive System 1 (rather than System 2) responses, which mirror typical purchasing behaviour to ensure data reliability.

Essentially, we ask the right questions of our community in the right way, ensuring our customer insight tools deliver robust predictive intelligence for your business. Using a mix of qualitative testing, quantitative testing, monadic testing and comparative testing, our data collection methods are more statistically accurate than any other.

5. The right team

Our expert Community team boasts over 150 years of combined experience in building and managing consumer panels on an international level.

We leverage this expertise to better understand our customers’ needs and to create a robust quality programme that ensures our data collection method is statistically valid. This results in data integrity you can trust to help you make informed decisions and drive product innovation.

Streamline your new product development process with Vypr.