In the world of market research, we have often seen examples of bad surveys, forms, and questionnaires. But what’s the point of gathering data when it's incomplete, inexact, or rife with errors?
That’s why in this blog, we will offer some guidance on how to counteract this problem and ensure that you are gathering high-quality data.
Data gathering projects: How do we run them?
Gathering high-quality data involves collecting and measuring information about variables of interest. In general, we achieve this systematically, in a way that, among other things, enables us to:
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Gather information from a variety of sources, with the goal of gaining a complete and accurate view of the area of interest.
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Allow individuals and businesses to answer relevant questions. This makes it possible for them to evaluate the results and better anticipate future probabilities and trends.
Thus, it is important to remember that exactitude in data gathering is essential, as it’s the only way to ensure the integrity of a study whose purpose is to help us make good decisions.
Though the methods may vary with the company carrying out the study, the emphasis must be on guaranteeing that we are gathering high-quality data.
The importance of guaranteeing we are gathering high-quality data
Regardless of the field of study or preference, and regardless of whether it is quantitative or qualitative, precision in a study is essential to maintaining the integrity of an investigation.
The consequences of incorrectly gathered data include:
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The inability to respond to research questions.
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Skewed findings that result in wasted resources.
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Loss of time for other researchers who end up pursuing unfruitful research paths.
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Compromised decisions with consequences for markets, businesses, and people.
What challenges must be overcome in order to obtain high-quality data?
Because high-quality data precedes careful processes, the main focus is based on planning. Some of the more common challenges include:
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Uncertainty about the moment, the methods, and the identity of the people responsible for reviewing the data.
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Vague description of the data-gathering tools that will be used.
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Lack of training for members of the staff responsible for data gathering.
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Hiring a certified and broadly experienced data provider.
Standards that high-quality data must meet
Project managers need to bear in mind that high-quality data must possess one trait to meet this standard: it must be reliable.
That said, there are several factors that we must consider in the initial planning process. It is possible to measure quality in every phase of the study by reviewing the following:
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Questionnaire design
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The definition of the sample: Will this survey be sent to the right people? Is it large enough?
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The panel selection process
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Administration and management of the field
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Results processing
But how can we guarantee the data we gather is reliable? Let's find out!
How can project managers be sure they have high-quality data?
One important part of ensuring you have high-quality data is developing a detailed plan at the outset. But quality in these terms goes beyond organization; it meets international standards.
The international standard ISO 20252 regulates and monitors social, opinion, and market research. This includes data and data analysis.
It establishes guidelines and requirements relating to the way market research studies are planned, carried out, supervised, and reported. Thus, businesses that handle this kind of project comply with strict guidelines to ensure maximum reliability and integrity.
What's to gain from having a regulatory body? Thanks to their involvement, we achieve cohesion and transparency in the way we carry out surveys. We also gain confidence in our results and in the providers of this service.
For example, if there’s ever a problem with the service purchased, the norm specifies a series of actions a project manager can use to appeal:
At the project level
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Requesting survey replacement.
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Requesting additional validation.
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Reviewing the data obtained.
At the fieldwork level
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Requesting complementary training for survey conductors.
In the case of fictional interviews. Revision and validation of the recent or simultaneous work done by that person.
Choosing a data provider that meets ISO 20252
Businesses that provide high-quality data fully comply with ISO 20252 requirements, and Netquest is one of those businesses. That said, in addition to meeting this standard, we are talking about one of the companies whose guarantee of quality is built on excellence.
With 21 years in the field and thousands of successful studies under its belt, Netquest uses tools and processes designed to deliver useful data. All of them comply with international standards and norms.
To achieve 100% reliable results, the company supports every study with three main pillars: Genuine people, Genuine panels and Genuine data.
Genuine data: A 360°-view for a unique portrait of the consumer
With a panelmade up of genuine people, at Netquest we offer a one-of-a-kind vision of the consumer. Thanks to high-quality and single-source data and our own technology, we are able to obtain richer, more complex data.
Here are a few examples:
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Digital behavior. Search terms, online browsing frequency, websites visited, etc.
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Online shopping. Transaction analysis.
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Geolocation. Identification of frequented areas and media exposure.
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Audio-matching: Measurement of exposure to advertising through ambient sound.
Genuine panels: Crafting more complete studies
At Netquest, we have developed a close relationship with our extensive group of panelists. This community, which now consists of over 2 million people, is the raw material we use to carry out studies that comply with the highest quality standards. Our studies are also ISO 20252 certified.
What are the advantages of working with a genuine panel? With a genuine panel, project managers can leverage the following:
High loyalty rates
Netquest panelists receive clear and timely communication through Netquest’s app. The app is used to gather high-quality data and ensure participation; in turn, that participation is rewarded, thereby helping build a loyal relationship.
This high loyalty rate enables us to play with hundreds of profiling variables, opening the door to true deep profiling. This makes it possible to learn from and build an extraordinarily detailed sample.
Ethical Data Handling
Compliance with ISO 20252 also involves compliance with legal obligations, such as the GDPR, the EU law on data privacy. Thus, none of our clients or partners can know the personal data belonging to the panelists involved in a study.
Continuous quality control
Under the quality standard ISO 20252, panelists are identified as unique and real individuals. Furthermore, Netquest, as a business that adheres to the most rigorous standards possible, ensures that every person's participation is high-quality.
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High-quality panelists: We avoid identity fraud through measures such as participation frequency monitoring, non-monetary compensation, IP monitoring, any many more.
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High-quality responses: To ensure high-quality responses, our recruitment is based on panel sampling, the opposite of river sampling. We recruit based on specific demographic and psychographic traits. We also apply additional protocols such as trick questions.
Genuine people: In contact with real human beings
Recruiting people for a study is one thing, but creating a community around that study is quite another. At Netquest, we build on this foundation to foster loyal relationships. This ensures transparency and fluid communication.
In conclusion, it’s important to remember that we obtain high-quality data thanks to processes and people. This data is based on the underlying perceptions panelists have of a product or service. To get the most out of the data provided by every variable and properly analyze it, you need to make a single decision: choosing the best ally.
At Netquest, we have the experience, the certifications, and the aptitude to create a trusting relationship with our clients. Send us a message and take another step towards conducting successful studies that lead to intelligent decision-making based on high-quality data.
