Potloc
How we’re making your data cleaner than ever
From missed investments to weak client strategies, poor-quality insights damage outcomes and reputations. IBM estimated that bad data cost the U.S. $3.1 trillion in 2016. A number that’s only grown since. Despite growing awareness, data fraud in surveys remains a serious issue, with problems like click farms, VPN usage, and now GenAI making it worse.
Definition
Data quality is the result of collecting reliable and authentic responses. When people are honest, attentive, and engaged while taking a survey.
This aligns with the standards set by the Global Data Quality (GDQ) Initiative.
At Potloc, we believe data quality depends on three key factors:
- The reliability of the respondent source
- The experience offered to participants—clear, fair, and non-repetitive
- The effectiveness of cleaning measures to detect and remove poor-quality responses
Who is impacted with data quality
Internaly
In our workflow, two key personas guide the success of every campaign survey:
- Supply team focus on reaching the right audience at the best possible cost. Their goal is to connect with people who match precise panel criteria while staying within budget.
- Project support team are responsible for delivering high-quality data to our clients. They ensure every response meets strict quality standards, balancing accuracy, reliability, and overall campaign performance.
Externaly
Two other personas that are not part of the scope of this project also play a key role in data quality:
- Clients often come with complex targeting needs, tight deadlines, and limited budgets. Their expectations directly shape how campaigns are set up and executed.
- Respondents are at the core of data quality. They need to match the target audience and clearly understand the survey topic to provide honest, thoughtful answers.
Challenges
To improve both speed and reliability, we tackled three key challenges in our survey workflow:
- Blocking poor-quality responses
- Simplifying internal tools
- Reducing delivery time
Blocking poor-quality responses
Imagine, implement and automate 14 quality checks
To fight against fraud and bad behaviours, we created a complex data cleaning system based on 14 quality checks and positioned at different steps of the journey.
- Pre-Survey Validation — Prevents bots, fraudsters, and duplicate respondents from entering your survey.
- In-Survey Validation — Removes ineligible and dishonest participants, while flagging potential low quality respondents.
- Post-Survey Validation — Checks the accuracy of open-ends and confirms bad quality respondents.
All respondents must pass the checks one at a time. If they fail a pre-survey check, they are blocked immediately. If they fail an in-survey check, they are redirected to an exit page based on their status. If they pass both the pre-survey and in-survey checks, they still have to pass post-survey checks.
Empowering teams with clear dashboards
Once respondents complete the survey and pass through our data cleaning process, key performance indicators are displayed in our internal back-office.
These KPIs help the Supply and Project Support teams quickly spot issues, adapt live campaigns, and remove low-quality respondents or sources. This data is accessible across several areas of the app, including the Data Quality section, Sources view, and Respondent Report.
Adjustments in a few clicks
To support teams in managing tight timelines or complex samples, we developed a set of features that allow quality acceptance levels to be adjusted in just a few clicks.
These tools give Supply and Project Support the flexibility to respond quickly to client constraints, reducing friction while maintaining control over data reliability.
Data cleaning level
Similar answers review
Wrap up
We built those tools to give teams visibility during fieldwork and the ability to act fast. This replaced hours of manual work in Google Sheets and delivered exactly what they needed.
Let's work together
Everything will begin around a coffee or a nice call to understand your expectations or the problems we can help solving
