Data analysis that gets you a clear action plan

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We translate raw data into business solutions for you

The most challenging part of any research is interpreting the results. As a partner in your success –and upon your request, we can crunch the numbers for you and create a digestible presentation of findings, so you can easily make data-driven decisions on the fly, and share the results directly from our platform.

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Get the full picture with high-quality insights

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Our research team applies weightings to address any survey bias, and creates data segments for cross-tabulation.

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We canvas your data to identify trends, benchmarks, and opportunities for you to take action.

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We categorize hundreds of long-form qualitative comments into themes and apply our sentiment analysis technology for easier classification.

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Our experts deliver strategic recommendations and highlights that offer guidance for your decision-making process.

Discover sampling on social networks

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Sampling on social networks is what makes Potloc different

Compare it to traditional research methods

Potloc logo consumer research company
Main features Online Panel (CAWI) Phone survey (CATI) Intercept survey
Non-incentivized surveys check_circle_outline check_circle_outline check_circle_outline
Incidence rate < 10% check_circle_outline check_circle_outline
Geo-targeted areas, up to 1km radius check_circle_outline check_circle_outline
High data quality check_circle_outline
Guaranteed quota sampling check_circle_outline check_circle_outline check_circle_outline check_circle_outline
Non-customer analysis check_circle_outline Get a quote check_circle_outline check_circle_outline check_circle_outline

Frequently asked questions

First, we clean data to ensure quality and robustness. Next, we ensure that it is a representative sample and we apply weighting where necessary to achieve acceptable minimums for the requested sample. Finally, we define robustness for analysis. Our expertise lies in defining which sub-samples are worth analyzing.
We acknowledge them! We know what biases we deal with and we know exactly how to address them. There are 4 types of survey bias when we launch a study:
  1. Coverage bias: Since we use social networks to target consumers, we definitely need them to meet certain conditions. They must have access to the internet, have a social media account, and be an active user. However, Canada’s adult population is 28.1M and 24.3M of them are active on Facebook. Coverage bias affects older populations as well so we might see an under-representation of men, older people, and less-educated people or with a low socioeconomic status.
  2. Facebook’s ad algorithm bias: Facebook’s advertising tool algorithm is set up in order to minimize cost-per-click (CPC). It basically pushes our survey ads primarily to the least expensive audiences. This might show an under-representation of men and older people.
  3. Cognitive load bias: Answering a 6-8 minute survey online is demanding from a cognitive standpoint, so some people might find the task too difficult to complete. This might result in an under-representation of older people, and less-educated people or with a low socioeconomic status.
  4. Self-selection bias: Unlike web panels, we have to communicate on the subject of the survey. People who click on our ads have an interest in that specific subject. And we never offer any incentives to respondents. People who complete our surveys do it because it matters to them that their voice is heard. So, what do you think is worse: Having respondents naturally interested by the subject vs. respondents seeking incentives? We think this actually increases our data quality.
  5. All methodologies have a bias, few are transparent. We address survey bias head-on by sampling enough people to ensure we hit the targeted quotas.

    For example, it is known that women answer more surveys and social media platforms have a higher representation of young people. However, it surprises most that there are sufficient elderly people on social media to collect needed responses.

    Traditional survey methods like phone, intercept or web panels, apply weight to their results and are not transparent about the impact on the data collected.

We use a variety of statistical tests to verify if the sub-sample variation to the mean is statistically significant and relevant to your business problem. Some of these include but are not limited to, linear regression, key square test, correlation, and cross-tabulation.