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Client satisfaction


Potloc's NPS

Qualitative comments organized into themes

We interpret the data for you

The most challenging part of any research is interpreting the results. We separate what you need to know from what’s merely interesting to know. As a partner in your success, we 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.

People illustrations working on laptops and interpreting data graphs

Quality and quantity, you can have it all


We categorize hundreds of long-form qualitative comments into themes.


Impact analysis, understand the result of inaction.


Guaranteed quota sampling to address any survey bias.


Segments for cross-tabulation designed by our research team.

We know what your consumers think

Do you?

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Why research experts are making the switch to Potloc

Compare us to traditional research methods

Potloc logo consumer research company
Main features Web panel Phone survey Intercept survey
Non-shopper analysis check_circle_outline check_circle_outline check_circle_outline
Geotargeted retail trade areas check_circle_outline
Non-incentivized surveys check_circle_outline
Quota sampling check_circle_outline check_circle_outline check_circle_outline check_circle_outline
Respondents sourced via social networks check_circle_outline
Supports use of media in survey check_circle_outline check_circle_outline check_circle_outline
Survey designed for mobile devices check_circle_outline
Comparative stats over time check_circle_outline check_circle_outline check_circle_outline check_circle_outline
Low cost per respondent check_circle_outline Get a quote 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.