Our guiding principles around good quality data
At Price Intelligently, we follow certain principles to maintain a high standard of quality data. While we carry out both qualitative and quantitative research, our focus in this article is on quantitative survey data.
When looking at the data we collect, there are four things to look out for:
1. Are you talking to the right people?
Are you better targeting current customers, or people who don’t know your product? It all depends on the questions you’re asking and the results you’re looking for.
Here’s a tangible example: If you want answers about the nitty gritty features of the product, you’re likely best leveraging current customers because they use the product and know what you’re talking about. If you ask an audience that hasn’t ever seen or used the product, you may be wasting your time (and money).
Ultimately, we can’t help you achieve pricing strategy success without generating high quality survey responses. So it’s in both our best interest and yours for the first-party data to be the highest quality.
2. Are people who they say they are?
The Internet is a wild place, so it’s perhaps unsurprising that many people lie to get paid for a survey—it’s rife with fraud. For example, they embody the Chief Marketing Officer at a SaaS company just to ‘qualify’ for the survey.
One of our prior clients ran research using Survey Monkey, and the results included many fraudulent responses. They blamed their questions; maybe they weren’t clear enough, or their target audience wasn’t specific enough. But although there may be faults with the survey design and targeting, it’s important to know how to spot bad actors and know how to design surveys to remove them.
At PI, we source and vet all survey participants to get the right quantitative and qualitative data on your target buyers. We work with various market panels, some of which use an entirely different participant recruitment strategy, for example sourcing participants via LinkedIn.
3. Do people understand the questions you’re asking?
Experimental design is really important. You can have the smartest, most qualified respondents, but if they don’t understand the questions, they won’t answer correctly and give you bad data.
At PI, we have significant experience creating surveys to elicit the most accurate responses from participants.
4. Getting clean, reliable data
To get accurate data, we leverage automation to uncover suspicious data patterns, which can be pretty impossible to catch with the human eye. But human elements play a large part in our data cleaning processes, too. We manually look through each and every survey response to ensure our clients are presented with the highest quality data.
We look for consistent survey responses, people who demonstrate an understanding of the product, remove duplicate responses and more. We want results that reflect the true market in the final research report.