Improve your marketing by better understanding your ideal customers.
Ask questions like:
- Why are you collecting qualitative data?
- What’s the purpose?
- What are you going to do with the answers?
If you’re enacting web/exit surveys:
- Will the widget distract from the highest priority goal on the page?
- Is your question relevant to the content on the page?
- Does the exit survey add value to the page or take away?
Here are some typical things businesses are looking to learn with customer surveys:
- Who are these people? What are their common characteristics? Can we form a hypothesis on some different customer personas?
- What kind of problem are they solving for themselves? We can use this information in our value proposition.
- What are the exact words they use? You can steal exact phrases from this for your copy, essentially having the customer write copy for you.
- What are the biggest sources of friction? Doubts? Hesitations? Any unanswered questions? Knowing this allows us to take steps to reduce friction.
- How would they prefer to buy?
- Do they comparison shop? How much? If they shop around a lot, it’s important to stress our unique benefits more. We need to be visibly different or better than the competitor.
- Can you uncover any insights about their emotional state?
- If you want to improve your buying process, survey brand new buyers and the people who didn’t buy.
- If you want to start a loyalty program to improve customer retention, survey frequent buyers.
- If you want to start a VIP program for top spenders, survey customers who spend a lot of money with you.
After 200 responses, the answers tend to get repetitive and don’t add value, and they take longer to analyze, using up more resources. But if you have fewer than 100, there might not be enough data to identify trends or to draw conclusions from. If you’re asking specific and quantifiable questions, such as Net Promoter Score, collect data from a large sample. But otherwise, balance resources with the insight you’d like to gain. If you have fewer than 100 people who recently bought from you, then make do with what you can get. 10 responses are better than zero.
To better understand your target audience, use questions like:
- Who are you? Get the demographical data and see if there are any trends.
- What are you using [your product] for? What problem does it solve for you? Understand the problem, and uncover unintended uses.
- How is your life better thanks to it? Which tangible improvements in your life or business have you seen?
- What do you like most about our product?
- Did you consider any alternatives to our product before signing up? If so, which ones?
- What made you sign up for our product? What convinced you that it’s a good decision? Why did you choose us over others?
- Which doubts and hesitations did you have before joining?
- Which questions did you have, but couldn’t find answers to?
- Anything else you would like to tell us?
For example, the creators of Monthly1K wanted to know why people were showing interest in the product and were happy once they bought it, but frequently not taking the step from interested to buying. Their survey had four questions:
- Were you at least interested in buying? Yes or NO.
- Be specific about your answer.
- What’s holding you back from starting your business?
- Should we make our support sumo do a dance video?
With the responses to this survey, they found the top four reasons that held people back from buying. They also used the customer’s language on the landing page, reordered the page so that the top questions were answered, and reduced friction by answering questions, fears, and doubts.
Develop a standard process for analyzing the information you get back from your surveys, and document it.
There is no consensus among qualitative researchers about the process of qualitative data analysis. There might not be a single best way to do it. Here’s one way that works for many people:
- Be clear about your goals. What are you looking for?
- Conduct an initial review of all the information to gain an initial sense of the data.
- Code the data. This is often described as reducing the data, and usually involves developing codes or categories, while still keeping the raw data.
- Interpret the data.
- Write a summary report of the findings.
When you review the data, look for natural groupings: for example, doubts, hesitations, and unanswered questions, and customer persona data. The goal in the initial review is to identify broad trends and then create a code for each trend. Then codify the data: now that you have a list of codes, go back and attach codes to as many responses as you can. For example, a client whose product was vegan healthy meal plans noticed that their survey data had 3 typical use cases:
- Busy mom, someone too busy to think about what to shop and what to cook.
- Overweight or sick people who want to get healthy by following the meal plans.
- Vegan/people with celiac disease, people who bought it because the meal plans were gluten-free and vegan.
Write down what you can about hypothetical personas, as many as you can spot, and count how many responses per code you have to prioritize issues. Write down key learnings and keep them at hand, and combine them with other forms of research to formulate hypotheses.
While analyzing your surveys, look for cognitive biases that skew your results toward the answers you want or expect.
There are a number of biases to beware of, including:
- Confirmation bias: Your opinions are the result of years of paying attention to information which confirmed what you believed while ignoring information which challenged your preconceived notions.
- Backfire effect: In light of new information that opposes your existing view, you double down on the prior and strengthen the inaccurate belief.
- Error of Central Tendency: make sure your survey is short enough to be interesting.
- Channeling bias.
- Experimenter’s bias: mostly applicable in telephone and in-person interviews.
- Recall bias: don’t survey people who purchased too long ago.