The first step is to choose a platform that allows you to create and distribute online surveys. There are many options available, such as Google Forms, SurveyMonkey, Typeform, Qualtrics, and others. Each platform has its own features, benefits, and limitations, so you should compare them and select the one that suits your needs and budget. Some factors to consider are the ease of use, the customization options, the integration with other tools, the data analysis capabilities, and the pricing plans.
The next step is to define the objectives and questions of your survey. You should have a clear and specific goal for your survey, such as testing a hypothesis, measuring customer satisfaction, identifying pain points, or evaluating product features. Based on your goal, you should craft relevant and concise questions that address your research questions. You should also avoid leading, biased, or ambiguous questions that might skew the results. Depending on the type of data you want to collect, you can use different question formats, such as multiple choice, rating scales, open-ended, or matrix.
I have found that it is critical to invite your key business partners in to this process. Even if they have no or little market research experience and may even slow you down, they will be more accepting of the results having been made part of the process. If they are the one ultimately implementing the insights coming from the research in to your product, it is best to involve them early on and bring them along for the whole ride.
Survey design benefits from iteration. There are online applications such as UserTesting that may be used to prototype your survey with a small representative group of users. In the is the case, the assessment is qualitative. The platform allows you to observe end-users completing your survey. Based on what's learned, you can update it. Repeat as necessary. Remember that surveys are always biased, so your goal is to minimize bias, not eliminate it. The perfect is the enemy of the good. It's best to keep your objective simple and singular. Multiple objectives will likely result in muddled surveys and ambiguous results. Your objective is likely, not unique, and you will benefit from leveraging existing survey methods and templates.
The third step is to segment and recruit your survey respondents. You should have a clear idea of who your target audience is and how to reach them. You can use various criteria to segment your audience, such as demographics, behavior, preferences, or needs. You can also use tools like personas or customer journey maps to define your ideal customer profile. To recruit your respondents, you can use different methods, such as email invitations, social media posts, website pop-ups, or referral programs. You should also offer some incentives or rewards to motivate your respondents, such as discounts, vouchers, or free trials.
Finding a representative group is challenging but crucial. Caution should also be used in sending multiple surveys to the same group. Depending upon your objective, this could lead to unwanted and avoidable bias. Personas should define factors that matter and have a measurable and significant impact on the design of your product (its use case). Use surveys and other methods to identify the factors that matter and eliminate those that do not. It's a bit of a chicken and egg problem such that you will likely use personas to define your target audience. Your personas, if it is unclear, are a source of bias.
The fourth step is to test and launch your survey. Before you send your survey to your respondents, you should test it yourself and with a small group of people to check for any errors, bugs, or issues. You should also make sure that your survey is compatible with different devices, browsers, and screen sizes. You should also optimize your survey for speed, clarity, and engagement, by using simple language, clear instructions, attractive design, and progress indicators. When you are ready to launch your survey, you should choose the best time and channel to reach your respondents and maximize the response rate.
As stated in previous comments, survey design is an iterative process. Start with qualitative survey assessments using small groups before sending your survey to the multitude.
The final step is to analyze and interpret your survey data. You should use the tools and features of your survey platform to collect and organize your data. You should also use various techniques to clean, filter, and sort your data, such as removing outliers, duplicates, or incomplete responses. You should also use different methods to analyze your data, such as descriptive statistics, cross-tabulation, correlation, or regression. You should also use visual aids, such as charts, graphs, or tables, to present your data in a clear and meaningful way. You should also interpret your data in relation to your survey objectives and questions, and draw conclusions and recommendations for your product development.
The most important point to remember when utilizing survey data is to analyze it effectively and derive actionable insights. This involves examining the responses, identifying trends and patterns, and understanding the significance of the data in relation to the business objectives. It's crucial to interpret the findings in context, considering demographic factors, customer segments, and any other relevant variables. By leveraging the survey data to make informed decisions, businesses can improve their products, services, and overall customer experience, ultimately driving growth and success.
The survey assessment step is where things often go wrong because of mathematical and statistical illiteracy. It's best to develop or acquire (hire) statistical expertise. If the data set is small (under 30), extreme caution should be used when analyzing results. Standard inferential statistical tests taught in a high school or college course are likely, not appropriate. I recommend approaching the analysis step humbly and making as few assumptions as possible. Start with visualizing the raw data and avoid the use of p-values. What can be presented to stakeholders is often constrained by the limits of their statistical maturity. Hence, the need to start simple and foster a data-driven culture.
Stop sending surveys. Start talking to humans. Stop soliciting predictions. Start running experiments. Interview and observe to discover. Trial purchase (intent) to validate. Don’t ask people what they “would” do or “would” like. Find what they “are” like and what they “do” do. Surveys trade false security and closed questions for real insights and validated learnings. Beware of survey monkeys and qualitative robots.