Product managers have access to a wealth of observational “events” data but often find this pales in comparison to the insights from direct customer feedback. While qualitative methods are popular with product managers, their approach is usually unsystematic and susceptible to bias or misinterpretation. To achieve a successful customer-driven product vision it’s important to understand how to design robust qualitative and quantitative research with specific use cases before you ever begin collecting customer feedback. Product managers should already be very familiar with Agile methodology, as it’s a staple of most development efforts. This research design should be done in quick sprint cycles, instead of a longer “One and Done” approach. The steps below give you a guideline to begin implementing this process into your own customer research, assuming you’ve already planned out your roadmap with SMART goals.
Leverage Existing Knowledge
The first step in your customer research is to understand your role in the market. A huge amount of free, secondary information is available to you during this process. Use what you learn to clarify your thought process and narrow your customer research to what you’ve found to be most important. Then, categorize your secondary information into product preferences (both what’s available and what’s desired), pricing & willingness to pay, messaging to connect with your research customers, and the right channel mix to reach them. Finally, implement a system to organize all the information you’ve collected for quick referencing (to view an example, click here). Be sure to include a way to access the original source along with a short description of how it can be used in your customer research.
Piggybacking On Expertise
Experts are separated from the masses by their ability to clearly articulate details (from memory) about your research topic and its connection to everything else in the market. Experts are identifiable because they can also cite all the sources of information that matter (helping you prioritize your secondary research) and can introduce you to other experts. Conduct 2-3 expert interviews. Although it’s technically considered a qualitative research method, expert interviews differ from IDI’s in their purpose. You want to use information gathered from the experts to form your questions for customer conversations (see below). During the interview resist the urge to treat them as an expert on every part of your research problem, because they aren’t. Identify their niche and keep questions focused. Their niche could relate to your business type (like SaaS or B2C), your customer group, your competitors, your industry’s technology stack, regulation, etc.
Qualitative Customer Conversations
This is the first transition into primary research. Customers outside of your organization can be accessed through panel companies or social media ads. Incentives are typically required, and the industry standard is $50 minimum. It’s a major key to exude professionalism and organization with your first impression, as your customer is judging your every move. Use tools like Calendly for scheduling. Although it’s a conversation, develop a script (what you want to learn) to frame your questions, but don’t type responses live; record the conversation and transcribe later. Have a “reschedule once and then move on” policy. You should conduct 20-30 of these IDI’s in a single research sprint, pay attention to patterns that appear 5 times or more.
Quantitative Confidence with Surveys
After combining the information you’ve learned through secondary research and industry experts with customer insights obtained through IDI’s, your next step is using quantitative primary research to increase your precision. The data you choose to analyze could be from your product itself or obtained through a survey (learn how to step up your survey research here). A number of different factors can affect your surveys completion rate, including your chosen method of solicitation (email, phone, etc.) and if you’ve chosen to offer an incentive (we highly recommend doing this). However, the overall length and design of your survey will ultimately determine it’s completion percentage. Your surveys length will primarily be determined by the type of questions you use (multiple choice, open-ended, scaled, etc.), and a general guideline is that a customer should be able to answer 3 questions per minute. Also anticipate open-ended responses to take slightly longer, up to 1 minute per question.
Research Driven Product Vision
The final steps in this research process are analyzing your data, extracting actionable insights, and then displaying all of this information into an easily understood report that answers your original hypotheses. It’s mandatory to revisit your SMART goals at this point. Doing this will help you keep focused on your original research question and find answers to the very specific hypotheses established before you began. Next, you need to follow your analytics “treasure map” to begin to mine-deeply into your data (to view an example, click here). “Mining” implies that there is value to be found in your data, and an analytics treasure map helps realign your research path as well as finding evidence to answer your hypotheses. Following this step, you can begin storytelling with your data. This phase of your research can make or break your whole cycle, as data is often displayed in what seems to be a cryptic fashion to those not familiar with it. In order to avoid this, you must explain your findings in simple terms. The key to success at this point is to emphasize the story you are trying to tell and use plenty of visuals.
After you’ve received answers to your original hypotheses, you’re ready to act. Conducting iterative waves of research can give you additional confidence in decision making, and that just involves repeating this same process with your hypotheses slightly altered by what you’ve learned from previous sprints. Success is ultimately determined by what you established as your objective in your SMART goals, and this research process gives you the precision needed to effectively guide your product vision.
For more tips on becoming a data-driven Product Manager, see our last post on The Best Way To Drive Your Product Vision.