AMA: Hims Former Sr. Director of Product Marketing, Alex Chahin on Market Research

Like many things, this can vary depending on your company. In some places, all research is unified under one leader, in one department. Other places, consumer insights may sit in one department (like Marketing) and UX research may sit in another (like Design or Product). With that in mind, there's definitely a venn diagram in terms of what these buckets cover. UXR often seeks to answer questions about how to best design an experience or interface for customers, but to best do so, it may be most efficient to ask general questions about attitudes and usage upfront. Consumer insights, on the other hand, often seeks to understand audience and market validation questions without necessarily putting UI stimuli or prototypes in front of customers. That said, as a product marketer, your goal should be clear regardless of where the research is coming from: You should help stakeholders understand which insights approach "truth" of the audience and market the best. People have a tendency to latch onto anything coming out of research as an "insight" with the same weight, but we need to help our counterparts understand which ones are actionable (i.e., because of sample size, because of frequency it comes up, etc.) and which ones are distractions.

To answer this question, first ask yourself a number of questions: * Business importance: Is this a smaller feature, or is it a big bet and large revenue driver? * Risk: Even if it's not a large revenue driver, there may be a substantial reputational risk to getting it wrong. Is this one of those situations? * Timing: When does this need to hit market? Can you convince management and stakeholders to wait for research, or do you need to act now? (Side note: Many projects will feel urgent, but it's rarely the case. Customers tend to forget who launches something first.) * Budget: Do you have the budget needed to conduct new research? * Current understanding: Saved the most important for last. What do you currently know about your audience, what they need, and what would encourage them to use your product more? Using the above, let's consider a couple scenarios. Scenario 1: Smaller feature, low risk, limited timing and budget Let's consider a relatable situation: A partner team is already pretty far along in thinking through a feature but brings you in to help take it across the finish line. In this case you may just want to make sure the feature is best set up for adoption with a quick quantitative survey (e.g., to existing customers). Qualitative research is less likely to help you here because feature scope is already relatively defined. Scenario 2: Big bet, meaningful risk, more resources to get it right If it's a new offering, category, or product line, you have a good case for several rounds of research that will help you get stronger insights as you go. Oftentimes this follows an arc where you start with qualitative research to get a broad understanding and probe deeper with customers. Then you take what you learned and do rounds of quantitative research to get greater confidence in the insights you learned and to put different offering configurations in front of customers.

We've all been there. First, you think through a problem and what you need to research. You dig in with your Insights team partner on the objectives and questionnaire. You get raw data back and sythesize. You pull together a thoughtful report with recommendations. You're excited to share it out with the team. Then you hear the doubt: "How was that question asked?" "How many people were asked?" "Are we sure we can believe that?" "Oh, yeah, I think that must be skewed." The thing I'd encourage you to remember is that the people skills part of insights work is as valuable as the work itself. I'd recommend three key tactics you can use to help ensure you get more, faster buy-in when sharing out results. 1. Bring them along on the journey. People are more likely to feel invested in the outcome when they feel they played a role in it. Once you have a draft research brief or questionnaire, for instance, share it with key members of your team who will be able to influence whether or not the research results are acted upon, whether that's Product, Design, Marketing, Analytics, or other teams. Giving them the opportunity to ask about the questions, phrasing, and objectives will help them feel more bought in. 2. Make it feel real to them. It's easier to divorce ourselves from how customers feel when the data is only presented in charts and stats. People often need to hear something for themselves for it to truly click. You can do this in a few ways: If you're conducting qualitative research (focus groups, calls, interviews), offer a chance for stakeholders to sit in on some of these as silent listeners. If that's not possible, play key clips from video interviews or show sample quotes from open ended questions in quantitative research. It's harder to ignore insights when we show the humanity behind them. 3. Give them sneak peeks. Look, we're all human, and you can use this knowledge to work for you when you share out research results. First, people love "social currency" -- having information that other people don't have. Second, people can react negatively when presented with new information that doesn't quite fit their world view. Third, we love modeling our behavior after others, and once somebody starts showing doubt in a meeting, it can pick up steam quickly. With all that in mind, you may want to identify a few key stakeholders who will be in the readout and offer to give them a sneak peek of the results before the formal shareout in your next one-on-one meeting with them. This can help prime them on the information they're going to see and feel like a valued partner.

If you have the ability to use both, it tends to help paint a richer picture. That said, a good rule of thumb is that behavioral data can help you understand what people are doing, and market research can help you understand why they're doing it. Let's imagine you lead product marketing for a subscription food delivery product (like a DoorDash's Dash Pass or Postmates Unlimited) and are seeing cancellations increase. That's a big behavioral data-based insight. Now, you might be able to use behavioral data to form some hypotheses as to what's going on. For instance, has there been a surge in demand recently (e.g., from COVID-19 increasing food delivery), and that's increased average wait times, making people unwilling to pay for a premium product? Has a certain cohort of customers been ordering less, meaning they might feel they're not saving enough in waived delivery fees? All of this is helpful, but it won't definitively get at the root of why customers are cancelling. Market research can then help you test those hypotheses you came up with to put more color behind it. Beyond these, there are other macro things to consider: Behavioral Data * How people are behaving with a current product or feature * What people are doing within your environment * See how people interact with messaging once in market Market Research * How people react to a new product concept * What people are doing in the broader category (not just your product) * See how people react to messaging and catch red flags or confusing things before launch

A quick thought experiment: Imagine you're launching a new product, and you only have the time and budget to run a quick quantitative round with your customers. Would you do it? In my experience, the product marketers I've worked with would jump at the opportunity. I say this to point out that just because data is limited, it doesn't necessarily mean it's bad. For instance, maybe limited means it was only one study, but you had strong sample size and statistically significant differences in answers. This should be seen as pretty reliable information -- quality trumps quantity. That said, I can see how some stakeholders might still be skeptical if they're coming from a place of a different hypothesis or perspective than you and the research are. There are a few things you can do to help: * Lean into people skills: Similar to one of the other answers on this thread, invest time in making the results feel more real and showing the humanity behind the insights (e.g., quotes from open ended questions, clips from interviews, etc.). You can also build consensus by having some one-one-one conversations prior to a broader shareout to help create momentum. * Contextualize with other internal data: There are certainly ways to show something is a trend or audience truth without needing to conduct more research. Check out another answer on this thread for some ways you can use things at your disposal within the company to show your results aren't just related to this particular study (e.g., trends/quotes from customer support tickets, NPS answers, ratings and reviews on your products, etc.) * Contextualize with other external data: You can also try to show something is a trend or audience truth by showing how it fits in the broader picture outside your company. What are competitors doing? What do market reports say? What are people talking about on social media related to your industry? Digging in here can also help you show that your findings aren't just limited to the study you have but can be extrapolated.

Starting a new project can certainly feel daunting, especially when you don't feel you have the right tools or budget. But there's good news: When you have to be scrappy, there's data all around you. Step 1: Wrap your head around the problem as best as you can Whether it's bringing a new product or feature to market or developing a new marketing campaign, I like to start with an unbiased hunt for existing information because it may trigger ideas you wouldn't have if you defined scope too early. To best wrap your head around a market, audience, or category, start with desktop research. There are often market reports available (e.g., from Mintel and NPD), and though the free versions will have limited information, they're often enough to start painting a picture. Do deep dives on social media to see what the conversation is like. Interview the key stakeholders on your team to unpack institutional knowledge from past launches. Dive into what competitors are doing and how they're marketing their products (e.g., MOAT and Facebook Ad Library should help give you a sense). Step 2: Synthesize everything you found Now take that disparate information and look for patterns. Is there something people on social wanted to see from the company that doesn't exist yet? Do the market reports speculate on growth in a certain area? Were you able to get a sense of the main competitive value props? If you see something come up with enough frequency, you can start to see it as a more reliable insight to build your GTM strategy off of. Step 3: Fine tune your objective to find gaps Once you feel you have a general landscape, fine tune your objective. Are you introducing a new feature and want to make sure it's something people actually want? Are you trying a new value prop? Are you trying to improve retention on an existing product? Let's say your goal is reducing churn on an existing product but you don't have a sense of why people are abandoning your product. Reach out to your customer support team for complaints or tickets and look for patterns there. Step 4: Create more data where you need it With all that in mind, you've gotten pretty far in your understanding of a market without spending a dime. Now ask yourself what you need to set your work up for success and how you might create that data. Identifying the risks of launching without certain information could give you an opportunity to ask management for increased budget. If not, ask your customer support and legal team if you can reach out to customers and offer to call them for feedback. Run focus groups and brainstorms internally (it's imperfect because it's biased, but it may help you gut check things). Then go forth and test!

In all of the places I've worked, I've had the good fortune of having talented Consumer Insights and User Reasearch partners. With that in mind, I tend to focus less on having a prescriptive take on specific methodology knowhow, but I do expect folks to have a strong ability to be thought partners with their research counterparts. In my experience, that often includes a few things: * Have an understanding of what data will be convincing to stakeholders across the business * Have an understanding of what type of research is best between qualitative and quantitative for the current stage of product development and business objectives * Partner with insights team on creating research briefs and providing feedback on questionnaires * Understanding which methodologies do or don't pass a "sniff test" (e.g., Does conjoint seem best here? Maybe MaxDiff is better?)