Alex Chahin

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?)

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

There’s so much B2C product marketing -- just look around you! Think about the laptop or phone you’re reading this on right now. The bluetooth headphones you used to listen to music recently. The shoes on your feet. The credit cards in your pocket. The toothpaste you used to brush your teeth this morning. What convinced you to buy those things? Maybe it was the description of the audio quality for the headphones. Maybe it was the extra cavity-fighting and whitening power of the toothpaste. Throughout my career so far, I’ve already worked on B2C product marketing for credit cards and shampoo and medication and therapy and beyond. All of these products require a lot of the same kind of B2C product marketing work as tech products do. You need to deeply understand the user, translate their pain points into business action, position and message the product, and know where your customers spend time or look for information. Rideshare apps and deodorant alike need these marketing exercises to succeed in the market. Now, I will acknowledge that even though these marketing skills are being used in many companies, they’re not always strictly labeled “product marketing.” In the example of consumer packaged goods (like Unullever, Procter & Gamble, etc.), the title might be Brand Manager instead of PMM. But many of the skills overlap regardless. I’ll also acknowledge that tech product marketing can feel very B2B heavy, but there are many companies that blend tech and B2C marketing well. If you’re looking for ways to build that skill set but are finding tech B2C opportunities limiting, look for DTC ecommerce companies that could fit the bill -- they usually offer a physical good that needs strong PMM work powered by a compelling digital experience.

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.

While there’s no perfect answer here, there can be too much of a good thing. Behavioral economics research has shown that we tend to have a hard time deciding from a large array of options. This phenomenon is known as choice overload. It’s also often referred to as the paradox of choice, being paradoxical, of course, because you’d think that more options would be better. In fact, not only is it harder to make a decision when there’s too much choice available, if we do end up making a decision, it can lead us to feel less satisfied with it than we otherwise would. Some experiments have asked people to select something from a small versus large catalog of options, and the people who choose from the catalog with far more options report higher levels of dissatisfaction. The thinking here is that when there are more choices, we’re left wondering if we really made the right call, and that if we had kept thinking about it, surely we would have been able to find something even better. Though choice can feel overwhelming, if we structure it in the right way, we’re more likely to get customers to convert. One study that we’ll look at first comes from an electronics store, where customers are deciding on buying different cameras. In the first variant, there were just two cameras available: a basic one for $169.99 and an upgraded one for $239.99. In this case, what do you think the split of customer purchases is? It turns out to be 50/50, with no lean toward one or the other. Now let’s add an even more premium camera into the portfolio, a premium camera priced at $469.99. In this variant of the experiment, what do you think happened? Well, only 22% bought the basic, 57% bought the upgraded camera, and 21% bought the new premium camera. That’s a 33% revenue increase, just by adding a third option! But it’s not just cameras and electronics. These researchers found that it applies across all sorts of different categories. On average, they found a 17.5% increase in the share of the middle option purchased in categories ranging from mouthwash to televisions to apartments. This pattern is known as the compromise effect. The compromise effect says that products whose attributes are not at the extremes tend to get chosen the most. Basically, people pick the middle option! This pattern especially holds when people don’t have a lot of information about the category they’re buying from, are inexperienced in the category, or don’t have particularly strong preferences about the good. Let’s look at another twist on crafting choice with prices. I want you to imagine that you’re on the website for the publication The Economist. You’re deciding between a couple different options for subscriptions. In the first scenario, you’re deciding between an online only subscription for $59 a year or a print and online subscription for $125 annually. In this case, what do you think most people end up buying? Well, in this configuration, 68% buy the online only version, and 32% end up with the combo online and print version. And this makes total sense. If I’m getting access to the same exact articles, why not go for the cheaper and more convenient option between these two? But what happens if we add another package into the mix? There’s still the online only version for $59 a year, a print only version for $125, and a combo online and print version for…huh…also $125! Wait…the last two are exactly the same price. Why would anyone ever want that middle option? Here’s why this works. It changes the focus of the decision. You’re telling me that I could get way more value — access to all of the articles online — and I don’t even have to pay more for it than I would for print alone? That combo option now looks like a steal! And the experiments bore this out. With this new configuration, only 16% of people purchased the online only option. Remember: that was 68% before! 0% bought the print only option, and a whopping 84% bought the combo option. This is a dramatic reversal of the previous consumer behavior and a great example of the decoy effect. The decoy effect happens when we add an option into the mix that we don’t actually intend to sell much of but changes the focus of the decision to be on the target product and the inferior one. The inferior one that we don’t really intend to sell is called a decoy. The psychology works here because it narrows the complexity of the choice you have to make. We call the complexity of a given decision we have to ask the consumer to make the cognitive load. Rather than think about a lot of attributes like the price, the different kinds of access you get, the value you’ll get from each, and so on, it zeros in on just one attribute and dramatically reduces the cognitive load. Now it’s a no-brainer decision. Of course I want more access for the same amount of money!

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.

Picture this: You’re working on the merchandising team at Williams-Sonoma, a home goods store that sells things like upscale kitchen appliances, utensils, and cooking supplies. Your boss has just asked you to figure out how to sell more breadmakers, which are appliances that mix the ingredients, form the dough, and bake it all in one. Many people enjoy fresh-baked bread, but not many people have the time or patience to make it, so the convenience of it makes it interesting for the brand to push in store. Okay, you might first think about running a promotion. Adding a discount would surely drive more breadmaker sales. But it also could be a pretty expensive way to get those sales. And the impact probably doesn’t last forever. Revenue probably reverts right back down to where it was once you stop running the promotion. You might consider setting up a demonstration stand in the store so people can see this unfamiliar appliance in action, hear about it from in-store experts, and — most importantly — smell and taste that fresh-baked bread for themselves. Yum. This tactic could be pretty effective…but it’s probably a logistical headache. You’d have to figure out if you can free up real estate in the store for this new station, and is it worth the revenue you might lose from whatever was in that space before? It would take some work to figure out how staffing the station would work, how often bread needs to be made, and how to clean everything up at the end of the day. Here’s what Williams-Sonoma actually did instead. They put a $429 breadmaker on the shelf. I know, that’s a lot of dough just to make some dough! Did they sell very many of the $429 appliances? Not at all. But breadmakers started flying off the shelf nonetheless because that $429 one was next to a $279 model. When they added the expensive model next to the $279, sales of the cheaper one doubled. In consumer psychology, this is what we call anchoring. Anchoring is the tendency to use the first piece of information we process to inform our decision. That piece of information acts as a reference point in the equation. Generally speaking, this means that when we encounter a higher number first, the next numbers we see feel smaller than they would if we saw them in isolation. Conversely, when we encounter lower numbers first, the next numbers we see feel even higher than they otherwise would have felt. In this breadmaker example, if we walk into the store and see just the $279 price tag on an appliance we know virtually nothing about, we feel like we have no idea how to evaluate whether or not that’s a good deal. Is that price just right for the value? Are they overcharging me? Is this a rare find? We get stuck on this and it impacts our chance of converting. But when that exact same breadmaker is placed next to the expensive one, now we no longer feel unsure. That $429 price acts as an anchor that puts the other one in perspective, and $279 feels like a fairer deal. You’ve probably seen this tactic at play in your own life. A lot of us shop on Amazon, and you’ve probably seen products often have a strikethrough price that’s more expensive above it. That acts as an anchor to make the actual price more appealing. The placement of that number is intentional. It’s not below or to the right where you’d see it second. It’s on top, where you would see it before and get anchored. Now, of course, this strikethrough price has to be truthful, but it doesn’t always have to be a promotion. It could just be something like the manufacturer’s suggested retail price, or MSRP, to provide that reference point. As a marketer, keep in mind how you can use surrounding numbers to make what you really want customers to buy more appealing.