So we discuss how to use customer interviews, which is a relatively cheap way of getting information about your idea. We're going to talk today about surveys, which is another way of getting information about whether your product, service, or venture idea is any good before you actually spend a lot of money launching it. Surveys are interesting. They should always be done after interviews. Right? And that's because you learn more about what questions to ask. And they could be very powerful. I do a lot of survey work. I've surveyed hundreds of thousands of people and I still get things wrong all the time. So doing this right is hard, doing this badly is very easy. And you could think about surveys as surveying two purposes. [COUGH] One better than the other. So one method is, as you could see still from the famous, four out of five dentists agree commercial that they should chew a particular brand of gum. That one way of using surveys is to convince people things are a good idea. Four out of the five people we surveyed said this is a great product. 90% of people said they would use it. This is something that you use in your pitch to venture capitalists or to other customers. And that's sort of marketing. I'm much more interested in how you use surveys to learn about your own assumptions, analyze your own business and come with more successful results, and I'm going to give you a few tips for thinking about surveys, it's again a complicated topic but this will hopefully be a useful introduction for entrepreneurs. So the first thing you do is find a sample, and I think this is the only math I've successfully shown in my lecture so far and you don't have to worry about too much. That is how you calculate sample size. It is not something you need to worry about hugely. But what you need to know is that the more people that you survey the better your estimate of the true value of a particular number is, right? So you've seen this in presidential polls before that the more people you survey, the shorter the confidence interval, the more certain you are of a number. So if you survey a 100 random people. You have a confidence interval of plus or minus 10%. So that means if 50% of people like your product, that true number can be somewhere between 50% and 60%. If you survey 267 people, that interval becomes plus or minus 6%. So it goes from 44 to 56% as a possible range, if 50% is the number you get in your survey. If you survey 384 people, that becomes plus or minus 5%, and so on, right? So the lesson here, I think more than anything else, is survey 100 people or you're not really getting numbers that are anywhere close to something that's useful for you. Where do you get the people you're surveying? There's really three sets of options. The first of what we call convenience samples. So this is the most common kind of survey and often the least useful. In a convenience sample you're surveying the people you already know around you, friends through Facebook or schools, or Twitter polls and trying to get answers back. And the problem with this of course is rarely is your group of friends the best representative customers. The best counter example I know is a successful start up that came to my class called common bond that has raised over $100 million and what they do is they refinance student loans, especially student loans for people from top business schools. So they survey their friends at work and that was actually a useful convenience sample because that was their end customers, but in most cases your customers are not the same things as your friends. So convenience samples need to be used with caution. A second option you could do is purchase a sample. This means buying basically answers from a more random group of people that are more representative. There are two pretty good ways to do this. One of these is Amazon's Mechanical Turk. Mechanical Turk is a service that has tens of thousands of people working for it. They work from home, and for small amounts of money, they do short tasks online on Amazon. And so Mechanical Turk is used for a lot of different things. If you ever are doing something that seems almost impossible for computer to do. It probably is because it's not being done by computer, it's being done by Mechanical Turk. So if you take a picture of plate of food and you're getting a calorie count back. In most cases that picture is being sent to Mechanical Turk, somebody somewhere in the United States or around the world is figuring out the calorie count and sending it back to you. So for very complex tasks that need to be done by humans, you can do Mechanical Turk to do these tasks very easily. For Mechanical Turk you can expect to pay for very, very short surveys $0.25 to $0.75 per survey. For more complicate surveys you'll spend more. Think about this as sort of minimum wage at a premium. But you could pay for a bunch of people to take surveys very easily on Mechanical Turk and get a much more representative sample. Google also does a survey tool that's relatively inexpensive, and you get some free credits for it with your Gmail account, so it could be very useful to do. And they generally let you ask one question, but you can pick a very narrow group of people to answer this. So Google knows a lot about you. So if you want a particular income level a particular region, Google can ask people just from that area. So again you can get a great sample that way. If you're looking to spend a considerable amount of money, but you need answers about a very narrow area like CTOS, of Indian technology companies or people who are interested in cyber security, who are buyers. There are a number of organizations that will let you hire a professional panels and you could just search for how to get a professional panel. And you pay each person involve $50 or $100 plus some fee to get the panel of people you want together to survey. So those are all approaches to getting purchase samples. You could also use ads to get samples. So you could put ads out on LinkedIn, Facebook, or use Google AdWords all are relatively inexpensive to actually advertise a survey, you could advertise it with a prize and get people to come that way. So these are ways of getting samples, convenience samples are easy and free. But not necessarily representative. Purchase samples are much better representative samples but they might be too broad for you if you're interested in their area. And targeted ads can be very good but it can be hard to get enough interest. You have to spend money on the ad side. So brief tips on how to do surveying. The first of these is about question types. So demographic questions, questions about gender, questions about ethnicity or about educational background. You might want to actually put those early on in the survey especially if they're not particularly offensive questions. And you might want to do that because those demographic questions often set people at ease and they let you compare later on the profile of people who took your survey. To the wider profile of people in the United States. So that could be a very useful set of tools. You generally don't want to ask yes or no questions. You want to ask questions that have multiple kinds of answers. You use yes/no questions only to qualify people. So if you're interested in contacting only people who bought sweaters, and we'll talk about a subscription sweater service as an example throughout the questions you'll see here. We might want to ask people up front, have you ever bought a sweater before? And if they say no, we drop them out of the survey. If they say yes, then they take the full survey. So only use yes/no questions to sort people into groups. And be very careful with open-ended questions. So open-ended questions are questions that give you an essay box to answer. So it might says something like, what do you like about sweaters question mark and they give your a box. There's a few reasons why open-ended questions are risky. The first is that, when I write surveys I tend to think of surveys as having a cost but the cost is a mandatory. The cost is the attention of the people who were taking the survey. So every question you ask cost a little bit of their attention and nothing costs more than open-ended questions. People hate writing essays and so if you give them a box to write a sentence or two, they're going to drop out of your survey at a much higher rate. Additionally, most of the data you get from open-end questions is pretty low quality. So you'll look through an open-end question you thought was very clever and you'll read something like Sesame Cat, I like cheese. And you're like, I don't know what this means. Why'd someone write this in? So it's very hard to interpret these sets of questions. So open-ended should only be used very carefully. At the same time you can use those open-ended questions for sensitive areas that you may not cover. For example, you're asking about a gender. You would probably want to include a male, female and an other box, so people can give you their own gender idenity. So even though these box aren't being used very much, they could be very powerful sets of tools. To let people feel like they have choices and they are being put in a particular boxes. So I want to go through a couple kinds of questions and tell you good and bad examples of them. So I want you take a look at this question, again we're asking about a sweater's cause we're interested in this example of starting a subscription sweater business. Not my best idea but probably not my worst either. So the question is, given the state of the economy, where do you buy your sweaters? Answer a. Amazon b. Mass merchandisers c. Clothing stores d. Other online sites. So I'll give you a second to think about what's wrong with this question. There's a bunch of issues here. So one of them is we're asking a leading beginning, given the state of the economy. So that's putting people to think about the state of the economy. That's not really relevant to a question. We are assuming they buy sweaters. They're not given the option of not buying sweaters. We're not giving the option of thinking about other choices that don't fit into these buckets. People may not know what a mass merchandiser is. And we have a bunch of different questions that ask me about different kinds of stores with known examples. How could we make this question better? How about this one? Wherever you bought the most sweaters from in the past 12 months. Now we've downloaded the question. We're asking the same thing but we're asking it over a particular time span and our answer is now Amazon, other online sites. So they're right next to each other as comparisons. Physical mass merchandisers such as Costco, Walmart, etc. Physical clothing stores such as GAP, Lands End, etc will give you options. I haven't bought sweaters in the last 12 months. And while letting them choose other options that might work for them. So is the better question because we've now specified exactly what we're asking. We've improved it in terms of the kinds of questions we're asking. So this is an improved version, maybe not perfect, but better. Here's another kind of question I see very often, which is a rating scale. It is hard to find the right sweater. Rate how much you comparison shop before buying a sweater, 1, 2 3, 4, 5, 6, 7, 8, 9, 10. So what's wrong about this? So again we're being leading here, right. We're saying it's hard to find the right sweater. That makes people feel bad that if they don't comparison shop, so thinking about this. It's hard to know what the question's asking. What does it mean to say rate how much you comparison shop. What is how much you comparison shop? And a one to ten scale's a very large range. And that could be a problem because people go through end point of advoidance. They don't like to answer one or ten. They'll end up reserving one for a sweater experience that involves them being stabbed and ten for a sweater experience that involves literal angels singing their praises, right. So they'll tend to not answer one or ten. Also it's not clear what one and ten mean here. What does it mean to be a one? What does it mean to be ten? What's better what's best? So a better way to answer this would be using what's called the Likert, or so what it's called Likert scale. How often do you comparison shop before buying a sweater? 1.never, 2.rarely, 3.sometimes,4.most of the time,5.all the time? That agreement disagree scale with that range is the Likert scale, it is a much better way in getting the answer. And notice also in the middle point here, it's very important for a scale type of middle point a three in this case sometimes which is a neutral point [COUGH] and you're trying to measure differences from that neutral point which can be really powerful way to asking these sets of questions. So those are two better ways of asking common questions. The other thing that I get asked a lot is to think about pricing, so surveys for pricing. So, take a look at this example. How much would you pay for a great new sweater delivered to you every month? $5, $10, $50, or $200. You'll notice there's a few problems. First of all, there's the fact that I haven't shown you what the sweater is. There's no real example it's not quite clear what sweater you're getting for this. But then think about the pricing area, so you may think that part of the problem is that the prices are too spread out and that's right. The pricing is kind of odd here but the bigger issue is that you can't just ask people pricing and expect them to give you an honest answer. The problem is that people will again, no one wants to seem like a sucker in these deals, so no one is going to pick $200. Even if they'd spend $200 on a sweater. And they may, and, as soon as, again, you've shown them pricing, it becomes a negotiation. So they're trying out to figure out what price they should pay. They want to answer correctly. They're not showing you what they really want to spend. And it's been shown that this kind of question is not useful for figuring out pricing at all. In fact most easy methods to figure out pricing don't really work. I'll give you one technique you can use without a lot of math, which is called monadic pricing. So monadic pricing we're avoiding setting expectations for price, which is one of the big problems we're giving people. Why don't you just tell people it could be $5? It's very hard for them to be willing to spend 200. So monadic pricing we ask the question, and in this case it's slightly improved, how much would be willing to subscribe to a service for $20 a month. It sends you a sweater every month like the one below and we can show pictures. So the question's clear. But what we're also doing, is that $20 a month, which is the pricing. Everybody who takes this survey is randomly shown a different price. Right, so there might be $20 a month, $50 a month, $100 a month. So people aren't anchored because they're only being shown one price each. In this case, that $20 price. And so, this is a way that you could start understanding how demand changes when half the people that take your survey see $20, half see $50. How much of a loss in terms of interest you find as you move from $20 to $50. That's monadic pricing. That works really well. What you can't do are other approach. You can't do a pricing ladder that does not work very well. A pricing ladder would be one where you ask people will they spend 200 for the sweater. If they say yes, you say, would you pay 500. If they say no, you say it would be 250. Again, you have the anchoring problem that does not work well. The Vaughn/Westerdorff approach, which is one of my favorite names ever, asks four pricing questions. How much would this have to cost for you to think it's a bargain? At what price would this start to seem like it's too good a deal, and something's wrong with it? And what price would this seem high? At what price would you never buy? And that has problems that makes it very hard to implement. And it's very hard to ask an open-ended question, too, where you would just say, how much would you pay? Again, that ends up being problematic. There is a better approach, and if you take the working class of marketing analytics, you'll learn about conjoint analysis. It's fairly complicated. There's no easy way to do it. But conjoined is the best way to get pricing information. It's just more complicated than most people engage in. Even people in my class who know conjoined tend to use it only for rare circumstances. So I'd ask you to think about conjoined if you pricing is a big deal and features are a big deal and you want a survey about that. That's the technique that you'd want to use, but it's not something we'll go into hugely here. So how do you you know you're asking good questions aside from using the techniques we just talked about? The most important thing to do is a pre-test. You need to give people the survey and, ideally, you're going to sit down with a couple people who will take the survey and ask them to take it while you're there. And ask them to narrate as they go. So this will give you a sense of, as their filling out questions, do they think a question is good or bad, do they understand the question, how long is it taking. And then after awhile you'll send that survey to a small sample of people and get responses from them. What you're looking for in results, to tell you whether it's good or bad, is interesting. So the first thing you're looking for is variance. What I mean by variance is you want some people to be answering ten to a question, and some people answering one. The reason you want to do that is if everyone is telling you they like your product, you're not getting any insight into why some people might like it and some people wouldn't. So you're probably asking the questions too leading. You want questions with multiple kinds of answers. You want to understand if people are comfortable with answering the sets of questions. Are they answering things in the right kind of way? Are questions making them nervous? Are they frustrated because they want to answer a different option than what's available? And you want to look for issues of annoyance and bias. Like I told you before one thing I learned about was asking about gender questions. So I did these fairly large surveys and the first surveys I did I asked gender and I asked whether people were male or female. And I got some frustrated replies back saying that I don't identify using these genders, I don't want to take your survey. And that's fine, it's well within those people's rights to do that. By adding in a box that said other and letting people fill that in, my response rate actually went up. And not only did my response rate went up, even though very few people actually filled out that box, most people felt more comfortable that I was asking the right sort of questions without bias. You also want to figure out timing. How long does it taking people to do the survey? You can give people an accurate estimate. When you're done, you need to think about your response rates. So when you analyze the results of your survey, if you have less than 20 people responding, you need to think about biases in response. If over 20%, as a general rule of thumb, this is not for every field, but in entrepreneurship, I wouldn't worry about it too much. And then you can use those census questions you asked about gender and about geographic area and income and education to compare the sample that took your survey to the general population to figure out whether or not your samples are representative and you could just use census data which is freely available to see whether or not there's a representation there. The last thing I urge you to do again, there's other classes that we teach at Wharton on this that you can find online. Go beyond just reporting numbers, just reporting that 50% of people said this, 20% of people said that. And actually think about running a rush analysis to figure out what the causes are, of the various kinds of answers that you're getting. So surveys are a really powerful tool, but doing one badly doesn't give you much information. It just annoys a lot of people that you're sending it to. So spend some time thinking about surveying. They're a very powerful tool and in the toolkits of entrepreneurs. They're not always intuitive doing it badly, takes very little time, doing it well takes a little bit more. But you can get really great results that are very powerful predictors of customer behavior in the future.