Are You Ready for Big Data Marketing? with Mike Moran

Hi, and welcome to today's Biznology digital marketing webinar. I'm Tim Peter, one of the authors of Biznology. I'm the president of Tim Peter and Associates, an ecommerce and digital marketing consultancy building brands and businesses via the social, local, mobile web. My company's award-winning work over the last 15 years for companies like Wyndham, Charles Schwab, Coldwell Banker, and others has generated billions of dollars in online revenue. Today, you'll be hearing from Mike Moran, who will present, Are You Ready for Big Data Marketing? But before we start, we do need to recognize our sponsors. BarnRaisers Group, a full-service digital and social media solutions company that builds brands using proven relationship principles and ROI. GaggleAmp, which empowers companies to amplify their social media content by leveraging employees, partners, and resellers. You can try Gaggleamp for free today. MENG, the Marketing Executives and Networking Group, New Jersey chapter. We aren't just about networking to land a great job. We are about staying current to grow in our careers. Tim Peter and Associates, digital marketing leadership to help you succeed in a rapidly changing world. And Unison, the unagency, your secret weapon in the noisy world of 21st century brand communications. Now, as we wait for more attendees to join, let me review the format of our webinar. Our Biznology webinars last just 30 minutes, so you can easily fit them into your busy schedule. We record each webinar and we'll email you that link later this week. During our speaker's presentation, you can use GoToWebinar to ask a question. That orange arrow that we're showing on screen opens and closes your controls. If you have a question, you simply type it into the box labeled Questions at any time during the event and press the Send button. I'll select a few questions at the end of our webinar and pose them to Mike. While we're waiting for the last few attendees to join, I'd like to remind you that the Biznology monthly newsletter and daily articles are available for free at biznology.com. So if you're not already a subscriber, we hope that you'll sign up now. Thanks again to all of you for spending 30 minutes with us. We know how valuable your time is, So let's introduce today's speaker. Mike Moran is the founder of Biznology, a well-known expert in all things digital marketing, and a senior strategist at Converseon, a leading social consultancy. Mike is the co-author of Search Engine Marketing, Inc., and sole author of Do It Wrong Quickly: How the Web Changes the Old Marketing Rules. Mike is a veteran of IBM, managing groups in IBM.com for 8 years. and retiring from IBM in 2008 as a distinguished engineer. So if you've ever struggled with how to get ready for big data, this is the webinar for you.

Mike, take it away. Thanks a lot, Tim. And thank you, everybody, for spending a half hour with us. So a lot of people are talking about big data. But I think in marketing, we don't always appreciate with how quickly things have changed. This is one of my favorite charts. It shows statistics that many of us have lived through, but it's kind of mind-boggling. I mean, if you look back in 1995 when 1,000 people really set the agenda for what was on television, and if something went wrong, a best practice was you could take days to respond. And just fast-forward 10 years to 2005, And you've got 100 times as many contributors, and people expect you to respond to news in hours. But what about today? A billion contributors, and you only have minutes to respond. So what's happening here is that there are all sorts of changes that are going on in terms of media that affect the way that we do marketing, but it isn't really media that is the cause. Of big data or any of these quickening of the pace of marketing that we see today. It's really something a lot more esoteric to the average marketer, something technologists call Moore's Law. Now, this is probably the most boring statement anyone will ever make in a marketing presentation. Transistor density doubles every 2 years and the price goes down by half. You'd say, well, that's really great, Mike, but what does that have to do with marketing? Well, it actually turns out it has a lot to do with marketing. If you can think about how much you pay for 8 gigabytes of memory today, it's around $12. If you could even have amassed 8 gigabytes of memory in 1970, it would have cost $3 billion. That's billion with a B. There's nothing that you can think of that has undergone such change and such cost curves in just many of our lifetimes. Here's the thing that's even more mind-boggling. That actually understates the change that we've lived through. Because if you think about the fact that inflation has changed how much a dollar is worth, it's actually $19 billion in today's dollars is what you would have had to spend in 1970. So $12 versus $19 billion. So well over a billion times difference. So that's kind of insane. No one can get their arms around that kind of change. In 5 years, computer technology that you have today costs about 20% of what it costs now. And so what is it that's happening here is that The data that's being amassed in marketing and the new media that is being uncovered is being driven by technology. So if we think about the changes that are happening, we can look backward as this did and say, hey, look, oh boy, we've been through a lot of change. But that's not really what the message is. The message is that the change that we're going to see over the next few years is going to happen at the same pace. So it's not a prediction that technology is going to continue to improve and drop in price. It's actually just an extrapolation of what we've already seen. And because of that, because of the fact that memory is dropping, processor speed is increasing, because of the fact that the prices of all these things are dropping, networking has increased. What it means is that there's more and more data And it's cheaper and cheaper to just hang on to it instead of throwing it away. That's what the underlying process is that's causing the big data transformation. So when we talk about big data, what are we thinking about? Well, some people like to talk about it in terms of 4 Vs. So the first one's volume. Now, you've probably figured this one out. That's the big part. Volume. Velocity is one of the other ones, which is that the data keeps changing. It's not that we're looking at data that's historical that goes back a year or 2 years or 3 years. We're looking at data from yesterday, data from an hour ago. Variety is another of the Vs. One of the things that's happening is that it used to be that you had analytic systems and CRM systems and other things that could hold on to the data, and they were designed, developed, and over a period of years you understood what data you were looking at. That's changing dramatically now. Think about just in the last 5 years. There was nobody 5 years ago who was looking at social media shares, or Twitter follows, or other kinds of data that just as the IT changes, as the technology changes, you are seeing more and more different kinds of data that becomes important. And some people will tell you that now Twitter and Facebook not quite as important as they were a year ago. You hear some people saying that for their business. And so you see forms of data show up, and you see them go away to be replaced by something else that we're trying to pay attention to. The last V is maybe the most important, veracity. It used to be that there was very little data around, but all of that data was curated. It came from a source that you understood. But now what you know is that you have to understand where the data came from, and you understand how it works, because otherwise you don't know what decisions to trust it for. That is an area that people are always going to have a role in. It's not going to be possible for computers to completely tell you what the veracity of their data is, because if someone ever claims that, you need to then understand what the veracity of the computer process is that told you that. And so one of the things we have to think about as we go through this is not how do we adjust to the big data that's available today, but what is it that we're going to do that lets us retain our skills, continue to be relevant, and move our companies forward in the face of these increasing changes? So let's look at some examples of big data to see things that are actually happening today. and to try and notice the contrast between how some of these leading companies are doing things and maybe how we're doing them. So let's think about Google. So what does Google know about us? Well, it knows who we are, what we're interested in, what we buy, and who our friends are. Now, how does it know these things? Well, it knows it because maybe we use Gmail. And so it's looking at all the content that we have in our emails. Maybe— And because we use Gmail, we're signed on all the time, so it sees everything we search for. Maybe we're in Google+, so it knows who else we're connected to. There are many companies out there that are using Google Analytics. So if you're on a website that uses Google Analytics, then Google might know what you're buying. All these things are things that Google understands. And the reason it knows all these things is because it gives away all these services for free. The reason it does that is because it knows that data it's collecting is more valuable than any price it could charge for the software function. So what does Amazon know? So it knows where we came from. It knows which ads we saw. It knows which products we already looked at. It knows what we bought already. Now, these are all things that we know are true. But when we think about our own companies, maybe we're not thinking that we do any of these things, or we don't do too many of them. And so the question we have for ourselves is, are we going to figure out how to start putting this data to work for us? It isn't just born on the web companies that need big data. It's everybody. You're going to be under pressure competitively if you're not figuring out how to collect the data and use the data to make better decisions. So what happens when you go to the mall? Some people think this is science fiction. It's actually out there now. The ad that is on that directory It's not just something that shows up. There are some places in the mall where it's not just a revolving ad that's showing up. It's one where there's facial recognition to see who is standing in front of it, and it's deciding which ads to show. So if this sounds kind of mildly creepy, well, it is. But this is how big data is being used. And so we have ethical decisions that we are going to make about whether we're going to do things that we think are good ways of using the data or bad ways of using the data. The truth is that it's up to us how we're going to use the data. But the truth is that the data is out there. And there are going to be companies using it. And we have to figure out what our stance is. Now what happens if you go into a store? One of the things you might notice is, hey, this store has Wi-Fi. Why do you think the store has Wi-Fi? Well, maybe it's because as soon as you connect to the Wi-Fi, it's store's Wi-Fi system, so it knows where you're located within the store by triangulating its routers. What do you think happens next? Gee, weird, this store just sent me a coupon. That's absolutely possible. There are stores that are rolling out systems like this today. What do you think the person does next after they get a coupon? Well, maybe he says, gee, I wonder if I can get it cheaper online. What does the store system find out? Gee, there's a customer checking Amazon. What happens next? Geez, can I help you with something? These are not science fiction systems. These systems are happening today. Now this is just the beginning. As I said, when you're looking at the amount of cost for technology that goes down every year and the capabilities go up every year, what you have to understand is that these things are things that are maybe interesting. They're stuff I can put in a presentation today. 5 years from now, especially 10 years from now, There won't be any store that isn't doing not only this, but many, many more things. And so the question is, what is it that we're going to be doing? How are we thinking about big data? How can we possibly be ready for something where we don't even know what the possibilities are? So let's look at a different example. So what do you think consumers are going to know when they buy a product? Right, if you've heard of RFID tags, you can put this beacon tag on a— it's a chip that you can put on, for example, the milk jug when it's milked. You can have a new RFID tag on the milk carton. These things can hold information about the milk. So when the dairy ships the carton to the store, the carton is now sitting on the shelf. You can use an app to actually tell you all the information that's on the chip. So you might know that the cow wasn't given antibiotics, it was milked 11 days ago, and the milk has never been Been higher in all of its transit than 42 degrees Fahrenheit. And those are all things that you might want to know. So when you look at kind of the back-to-local food movement where people are saying, well, I'd like to know what's in my food. Well, there are other ways to know what's in your food, and you can bet that this kind of technology is going to be applied. These RFID tags cost only a few cents nowadays, and think of what's going to happen in 5 years and in 10 years. They're going to cost a fraction of a cent. And so these kinds of things are going to be everywhere. Now, are there privacy implications here? Are there all sorts of regulatory things that may happen? All of these things are things that you can anticipate. But I don't know how any of those things are going to come out, and neither do you. What I do know is this technology is going to continue its relentless improvement. And so we need to think differently about how we're doing marketing, because we can't just think that we're going to do the same things we do today faster or better. We have to think that there's going to be a sea change coming every few years. Things are going to be very different than they were a few years ago. That can sound scary, but it also is a huge opportunity if you take the right attitude and you're thinking the right way about how to adjust the way you do marketing. IBM has a statistic that said that 98% of the data currently in existence was created in the last 2 years. That's kind of mind-boggling. We think about all the data created by email and photos and Twitter. But start thinking about this. Start thinking about the fact that security cameras are everywhere. There are sensors that could be on every item anywhere. And so the question is, in 5 years, will 98% of the world's data have been created in the last 2 months? We don't know, but that's possible. And so this is what we have to cope with, is how much data are we going to be able to analyze, and what are we going to do with it? Because the data is going to exist. It's going to be incredibly cheap to collect. And it's going to be very cheap to store and to analyze. And so the question is, what are we going to do with all that information? So big data is here, but the question is, is it changing your marketing? That's really what all of us need to ask ourselves. And my experience with clients is probably not. Big data really hasn't changed the marketing of most of the companies I'm working with. And most of us don't really have budgets to do analytics. The question is, how do you get bigger budgets? What is it that you're going to do that's going to make this change happen? Well, what you're going to do is you're going to start using the data that you have now to show improved return on investment. That's really what you're going to need to do. So most of us are concerned a bit about big data, but the truth is that right now we do a crappy job with little data. We don't really use the data we have now. We don't use our web analytics systems the way we need to. We don't use our customer relationship management management systems the way we need to. We typically don't know how good our marketing is. We typically don't know when our marketing has really made an impact and when it hasn't. And those things are possible now without investing any money in big data analytics. It's very simple stuff that you can do with inexpensive or even free technology. So that's really what we need to do. We need to make the change to say that we are going to be data-driven. It's really a process change. So if we think about how we want to adapt, we need to make a few big changes, and then we have to let automation respond to lots of little changes. So if you are still out there deciding what are the exact pictures that you are going to put in a new email marketing campaign, that's probably not what you are going to be doing in a couple years. And the sooner you stop, the better. You would be much better off to send out 10, 15 versions of the email all with different pictures to a small subset of the list, see which ones are getting the best response, and then let the machine decide to send out the rest of the list with the one that worked the best. I mean, this is more likely what you're going to be doing. So we need to make some big changes in the way we work to become much more process-oriented where we're identifying the data and making decisions based on the data. But that also means we have to change speed. Remember that one of the Vs was velocity? We can't take data, analyze it 6 weeks later, and then make a decision. We have to be able to analyze data and make decisions in real time. And one of the things that we need— we can do right now to be ready for that is to change our process. So right now, many, many companies are using old-style development and marketing techniques where they're kind of doing big bang projects. Where they do something called the waterfall method where they come up with all the requirements. Maybe you are writing a brief for the agency and asking them to do 50 YouTube videos for you. So they will come back and they will give you an estimate for that. And then you will lay out what all 50 should be. And then you will commission them to go do the work. And then they do it all and you load it all up and you see how it is going. But that is kind of the waterfall approach. It is like baking. You have to know what you want up front, precisely measure all the ingredients, and nothing is done until the end. So it is not food until it is all completed. So until you have all 50 videos and you put them up on the site, you don't have anything. Now agile marketing is more like making soup. So you can throw all the ingredients in. You can focus on tasting it and letting it simmer. And you can even change your mind a little bit and say, hey, it is not just vegetable soup. We have some cooked chicken in the fridge. Let's make a chicken vegetable. You can eat it at any time, but obviously if you wait longer, it will probably be better because you are tasting it. You are seasoning it. Now we could bake the way we make soup, but we don't do it because it is too hard and the soup wouldn't taste as good. But this is how we do most of our marketing. It is also how we do a lot of our IT development. It is how we do websites. We have these long drawn-out projects. Now what we need to do instead is to do small tests. So instead of writing a brief for the agency to ask for 50 YouTube videos, why not do 5? See how they work. Test to see which ones cause more people to view them, which ones cause more people to share them, which ones cause more people to click to your website, which of those people who came to your website converted. Try and understand which of the videos were working and which were not. And then use that to decide how you are going to do the next few videos and the next few. Constantly iterate. Constantly do your approach based on the data, based on a feedback loop. Those are changes we can all make right now. If you start to work out how your processes change for a feedback loop, then what you're going to find out is as you do that, you're going to be ready for the onslaught of data that's coming. And it is an onslaught. I mean, because the way you'll know things are better is by looking at all this kind of data. Many of us can collect some of this data now, but the truth is that we can't collect all of it, and we don't use all of it. So even if you only look at a little bit of it, it's really okay. It's really okay to just look at some of it. It's not a problem. What you really need to do though is to start being data-driven. Start looking at some of that data. And if this is too overwhelming for you, start by looking at something very simple. For any piece of marketing, look at whether someone saw your marketing. So on YouTube it would be how many views there were. Did they choose it? So for example, if you have like a call to action overlay, did they click on it to come to your website? And then you want to look at conversions. Did they actually buy something? So look at that traffic that came to your website and see if you actually got a conversion out of it. These are simple feedback loops that we can do for any kind of marketing. The other thing we need to be doing is testing. So I talked earlier about the test email, but testing is a very old idea. This is A/B testing. The left side is A, the right side is B. Now if you look at this, I don't know how many of you have ever seen the shopping cart at Amazon.com on the left. But they put it here for 1 week in 1997.

Why?

Because they didn't know where the cart should go. I mean, how would it be decided at your company? Would you flip a coin? Would you run it down the middle of the page because nobody could agree whether it should be on the left or the right? I mean, what Amazon did is they asked their customers' opinion rather than their employees' opinion. And there were 1% more sales on the right, so that's where it's been ever since. So this one decision was worth 1% of all the sales of Amazon since 1997. Now, you might say to yourself, hey, Amazon's a big company. They can afford to test all this stuff. But this was 1997. It isn't that they have a lot of money so they can afford to test. It's that they test everything. Therefore, they have a lot of money. And what you need to do also is to focus on the speed that you're going to run these tests. So how many tests can you run? How quickly can you check the results? I mean, if you only run one test a month, you're going to quickly fall behind a company that can run a test a week or a test a day. Right? If you can run a test a day, you have 30 more chances at the right answer than a company that runs only one test a month. So understanding how big data is going to change our processes is the really first step that we need to take. So by going after feedback loops, looking at being data-driven in our process, and understanding how agile marketing works, are all steps we can take right now that's going to help us get where we need to go as the data becomes bigger, faster, and in some cases overwhelming to our ability to process them manually. That's all I've got, Tim.

Well, thanks, Mike. I'm sure our attendees have a much stronger idea about how to take advantage of big data. Now, we do have some questions from our audience teed up for you. And I'd like to remind our audience that it's not too late to ask your own question by typing it into the questions box in your GoToWebinar controls. However, before we get to those questions, I'd like to remind you that the Biznology team provides jumpstart workshops for corporations who want to apply what they learn directly to their own business and have their team create a plan for executive approval right in the workshop. Mike, Rob Peterson, and I teach the search marketing course. We'll be there to guide you and help you persuade that executive to say yes. You can go to biznology.com/training for more information on all the Biznology offerings. Now, while we're about to start firing questions at Mike, we need to thank our sponsors once again. BarnRaisers, a full-service digital and social media solutions that builds brands using proven relationship principles and ROI. GaggleAmp empowers companies to amplify their social media content by leveraging employees, partners, and resellers. We encourage you to try GaggleAmp for free today. MENG, the Marketing Executives Networking Group, New Jersey chapter, who aren't just about networking to land a great job, They're about staying current to grow your careers. Tim Peter and Associates, digital marketing leadership to help you succeed in a rapidly changing world. And Unison, the unagency, your secret weapon in the noisy world of 21st century brand communications. Now, on to your questions. Our first question is, Mike, what can a smaller company do to use their data most effectively?

So I know when you hear big data, you think big company, but it really isn't true. I mean, you know, when we gave the example for Amazon, Amazon was a small company when they were doing that. So it doesn't really matter if you're a big company or a small company. The truth is that small companies become big companies by being data-driven. And so all of the process things we talked about are actually easier for small companies to do than big companies to do. Think about how hard it is to get all the people across a really large company to change the way that they're doing their job and to become more data-driven. Small company, you can just make that decision and say, hey, you know, we've only got 4 people working here. We're all going to sit down. We're all going to talk about what it means for us to do this. And so instead of making decisions based on our opinion or whether we think things look good or that we liked the way that this seemed to come out or whether we heard a customer who said they liked it and so therefore that's the only decision data point we have, let's really look at whether the marketing is changing how many people see it, How many people are sharing it out there with us? How many people are coming to our site? How many people are converting? How many of those conversions are actually buying offline from us? And so looking at that, kind of beefing up all of those systems and getting people to change the way they work is actually easier to do in a small company than in a large company. So I don't think there is any difference between the way they do it. It's just a question of how difficult it is.

That's great. That's great. As a follow-up to that, if you're just starting out, is there an area where you'd put your focus?

I think the focus you always need to put on is in sales. I think that a lot of times I talk to people who are just starting out in business and what they want to talk about is what they sell, what their product is, what their service is. They want to basically talk about this thing that they're going to do for people. They build up a lot of work in marketing, where they're explaining how all this stuff does. But here's the thing. If you're focused on your customer, there's 2 ways that that comes out. The first is that instead of focusing on what you sell, focus on what the benefit is to your customer. That's going to be a lot more attractive to them than them having to understand what you sell and figure out how that will help them. But the other way to focus on your customer is to really focus on these numbers that tell you whether you're selling things or not. That tell you whether you're making progress. It tells you whether you're getting people deeper into the traditional funnel where you're getting closer and closer to sales. That's really how to focus, right? So the idea that somehow measurements get you away from kind of your focus on the customer is completely wrong, and I've heard people say this. The truth is that measurement actually helps you have a maniacal focus on the customer because what you're doing is only pleasing the customer if they're buying from you. I mean, if there's something wrong with what you're doing, you're going to see it in sales, and you're going to see it in all those other numbers earlier in the process than a sale. And so if you're just starting out, focus on what the customer benefits are. Focus on the things that you're really doing for your customer, not necessarily the things that you do or the way that you do it. Focus on what it is that they're going to get out of it And have a relentless focus on looking at those numbers so that you know when you're on track or off track.

That's great, Mike. And it looks like we have time for just one last question. And since you were just talking about customer benefit, can you talk a bit about big data and customer privacy? I mean, some of the things you mentioned during the presentation could create some concern for some businesses. Can you just talk a bit about how, you know, you can protect customer privacy in a big data situation?

I think the most important way to do that is with transparency. I think that if you understand what data you have and how you're using it, and you disclose that to your customers— and I don't just mean in some privacy page that's buried on your website— but if you have open discussions with your big customers and say to them, this is what we're doing. This is the thing we're doing. Other companies in our industry are doing it. We wanted to talk to you about it to see how you feel about it. To me, there isn't an issue with people, with companies taking data and using it. There is an issue when people believe that they are not really getting the benefit from what companies are doing. So there are all sorts of times that we are happily giving up our privacy. I mean, we all give up our privacy to use Gmail for free, because getting free email is really important to us. Now some of us don't realize what privacy we are giving up, and so when that happens then we become upset. So what you want from a company point of view is to be so open about what's going on that people understand what you are doing. They understand that the reason you are giving them free email is because you want to look at this data. And you are not going to share data with other companies. You are just using it to personalize their experience so that you can provide more of the information they want. I think if Google was more open and transparent about what they're doing, I think they would not have any kind of backlash in that area. And I think that that's the lesson for all of us, is to really think about— if you look at companies that have been criticized for privacy, it's mostly because people didn't understand what they were doing. If on the other hand, you look at other companies where you are compensating the customer in some way for what it is that you're taking the data from, people willingly give up their privacy in situations because there's something that they want. And so I think we have to look at what the value proposition is in order to know that we're doing something good for the customer and not taking advantage of them.

That's great. And that sounds like a perfect time to transition out. That is all the time we have for today. Mike, thank you so much for these great ideas. And thanks especially to our audience for your participation and your questions. If any of you had any questions that you did not— that we did not have time to answer, please email them to eileen@mikemorangroup.com. That's eileen@mikemorangroup.com, and she'll be sure to get them to Mike for the answer. Later this week, we'll send you all a link to the recording of this webinar to listen to again and share with others. And we also invite you to mark your calendars for our next Biznology webinar. You've Got Great Content, Now What? with Andrew Skulkin, scheduled for 11:00 AM US Eastern Time on July 15th, 2014. We hope to see everybody back then. At this point, take care, everybody. Have a great rest of the day.

Bye-bye. Bye-bye.