Weigend_WorldMarketingForum_MEX_2013.06.27

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Big Data, Social Data, and Marketing

by Andreas Weigend, Social Data Lab

Keynote at World Marketing Forum, Mexico City, Mexico on June 27, 2013

Andreas:Thank you. Good morning ladies and gentlemen. In the next hour I will talk to you about data. I'll talk to you about big data, and big data has been in the media, in the mass media very recently. You learned that the NSA collects enormous amounts of information just across the border from here. The amount they know about us is more than most people ever imagined.

But what it means for you: big data is not the size that matters. What matters is what you do with it. So I want to start and tell you that for me, big data is a mindset. It is the mindset to turn a mess into meaning. Some people have said big data is the new oil.

Now let's think for a moment. What does that mean? Oil is a raw product, crude oil, and what you need to do is you need to refine that oil for it to be useful. So we'll be talking about data refineries today. The goal of course is to help people make better decisions.

I want to give you a couple examples of end-user decisions and how these data refineries affect them. The first example is when I went to the airport yesterday, I needed to figure out what route do I take. I live in San Francisco and there are two ways to get into the airport. In the past, we would've looked at a map.

Then 20 years ago when I was a post-doc at Xerox Park where we had complicated computers building complicated models with lots of assumptions trying to figure out how the traffic was going to be, we had very little data. Now all we do is observe how cars are moving and based on knowing how the traffic is moving, we can easily read out the solution on whether we should take this route or that route. We moved from a data-poor to data-rich world. That helped me as an individual to make the decision which route to take. Note, what does that mean for marketing?

In the old world you needed to make lots of assumptions. You needed to make assumptions about your target groups, and you segmented your customer base. No need anymore. You now know each consumer individually, so the dream that in the olden days you had somebody follow a shopper, maybe follow them home, that dream has become true. We have reached the market size of one, the segment size of one -- maybe not even of one, maybe one-tenth because it depends on the time of the day, whether it's 3 a.m. or 3 p.m. whether I'm interested in certain items. It depends on whether I'm in a hurry, on whether I'm with other people, on whether you sitting in this audience listening to me -- what message should we send you on your mobile phone.With all this, big data is the new oil.

Sometimes we also say social data is the new oil because the real change in data is not the transactions. 20 years ago people knew as much about transactions, what you're selling, as they do now. But the real change is social data, data people create and share.

I want to make a distinction between social data and social media. Social media, Facebook for instance, is also a platform where you create and share things. But social data is much deeper than that. When i talk about the data strategies of specific companies, I'll give you some examples which hopefully will make the difference quite clear.

What I want you to know is that last year in 2012, people generated more data than mankind has from its beginning, through 2010. We live in amazing times. We live in a time where the world got connected, irreversibly so.

Why now do people share so much data? This year is the first lesson for marketing. I call it the ABC of social data marketing. A stands for Attention. You want to get the attention of people and people share because they want the attention of their friends. B stands for belonging. In this fragmented world where there are so many ways of where we could be, we want to belong to something. We want to belong to a tribe, so
B -- think Facebook -- allows people to find a group to belong to.C stands for connection in social data marketing, for really forming connection but not the connection between the brand and the person; the connection between a person and another person. That is a very important distinction.

When I talk about connections, I'm not talking about you as a company connecting to the consumer, but I'm talking about you giving a reason for the consumer to connect to another consumer. Compare this to the old marketing. Some people say the ABC of the old marketing was Advertising, Branding (although some people say bullshitting), and what did C stand for? Confusion?

Here it is now people share because they want attention; people share because they want to belong; and people share because they want connection. Underlying all of this is a huge shift in communication. I've given you five dimensions how in this communication revolution we have changed.

1)Marketing primarily used to be one-way communication. It now is two-way communication, and mind you; two monologues do not make a dialog. They are genuine dialogs and in many cases the conversation is between consumers.

2)Communications used to be asynchronist, so you think about a message and you push that message out. Now communication is synchronist. People expect to get a response, right now, right there. It's no longer acceptable to say we will get back to you. No, they want now.

3)People used to plan. You as marketers gave them information so they could figure out at home what they wanted to get. Now they want to interact. My students at Stanford, when they come up to San Francisco, it's impossible to get them to actually figure out beforehand which restaurant they want to go to. They get in the cars, they get on their phones or they start texting and say, "Where are we going now? Where are you? Okay, that sounds good." They figure it out as they go along.

4)Most of us grew up in a society where we kept lists: check, check, check. Now people live in something called the "flow". There's such an abundance of things, of messages, of products, that it's like when you're fishing in a river with lots of fish. You don't carefully plan which fish to take. You just reach in, and in this flow, you grab something. You grab the next thing. If you like it, you stay with it. If you don't like it, the next thing comes along. That's a shift from a list mentality to a flow mentality.

5)The most interesting one, when I say social data I talk about data people create and share. That is our shift from keeping things private to making things public. Sometimes when I work with companies I suggest to them as a thought experiment that they should change from assuming you need permission to share something to assuming you need permission not to share something. Think about how that changes within the organization how work gets done. You need to go to your boss and ask your boss, "Sorry, I don't want to share this. Will you give me permission?" As opposed to now, when you say, "Here's data. I would like to share this data across the organization. Will you give me permission?"

Those are the big shifts which you are seeing in communication and applications to marketing. Let's go through a specific example. The example here is how we moved from Web 1.0 to Web 2.0 to Web 3.0

Web 1.0 was about digitize: B2B, business-to-business; and B2C, business-to-customer. Web 2.0 was about creating and sharing, the consumer-to-business, but also what I call C2W, the consumer to the world. Web 3.0 is about connecting, about C2C. There's another way of saying this; we moved from e-commerce, where the company is in the center, to me-commerce where the user is in the center, and says I'm interested in traveling to Mexico, make me some offers; to we-commerce where we focus on the connections between the consumers.

Sometimes it's worth wondering whether this is relevant just for a few people or for the world. Let's reflect. I think that Amazon with its way of letting people write reviews, of its way of creating transparency in products has changed the way a billion people think about purchase. I know Amazon isn't really here, but I think you all know Amazon; that you can look items up, and you can make better decisions, based on the information that Amazon has.

Google has changed the way a billion people think about information. Facebook has changed the way a billion people think about identity, about who they are, how they relate to others, how they relate to products, how they relate to brands. This is a huge shift. I call it a revolution, the social data revolution.

It doesn't only (indiscernible) commerce. It goes way beyond that. Think about it, education. Most education is still run the same way we ran it 100 years ago. But we see elements of revolution there, of how our data dramatically changes the way education is done; where without the barriers of big institutions, people anywhere if they are curious can have access to information. Or think about health and how nowadays by instrumenting people 24/7 we can know how they are. We can detect things much earlier. Think about cities. By instrumenting cities or any community we know what to build and what not to build; what's going on.

Think about insurance and many other areas. What we are facing now is a culture shift where we can't hide behind technology anymore, behind the absence of technology, but where we are empowered and that means we both have the responsibility and power to hopefully do the right things.I want you to think about where you want that to go.

In Buenos Aires, they call me Mr. Lomo because I love the beef the down there. Even when they picked me up at the airport here, they had a sign that said not my name, Andreas Weigend, but it said, "Mr. Lomo." So you might have heard of SoLoMo: social, local, mobile.

Social, local, mobile is where the data which we're talking about, that I want you to think about how to use them for marketing come from. Social we talked about, is about connection and about sharing. Local means that you know where the person is, the situation they're in. Mobile is of course if you think about it, what do you touch more often than your mobile phone? It's a proxy of the person and it is also allowing for very easy interactions, two-way interactions.

A specific example for a SoLoMo is Nike+. Here's how it works. You put an app on your phone and the app finds out through GPS where you are. You then socialize your run. You contribute this data and you can connect with other people. You belong to running clubs and Nike is the largest running club in the world. I have one house in San Francisco, and one house in Shanghai, China. Those two cities are sister cities and they're running against each other on Nike+.

Why am I using this as an example for marketing? On average, people come three times per week. No brand that doesn't allow people to express themselves has any hope of getting somebody to come several times a week. But here, it's about identity and about self-expression.

That was the first part where I talked about data in general. I'm now moving to the second part. I have a simple question. What is marketing? My friend Phil Kotler will come later today.You all know the 4 Ps of Product, Pricing, Placement, and Promotion. They will always be true. Some of you also know the fifth P of marketing: pornography.

But what is marketing? Marketing for me is to help people make better decisions. There are two words: better and decisions. Last quarter at Stanford I taught a class on neuromarketing where we put people into magnets called FMRI and we tried to understand what is going on in their brains, as we showed them one picture compared to another picture.

I contrasted the neuromarketing to looking at the "digital exhaust," looking at what people produced in the world, the social data they leave. It's absolutely fascinating how I think we will see a revolution in marketing by having those two data sources; how we can understand people. Understanding people, we are right now on a verge of a revolution because of the data people create.

What does it mean for companies? For companies it means they need to adopt a scientific approach. Better is an equation you write down and then we measure how well are we doing on that equation. I just saw Jeff Bezos, the CEO and founder of Amazon two days ago. I was a chief scientist at Amazon so we worked together ten years ago. The fact that Amazon had a chief scientist meant she took it seriously, that they wanted to understand what better means.For marketing it is helping people make better decisions.

I now want to give you the data strategies of three companies to help you understand how differently those three companies look at helping people make better decisions. What it means for you is in each case you should think how can you leverage what those companies have done for your companies and for your marketing. There is no hope that any of you will build an Amazon, Google or Facebook. But they're all partnering and have APIs, interfaces with their data. Figuring out how to make that useful is how I want you to think about it.

Let's talk about the data strategies and start with Amazon.com. Jeff Bezos always says the goal is to make it trivially easy for people to contribute, connect, and collaborate. Ultimately Amazon is a data refinery. It takes that raw material from people, hundreds of millions a day of use, and help people make better decisions. I have five examples for that.

Amazon has these recommendations that easily make 20-30% of its revenues. I want to take you through the five steps of the last 15 years. I want you to understand how the underlying data in each case have changed.

Step one, manual merchandizing. They are experts that figure out that the white shirt goes well with that striped suit. Maybe they have some access to product descriptions but it's mainly the people, the same people who used to create catalogs beforehand, or the same people who are good in shops at putting the right clothes on mannequins.

Step two, implicit data. Implicit data are for example clicks or searches. Let's take clicks, for example. If you click on an item, a whole host of stuff will come up. You use your intelligence to figure out what to click on next. You click on that item next. Whoops, Amazon takes into a big matrix that you clicked on this and that item within a few minutes. Over the years, Amazon built up this big database called "item-by-item filtering" or recommendations. Now if you click on that first item, we'll tell you people who clicked on that item also clicked on those items. Somewhere we can see this as collective intelligence, as harvesting the intelligence of a billion people in order to help you make better decisions.

Step three, allowing people to write to the website, to produce explicit data, such as reviews. What do you think; if someone says this item sucks, it blew up the moment I plugged it in, don't by this; is that something Amazon should show on the website or something to suppress?

It turns out in all of these things we've done experiments; it's increasing trust to actually show negative reviews. Naively you may say we only want to have good things being said about our products on the website. No, nobody trusts you if it only says it's the best. There are always tradeoffs in helping people make better decisions. You highlight those tradeoffs.

These are explicit data, a platform where people can write reviews, but also where people can write lists of items they might buy together. For instance for a camera. What UV filter do you think you might be working with it?

Fourth step, is to try to model the situation someone is in. That is not that old. That only is a few years old. That goes SoLoMo again, that you understand where is the person, what does their mobile say about it, are they ready to buy? Maybe I should give you an example for the data strategy here.

Amazon has an app for your smart phone where you can take a picture of an item, let's say that water bottle, and Amazon tells you how much it would cost at Amazon. Amazon also tells you what you might buy instead, what the pros and cons are. Think about this. Why does Amazon do that? Why does Amazon invest in writing an app with image recognition so you can learn more about a product?