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Digital Marketing & Artificial Intelligence - Zenith 2016


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Digital Marketing & Artificial Intelligence - Zenith 2016

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Cindy Krum speaks about Digital Marketing, Artificial Intelligence, Learning Algorithms and API's at Zenith in Duluth in 2016. Topics in this presentation include, but are not limited to:

-Exponential Growth of Technology
-Learning AI/Bots
-Application Programming Interface
-Automating Marketing

Cindy Krum speaks about Digital Marketing, Artificial Intelligence, Learning Algorithms and API's at Zenith in Duluth in 2016. Topics in this presentation include, but are not limited to:

-Exponential Growth of Technology
-Learning AI/Bots
-Application Programming Interface
-Automating Marketing


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Digital Marketing & Artificial Intelligence - Zenith 2016

  1. 1. Digital Marketing & The Age of Artificial Intelligence, Learning Algorithms & API’s Cindy Krum, CEO, MobileMoxie
  2. 2. Gordon Moore Founder, Intel Corporation “The power of computing devices doubles about every 18 months.”
  3. 3. Gordon Moore Founder, Intel Corporation “Frankly, I didn’t expect to be so precise.”
  4. 4. Exponential growth makes digital marketing unique…
  5. 5. Dynamic Ecosystem
  6. 6. Open Technology
  7. 7. “If GM had kept up with technology like the computer industry has, we would all be driving $25 cars that got 100,000 miles per gallon.” -Anonymous
  8. 8. The exponential growth in technology is becoming more and more apparent…
  9. 9. Cross Device – Multi Device – Inter Device
  10. 10. https://maps.google.com/locationhistory/b/0
  11. 11. Internet of Things
  12. 12. Casting
  13. 13. Cindy Krum | MobileMoxie@Suzzicks
  14. 14. New concepts & technologies make the data more powerful…
  15. 15. Big Data Volume – Velocity - Variety
  16. 16. Machine Learning
  17. 17. Artificial Intelligence (AI)
  18. 18. Supervised Learning AI (Bots Trained By Data & Sometimes Humans)
  19. 19. What do We Mean when we say ‘Bots’?
  20. 20. Invisible Robots…
  21. 21. Digital Marketing Singularity
  22. 22. Time to Get Serious
  23. 23. Time to Get Serious
  24. 24. Smarter Platforms could eliminate the need for digital marketers. Platforms already collect lots of the data that we want & they already leverage Machine Learning & AI
  25. 25. Machine Learning Platforms
  26. 26. Learning Algorithms & AI are Game Changers
  27. 27. Learning Algorithms & AI are Game Changers
  28. 28. Learning Algorithms & AI are Game Changers
  29. 29. “Lasers”
  30. 30. TacoBell TacoBot
  31. 31. Google RankBrain
  32. 32. Google NOW
  33. 33. To stay relevant, marketers need to integrate with new platforms & new data sources…
  34. 34. Data Companies You Never Heard of…. Acxiom, Experian, Infogroup, RapLeaf, LocalEase, Marketo, CoreLogic…more • EX: Acxiom – 23,000+ servers in Little Rock collecting, collating & analyzing more than 1,500 data points on the average American including things like:  Right handed or Left Handed  What kind of car you drive  Your psychological outlook on life  Contents of all public records  Where your kids go to school  What size clothing you wear  Where you live  Where you shop  What pets you have  Social media profiles  Where you travel  How much you make
  35. 35. API = Application Programming Interface
  36. 36. API’s Plug into Bigger Platforms • Google Places • Yelp • DocuSign • LinkedIn • Salesforce • Amazon • eBay • Bing/Google Maps • Foursquare/Swarm Insights • Facebook Insights • AdWords • Instagram
  37. 37. • Fitbit • Zillow • BBC • Open Weather • PostCodes • NFL Arrests • US Census Bureau • World Health Org • City-Specific API’s from City Gov’s • ESPN • Reddit • OpenSecrets (Campaign Contributions) x API’s Plug into Bigger Platforms
  38. 38. APIs Connect Disparate Data in the Cloud
  39. 39. AI & Machine Learning for Competitive Research & Strategy
  40. 40. Foursquare API = Passive & Active Check-ins
  41. 41. Automating Lookalike, Cohort & Custom Audiences
  42. 42. Marketing will be more about modeling data from a variety of sources (including but not limited to the platforms) then anticipating & maximizing relationships within the different data sets …
  43. 43. Dashboard Creation & Interpretation
  44. 44. Process Constant Streams of Data
  45. 45. Visualize & Interpret Multi-Dimensional Data
  46. 46. And success will be about using Machine Learning & AI to write rules to respond to the constant streams of data…
  47. 47. Base 2 vs. Base 4
  48. 48. And about using the data in an authentic way…
  49. 49. People Already Feel Isolated
  50. 50. Respond Accordingly
  51. 51. “The level of employee required to run Google’s machine is going down. Creative is going to be the driver and difference maker. Your job is about maintaining the machine.” -Marty Weintraub
  52. 52. Importance of Connection & Humor
  53. 53. Importance of Story
  54. 54. It’s All About Proper Customization, Automation & Timing
  55. 55. Thanks!!!
  56. 56. Thanks!!! @suzzicks @mobilemoxie facebook.com/mobilemoxie www.mobilemoxie.com 2 Months Free: ZENITH

Notas do Editor

  • Thanks for the awesome introduction Marty! Good morning everybody! Like he said, my name is Cindy Krum, and today I am going to be talking about the future of digital marketing, and how our jobs are going to change to meet interact with a growing amount of data, and how we can prevent being replaced by automated marketing systems and bots. And if this is a new concept for you, just do a quick search and you will find that ‘marketing singularity’ is something that lots of people are talking about – not just me!

  • So lets start at the beginning – does anyone know who this is?
    -not my grandfather, or an uncle….
  • Here is an older picture of him – this is Gordon Moore, the founder of Intel, and also the person credited with determining ‘Moor’s Law’ which basically says that “the power of computing devices doubles about every 18 months.”

    He was actually talking about transistors at the time, but the law is now, more broadly applied to many different kinds of technology.

    He was a super smart guy, and I bet most of you thought that the guy who invented ‘Moore’s Law’ would be dead, but he is still very much alive, though not running Intel as actively any more.

    I also like him because he had a sense of humor and humility. In addition to this quote, he also said….
  • “Frankly, I didn’t expect to be so precise.” He was just trying to plan for market demand and purchasing, at the time, but the law ended up to be broadly applicable. And what is interesting for us as digital marketers, is that the constant doubling described in Moore's Law sets us up for ‘exponential growth.’
  • And Exponential Growth looks like this. The key to understanding Exponential Growth, is understanding that it looks linear until the vary end. Doubling is powerful stuff, but hard to detect when numbers are small. By the time you realize you are in the super-rapid, parabolic growth phase of a technology, it is already too late to plan for it, or respond to it – it is all just moving and changing too fast. So you have to anticipate as early as possible, to have time to respond.
  • And FYI – Exponential growth does not happen like this in all industries. It is unique to technology.

    -90% of the World’s stored data was created in the past 2 years

    -This also is expected to double every 2 years
  • Part of what allows technology to grow at such an extreme rate, is that it is a dynamic ecosystem. The technologies that we rely on don’t stand on their own, but rely on a network of other technologies to work in consort. Innovation means that technologies are constantly being developed, and those new technologies create new opportunities for existing technologies so new gaps in the supply and demand are constantly being created and filled.

  • Beyond that, lots of the innovation is now done in somewhat open environments. Lots of the software is Open Source and platforms are even incentivizing developers to build in their open source environments.

    There are also open data sets and API’s that are available for cheap or free, to help companies supplement their data with external information.

    Even when software and technology is not open source, when it is made publicly available, it can often be reverse engineered and replicated, to some degree.

    This makes innovation rampant, because once knowledge or systems are gained or created, they are widely available, and can be built on by multiple groups at a time, rather than requiring unique, siloed efforts that are slow and less dynamic.

  • This is one of my favorite quotes to illustrate that point – “If GM had kept up with technology like the computer industry has, we would all be driving $25 cars that got 100,000 miles per gallon.”

    And obviously this isn’t the case – the auto industry is much slower at growth, but technology is being incorporated into cars, and that technology is advancing independent of the auto industry

    – More and more driverless cars are being designed & tested, and those rely on the same technology that we will be talking about today.

  • So those things are what get us to here in the curve.
  • And this rapid growth rate is becoming more and more apparent every day.
  • As a society 2.5 QUINTILLION bites of data every day. We are constantly connected – usually to a minimum of one device, but often carrying 3 or more connected devices at one time.

    ComScore actually thinks that desktop internet access may have peaked in 2015, and “ Over 1 trillion minutes were spent online with mobile devices in March, almost double the time spent on desktops, the company said.” http://snip.ly/6lt4y#http://www.wsj.com/articles/has-desktop-internet-use-peaked-1460714718
  • So we are all constantly connected, for working, socializing and shopping, and these behaviors don’t just happen on one device at a time. They are cross device, as users move seamless from one connected device to another.

    According to a recent study on mCommerce, over 50 percent of time spent on eCommerce sites occurs on a mobile device, and the average shopper will use between two to three devices before they buy

  • Mobile devices are giving more and more offline data about your behavior too.
  • And the growing number of connected and interconnected devices is what gets us to the Internet of Things. We are living in a world where our stuff can communicate with our other stuff, using the internet, and auto-adjust or respond, based on the preferences that we pre-program.

  • Our phones and tablets can communicate with our TV’s, projection systems and and computer monitors by casting.
  • Or doorbells can send alerts and stream video from your front door, so that you know who is ringing the doorbell.

  • And that is what gets us to a home that looks like this – the ‘things’ communicate with apps on the phone, over the internet, to respond in an automatic or active way to our every wish. And that is great, for us as marketers, because it represents lots of data about our consumers, but there is risk, because it could easily border on being creepy

    - Like here…..
  • It is a guy sitting at his desk talking to a colleague, and he says “I think my Nest Smoke alarm is going off. Google Adwords just pitched me a fire extinguisher and an offer for temporary housing.”
  • And even more things are passively communicating signals with little RFID chips that are embedded. These are in your car tires, ID badges, key fobs, pets, that are micro-chipped, products you get at the store, and more.

    They cost only about $0.03 to make and they can be activated to send a short signal by the technology around them.
  • And this is an exaggeration of a very likely reality. The more and more things that are connected to the internet, and to each other, we as marketers have more and more channels from which we may be able to receive data, but also, more and more in which we my be able to market. Think about the connected watches, TV’s and car products. These things are just starting to get onto the radar of most digital marketers, but they represent a significant and growing opportunity.
  • So that gets us to the stuff that is more foreign and future-y. Big data, Machine Learning and AI, because they are the models through which we will leverage all of this new data. So lets dig in here, because I know these words are not new to you, but their meanings could be a bit vague.
  • Big Data has been described as being ‘a lot like sex in high school. Not everyone who is talking about it is actually doing it.”

    Big Data is roughly defined as “computations using data sets So Large that Distributed/Cloud Computing Must be Used, and often made up of constant streams of lots of different kinds of data.

    This is most easily remembered with the 3 V’s of Big Data – Volume, Velocity and Variety. Generally ,if you are not doing the computations in the cloud, by processing lots of different streams of data, you are not really dealing with Big Data.

    Another characteristic of Big Data is that Micro-Decisions must be automated, to respond automatically to changes in the streams of data.

    The best example might be a self-driving car. All the different sensors are sending information to the internet to be processed all the time. The internet must respond quickly to update the behavior of a car, and prevent a crash.

    It is a lot of data, from sensors all over the car, looking at other cars, as well as weather and things like that. One self-driving car generates loads of data
    1 gigabyte of data per second
    2 petabytes of data per year

    Incidentally, this is actually too much data for the cloud to handle, in a wide-spread way. If Everyone Drove Driverless Cars we would Melt the Cloud, but the cost and availability of cloud storage is also advancing exponentially, so by the time it is relevant, it is expected to not be a problem.

  • Machine learning is the process in which server software process existing data, and relationships and use that information to process and evaluate new sets of data and relationships. The more information that the system has to evaluate, the better it is at anticipating a precise reality.

    The easies way to understand this is with thigs like Pandora and Sotify. The more they know about what you like, the more they hone in on the algorithms that they have to predict more about what songs you will and won’t like.

    Digital marketing is becoming more and more about machine learning algorithms that are used to show ads to the right audience at the right time. In fact, lots of digital marketing is becoming a mix between old-school traditional marketing – like buying air time for tv and radio commercials, and buying banners – but all with a Machine Learning twist.


  • Artificial Intelligence or AI takes Machine Learning a step further. It is defined as “a system that perceives its environment and takes actions that maximize its chances of success.” The main difference between machine learning and AI is that AI can determine which data it needs to achieve the most learning, and it can seek it out actively.

    Pavel Alpeyev Technology reporter from Bloomberg Tokyo says: “Investments in AI startups tackling everything from education to retail and agriculture reached $310 million in 2015, almost a seven-fold increase in five years, according to research firm CB Insights.” http://www.bloomberg.com/news/articles/2016-04-13/artificial-intelligence-s-next-phase-sooner-and-more-accessible-for-everyonex

    From a platform perspective, is no surprise that Google wants deep links in all these apps – it turns them into API’s that feed it engagement data – Google can crawl and index all the app behavior – especially if it is deep linked, but even if it is not - at least on Android – so based on private and public indexing on your phone, Google has all the data about what you like. They think they can learn more about consumers from their phones. Facebook feels they can learn more about you from your Friends & Messaging.
  • What is funny, is that some of the things that are supposed to be run by bots, are still strongly influenced by or at least reviewed by humans – or more accurately, “Humans, pretending to be bots that are pretending to be humans.”

    GoButler, X.ai, Clara, Facebook M, Operator, Mezi ---Personal assistant bots that are trained & supervised by humans.

    Even RankBrain is checked & verified by humans


  • If their technology becomes smart enough, how will we as digital marketers keep our jobs? Will we be irrelevant? Will we be replaced by robots?

    Bots are great because they can work 24/7 without breaks, sleep, health insurance, birthday cakes or foosball tables. They also don’t make mistakes when they are having a bad day, and don’t disturb other colleagues. They just work all the time. They are a really great long term investment when compared to lots of employees – especially low level employees that require training from other employees.

  • And the reality of course, is that we are not talking about these kinds of bots – we are talking about invisible bots, that live on servers, and just make decisions all day.
  • So that gets us back to the cover slide

    Marketing Singularity is near! Facebook, Google and others are beginning to employ machine learning and AI to make marketing on their platforms more intelligent, and automated.


  • So it’s time to get serious.
  • Like….Really, really serious.

  • But it is important to understand that everyone is competing for the same data. Some of it will be easy to get, and other will be harder. In general, platforms will only trade data for data – they will give you some data, in return for you sharing your data with them. And in order to sweeten the pot, all of the platforms are trying to make it easy and beneficial for other companies to integrate, and make them the platform of choice.
  • Almost all the apps you love the most use machine learning, either to show content, or ads or both. And if you think about it – People expect their media on – demand, and to some degree, automatically personalized. Lots of digital marketing is becoming a mix between old-school traditional marketing – like buying air time for tv and radio commercials, and buying banners – but all with a Machine Learning twist.

  • And at this year’s F8 conference, these are the plans they announced
  • – specifically calling out AI as the final stage in their current 10 year roadmap.
  • – specifically calling out AI as the final stage in their current 10 year roadmap.
  • Which may look a bit like it was aided in design by an ambitious 10y/o but whatever. :P

  • Facebook has already lined up 30 businesses for bots including CNN news bots, weather forecast bots, Uber, Lyft, Burger King, Bank of America etc.
    15million Facebook business pages, over a billion messages to businesses per year without bots. Facebook Messenger/WhatsApp has 900million users – Not monetized yet, but it will be.


  • http://orig07.deviantart.net/30f4/f/2010/018/e/7/tacobot_i_by_houseoftryst.jpg
  • By now we have all probably heard of Rank Brain, which is Google’s search learning algorithm. It allows Google to hone in on what individuals and groups are looking for, by using filtering queries, results and user behaviors through an internal learning algorithm. But the truth is, this is nothing all that new. Google has been using a searcher’s personal and aggregated response to personalize results and inform the larger algorithm for many years.

  • And in some ways, it is a race between the platforms, that allow bot and machine learning integration, to reach users, and companies, at the right time, to drive the right amount of adoption and integration to hit critical mass for adoption.

    Facebook is trying to make Messenger one of its primary AI platforms, but Google Now is reaching cross device data through the OS itself, which is likely more powerful. It has access to all of your Google account information, including your app downloads, search history, Gmail, Maps, Photos, Music, Plus and more, but it can also crawl and scrape push notifications and app interactions on your phone –including the ones from Facebook.

  • And lots of us are even storing work documents and personal files in Google’s cloud, and Google has full rights to crawl this stuff too – though there is pressure to limit that, we can’t know exactly what and how that information might be used.
  • Google and Apple are very interested in voice commands, and even these are getting more powerful as the world between apps and web merge. Both voice commands and silent triggers can now launch an app from a browser. This is only made possible by code in the app and the browser. This provides a voice activated connection between on and offline. Voice recognition technology is also made possible because of machine learning, but in this use-case, voice commands and visual responses like images and video could easily be leveraged even more for AI technologies later.
  • Ok – so Facebook and Google are working really hard to get lots of data, and they are building platforms that will encourage companies to share data. But, as marketers, we need to know about all the different places where we can get data – and it is not always just from platforms.
  • The thing about the internet, is that it is kind of like a microscope and a telescope at the same time. It can give you generalized information about lots of people, or specific information about lots of people, depending on what you want, need or can process.
  • Lots of information that you might not expect is already out there.

    Other companies like this include Experian, Infogroup, RapLeaf, LocalEase, Marketo, CoreLogic. They are working with social and ad platforms, as well as working directly with businesses to share and model data for marketing.

  • Most large digital companies use API’s to make the various technical parts of their business work together – weather it is inventory management, or ordering or pulling products into a website. And these make the company powerful, and the data more seamless and useful, but similar technology can also be used to pull in the external data sources, and that data can be used for machine learning.
  • API’s can be visible – like enabling plugins like Gify to work in Facebook Messenger. But they can also be invisible, just sharing information.

    Or if someone is not sharing information, you can always scrape the public website to get the information you need.

    Pavel Alpeyev Technology reporter from Bloomberg Tokyo says: “Artificial intelligence was once the exclusive playground of Google Inc., Facebook Inc. and other tech leaders. Now, any deep-learning startup can access cloud-based platforms, with Microsoft Corp., Nvidia Corp. and Amazon.com Inc. selling AI like a utility.” http://www.bloomberg.com/news/articles/2016-04-13/artificial-intelligence-s-next-phase-sooner-and-more-accessible-for-everyone
  • API’s can be visible – like enabling plugins like Gify to work in Facebook Messenger. But they can also be invisible, just sharing information.

    Or if someone is not sharing information, you can always scrape the public website to get the information you need.

    Pavel Alpeyev Technology reporter from Bloomberg Tokyo says: “Artificial intelligence was once the exclusive playground of Google Inc., Facebook Inc. and other tech leaders. Now, any deep-learning startup can access cloud-based platforms, with Microsoft Corp., Nvidia Corp. and Amazon.com Inc. selling AI like a utility.” http://www.bloomberg.com/news/articles/2016-04-13/artificial-intelligence-s-next-phase-sooner-and-more-accessible-for-everyone
  • Lots of these companies, and the companies that are the most successfully integrated work with API’s,. APIs allow different, disparate data sets to connect and combine with each other.

    -Sometimes it is just about matching up your internal data sets – and creating API’s between your internal siloed data – including things that are not just digital, like TV commercials or offline information.

  • And you can get pretty creative with using API’s – like pulling in a Linkedin API to know when the geographic distribution of an industry begins to change, or knowing when the availability of candidates or competition in a specific industry changes.


  • Or like Foursquare using their own data to predict the most recent losses in foot traffic - just using active and passive check in data from their own API.

  • We can even automate customer segmenting in advertising platforms using their own data API’s and thinkgs like lookalike segments, and cohort analysis to automatically generate new campaigns or adjust bids. These can be based on things as specific as purchase history or as broad as websites that the users visited.

  • With all the extra data from platforms and external sources, success will be about predicting and modeling. The constant streams of data from the API’s should be used to drive internal automation of business and marketing decisions will make you more successful.
  • We are used to dashboards like this, that show a snapshot or a time over time comparison of data.

  • But more and more, we are going to be expected to be like weather reporters, not only talking about the current conditions, but understanding and interpreting the ongoing trends of large quantities of data and predicting the future based on the different factors.

  • And then help people visualize not only the existing data, but also the predictions associated with that data.

    Internet connections by OS around the world
  • Migratory paths of birds

  • Carbon creation and Global Warming

  • Deaths from gun violence

  • Tweets from the first 140 twitter employees

  • Do you have a wearables strategy? Will you need one? Do you even know the fundamental components of what it would take to make an app interact with wearables?

    Most companies don’t but those that do will have an open field of opportunity. People want this. Look at all the people wearing fitbits in the room. Those are very simple technology.

    It is data from technology like this that is going to be highly actionable – in a very personalized way, but also useful in aggregate. We can easily automate advertising to show different ads to people who have not hit their goals in steps, or calories burned – no problem.
  • But what about this? This is a Samsung contact lenses that interacts with the Google Explore program.

    If the contact lense indicates that we are struggling more and more with diabetes, can Google use that to segment us for different ads? Can it use that info to segment our family members for different ads too.

    The thing about Exponential Growth is that by the time you realize it is happening, it is too late to really do anything or plan for it.

  • We talked about mapping the consumer gnome, but what if it got more personal that that? Marketers may even be able to tap into huge data sets to make even more advanced audience segmentation decisions based on personal DNA reports and family profiles. This data may not be able to tell us what kind of tired a person needs to buy. But maybe this is exactly that data set we want if we are selling life insurance, health insurance, fertility treatments, weight management products, and things like that.
  • And FYI - some futurists believe that widespread mapping of the gnome is something that will happen in the next 10 years, and will allow us to beat cancer and other killers, by hacking the responses to diseases – Since computers operate in Binary code – base 2. DNA operates in quaternary code – base 4. An interesting concept to think about, but hopefully not at all related to marketing for a long time!!!


  • One of the unfortunate realities of our time is that more and more people are depressed and feel isolated, possibly because of the wide-spread heavy reliance on digital technology. Bots and AI, especially when used for marketing, are at a severe risk of making this worse, and creating a negative emotional association, rather than a positive one.
  • But it is not Just About Collecting It & Visualizing It – it is About Responding to it. -Automatically increase advertising budgets, bids or thresholds
    -Dynamically adding new demographic & psychographic groups or segments to advertising targeting
    -Or seamlessly letting events trigger changes the purchase of products or raw materials
    -Or simply triggering email responses

    And some of us, if we are sophisticated, may be doing this with our own data sets, but the new challenge will be incorporating & fine-tuning other data sets.

    And this automation may require new skill sets or deeper integration with the development teams.
  • So to do this – we have to understand the machine –or machines. The data that we are using comes from all over and it is what drives the machines – so we have to understand the machines.
  • But we are still just people. This is why companies like Apple, Google and even TacoBell are working hard to give their bots personalities and senses of humor. They often know jokes, or have Easter Eggs that make using them more fun and interesting, and make it feel more genuine.
  • Even if we are working with bots, we need to remember that our customers are looking to connect with companies that have a story that resonates with them. The internet has democratized so many goods and services, that everything is reasonably easy to get, so there is a new level of evaluation in the customer journey, and this is becoming more and more important.

    We are all still human and programmed for story. Automation may be used to reinforce a corporate mission or story.

    Where does it come from?
    UGC/Story Telling becomes less tactic & more central strategy
  • As customers give over more and more of their data, they expect it to be used to improve their interactions and make their life easier and more pleasurable – they are not just giving you data so that you can market to them better and take more of their money.

    In a digital world, lack of customization and personalization = death Newspapers
    Automated Personalization in Communication – Esp Email & Text
    Combination of Product & Service – Like Amazon Prime
    Customized Remarketing Emails
    GeoSpecific Remarketing – “You came in our XYZ Store”