Big data and AI in digital marketing
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Big data and AI will disrupt every aspect of digital marketing. Deep learning uses neural networks like the human brain to make predictions. AI can be used for tasks like predicting customer buying propensity, generating art, composing music, and personalizing ads and messages. By 2020, over 85% of customer interactions may be managed without humans as AI becomes more integrated into companies and used across the entire customer journey.
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Big data and AI in digital marketing
- 1. Big data and AI will disrupt every aspect of digital marketing Niko Nelissen, founder Blendr.io v1.1, october 2017
- 3. The rise of big data & AI
- 4. Explosion of big data technologies
- 9. What is deep learning, how does it work ?
- 11. Let’s take the example of retargeting
- 12. RTB (real-time bidding) to retarget
- 13. Predict buying propensity RTB Pay for display ad ? Profile Looked at pricing Used car configurator
- 14. Predict buying propensity without AI Profile Looked at pricing Used car configurator SCORE +3SCORE +5 TOTALSCORE 8
- 15. What if we would use deep learning ? Introducing “neural networks”, connected neurons that operate similar to the human brain
- 16. Predict buying propensity with deep learning Profile Looked at pricing Looked at car configurator Looked at specifications Looked at financing Ready to buy Not ready
- 17. Predict buying propensity with deep learning 1 1 0 0 .2 .5 .1 .3 .7 Looked at pricing Looked at car configurator Looked at specifications Looked at financing Ready to buy Not ready
- 18. Training the AI model Thousands of profiles Looked at pricing Looked at car configurator Looked at specifications Looked at financing Ready to buy Not ready For each profile, we ”label” it, telling the model if that person converted or not.
- 20. Neural networks “learn” similar to humans: by viewing many examples and discovering commonalities Deep learning
- 21. OK, but can AI be creative ?
- 23. Art generated by AI (neural network)
- 24. Pikazo app
- 26. AI in digital marketing
- 27. AI is at the very beginning of the hype cycle
- 28. AI companies in digital marketing
- 29. Personalized real-time ads (IBM Watson)
- 30. Face recognition in store and DOOH
- 31. Personalize messages based on personality using NLP
- 32. Personalized call-to-actions in text ads
- 35. The future of online interactions 30% of web browsing sessions will be done without a screen. 50% of all searches will be voice. >85 % of customer interactions will be managed without a human. By 2020…
- 36. Google Deepmind can even do lip reading
- 37. Google AutoML: AI that builds new smarter AI
- 38. AI in your company ?
- 39. Apply AI in every step of your customer journey
- 40. Build your own chatbot using api.ai (Google)
- 41. Build your own chatbot using Motion.ai (Hubspot)
- 42. Data integration – blend your data
Hinweis der Redaktion
- BIG DATA AND ARTIFICIAL INTELLIGENCE ARE DISRUPTING EVERY ASPECT OF DIGITAL MARKETING Join us on a trip to the future, as we explore how big data and AI (artificial intelligence) are already changing digital marketing, and how they will disrupt every aspect of digital marketing even more in the future. We'll have a look at machine learning, deep learning, chatbots powered by NLP (natural language processing) and other mindblowing technologies. We'll also see how companies such as Google, Facebook and others are using AI to fundamentally change how we profile individuals and how we target them. Finally, we'll explore how AI can help your company today. Selection of sources: http://customerexperiencematrix.blogspot.be/2016/02/landscape-of-machine-intelligence.html https://www.linkedin.com/pulse/15-applications-artificial-intelligence-marketing-robert-allen https://www.conversica.com/ https://www.amplero.com/blog/posts/11-startups-that-prove-deep-learning-and-ai-are-changing-marketing-forever/ http://contentmarketinginstitute.com/2017/08/marketers-use-artificial-intelligence/ https://www.slideshare.net/eMarketerInc/emarketer-webinar-artificial-intelligencethe-future-is-already-here http://www.cmo.com/features/articles/2017/8/24/15-mindblowing-stats-about-artificial-intelligence-dmexco.html#gs.s=KX0iM https://marketinginsidergroup.com/content-marketing/machine-learning-ai-marketing-trends-watch-2018/
- Blendr.io is a data integration platform, allowing you to easily extract data from your sales & marketing tools, and connect your tools for orchestration. Blend your marketing & sales data, with a simple "point & click" interface. Blend data from your CRM, marketing automation, mailing tools, social media and any sales & marketing tool in your organization. Unleash your data by sharing "data blends" for analytics, data enrichment, unified access, data democratization, and much more. Your development team can use the Unified REST API and SDK's for easy access to all your datasources and social media channels. SaaS companies use Blendr.io to supercharge their data integration efforts, or to outsource their data integration services to a trusted partner. The Blendr.io email extension (MailMine.io) bridges the gap between sales & marketing in terms of lead discovery. MailMine.io allows your sales reps to discover leads in their email mailbox and push them to your CRM or marketing automation through Blendr.io. https://www.blendr.io
- Why exactly is AI being adopted so quickly nowadays ? Let’s have a look at the technologies driving this evolution.
- Overview of big data technologies: big data solutions are now readily available in the cloud. Big data is a main component of AI, since large amounts of data are typically needed to “train” an AI model, as we’ll see in the next section.
- Various AI algorithms are now readily available in the cloud.
- This is an extrapolation, made in 2000: the cost of computing power: by 2020, the cost of computing power equal to a human brain will be less than $1000. AI typically requires large amounts of computing power, so this evolution makes AI (and especially deep learning) affordable.
- NLP (natural language processing) is a part of AI which allows computers to understand human language.
- NLP is evolving at a rather slow pace, compared to other aspects of AI, because of the complexity involved. 2015. Source: http://sentic.net/jumping-nlp-curves.pdf Syntactics curve: based on words, e.g. recognizing subject, verb etc. Semantics curve: adding knowledge, e.g. chair = furniture, but also “things fall downward” Pragmatics curve: look at “mini stories” in text, recognize “intent”: e.g. in sentiment analysis: “small seat” is negative, except when looking for a child seat in a rental car
- Why do we need AI and big data ? What is it, and how does it work ? Let’s look at a concrete example from the AdTech world: RTB (retargeting).
- If you look up neural networks in Google, you always get the “cat or dog” example. But we are not in the cats & dogs business… See let’s look at another example.
- Let’s use retargeting as an example. A website autodealer.com wants to retarget visitors that left its own website, by displaying ads on carreviews.com. The advertiser uses for example Google ads, which uses the same cookie on both websites to recognize the website visitor on other websites. The visitor sees the ad on carreviews.com, clicks it and revisits the autodealer.com website.
- Let’s assume that the advertiser is using real-time bidding (RTB) as a mechanism to buy ads on other websites, for the purpose of retargeting. RTB works as follows: an automated process takes place in a short time frame of under 100 milliseconds. When a user visits a website, the advertiser is alerted and can bid for displaying an ad. Source: https://www.semrush.com/blog/real-time-bidding-leveraging-display-advertisement-performance-with-real-time-data/
- As an advertiser, we have to decide if we want to pay for the ad. That will depend on the interests (profile) of the website visitors.
- Typically a scoring algorithm is used to calculate the level of interest of a website visitor. We increase a score, for webpages that have been visited. This is a method that does not uses AI, instead we are using “heuristics”. We have to decide which scores to apply for each webpage, these are called the “features” of a website visitor. The outcome of the heuristics is a total score. When the score is above a certain treshold, we consider the website visitor to be interested and we’ll pay for an ad.
- Now let’s have a look at what RTB would look like of we use AI instead of heuristics. More specifically, we’ll use deep learning, which means that we will use a neural network. Neural networks are computer algorithms, that operate in a manner similar to the human brain. A neural network consists of interconnected neurons that process data.
- Here’s an example of a neural network, showing neurons and connections between neurons. In reality, a neural network in a computer has thousands or millions of neurons. We use the same “features” as before, as input for the neural network. The features are the webpages visited by a person. The output of the neural network can have 2 values in this example: The user is interested in our product (or “ready to buy”) The user is not interested (“not ready to buy”) In case the outcome is “ready to buy”, we will pay for an ad in the RTB cycle. The power of the neural network is that it will decide by itself which features to use for the prediction. This is very different from the previous method with heuristics, where we had to decide what score to apply to each webpage.
- Let’s have a closer look at how the neural network works. For each input we use a 0 or 1, one means the user has visited said webpage. The neurons in the middle will add the values of their connected neurons using a weight. This process continues from left to right until we reach the “output” neurons. A high value for the output neuron means that there is a high probability that this output is the correct one. In this example there is a 70% chance the user is “ready to buy”, so we should buy the RTB ad.
- The challange in a neural network is to come up with the correct “weight” factors for all the connections. This is done by so called “training” of the neural network. Training means that we feed the neural network with data from many website visitors, including their “features” (which webpages they have visited”) and we also indicate if they eventually purchased a product from us yes or no (the so called “labels”). The neural network processes all this data from right to left, and adjusts the weights of each neuron until the neural network makes almost correct calculations for each person in the training data. Once this is done, the weights are fixed, and the neural network is able to predict the outcome for new website visitors.
- Google TensorFlow Playground: a neural network in action as it is being trained.
- As you can see, AI learns from examples, similar to how humans learn.
- OK great, so the technology is there (big data, AI, NLP). But marketing is not just about processes, it’s also about creativity. Can AI be creative ?
- EyeEm developed machine learning to curate photos, the AI is able to decide if a picture is beautiful or not. Source: https://magenta.as/ai-and-the-future-of-creative-jobs-36f172ca15c3
- AI can also create art. Here’s an hallucinatory image created with the Dreamscope app at Google. Google engineers discovered this technique by reversing image recognition neural networks trained on dogs. Source: http://techonomy.com/2016/05/the-coming-age-of-creative-ai-from-roboadvisors-to-roboartists/
- The Pikazo app is a free and fun app in the app stores, that creates art by combining 2 pictures using AI. Source: http://www.pikazoapp.com
- Jukedeck created AI that composes music. Source: https://thenextweb.com/artificial-intelligence/2017/02/11/how-creative-ai-can-change-the-future-of-music-for-everyone/
- Conclusion: AI is intelligent, capable of solving difficult problems, and it can be creative as well, so we have all ingredients needed in digital marketing. Let’s see what various companies are doing with AI in digital marketing.
- The Gartner hype cycle for Digital Marketing and Advertising positions general purpose machine learning (AI) at the beginning of the cycle.
- Here’s an overview of companies applying AI in different aspects of digital marketing.
- IBM Watson is the general AI solution from IBM. IBM Watson became famous when it played Jeopardy on TV and won, in 2011. Watson is also applied in digital marketing, e.g. for generating personalized real-time ads. Sources: https://www.linkedin.com/pulse/15-applications-artificial-intelligence-marketing-robert-allen/ http://contentmarketinginstitute.com/2017/08/marketers-use-artificial-intelligence/
- AI is used in “Digital out of home” (DOOH) advertising to do face recognition using a camera in the bill board. This allows the personalization of an ad, e.g. based on the gender and age of a person in front of the bill board.
- Crystal uses NLP to assist sales people in writing better emails, tailored to the profile of the recipient. Of course similar technology can be used in e.g. text ads. Source: https://www.crystalknows.com/
- Persado uses NLP to personalize text ads.
- Blueshift automates the overall customer journey, powered by AI.
- What is Salesforce doing ? Salesforce Cloud “Einstein” is the general AI solution from Salesforce. Einstein is capable of doing predictive scoring, building predictive audiences and optimizing the send-time of mailings.
- Here are some numbers on the future of online interactions. Digital marketing will have to follow, meaning we’ll have to start adopting chat bots and spoken voice as an advertising channel.
- So it’s no surprise that Google is focussing so heavily AI-improved computer voice, and even lip reading. Don’t forget Google is an advertising company, so everything they build will have its effect on digital marketing !
- Google is even doing more, they are building AI that builds new and better AI, with Google AutoML. The image shows a neural network architecture, to the left built by a human, to the right built by AI. Let’s imagine what this will bring to e.g. marketing automation: AI will be able to build fully automatically generated retargeting & nurturing campaigns !
- How can you adopt AI in your own company ?
- You can find AI-driven products in the market, for each step of your customer journey. Source: https://www.linkedin.com/pulse/15-applications-artificial-intelligence-marketing-robert-allen
- One interesting aspec is to build your own chat bot. API.ai is a platform from Google that allows you to easily build a chat bot for your own company or service.
- Motion.ai is another platform to build chatbots, part of Hubspot.
- A data integration platform such as Blendr.io is a crucial element when you start experimenting with AI. Blendr.io allows you to extract for example customer data from your sales & marketing tools, such as your CRM, mailing tools and marketing automation. Next, you can easily analyse the data, combine the data and send it to a machine learning platform in the cloud.
- With the current pace of evolution, we will soon reach so called “singularity”. Singularity means that several technologies come together, resulting in new solutions for which we are currently unable to predict what they will look like, and what they will be able to accomplish. Singularity will bring unprecedented new opportunities in digital marketing, make sure you’re prepared!
- If you have questions or remarks, you can reach Niko Nelissen on Twitter. Send a direct message to @nikonelissen