3 New Ways Artificial Intelligence Is Powering The Future Of Marketing

Artificial intelligence is top of mind for many in the marketing and communications world. Many Marcom departments already use AI to analyze consumer behavior and try to predict future needs. Many brands use algorithms to recommend personalized content, show personalized ads, as well as power customer service chatbots. But what if AI can help brands take their brand voice to the next level?

Brands usually spend thousands, if not millions of dollars, fine-tuning their brand voice, which describes a company’s personality. Now, AI in digital marketing has a whole new face – literally, as digital avatars and synthetic beings enter the digital marketing world. Here are three examples of how brands are using artificial intelligence in interesting new ways to power campaigns beyond what’s been done before.


Cost Saving Power of AI

Before AI, companies would have to shoot the same commercial multiple times but with a different brand name or item, but now AI lets brands reach all markets they operate in with reduced time and money. Synthesia, an artificial intelligence, and video synthesis company, showed that’s no longer the case. 

In a recent project posted on their website, Synthesia partnered with global marketing and ad agency Craftww to save their client, JustEast, a lot of money. The agency had worked with JustEat on a widely successful Snoop Dogg advertisement. JustEat’s Australian subsidiary is called MenuLog, and the client wanted to leverage the successful campaign in all the markets they operate. However, they only recorded the original “JustEat” version of the ad. 

You can see their first video here:



With Synthesia’s help, they were able to transcreate the ad. They took the Snoop Dogg commercial for Craftww’s JustEat brand and morphed it into a commercial for JustEat’s subsidiary called MenuLog. How they did it is the interesting part of this story. 

Synthesia did more than swap out logos. They changed Snoop Dogg’s lip movements in all the shots of the commercial. The outcome was a considerable cost savings for the client because they didn’t need to produce the ad twice.  

You can see the version for MenuLog here:



Synthesia believes that synthetic media and deep learning will create a new generation of content creation tools that are “are empowering, effective, and ethical, for everyone.” 


Building the ‘Business to Robot to Consumer’ Business Model

The connected world of devices combined with artificial intelligence has created a new frontier for companies to explore: Business to Robot to Consumer (B2R2C). While the new business model may be a lot to take in, the applications are simple but powerful. Marketing to robots happens when more virtual assistants, digital avatars, and even robots become the gatekeepers between brands and consumers. For example, when your voice assistant knows what you need to buy for your pantry before you even know you need it. 

Enter the AI Foundation, an organization at the forefront of this new trend. It has been partnering with personalities like Deepak Chopra and Sir Richard Branson to further their mission of responsibly moving the world forward by giving each of us our own AI that shares our personal values and goals. 

For the Foundation, AI will revolutionize how people connect, communicate, live, and work. This, in turn, will create a massive potential for those who own it, but AI can pose grave threats when used by a few to manipulate us, when it malfunctions, and when it divides us, but AI itself can help prevent this. For them, AI must be for all, and we each need our own AI — to protect us, make us wiser and more powerful, and unlock the full potential of our world. 

The AI Foundation is working on use cases where people can benefit from AI in their day to day life — cooking is one that recently became increasingly important as we’re spending more time in our kitchen’s lately. The are working on an AI chef “who’s familiar with what is in your kitchen, pantry, and even knows what is on your mind for dinner!” The AI chef helps customers perfect their favorite dish or share new recipes based on their favorite ingredients. 

“With our technology, companies, and brands can build a stronger connection with their customers/fans through more interactive and intelligent engagement. They can learn exactly what users want and are interested in, and deliver better value and less noise through 1-1 conversations,” said Chris Acosta, VP of Product Innovation & Growth at the AI Foundation

In a video on the Foundation’s Facebook page, a woman talks to her AI chef. They have a conversation about making a meal or start prepping for dinner. The woman says that she’d like to make stuffed bell peppers. The AI Chef responds that “great stomachs think alike.”


Digital People make powerful brand experiences 

Synthesia & the AI Foundation aren’t the only organizations working to make digital people available to brands for engaging and compelling experiences. Soul Machines announced in May that it’s making their Digital DNA™ Studio (DDNA Studio) available for companies so that “brands can connect with every customer in a personalized way at scale with Soul Machines’ Digital People.”

Soul Machine’s Digital people are “designed to deliver the personalized, emotionally engaging experience that customers crave with brands at a cost that allows them to scale.” Creating digital people no longer takes a team of developers and CGI experts. Brands can now use the Digital DNA studio to develop people that match their brand’s culture and customers to create a more engaging experience. Soul Machine calls the artificial intelligence that powers their digital humans the “Digital Brain,” which drives the Autonomous Animation functionality. 

Soul Machine says use cases for digital people range from training, entertainment, and financial services – any type of repetitive business function. But digital people can perform them in ways that seem more helpful than an automated chatbot. 

Soul Machine’s Digital People are designed to evolve over time based on user interactions. Soul Machine believes it’s important to see through the “smoke and mirrors of high-tech marketing [it] requires a clear definition and taxonomy for understanding the new generation of Digital Humans that will soon surround us.” In order to give these new digital humans a personality that matches their brand, companies will have to define their brand values, the role they need the digital human to fill, and do it in a way that appeals to their client base.


Let’s Go Beyond the Deep Fake

These examples show that artificial intelligence, when used creatively, can do more than the grunt work. It is a way to build relationships with customers on a personal level while at the same time scaling at large in a cost-effective way. It’s important to go beyond equating AI to only chatbots and have conversations around how AI can actually work in service of brands and, in turn, be used to better their customer’s experience. 

We should continue to have meaningful discussions around deepfakes and setting up safeguards and ethics around synthetic media. But, we must also move the conversation beyond just focusing on that element of synthetic media. It’s also time for brands and businesses to better understand the broader trends that are on the horizon, so they are prepared for the future of marketing to come.



This article is written by Cathy Hackl and originally published here



How AI Is Replicating Human Cognition To Find Answers

  Based on an internal survey conducted of over 375 Global 500 customers, roughly 80% of concierge and customer service efforts are spent on providing informational services to their end-users, be they, employees or customers. Though there are of course situations where it’s necessary to speak to a human being, oftentimes these are requests for items that are buried in an employee handbook or a customer FAQ, and it’s inarguably quicker to simply call someone and have them find it for you. It makes sense, but it’s also a heavy operational burden on any enterprise, as many times customer and concierge services are managed via a manual workforce and a dedicated team. Informational services are a necessity but also a time sink. In an ideal world, you would want the team to spend their energies on other tasks that are more judgment-intensive that require customer handholding. This is also compounded by the increasingly more-complex documents that we sign both in business and as consumers — multilayered terms of service and privacy policies, agreements with clients or contractors, things that are commonly dense.  

Search Can Be Too Complex And Cumbersome

This problem is compounded in the enterprise. Enterprise content is often buried under various data sources in a variety of data hosting channels, making it extremely difficult for a layperson to figure out by themselves. A reasonable expectation may be to think that there is a central repository to host all the content — and that a search product could fix that problem.  The concept of content search works on the premise that the user is interested in filtering down topics from indexed content based on relevancy to their search query. So, potentially, all that a user can do using search is to filter down topics that they have read through and figure out what they want from the keyword-based content. They’re searching, clicking and searching again based on the information that they have, and then hopefully coming to the “right” answer, based on what they need to do. Results are just that — results. They’re not computations of an actual answer and consideration of a particular question. They’re not answering the question directly — they’re giving what might be possible — without verification. So, what does it take to get from here to give the end-user an answer to their question?  

How AI Is Replicating Human Cognition To Find Answers

This is an extremely hard problem to solve technically because this involves not just understanding the semantics of a user’s question, but understanding the context of the content personalized to the end-user. Think about answering a question that someone asks you — even something simple, like “Can you meet me at 5 p.m.?” This requires you to consider not simply whether you’re free that time, but whether you will be able to speak (e.g., location-wise — are you in a crowded room?), logistically (will you have phone signal where you are?), mentally (will you be alert enough?) and so on, at that time. These calculations are done by our brains, but in a massive repository of information, you have to contextualize based on the information available, the question itself and potentially external factors. Once you respond back to the end-user, you have to be extremely precise — answering the question but also potential other questions, or aid in finding information to further help the user. Now, add the complexities of figuring out content formats, incorporating domain-specific knowledge and giving real-time responses to the end-user, further compounded by doing this at an enterprise scale with a variety of content and loads of data.  At that point, you get a sense of how challenging this problem is to solve.   

Using Conversational AI To Find Answers

Using AI, deep learning techniques and natural language can synthesize and understand content available in the siloed information repositories and enable the user to ask and get an answer to a question in plain speak. It’s an incredibly complex problem to solve, one that requires you to combine different techniques to create something akin to the decision-making of a human brain. Content understanding is a multilayered problem. It involves not just extracting data from highly unstructured content within various formats but also interpreting underlying semantics, identifying entity relationships, structuring and categorizing information that is essential for answering end-user questions in the context of content ingested. It’s not simply a case of ingesting — it’s a case of having the ability to use unsupervised learning to “comprehend” the document and be able to come up with answers to questions a user might ask.  It’s ultimately a reengineering of how we communicate with documents. We’re used to reading them and then asking someone (or searching) for an answer if we can’t find it from the text. The real solution is using new AI algorithms plus machine learning techniques combined with natural language understanding to give people the ability to ask a document a question and get a precise answer, much like a human would respond.    

Challenges To Consider

Conversational AI is a rapidly advancing technology that is moving at light speed. As technology suppliers, we do not do ourselves any favors by liberally applying “AI washing” to everything we do. As a prospective technology buyer, consider applying conversational AI initially to a specific use case, one that consumes time and has high user frustration. It’s vital to verify the results from two dimensions — productivity as well as user experience. User experience to a large extent dictates success and adoption. Once you have a successful initial project, broader adoption is easier.  Implementing conversational AI technology at the enterprise level could free up more time for the 21st-century knowledge worker — making your business significantly more efficient.

This article is written by Ram Menon and originally published here


How artificial intelligence is transforming the future of digital marketing

Digital marketing relies on the copious amounts of data that gets created every time by customer interactions. Algorithms optimize various factors and data points that influence digital marketing success. In 2020, we anticipate a significant uptick in the mainstreaming of AI uses cases in digital marketing across several areas. 

Search will get very smart

In the past year, online search has had several AI and machine learning developments. Google is leading the pack with exciting applications in information retrieval. For example, Google’s BERT  technology can process a word in the context of all the other terms in a sentence. BERT also enables anyone to train their own state-of-the-art question answering system.   Customization of search results and the results page based on learnings from past interactions and preferences of a user is another application of machine learning used in the search.

AI-driven personalisation of messaging 

Several adtech companies have been focussing on using AI and machine learning to find the right audience to write better ads than humans and to increase conversion rates and engagement. There are also several AI-led developments in the area of creating dynamic ads to personalise marketing messages on the fly.  AI has an application in terms of determining the logic of personalisation, using techniques such as natural language generation (NLG).

Use of machine learning in campaign operations 

Platforms such as Google and Facebook have been at the forefront of AI/ML applications in marketing. Starting from smart bidding and smart campaigns to auto-generated ads, Google is making it easy for advertisers. Smart bidding options such as TROAS, TCPA, and others use advanced machine learning algorithms to train on vast data to make accurate predictions on bid amounts impacting conversion and assist advertisers in optimising without getting into too many details.  Google factors signals to predict user behavior and to influence auction time bidding as per the goal set by advertisers. Facebook has also incorporated machine learning across campaign planning and execution, as also in ad placements and ad delivery. Similarly, on the organic search side, machine learning-based product ALPS reverse engineers Google’s ranking algorithm and is able to accurately quantify ranking drivers, provide precise recommendations for changes, and predicts the impact of SEO actions before they are implemented. Similar technology to drive improved ad copy testing in digital marketing exists. These help in evaluating ad copies and landing pages on various parameters like relevancy, use of action promoters/inhibitors, urgency inducers, page layout, load times, etc., to gauge the impact on ad relevance, expected CTR, and landing page experience. 

Future trends  AI

will also have additional application in digital marketing with the uptick in the adoption of technologies such as VR and AR, as commercial use cases of these technologies find wider adoption in retail and other sectors.
Many retailers are also testing AI and VR/AR technologies together to make the user experience personalised to an individual. Other areas of impact include voice search. We will increasingly see ads about things which we just said or talked about but haven’t searched for yet. Similarly, image search is also being used by many brands for their consumers to identify products. 

This article is written by Aditya Saxena & Ajay Kumar Rama and originally published here