Voice Assistants – The Next Big Opportunity For Digital Marketers

I distinctly remember when Apple first released Siri and everyone was enthralled with asking her silly questions. In 2011, most people thought the idea of voice assistants were a useless function, something you could kill the time with as opposed to a practical application. Now, seven years later, the capabilities and functions of voice assistants have grown to be a part of people’s everyday life, and their function and adaptability are projected to grow even more in the coming years.

Since the release of Siri, many companies have released their own voice assistant. Google released their voice technology in 2012, Microsoft released Cortana in 2013, and Amazon released Alexa and the Amazon Echo in 2014. In the proceeding years, these technology powerhouses have continued to perfect  their products – advancing both their capabilities and product lines to accommodate all possible voice assistant function. People have built voice technology in their homes – locking doors, turning on lights, controlling the television, stoves, refrigerators – at this point, you could nearly talk your way through your morning routine (which might have its positives and negatives as seen in this ad released by TRY). With all the recent additions and advancements in voice technology, many people are projecting what to expect in 2019.

1. Differentiating voices: One of the main reasons people enjoy voice assistants is because of the personalization level. Devices will continue to get better at differentiating different voices. In 2017, Google announced that their home devices could detect up to six different users. This allows for more personal responses to questions like “what does my calendar look like today?” or receive a more tailored news report. A few months ago, Amazon caught up with this trend and announced unique user profiles for the Alexa devices. This will be a key focus in development advances in the months to come for all voice assistant devices. 

2. Automotive Breakthroughs: In 2018 car models, voice assistant became a standard feature. Mercedes A-Class and Audi A8 are using a system developed by Nuance, called “Just Talk.” Just as with Siri or Alexa, the system is programmed to recognize casual conversation cues, so users don’t have to memorize a set of commands. Examples of its application include phrases like “It’s warm in here” which would activate the air conditioning or asking for navigation directions. This type of tech, or apps such as drivesafe.ly allow drivers to safely get more done on their commute to and from work, keeping their eyes off their phone and on the road. The less buttons that drivers have to push, the safer driving while using tech will be, which is why many automotive companies are turning their attention to voice assistants.

3. Search Ad Revenue Increase: Although voice advertising will always pose some challenges and discrepancy because there isn’t the visual confirmation as with traditional search advertising, the popularity of this ad form will grow in 2019 due to the popularity of voice search apps on mobile devices. The use of voice searches, which in turn creates opportunities for voice ads, comes from an improvement in voice assistants accurately capturing the conversation. As I mentioned previously, in 2011, when Siri was first released, it was rare that she accurately picked up what you were asking. Now, voice assistant companies are reaching for a 99% accuracy level. With these ambitions, people will get used to using voice assistants for more searches. The result, this channel is a wide open opportunity for advertisers so much so that Juniper predicts voice-based ad revenue could reach $19 billion by 2022. If this prediction reigns true, Google and Amazon will not hesitate to open their platforms to additional forms of paid digital advertising.

4. Incorporation of screens: As voice assistants products became more popular, companies had to differentiate themselves. Google released the Google Assistant which featured all the favorite Google Home voice assistant features, but also has a 7 inch screen. This allows user to engage with the assistant even more, providing more opportunities for advertisers. The addition of the screen is a trend that we will see more of in 2019 as it provides even more opportunities for brand marketers.

Nearly half (46%) of adults in the United States use voice assistant making them a normal part of everyday life. With stable and growing usage, the  opportunities for this channel is unquestionable. With its many adaptations and potential for marketing revenue, advertisers need to keep an eye on the future of voice technology to ensure they aren’t missing an opportunity to diversify their media mix.

Digilant Announces MAIA: Programmatic Power With Human Insights

Marketing, Artificial Intelligence & Analytics for the Modern Marketer

Boston — December 18, 2018 Today, Digilant — a programmatic media buying services company — introduced MAIA: Marketing, Artificial Intelligence and Analytics. This straightforward yet powerful infrastructure will be used to power DIGILANT and offers advertisers the harmonious combination of programmatic power and human derived insights, in one easy-to-use solution.  
According to a recent report by Forrester, no one DSP or ad-tech platform exists that addresses the full range of omni-channel buying needs. As a result, marketers are forced to stitch together a patchwork of disparate platforms, inventory and data sources to address their requirements — and then rely on these programs built on wobbly infrastructure to measure, analyze and predict consumer behavior.

“The existing set of online advertising solutions is conceptually inadequate for what marketers need to be successful in 2019 and beyond”, says Raquel Rosenthal, CEO of Digilant. “This year we made it a priority to address this gaping hole in the digital advertising solutions landscape, and we think we nailed it with MAIA.”

Never before have marketers been forced to be so adept across so many channels, or has it been such an ongoing challenge to serve up “the right ads, to the right audience, at the right time and cost”. With dozens and sometimes hundreds of dynamic digital channels to manage on even a single campaign, media buyers find themselves at the mercy of systems and software that are often either disconnected altogether, have loose and unreliable connections, or report on questionable metrics. The result is advertisers exposed to potential overspending, fraud and brand safety concerns.

Enter MAIA – Marketing, Artificial Intelligence and Analytics for the modern marketer.

According to eMarketer, 73% of U.S. marketing professionals find it difficult to get quality reporting and insights from their data sources. And in our experience at Digilant, the crux of the problem is a combination of an over-reliance on technology and not enough involvement of insightful and experienced digital advertising professionals.

This is the issue MAIA addresses head-on. Unlike existing marketplace solutions, MAIA brings people and technology together, creating the perfect blend of strategy, insight and efficiency that helps drive marketing teams to successful outcomes. MAIA intelligently makes sense of massive data sets, and with the constant support and guidance from the Digilant operations and analytics teams, leads advertisers to more efficient media buys, better decision-making, and media optimization across multiple channels — resulting in superior ROAS and overall performance.

MAIA Combines Best-of-Breed Technology + Talent

With the introduction of MAIA, Digilant is now able to offer Digital Advertising Solutions for agencies and brands that blends both human expertise and curated technology to generate intelligent insights activated through cross-channel campaigns.

“I’ve been in the Internet advertising industry for more than 20 years,” says Digilant CEO Raquel Rosenthal, “and I can tell you that MAIA solves the single biggest challenge that media buyers face today, and that’s multichannel management and optimization. There’s nothing in the marketplace that comes close to the features and functionality of MAIA.”

MAIA’s infrastructure was built to combat the future complexities of the advertising industry and allows marketers to effectively use billions of collected and analyzed data points. This data is sourced from over 250+ log level feeds, API’s and server-to-server level integrations with partners and channels. MAIA’s AWS powered technology directly feeds in to Digilant’s fully integrated Data Management Platform and Data Mining Console.


MAIA, by Digilant, offers media buyers and brands:

  • Omni-channel media buying and execution
  • Privileged access to the highest-quality markets and media
  • Real-time, extensible data management based on people, not cookies
  • A proprietary data layer combined with AI, built from real transactions
  • Extensible decisioning and machine learning platform, and customizable attribution framework
  • The best human insights from people with programmatic know-how

The modern marketing operation requires both programmatic power and human expertise to be successful, and MAIA accurately measures, analyzes and predicts customer personas, behaviors and motives. The platform uses data as a currency, to not only build the most accurate audiences to target, but to give brands the ability to see the whole picture – how strategy, data, inventory and reporting work together. And then the ability act on those insights to consistently learn and achieve better results campaign-over-campaign.

About Digilant

Digilant is a programmatic buying company, designed for both agencies and brands. We connect people and technology to create a perfect blend of strategy, insight and efficiency that will elevate any marketing team to find massive success. We also support advertisers who are moving towards programmatic self-sufficiency by aligning them with and training them on the right set of programmatic platforms and technologies.

Using MAIA (Marketing, Artificial Intelligence and Analytics) – the harmonious combination of machine power and human expertise behind all things DIGILANT — we intelligently navigate massive data sets. MAIA enables marketers to use data as a currency to generate more efficient media buys, make better informed decisions, optimize and drive performance across all digital channels and campaigns.

Digilant is an ispDigital Group Company. For more information, visit us at www.digilant.com, read our blog or follow us on Twitter @Digilant_US.

Programmatic Media Buying 101: What is the Difference Between AI, Machine Learning & Programmatic?

The world of digital advertising and programmatic advertising has developed its own language in the last couple of years, full of terms that are commonly heard and used everywhere but mean something very specific when attached to the word advertising. Most recently it’s almost impossible to read an article or even talk about media buying without bringing up the terms Artificial Intelligence (AI) or Machine Learning. The terms AI and machine learning are often used interchangeably but they are different. What is the difference between the two and what should they mean to us or me as a marketer or CMO?

AI for Programmatic Buying

Artificial intelligence is the concept of reproducing human intelligence in machines so they can execute on activities that normally would require a human brain to be involved in, such as making data-based decisions.  By using AI-powered systems brands and advertisers case save money and time by completing tasks faster than us mere humans and make less mistakes.  When you apply this to the programmatic media buying industry, you bring efficiency to the media buying process, freeing people who’s job it is buy media from the more tedious and allowing them to focus on the strategic and creative elements of their jobs.

The reason digital media executives keep talking about AI technologies is that they allow us to have algorithms that analyze a user’s behavior, allowing for real time programmatic campaign optimizations towards consumers who are more likely to convert. Advertisers then have the ability to gather all this rich audience data to then use it to be more accurate with their media buys and overall targeting tactics – ultimately spending less money and time and bringing in a higher ROI.

Will Machine Learning Replace Media Buyers?

The words Machine Learning can conjure up images of old sci-fi movies in which someone develops an intelligent robot that then dominates its creator or destroys a large city… leading to many questions about how this technology could affect the digital media industry.
Machine learning is a type of Artificial Intelligence that provides computers or robots with the ability to learn things by being programmed specifically to take certain actions, improving their knowledge over time, much in the same way our brains do.
Computers using machine learning focus on imitating our own decision-making logic by training a machine to use data to learn more about how to perform a task.

Imagine you ride your bike to work every day. Over time, after trying different ways to get to work, you will learn which route is faster or maybe which road or path is better according to the day of the week or based on the weather outside. This is exactly how machine learning works. You feed the computer or algorithm with large amounts of data so it will analyze information from the past and learn from it to apply the learnings to any new data it receives in the future.

When applied to programmatic advertising, machine learning algorithms can analyze large volumes of data from difference sources and draw conclusions from it. It means you can almost replicate the brain of an experienced media buyer in a machine or algorithm so it becomes capable of  predicting, planning and optimizing media. Almost…. but not yet, though the machines can certainly make programmatic advertising more efficient, faster and easier to implement, there remain many factors which need human brains to input link the machine learning to an overall media buying strategy.

So How are AI and Machine Learning Connected to Programmatic Advertising?

Programmatic advertising is the automated process of buying and selling ad inventory through an exchange, connecting advertisers to publishers rather than having to make individual deals with each publisher. This process uses artificial intelligence technologies to improve efficiency and make better decisions for the advertisers with their budgets.
There is a lot of investment being made in marketing and ad buying technologies to leverage AI.  Companies like Xaxis, are betting heavy on AI for improving their future Programmatic Buying Platforms.  Fo right now marketers are using AI to stitch massive amounts of their data together, but it still hasn’t replaced human analysis.  For media agencies, Artificial Intelligence is still more a buzzword or a catchphrase to get peoples attention.

David Lee, programmatic lead at ad agency The Richards Group, said that he regularly gets pitches for AI-enabled products but the AI part of the products usually “doesn’t seem to affect performance outside of being a buzzword.”

You need Machine Learning to feed AI but you don’t need AI for Machine Learning. What that means is that machine learning is the technique — using algorithms to process data, learn from insights and make predictions for future programmatic campaigns which then trains the AI.
Both Machine Learning and AI are here to stay.  If you are a marketer or a media buyer, get familiar with these terms as they will continue to occupy the press and blogs like ours.  But for now they are not taking over for humans, that’s still in the sci-fi section of the video library.

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