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.

eSports: How Will Programmatic Emerge Into This Growing Industry?

The world of esports, has been evolving and attracting attention in pop culture since the turn of the millenium. Esports, as the name implies, is defined as electronic sports. They differ from traditional sports and sport leagues because the games are constantly changing, rather than set rules, each unique game poses new challenges, obstacles and intrigue. This variance in action and energy is what is making the concept of online sports so attractive to millenials.

Esports have two major viewing and engagement formats: live in-person tournaments and online events. Huge esport tournaments take place all of the world, where people come to compete in large arenas, just as in traditional sports. In 2013, the Staples Center hosted 13,000 fans to watch a South Korean team defeat a Chinese team in the championship final of League of Legends. This mass gathering of esports fans confirmed their popularity and influence. As watching sports online has continued to grow in popularity with more
streamers, it has given esports fans more ways to watch their favorite players compete. Twitch, a online subscription service owned by Amazon, allows user to watch and stream digital video broadcasts. This gives fans a chance to interact directly with their favorite players in a way that traditional sports cannot offer. This is one of the reasons esports have grown in popularity among the 21-35 male demographic. And, with such an important demographic as a fan base, advertisers, now more than ever, are figuring out ways to deepen their understanding and presence in this market.

In most recent years, the only way to advertise with esports was through sponsorship, which actually accounts for 40% of esports’ profit growth. However, esports is weary to accept sponsorship from big companies who are only interested in short-term deals. Esports wants to make sure that the companies will support them for the long-haul. However, because esports, at this point, are largely unregulated, it can also be very difficult to convince companies to invest large sums of money.

With this combination of factors, Twitch has begun to move their focus toward programmatic. Digiday reported that an agency exec said that Twitch has been “asking for advertisers to commit to spend at least $50,000 per campaign in exchange for a certain number of impressions.” Twitch hit $1 billion in ad sales in 2018, so combining this substantial ad revenue stream alongside Amazon’s advertising platform, they have set themselves up to be a huge player to watch in the programmatic marketing space. As advertisers have realized that this is a great platform to reach 21-35 males, Twitch is pitching  themselves as more than a online game streaming service.

In the next few month, it will be interesting to watch how esports continue to influence sports, advertising and programmatic markets. New platforms like Twitch and esports are great new programmatic opportunities for advertisers to jump on.  Advertisers are always searching for new ways to target audiences and this niche is definitely an opportunity that media buyers can keep in their back pocket when setting strategies for 2019.

How Advertisers Can Wield Data Exhaust

Read the full post at emarketer.com

Authored by Ross Benes

Even when an advertiser’s programmatic bid fails to win an impression, they can still gather data from the auction to use across their campaigns. But doing so can be time-consuming and requires technical expertise, so many advertisers discard this data instead of utilizing it.

The amount of data that programmatic bidding creates has increased as header bidding has become more popular. Before header bidding’s rise, programmatic advertisers relied on a system called “waterfalling,” which sequentially passed bids from one ad exchange to the next. Header bidding allowed programmatic platforms to bid simultaneously on the same piece of inventory that was being offered across multiple exchanges. Three-fourths of the 1,000 most popular sites that sell programmatic ads use header bidding, according to Adzerk.

As the data that users generate has increased, advertisers have found themselves scrapping lots of data. In an August 2018 survey of 100 digital marketers worldwide conducted by Digital Element, 15% of respondents said they throw away at least half of their data.

Wesley Farris, director of partnerships at programmatic agency Digilant, spoke to eMarketer about how advertisers can make use of data exhaust.

What sort of data can a DSP store from a programmatic auction?

“Every time a DSP gets a bid request, there are a number of data variables that are passed on: the anonymous user ID, the domain, the time stamp, the location of the page, the creative size. There could be upward of 50-plus variables that are passed in the bid request, and the DSP stores that information in what I call ‘log files.'”

How can an advertiser use that information?

“You can use it for things like the customer journey. If you pull that information out, you can start to piece together a picture of how different channels and variables are affecting the campaign and put together that customer journey of how they engaged with your media. If you ingest that data across your search and social efforts, you can then more or less combine your search efforts with your social and display efforts and see how those are impacting one another.

“You can also use it for data science to find which variables within the bid stream are driving conversions or whatever key performance indicator [KPI] that’s being measured. You can use it for audience discovery, bucketing users by similar variables, and reaching them on a more granular level. The data is definitely used at the DSP level for optimizations and reporting insights.”

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