Why Marketers Should Tap Into the Potential of Bing Ads, the Dark Horse of the Search World

With the introduction of the Microsoft Audience Network (MSAN), enhanced AI capabilities and increased partnerships within the last 12 months, Bing Ads is becoming an even more advanced channel that should be tapped to effectively reach the right audience at key moments.

Bing has often been an overlooked publisher in the search world, left in the shadows of its older rival, Google Ads, and simply not given the credit it’s due. However, marketers shouldn’t overlook the dark horse that is Bing Ads. With the introduction of the Microsoft Audience Network (MSAN), enhanced AI capabilities and increased partnerships within the last 12 months, Bing is becoming an even more advanced channel that should be tapped to effectively reach the right audience at key moments.

Partnerships and AI

The long-standing partnership between Microsoft, AOL and Yahoo continues to evolve; starting in March 2019, Bing began exclusively servicing Yahoo Search traffic, which included traffic currently acquired from Oath Ad Platforms (previously known as Yahoo Gemini) and other search platforms. With Microsoft’s acquisition of LinkedIn came the ability to target LinkedIn users based on job function and title, an exceptionally important development for those in the B2B sector, and a feature that Google simply cannot match.

A few key placements and sites unique to Bing that marketers should consider adding to their advertising efforts include the trifecta of MSN, Microsoft Outlook and Microsoft Edge. This trifecta enables marketers to deliver high-quality native ad placements across devices regardless of audience, while benefiting from Bing’s promise never to show ads next to sensitive categories such as tragic current events to help protect brands. Bing offers two layouts for native ad formats: image-based ads and feed-based ads. Imaged-based ads are highly visual and appear across multiple types of platforms. Plus, a big bonus to marketers is the ability to import their current assets from what they’re already running on the Google Display Network (GDN) or Facebook. Feed-based ads are product-based and require the use of product audiences which retarget customers on products they’ve already viewed or even added to cart but didn’t finish the check-out process.

Chatbots offer another great way to provide on-demand answers to customers, and Microsoft and Bing stand are at the forefront. This real-time ad extension format can inspire users to purchase an item or answer specific questions to help better service their needs. In fact, Bing projects that 95 percent of customer interactions will be powered by AI bots by 2025. This is something that Bing has been testing for some time now, but Google has barely set in motion.

The MSAN Factor for Bing Ads

There’s been a lot of talk about keywords becoming a thing of the past and looking toward audiences as the means to effectively reach consumers in the future, causing a ripple effect across the industry. In fact, Google AdWords dropped ‘Words’ from its name last June (announced at Google Marketing Live 2018), as the company transitions its focus to the ads themselves. But what does Bing have going for it in this aspect that Google doesn’t? The MSAN component. MSAN is powered by AI and machine learning known as the Microsoft Graph. This intelligent tool contains search and web activity and helps isolate trends to help reach a marketer’s target audience. Bing does not allow for commercial data contained in the Graph to be used for targeting ads; any data is privately stored, owned and anonymized by Bing — a critical factor in a world where privacy is at the forefront of both consumers’ and marketers’ minds.

MSAN and Google Ads’ audience network have similarities like remarketing, in-market, custom audiences and product audiences. Additionally, advertisers can target by age, gender location and device. But the real shining star of MSAN and Bing Ads is LinkedIn profile targeting. This unique feature allows advertisers to apply LinkedIn targeting to campaign and ad group levels and target by industry (with up to 145 unique industries), by company name (over 80,000) and by job function (26). Marketers can apply these targeting settings for text ads, shopping and dynamic search ads.

Artificial Intelligence Ethics

Marketers are not the only ones watching Microsoft’s next move. In a surprising revelation, the Vatican is teaming-up with Microsoft for a prize to “promote ethics in artificial intelligence.” Pope Francis even met with Microsoft’s President Brad Smith on Feb. 13 to discuss the Catholic church’s position on AI. The person who best defends their dissertation on ethical concerns involving AI will win a trip to the Microsoft headquarters and a prize of 6,000 Euros.

With all the recent talk around privacy concerns and the role tech giants play, it’s a smart move for Microsoft to approach the apprehensions head-on. It’s particularly timely since President Trump announced an executive order earlier this month outlining a plan on how the country will get ahead of AI and how the government can work directly with AI companies. However, with the public scrutiny of Facebook’s Cambridge Analytica data leak scandal and Google’s share of privacy concerns, Microsoft is proving its reputation with no major incidents top-of-mind. The Microsoft Graph provides   another layer to help reassure their commitment to protecting consumer data.

If marketers have been on the fence about tapping into Bing Ads’ potential, there is no greater time to start acting on it than now. Bing’s increased partnerships, addition of MSAN and intelligent solutions, and commitment to ethical responsibilities shouldn’t be underestimated. The odds may not have favored Microsoft products like Bing in the past, but these innovations mean marketers’ investments now will pay high dividends in the future.

How Machine Learning Is Changing the SEO Rules

More than 40 updates in four years — that’s how often Google updates its search engine algorithms. And while most of these updates only caused ripples, others made waves that left digital marketers scrambling for solid ground. What if search engine algorithms evolved seamlessly without updates?

Google Panda Penguin ConceptMore than 40 updates in four years — that’s how often Google updates its search engine algorithms. And while most of these updates only caused ripples, others made waves that left digital marketers scrambling for solid ground.

What if search engine algorithms evolved seamlessly without updates?

Thanks to machine learning, the days of potentially jarring updates could someday be behind us. Machine learning occurs when programs can make predictions or determinations based on a wide range of signals or parameters. Uber, Auto Trader and Expedia are among the many large companies that employ machine learning; the technology is also proving useful in the fields of fraud detection, data security and financial trading. And yes, machine learning is already commonplace within Google and Microsoft, two of the world’s largest search and technology giants.

Don’t expect Google’s programmers to bow down to artificial intelligence anytime soon. However, there’s no denying that machine learning will play a big role in SEO.

Machine Learning’s Place in Google

You don’t need to travel far back in history to find Google casting doubt on the quality of machine learning.

Back in 2008, Google officials still believed their human programmers were more capable and less error-prone than the artificial intelligence available at the time, according to the marketing analysis blog Datawocky. In a 2011 discussion on Quora, a poster who claimed to work at Google from 2008 through 2010 said the company’s search team preferred a rule-based system over a machine-learning system because it could implement faster and more definitive algorithm changes.

However, machine learning was a core component of Google AdWords by 2012. The platform’s machine learning system – referred to as SmartASS — could determine whether users would be interested in ads enough to click them. One year later, Google officials were speaking publicly about working machine learning into their search engine algorithms.

Today, Google uses machine learning with its search algorithms mostly for “coming up with new signals and signal aggregations,” Gary Illyes of Google told Search Engine Land in October. He explained how Google’s search team uses machine learning to predict which algorithm adjustments are most worthwhile.

Illyes also talked about RankBrain, a machine-learning system implemented by Google in 2015.

RankBrain plays a vital role in Google’s ability to interpret long-tail search terms – like those often spoken into smartphones — and return relevant search results. In a Bloomberg article published in October 2015, Google senior research scientist Greg Corrado said the machine-learning system had become the third-most important page-ranking factor out of roughly 200 signals that impact the search algorithm. RankBrain was rolled out after a year of programming and testing, and it’s regularly fed loads of new data to improve its capabilities, Corrado said.

So, we know Google uses machine-learning to test and shape its algorithms. We also know Google is much more open now to embracing this technology. That begs the question: What’s next?

What Machine Learning Means for SEO

The more machine learning plays a role in search engine algorithms, the more digital markers will need to be proactive about maximizing the user experiences of their websites and landing pages. Machine-learning systems will result in more fluid search algorithms that make real-time determinations based on positive and negative reactions to content.

With that in mind, SEO experts can prepare for the machine-learning revolution by focusing on the following questions.

  1. Is your landing page relevant?
    Visitors who arrive at your site on the most appropriate landing pages are much less likely to bounce back to the search engine results page (SERP), and high bounce rates are easily detectible red flags of a poor user experience.
  2. Could my landing pages be more engaging?
    You’re halfway there if your visitors are arriving on the right pages. Now, think of new ways to capture their attention. Can you add videos, guides or additional products that add value for your visitors and make each visit more compelling?

Marketing in the In-Between of an AI Revolution

“To be good at the digital and physical is what the future’s about. … Get used to living in the in-between.” That was something Beth Comstock, vice chair of GE, said during her keynote at &THEN16. And it really got me thinking: Marketers are living in a whole lot of in-betweens. It’s not just the in-between of physical and digital. We’re also on the cusp of an AI revolution.

“To be good at the digital and physical is what the future’s about. … Get used to living in the in-between.”

That was something Beth Comstock, vice chair of GE, said during her keynote at &THEN16. And it really got me thinking: Marketers are living in a whole lot of in-betweens.

It’s not just the in-between of physical and digital. We’re also on the cusp of an AI revolution. A few weeks ago I was at Dreamforce where I saw this slide from Wired’s Kevin Kelly:

At that same show, Salesforce announced its Einstein cloud-based AI, which can do some pretty cool things. For example, Einstein can analyze a sales person’s email string and determine if it’s likely to convert. It may notice there’s no one with purchasing power at the appropriate level on it, and recommend the email for you to send and who to send it to to fix that.

Yes, Einstein will write the email for you too.

Meanwhile, at &THEN, Adobe just announced that they’re bringing aspects of their own AI-facilitated product, Adobe Analytics, more directly into its marketing tools. That includes adding the analysis workspace directly into Adobe Campaign, which will allow users to analyze customer segments and campaigns with real-time visualizations and AI insights.

Adobe is also adding predictive remarketing to its suite. Predictive remarketing will look at your website visitors, for instance, individually and identify ones who are less likely to return. It then automatically creates and deploys a remarketing trigger to try to re-engage that customer through email, SMS or other channels.

We’re essentially automating the automation. And as we begin to add a flood of passive data from the Internet of Things to that analysis, the automation is going to get very, very smart.

It’s all part of a revolution in what humans can accomplish with data and machines.

Kelly made an analogy: You could think of the Industrial Revolution as the introduction and mastery of artificial power — steam and electricity replacing human, animal or other forms of natural muscle. That power became controllable; it enabled many, many times the output of the old natural power; and it became cheap enough that we soon put electricity in every household.

He said that’s exactly what we’re about to see in the revolution of artificial intelligence. Hence the next 10,000 startups.

The AI Revolution Will Not be TelevisedWe’re collectively and spontaneously reorganizing our culture around digital information structures, according to Comstock. I think the acceleration of machine learning tools is a testament to that.

The fact it’s happening here in marketing pretty early in the lifecycle of true AI is testament to another thing she said: “Marketers are behaviorists, we recognize and anticipate change.”

And the role of shaping, communicating, and promoting (or discouraging) that change also lies in the hands of marketers. “Communications is where change happens,” Comstock said. “Communications is how you structure a culture.”

Turns out it’s also how you structure a machine culture. And perhaps how a machine culture will continue to restructure us.

So we are all very much navigating the in-between: Of physical and digital customer interactions, of human and machine intelligence, of a revolution that will not be televised because it’s already streaming live on a dozen social networks to audiences carefully chosen by algorithms to be most likely to engage.

Get used to it.