Conversational search engines such as YouChat and Microsoft Copilot use large language models (LLMs) to generate answers to queries. It is only a small step to also use this technology to generate and integrate advertising within these answers - instead of placing ads separately from the organic search results. This type of advertising is reminiscent of native advertising and product placement, both of which are very effective forms of subtle and manipulative advertising. It is likely that information seekers will be confronted with such use of LLM technology in the near future, especially when considering the high computational costs associated with LLMs, for which providers need to develop sustainable business models. This paper investigates whether LLMs can also be used as a countermeasure against generated native ads, i.e., to block them. For this purpose we compile a large dataset of ad-prone queries and of generated answers with automatically integrated ads to experiment with fine-tuned sentence transformers and state-of-the-art LLMs on the task of recognizing the ads. In our experiments sentence transformers achieve detection precision and recall values above 0.9, while the investigated LLMs struggle with the task.

Researchers from the Netherlands have developed a new machine learning method that's capable of distinguishing sponsored or otherwise paid content within news platforms, to an accuracy of more than 90%, in response to growing interest from advertisers in'native' advertising formats that are difficult to distinguish from'real' journalistic output. The new paper, titled Distinguishing Commercial from Editorial Content in News, comes from researchers at Leiden University. The authors observe that though more serious publications, which can more easily dictate terms to advertisers, will make a reasonable effort to distinguish'partner content' from the general run of news and analysis, the standards are slowly but inexorably shifting to increased integration between editorial and commercial teams on an outlet, which they consider an alarming and negative trend. 'The ability to disguise content, willingly or unwillingly, and the probability that advertorials are not recognized as such even if properly labelled is significant. Marketers call it native [advertising] for a reason.'

So-called "native advertising" online, where advertising is presented in a similar way to editorial, has failed to take off. A US study last year from Stanford University found native advertising is no better at getting us to buy than standard online ads. "Consumers are very good at filtering out messages," explains Lisa Du-Lieu, a senior lecturer in marketing at Huddersfield University. "If you don't get their attention within the first couple of seconds, it just bounces off them." For that reason, brands are shifting their attention to platforms and formats that they know we are engaged with.

This piece was coauthored with Kent Huffman, Chief Marketing Officer and founder of DigiMark Partners. In 2017, B2B marketers will continue to deal with tangible growth and marketing ROI pressures. Below are 13 primary B2B marketing trends that deserve your time and attention and will help you address the challenges, opportunities, and complexities you'll undoubtedly be presented with this year. Probably the most impactful B2B marketing trend in 2017 will be a tighter focus on improving the customer experience while driving growth. Strive to use a balanced approach to building customer loyalty. Instead of focusing solely on "customer-centric" methods based on your customers' lifetime value, offset it with "customer-focused" techniques that enable you to provide relevant experiences across all touch points and concentrate on what your customers value most.

According to Chartbeat analytics, two out of three clicks on native advertising bounces in 15 seconds or less. From Facebook to Outbrain, it costs three times the cost of a click for a content marketer to get any meaningful engagement with their work. According to that same study, users who spend 15 seconds or more on a webpage will read 80% of the content or more. For every $1.50 spent on native advertising, $1.00 of it is wasted. Half the money I spend on advertising is wasted; the trouble is I don't know which half.

According to Chartbeat analytics, two out of three clicks on native advertising bounces in 15 seconds or less. From Facebook to Outbrain, it costs three times the cost of a click for a content marketer to get any meaningful engagement with their work. According to that same study, users who spend 15 seconds or more on a webpage will read 80% of the content or more. For every $1.50 spent on native advertising, $1.00 of it is wasted. "Half the money I spend on advertising is wasted; the trouble is I don't know which half."

This piece was coauthored with Kent Huffman, Chief Marketing Officer and founder of DigiMark Partners. In 2017, B2B marketers will continue to deal with tangible growth and marketing ROI pressures. Below are 13 primary B2B marketing trends that deserve your time and attention and will help you address the challenges, opportunities, and complexities you'll undoubtedly be presented with this year. Probably the most impactful B2B marketing trend in 2017 will be a tighter focus on improving the customer experience while driving growth. Strive to use a balanced approach to building customer loyalty.

This year I took on a couple of ambitious tasks. One was part of my professional development, to learn everything I could about artificial intelligence (AI) and marketing, and the other focused on annual native ad tech research, similar to what was presented here last year – the 2017 Native Advertising Technology Landscape. Little did I know at the time, but an entire ebook came out of the subsequent AI research, "Everything You Need to Know About Marketing Analytics and Artificial Intelligence." It literally is everything you need to know about marketing and AI today and its impact on analytics, earned, owned and paid media. As a result, I'd like to share what I learned conducting all of this recent research in a two part series.

Does artificial intelligence (AI) have a place in native advertising? Can it help marketers succeed with native advertising? The Native Advertising Institute asked Dale Lovell, chief digital officer at Adyoulike, an ad tech platform that integrated IBMs Watson AI software in 2016. Below are highlights from the interview which have been slightly edited for clarity. "I believe that AI tools, AI native will really free marketers up to be more creative. Today, marketing departments are tasked with analysing so much data and compiling reports that go to clients that sometimes don't get read because the client is sifting through reports that they can't understand. There's just too much data in many ways for the human mind to process. So you could either hire a thousand people in your team -- which is not scalable -- or you could use an AI tool to help create insights that inform your marketing and effectively lets marketers do what they do best which is be creative."

The real progress in machine learning is that, beyond quantitative and categorical data, we can now build models for images and text. These models can recognize objects and process language at the level of an average six-year-old human (yes, "human level"). Major tech companies -- many of which are in advertising too -- already leverage these models: think Facebook friend-tagging and Amazon answers. We believe there are opportunities to use these models for native advertising. More specifically, we can build image and text models to analyze the content of native ads and the content of publishers at scale, in order to find the best "fit." In addition, we want to build these models in an open and interpretable way, rather than simply using a catchy name that includes "AI." In doing so, Qchain hopes to help both advertisers and publishers meet their goals more efficiently with the right audiences -- forging a path for more authentic marketing.