Singapore – Global media measurement and optimization platform Integral Ad Science has recently announced the general availability of its ‘Quality Attention’ measurement product – a first in unifying media quality and eye tracking with machine learning. 

The new offering provides transparent metrics to help global advertisers increase return on investment, drive brand consideration, and boost conversions.

With Quality Attention, advertisers can capture higher attention to drive campaign performance and unlock proven results. Quality Attention uses advanced machine learning technology, actionable data from Lumen Research’s eye-tracking technology, and a variety of signals obtained as part of IAS’s core technology. 

This includes viewability, ad situation, and user interaction, and weighs them into a single attention score. IAS’s attention model is designed to predict if an impression is more likely to lead to a business result including awareness, consideration, and conversion.

In detail, Quality Attention provides global advertisers with an advanced machine learning model that views campaigns’ attention performance based on a vast data pool, a 130% lift in conversion rates when comparing high attention impressions to low attention impressions, and the combination of large consumer attention biometric data sets with media quality metrics to provide the most accurate picture of attention for global advertisers.

Talking about this initiative, Yannis Dosios, chief commercial officer at Integral Ad Science, said, ”Attention measurement must inform actions that drive superior results for advertisers. Our Quality Attention offering is purpose-built to help brands and agencies navigate through media clutter to seamlessly understand how media visibility, the ad environment, and customer interaction impact campaign performance.”

“According to our research, brands that focus on driving higher IAS attention scores achieve up to a 130% lift in conversion rates leading to a better return on their investment,” he added.

Kuala Lumpur, Malaysia – Google Cloud has announced new artificial intelligence (AI) innovations dedicated to retailers to aid in their in-store shelf checking processes and enhance their e-commerce sites with natural online shopping experiences for consumers. It has also integrated its technologies with Accenture’s ai.RETAIL platform as part of its expanded strategic partnership.

The first on the list is Google Cloud’s new AI-powered shelf checking solution that can help retailers improve on-shelf product availability, provide better visibility into what their shelves actually look like, and help them understand where restocks are needed. 

Built on Google Cloud’s Vertex AI Vision and powered by two machine learning (ML) models—a product recognizer and tag recognizer—the shelf checking AI enables retailers to identify products of all types, at scale, based solely on the visual and text features of a product, and then translate that data into actionable insights.

The company has also announced a new AI-powered browse feature in its Discovery AI solutions for retailers. This capability uses ML to optimise the order of products (i.e., which products the shopper sees first) on a retailer’s e-commerce site once shoppers choose a category, such as ‘women’s jackets’ or ‘kitchenware’.

Other Google Cloud announcements include more personalised search and browsing results with machine learning (ML), and the ‘Recommendations AI’ solution uses ML to help retailers bring product recommendations to their shoppers.

Meanwhile, Accenture’s ai.RETAIL is an integrated solution that helps retailers better utilise data and AI to optimise common systems and programs, such as customer acquisition, pricing and promotions, assortment, and supply chains. Retailers can now deploy the ai.RETAIL platform on Google Cloud, meaning it is extended to Google Cloud’s trusted infrastructure and is integrated with multiple Google Cloud products and capabilities.

For Megawaty Khie, country director for Indonesia and Malaysia at Google Cloud, the upheavals in the past few years have reshaped the retail landscape and retailers are now seeking new ways to be more efficient, more compelling to shoppers, and less exposed to future shocks.

“The leaders of tomorrow will be those who address today’s most pressing in-store and online challenges with the newest AI tools. Our work with Accenture will also help local retailers quickly adopt integrated solutions that amplify the true benefits of AI, so that they can holistically understand their business across functional boundaries and continuously optimise their offerings and operations to thrive in a complex retail environment,” Khie said.

Sridhar Subramanian, managing director of Accenture’s Google Business Group in Asia Pacific, commented that with shifting consumer buying habits, now more than ever, retailers need to invest in building a digital core – which includes a solid data foundation, ML, and AI. 

“With the best of Accenture’s integrated ai.RETAIL platform and Google Cloud technology, companies can now access products and capabilities to help improve consumer engagement and conversions, and make their supply chains more sustainable,” Subramanian said.

Google Cloud and Accenture are also collaborating on a broad, new initiative to address complex challenges facing retailers today, including applying intelligence from ai.RETAIL to help businesses optimise their customer, workforce, and storefront experiences, and utilising other technologies and offerings from both companies.

Singapore – Global computer software company Adobe has announced a slew of generative artificial intelligence (AI) models to improve customer experience (CX) delivery for businesses, ranging from content personalisation and editing, to marketing copy generation and conversational experiences.

The new AI models will be part of Adobe Sensei’s collection of enterprise applications which enables customers to work and collaborate in new ways. Adobe Sensei is the company’s artificial intelligence (AI) and machine learning (ML) arm for its Adobe Experience Platform.

A major AI model being announced is ‘Adobe Firefly’, a generative service which is trained on Adobe Stock images, openly licensed content and public domain content where copyright has expired, and will focus on images and text effects and is designed to generate content safe for commercial use.

“With Adobe Firefly, producing limitless variations of content and making changes, again and again — all on brand — will be quick and simple. In the future, marketers will be able to also train Adobe Firefly on the brand’s own collateral, generating content that reflects the brand’s style and design language,” the company said in a press statement.

David Wadhwani, president of digital media business at Adobe, said, “Generative AI is the next evolution of AI-driven creativity and productivity, transforming the conversation between creator and computer into something more natural, intuitive and powerful. With Firefly, Adobe will bring generative AI-powered ‘creative ingredients’ directly into customers’ workflows, increasing productivity and creative expression for all creators from high-end creative professionals to the long tail of the creator economy.”

Meanwhile, Adobe’s Sensei GenAI will enable brands to instantly generate and modify text-based experiences across any customer touchpoint and leverage different large language models (LLMs), including ChatGPT through the Microsoft Azure OpenAI Service and FLAN-T5. The selection will align with the unique needs of each business, stemming from brand guidelines, product vocabulary and customer insights.

Some of the business uses for Sensei GenAI include marketing copy generation, dynamic automated chat, creation of rich audience segments which provide precision for personalisation campaigns, and caption generation.

Amit Ahuja, senior vice president for digital experience business at Adobe, commented, “Adobe has a long history of unlocking AI as a co-pilot for marketers, and we have a vision for generative AI that covers the full lifecycle of customer experience management, with the enterprise-grade security and data governance that our customers expect.”

He added, “Business growth is driven by customer experiences, and generative AI is a transformative, foundational technology that will impact every aspect of how brands connect with their customers.”

Singapore – Machine learning company Moloco has announced the latest updates to its cloud-based programmatic advertising platform, ‘Moloco Cloud Demand-Side Platform’ (DSP). 

The Moloco Cloud DSP latest updates will focus on improving performance through intelligent budget allocation, automating workflows through smart campaign user interface, user experience (UI/UX), and ad creation. 

Powered by Moloco’s proprietary machine learning algorithms, the updates include ‘optimised budget allocation’, where real-time data is continuously incorporated into the machine learning engine, while the ‘intuitive campaign setup interface’, with Moloco’s new UI/UX updates, enables marketers to set up their campaigns through an intuitive guided flow and experience built-in recommendations. With this, customers can now create new campaigns in four easy steps with built-in recommendations presented based on the campaign’s goals. And lastly, the ‘auto-generated video end cards’, which allows the platform to now auto-generate video end cards in portrait, landscape and square formats when uploading a video as the campaign’s creative.

Anurag Agrawal, VP of product at Moloco, said, “Our DSP solution empowers growth marketers to leverage their own unique, first-party data to increase the returns on their advertising campaigns.”

He added, “These latest updates, combined with our powerful machine learning algorithms that pinpoint target audiences and adjust bidding strategies in real time, help our customers not only improve their overall campaign experience, but also help achieve greater accuracy and higher performance at lower cost.”

Fraudulent advertising traffic could be costing you more than just lost budget. Any budget lost to fraud is a lost opportunity for engagement.

Invalid traffic is any advertising engagement that isn’t out of genuine interest in the advertised offering. Malicious invalid traffic, or ad fraud, is often difficult to detect until it’s too late, and then little can be done. As a result, it can drain a marketing budget quickly, with no reward in return.

Juniper Research’s report on the future of digital advertising found that advertisers’ total fraud losses will rise to USD $100b by 2023. This figure doesn’t include the unseen losses, which can be difficult to quantify. Advertisers lose out on budget as well as lost time, poor campaign performance, and lost opportunities for engagement.

Moving into 2022, businesses have accepted the fact that they need to develop and execute an in-depth marketing strategy to increase market share but may not be achieving maximum return-on-investment (ROI) from their digital marketing budgets. Organizations looking to maximize ROI must build a business case for ad fraud detection to get value for money in the new year and beyond.

Innovation Equals ROI

Ad fraud and the sophistication of perpetrators have evolved at a rapid pace in the last 20+ years. As budgets for online advertising have grown, so has the lucrativeness of ad fraud as a business opportunity, attracting more sophisticated players to the arena. Now ad fraud is an industry in its own right, evolving and adapting to continue to outsmart marketers. This industry will only continue to thrive as time goes on.

For businesses, it’s becoming harder and harder to add to blacklists and manage increasingly complex rule sets, without catching legitimate traffic in the crossfire. There are significant barriers to detecting ad fraud such as difficulty differentiating between a genuine and a fake click, evolving methods used by fraudsters, and a lack of industry standards against it.

Without proactive measures, the consequences of ad fraud will be exacerbated further. In addition to the wasted media spend already mentioned, fraud will inflate volume metrics, making low-quality sources appear to be high performing. The result will be advertisers unknowingly increasing investment in sources of invalid traffic, compounding losses further.

Fighting Ad Fraud

Fraud prevention will be the best way to beat these organizations in 2022. By stopping the fraud, fraudsters don’t get paid and the business of fraud is made less profitable. To build a successful business case for ad fraud detection, you have to weigh up which options work for your business.

· Blacklists

Blacklists are an important part of any ad fraud defense because they quickly identify sources that categorically don’t have human traffic such as servers. Blacklists are a very basic first step as fraudsters can easily circumvent them by changing the IP addresses of their traffic. Also, when blacklisting IPs that can have human traffic rather than isolating the fraud itself, blacklists can actually result in high volumes of false positives.

· Rule-based detection and mitigation

Rule-based mitigation involves identifying characteristics and thresholds that, when exceeded, block traffic or a traffic source. Rule-based mitigation is appropriate when you know the characteristics that define a particular fraud tactic, such as an impossibly short click to install time. However, it is near impossible to formulate rules for fraud tactics that you have never encountered before. For a rule to be created, a new fraud type must be observed at scale which means it is impacting your budget and taking up time. Rules are reactive – a new fraud type exists, then a rule is created. It is always fraud first, then rule.

· Machine learning

A subset of artificial intelligence, machine learning extracts patterns and relationships from data and expresses them as a formula that can be applied to new data sets. Over time, as the data changes, new patterns are learned by the model without the need to explicitly program them. Because of the scale of data processed, insights can be more valuable and derived much faster using machine learning than by using human analysis alone.

Machine learning can provide more thorough and efficient analysis. In fact, according to research by TrafficGuard, machine learning, by 2022, could save advertisers over $10b a year in ad spend that would have been wasted on fraud. Instead of reacting to fraud as it evolves with new rules, machine learning can be part of a proactive defense that is tactic-agnostic, more accurate, and able to stop fraud before the fraudster gets paid.

Balancing the Business Case

These key options should all be considered in a valuable business case that accurately weighs up benefits, risks, and costs. As digital transformation evolves, ad fraud will only increase in sophistication and severity, impacting advertisers’ efforts to secure a return on their advertising spend.

Ultimately, machine learning provides a win-win for organizations looking to strike a balance between effectiveness and affordability, as businesses can easily access powerful infrastructure on a scalable subscription model. As we progress into the next year, organizations must prioritize prevention, not just detection, if they are to manage the risks to marketing budgets at such a critical time for businesses. 

This article is written by Luke Taylor, chief operating officer of digital ad verification and fraud prevention, TrafficGuard.

The article is published as part of MARKETECH APAC’s thought leadership series What’s NEXT. This features marketing leaders sharing their marketing insights and predictions for the upcoming year. The series aims to equip marketers with actionable insights to future-ready their marketing strategies.

If you are a marketing leader and have insights that you’d like to share with regards to the upcoming trends and practices in marketing, please reach out to [email protected] for an opportunity to have your thought-leadership published on the platform.

Sydney, Australia – Experience improvement (XI) solutions company InMoment has appointed Mehul Nagrani as the company’s new general manager for AI product and technology. He brings in extensive experience in leveraging machine learning (ML) and natural language processing (NLP) to deliver artificial intelligence (AI) products and technology that operationalize experience data to drive better business decisioning.

He was most recently the founder and CEO of Fokal AI, an AI automation company and platform for ML applications. Prior to Fokal, he also served as the EVP and general manager, digital for Univision Communications where he transformed the division including its technology stack, personnel, products and overall financial performance. He was an engagement manager for McKinsey & Company, and an IC design engineer for Micron and Intel.

Nagrani’s appointment follows the recently announced acquisition of Lexalytics, a provider of cloud and on-premise natural language processing and machine learning. The Lexalytics technology team will report to Mehul, and he will report to Andrew Joiner, InMoment CEO.

For Joiner, Nagrani’s appointment comes at a significant time of growth and investment for the company, adding that when they introduced the idea of experience improvement last September and challenged the industry to do more, they did so with the knowledge that acting on data is paramount. 

“Experiences are changing every day, and expectations are dynamic. Our vision of AI is to offer faster progress and improved decision making by automating tasks that can easily be facilitated through technology. We are tuning our approach to the tasks of CX professionals to give them more scale,” Joiner stated.

Speaking about his appointment, Nagrani commented, “InMoment has consistently been recognised not only for its future-proof vision but also its ability to execute on that vision. I was drawn to InMoment largely because of its comprehensive vision, innovative approach and caliber of talent.”

He added, “While already an industry-leader, accelerating the advancement of AI-based technology that leverages all types and forms of data, will help InMoment and our customers better deliver on the promise of experience improvement. I look forward to working with the combined InMoment and Lexalytics teams to accelerate this progress.” 

InMoment has also recently appointed Eric Weight as its VP of solutions consulting for APAC.

Melbourne, Australia – Thrive, an AI-powered fintech aimed at small and medium enterprises (SMEs), has announced the initial stages of its crowdfunding, targeting A$3M, on the equity crowdfunding platform Birchal.

The crowdfunding comes after the platform’s interest in launching the Thrive app for its SME waitlist, which has numbered over 7,500 businesses.

According to Thrive’s data, financial admin has been the most disliked activity in running a business and that business owners waste over 42 days a year in managing their financial affairs. This is something that the platform aims to solve by automating banking, accounting tasks, and lending for SMEs. 

Furthermore, Thrive combines a smart business account with value-added services like receipt scanning, invoicing, tax forecasting, payroll, and more. Using artificial intelligence (AI) and machine learning (ML), these tools are designed to run on autopilot, winning back time for busy business owners and making it easy for them to stay in control of their financial destiny.

“We have been running a number of focus groups with small business owners as we put the finishing touches on our product. After we kept getting asked about investment, we decided that we couldn’t think of anything better than to allow our potential customers to become investors in the business as well,” said Michael Nuciforo, co-founder & CEO of Thrive.

Meanwhile, Thrive Co-founder and COO Ben Winford added, “We were really impressed with the Birchal team and platform. We can’t wait to launch our campaign and to get our members behind us. Crowdfunding presents an exciting opportunity to build our brand, grow our member base and build further advocacy for our business.” 

San Francisco, California, USA – Marketing technology company Kenshoo has announced its acquisition of market intelligence company Signals Analytics, which entails enhanced accelerated e-commerce adoption for their clients in the midst of the pandemic.

As businesses are facing the need to rapidly transform engagement from physical to digital, there is a rise in the emergence of disruptive direct-to-consumer models and increased sensitivity to consumer privacy. Through the establishment of an AI-powered platform that connects internal and external data sets to surface insights across the entire marketing value chain, Kenshoo will empower enterprise clients to make stronger predictions and unleash their growth potential.

“Given the exponential growth we are experiencing in performance marketing, specifically around e-commerce, Kenshoo sees firsthand how brands make decisions to bring products to market online. The channel discussion is changing from media platforms to distribution types—direct-to-consumer or retail—and we are relied upon to support those decisions,” said Kenshoo CEO and co-founder Yoav Izhar-Prato.

He also added, “We looked for a powerful platform that best captured holistic consumer and market insights by connecting external data sets layered with cutting-edge, advanced analytics capabilities, and we found both in Signals Analytics. With a proven record in curating and augmenting external data and utilizing unique assets in artificial intelligence/machine learning (AI/ML) to infuse decisions with relevant, actionable insights for very prestigious brands, the team wowed us.”

Through the acquisition, the combined company assets will help create a connected knowledge graph across brand, consumer, product, campaign, publisher, and market data silos. This then allows consumer insights and analytics teams to streamline trend analysis in order to identify white space opportunities; provide marketers the ability to build more effective strategic plans, and give social, retail, and publisher partners access to broader cross-channel intelligence to generate value.

“Signals Analytics was founded on the premise that more sound, timely market intelligence could improve business outcomes as a critical bridge to fast-moving customers. My co-founder Kobi Gershoni and I recognized that the way to get there was by extracting available market signals from the noise that were often missed given the sheer volume of data constantly generated online,” said Gil Sadeh, Signals Analytics co-founder, and CEO.

“By connecting these signals in a robust, configurable data fabric using patented AI and natural language processing, we have helped some of the world’s most discernible consumer brands accelerate product innovation, improve launch metrics, support marketing teams, and ultimately drive growth. Joining forces with Kenshoo means we can advance our collective mission of enabling smarter, faster go-to-market decisions in the current, highly dynamic digital commerce era,” Sadeh added.

Kenshoo has established its presence in Asia Pacific and Japan back in 2014, with its regional headquarters located in Hong Kong and two satellite offices in Singapore and Japan.