Singapore Integral Ad Science, a worldwide media measurement and optimization platform, today announced a partnership with Snap Inc. This agreement seeks to provide advertisers with greater transparency during their Snapchat campaigns by employing IAS’ AI-powered total media quality (TMQ) brand safety and suitability measurement tool.

Compliant with the Global Alliance for Responsible Media (GARM) guidelines, the newest product offers marketers the benefit of independent verification together with reliable and open industry data. Frame-by-frame analysis of images, audio, and text is used in IAS’s Total Media Quality solution to provide insights into video content while guaranteeing the most accurate assessment at scale. 

Speaking about the partnership, Lisa Utzschneider, CEO of IAS, said, “We are excited to partner with Snap to deliver our best-in-class measurement solution for marketers to safeguard and scale their businesses on Snapchat. Snap is focused on developing ad offerings in a premium and safe content ecosystem, and our partnership will give advertisers actionable data to maximise their investment across Snapchat.”

Meanwhile, Patrick Harris, president of Americas at Snap, expressed, “We’re thrilled to partner with IAS to offer Snapchat advertisers an additional layer of brand safety and suitability. We’ve built safety into the fundamental architecture of our platform and are dedicated to providing our community and partners a healthy and safe experience. We look forward to continuing to invest in products and partnerships across the brand safety ecosystem.” 

Singapore Accenture has formed a partnership with Adobe to jointly develop industry-specific solutions. They intend to help enterprises create content at scale and alter their content supply chains by leveraging Adobe Firefly, Adobe’s family of creative generative AI models. 

Accenture intends to include Adobe Firefly Custom Models in its Accenture Song marketing services. With the help of this integration, clients will be able to use their proprietary data and brand guidelines to train customised models with the industry-specific insights that are needed. Designed for commercial use, Firefly is accessed through Adobe Creative Cloud and Experience Cloud apps, as well as APIs via Firefly Services. 

Marketers can create particular campaigns by producing content that matches their brand’s aesthetic and visual language. Then, by utilising effect analysis and performance statistics, these programs may be continuously improved. Iterative strategies reduce the need for manual modifications and streamline the process of creating content. 

Accenture’s data and AI engineering skills will be leveraged by the new solutions, which are first aimed at the automotive, retail and consumer goods, financial services, and health industries. They will also integrate Accenture’s strategy for creating cohesive brand experiences with its approach to responsible AI.

Through the integration of these solutions with Adobe’s wider range of generative AI-powered tools and client systems, businesses can gain the benefits of content that is industry-specific, locally relevant, and internationally consistent. In addition, Accenture engineers will receive specific training in Adobe Firefly so they can help clients with generative AI initiatives. 

Accenture will use Adobe Firefly in its marketing division as part of the partnership to help staff members create creative material effectively. Accenture is able to customise content for each of the 19 sectors it services by using a Firefly Custom Model that is adapted to its unique brand style and design language. 

Speaking about the partnership, David Droga, chief executive officer, Accenture Song, said, “Brands today are looking for ways to go beyond experimenting with generative AI to achieve real impact. Whether it’s consumer goods companies scaling their product data and images in e-marketplaces worldwide, or healthcare providers ensuring brand standards for patient safety, the demand for scalable generative AI solutions is increasing. By bringing together Adobe technology with Accenture Song’s tech-powered creativity, we can help democratize the ability for teams to develop creative assets and accelerate content supply chain transformation.”

Meanwhile, David Wadhwani, president, Digital Media Business, Adobe, stated, “Businesses have an unprecedented opportunity to leverage generative AI to deliver truly personalised experiences that connect with their customers. Firefly is an enterprise grade solution that powers a full suite of generative capabilities – from content generation to editing to assembly – through our industry-leading applications and enterprise automation APIs. We are excited to partner with Accenture to define and implement solutions that empower organisations around the world to harness the power of AI.” 

Furthermore, Jim LaLonde, lead of the Accenture Adobe Business Group, commented, “In recognition of our technology and industry experience and decades-long relationship, Adobe has selected Accenture to help develop and deliver industry-specific generative AI capabilities that will give organisations the tools they need to unlock new value. Together with Adobe, we’re continuing to invest in the talent and technology needed to drive next generation experiences for our clients.”

California, USA – S4Capital’s operating brand Media.Monks, a digital-first, data-led advertising and marketing services company, has launched ‘Persona.Flow,’ a professional managed service that gives brand marketers the ability to talk to their data. According to the agency, said solution addresses a unique need within enterprise AI workflows by converting owned customer data into dynamic consumer personas. Said transformation then allows marketers to interact in real-time, significantly augmenting their speed to market.

The ‘Persona.Flow’ solution offers a user-friendly interface to help brands streamline strategy workflows, increase their precision, and democratise deep data insights. Its core technology is driven by a robust retrieval-augmented generation (RAG) framework that synergises brand-owned data and factual data from extensive consumer and market research libraries provided in partnership with data-driven marketing firm Claritas. 

Moreover, the solution negates the need for the weeks of planning and deliberation that are commonly associated with building consumer intelligence. The service amplifies speed to market by delivering detailed, granular insights in real-time and in natural language, making it easier than ever to respond to market trends as they emerge.

According to Media.Monks, owning a comprehensive dataset is crucial for brands to future-proof themselves and stay competitive in today’s data-intensive and AI-dominated creative landscape, and with ‘Persona.Flow’, it serves as a reliable, single source of truth for all of a brand’s marketing data, fostering the discovery of new creative concepts that may in turn generate further data based on their performance with real audiences.

“As a consultative AI partner, Media.Monks aids brands in leading the new economy and becoming AI-first through the deployment of customised solutions. Unhindered by traditional agency compartmentalisation, Media.Monks’ in-house team of data scientists, machine learning engineers, and creatives boast profound expertise in AI and machine learning technologies. Consistently at the forefront of integrating and applying these technologies, Media.Monks is pioneering a revolutionary commercial model for the marketing and advertising industry that addresses brands’ most urgent commercial requirements,” the agency stated.

As Google prepares to phase out third-party cookies, marketers are exploring alternative strategies to directly gather valuable customer data. In their quest to drive customer engagement on social platforms, AI-powered chatbots have emerged as both a viable and effective tool for boosting conversions. These intelligent chatbots excel at facilitating meaningful conversations, offering real-time personalised assistance that aligns with a brand’s tone and identity.

Training language models typically involves three techniques: pre-training, fine-tuning, and in-context learning. While these approaches aim to improve model performance, they vary in methodologies and objectives. Therefore, when implementing an AI chatbot for your brand, it’s crucial to understand the key differences between these training approaches and, more importantly, recognise why in-context learning models provide the most compelling solution for businesses looking to optimise marketing efforts while minimising costs.

Understanding Training Language Models: Pre-training, Fine-tuning, and In-Context Learning

Having a basic understanding of training language models is advantageous for marketers as it allows them to establish realistic expectations for chatbot capabilities, tailor chatbot responses to align with marketing objectives, and foster effective collaboration with technical teams engaged in chatbot development. Here’s a concise overview of the three techniques:

1. Pre-training:

  • Purpose: Establish a foundation of knowledge and generate coherent and contextually relevant responses in a wide range of topics.
  • Benefits: Learns general linguistic patterns, grammar, and semantic relationships.
  • Limitations: May not be domain-specific or tailored to specific tasks.
  • Potential Marketing Use Cases: Text generation, language translation, news articles entity recognition, sentiment analysis for social media.

2. Fine-tuning:

  • Purpose: Adapt the model to a specific domain or task and improve task performance.
  • Benefits: Learns from task-specific examples and becomes more relevant to a particular application.
  • Limitations: Requires a labeled dataset specific to the target domain, which can be expensive and time-consuming to create.
  • Potential Marketing Use Cases: FAQs, text classification for document categorisation, sentiment analysis for market research.

3. In-context learning:

  • Purpose: Enable the model to understand conversational dynamics and generate contextually appropriate replies based on the given instructions.
  • Benefits: Learns from conversational exchanges, including user inputs and system responses.
  • Limitations: Requires a dialogue dataset specific to the target application, but can be curated and augmented more easily compared to creating a fully labelled dataset from scratch.
  • Potential Marketing Use Cases: Chatbots and virtual assistants, dialogue systems, personalised recommendations.

Characteristics of In-Context Learning AI Chatbots for Marketers

In-context learning AI chatbots offer three useful characteristics that enhance organic customer interactions.

Firstly, contextual prompts can boost the effectiveness of personalised email marketing campaigns. Marketers can use in-context learning language models to ask subscribers about their fitness goals and preferences via email. Analysing the responses enables the generation of personalised product recommendations, leading to a more tailored and impactful email marketing experience.

Secondly, reinforcement learning or structured feedback can drive sales in e-commerce chatbots. Marketers can integrate a reinforcement learning mechanism by asking customers to rate the helpfulness of the chatbot’s responses after each interaction. This feedback allows the chatbot to prioritise and generate accurate, relevant responses, elevating the overall customer experience. Continuous reinforcement learning enables the chatbot to better understand customer queries and provide satisfactory solutions.

Thirdly, through multiple iterations of training, in-context learning AI chatbots can adapt to evolving marketing requirements and improve their responses. For instance, a travel agency’s chatbot can undergo iterative training to stay up-to-date with the latest travel destinations, flight schedules, and hotel availability. As new information becomes available, the chatbot learns and adjusts its responses, delivering customers accurate and timely travel recommendations. This iterative training process ensures that the chatbot remains well-informed and capable of meeting customers’ changing travel needs.

Cost-Effectiveness of In-Context Learning AI Chatbots

In-context learning AI chatbots offer a cost-effective solution for marketers compared to other training approaches. Here’s why:

Reduced Data Labeling Costs

In-context learning AI chatbots require a dialogue dataset specific to the target application. While effort is still needed to collect and curate this dataset, it is often less expensive and time-consuming than creating a fully labelled dataset from scratch, as required in fine-tuning approaches. This cost advantage makes in-context learning more accessible, particularly for businesses with limited resources.

Continuous Learning and Adaptation

In-context learning AI chatbots can continuously improve their performance through reinforcement learning or structured feedback mechanisms. This iterative process allows the chatbot to adapt to changing customer needs and refine its responses over time. Instead of investing in periodic retraining or fine-tuning, marketers can leverage the ongoing learning capabilities of in-context learning models, saving both time and resources.

Improved Operational Efficiency

By automating customer interactions and handling a wide range of queries, in-context learning AI chatbots reduce the need for human intervention. This streamlines operations, enabling marketing teams to allocate their resources more strategically. With AI chatbots taking care of routine queries and tasks, marketers can focus on higher-value initiatives, maximising their productivity and cost-effectiveness.

Enhanced Conversion Rates

In-context learning AI chatbots excel at delivering personalised and contextually relevant responses, which significantly impact conversion rates. By providing tailored recommendations, addressing specific customer needs, and fostering engagement, these chatbots create a more compelling user experience. Higher conversion rates translate to a better return on investment (ROI) for marketing efforts, making in-context learning AI chatbots a cost-effective choice.

Drive Customer Acquisition through AI-Enhanced Conversations

AI and predictive analytics are essential components of a comprehensive marketing strategy. In addition to its cost-effectiveness, in-context learning chatbots enable forward-thinking marketers to identify precise customer segmentations, optimise human resources, and drive conversions more effectively. Are you ready to take the lead and unlock the full potential of your customer interactions? The choice is yours. 

This article is written by Henson Tsai, founder and CEO of SleekFlow

The insight is published as part of MARKETECH APAC’s thought leadership series under What’s NEXT 2023-2024What’s NEXT 2023-2024 is a multi-platform industry initiative which features marketing and industry leaders in APAC sharing their marketing insights and predictions for the upcoming year.

Malaysia Malaysia Airlines and the international technology company Google have formed a partnership to promote Malaysia’s growth as a prime tourism destination while also driving digital advancement in the aviation industry.

Expanding on its commitment to promoting Malaysia as a core hub for international travellers, the dynamic relationship marks Malaysia Airlines’ drive towards digitising its commercial advancement to promote growth and demand from important markets. 

The partnership intends to support continued development and innovation in a number of product areas, such as Google Pay, Google Flights, and AI-driven marketing solutions. They may develop a comprehensive ecosystem with a client experience as the top priority for this endeavour.

The airline also hopes to use creative solutions to propel its growth and marketing tactics, positioning it for major network expansion plans this year, as travel demand is expected to approach pre-pandemic levels.

Malaysia Airlines aims to increase the reach, relevancy, and return on investment of its marketing initiatives in the very competitive travel market by utilising Google’s AI-powered Performance Max. 

Speaking about the partnership, Dersenish Aresandiran, Malaysia Aviation Group (Airlines) chief commercial officer, said, “By harnessing the power of Google’s technology innovation and expertise, we are confident that we can unlock new opportunities, elevate the travel experience, and strengthen Malaysia’s position as a leading tourism hub in the region, aligning with the government’s vision for Visit Malaysia Year 2026 (VMY2026).” 

Meanwhile, Farhan Qureshi, Google Malaysia, Pakistan and Frontier Markets managing director, said, “Malaysia’s tourism sector is poised for significant growth, and Google is committed to supporting Malaysia Airlines in capitalising on this opportunity. By teaming up with Google, Malaysia Airlines intends to harness cutting-edge artificial intelligence (AI) and digital technologies to enhance its commercial operations, streamline processes and provide tailored experiences to travellers.”

In today’s dynamic economic climate, brands constantly navigate budget constraints within their digital marketing efforts. The digital advertising landscape is evolving rapidly, driven by trends such as AI optimisation, increased personalisation, and growing mobile usage. In this context, cost-efficiency has emerged as a critical factor that can significantly influence the success of marketing campaigns while ensuring financial sustainability.

The Transformation of Digital Advertising with AI

The digital advertising landscape is undergoing a paradigm shift with the integration of AI. AI has become indispensable for engaging, converting, and reaching consumers effectively. Insider Intelligence predicts that over 50% of digital ads will leverage AI and machine learning by 2024, creating seamless, personalised experiences across devices and formats.

AI enables advertisers to create hyper-personalised messages for each individual, enhancing relevance and engagement. They can continuously experiment with and optimise campaign elements such as captions, images, and calls to action, maximising impact. AI also helps advertisers predict the consumers most likely to react positively to tailored value propositions, enabling more targeted and effective campaigns. Furthermore, AI facilitates the automation and scaling of cross-channel advertising while preserving personalisation, resulting in significant cost savings and increased efficiency.

AI’s Impact on Cost-Efficient Mobile Advertising

AI is transforming the landscape of mobile advertising and making it more cost-efficient. Industry reports from eMarketer reveal that AI has led to a 27% decrease in customer acquisition costs. Furthermore, Epsilon’s research found that AI-driven Personalisation Images influenced 80% of consumers to purchase. By leveraging AI, brands can create more targeted and personalised ads, reducing waste and improving return on investment.

AI-powered mobile advertising allows brands to leverage buying intent signals to diversify their digital advertising strategies. By harnessing these signals, brands can gain insights into consumer behaviour, allowing them to target their advertising efforts more effectively and efficiently. This approach increases the likelihood of conversions and ensures brands get the most value from their advertising spend.

E-commerce businesses could benefit from using AI and machine learning to create personalised product recommendations, dynamic pricing, and targeted ads based on consumer behaviour, preferences, and purchase history. AI can also help e-commerce businesses improve customer service, loyalty, and retention by using chatbots, voice assistants, and sentiment analysis. Xtend’s Whitepaper showcases an Indonesian e-commerce platform.

AI-powered solutions activate dormant buyers and targeted micro-cohorts. An effective pacing strategy was developed. As a result, the platform has gained three times the amount of new buyers monthly, with click-through rate value that is four times higher than the industry average rate.

Personalisation, another key digital advertising landscape trend, influences consumer behaviour and advertiser strategy. AI-powered personalisation enables brands to deliver tailored messages to consumers, increasing engagement and conversion rates. By delivering content that resonates with consumers personally, brands can enhance their relationships with consumers and drive loyalty, all while optimising their advertising spend.

In 2024, advertisers are expected to embrace immersive and cohesive ad formats, creating a holistic advertising experience. The shift towards a more automated, data-centric, and integrated advertising experience is anticipated.

In conclusion, as digital advertising evolves, brands must prioritise cost-efficiency in their AI-powered mobile advertising strategies. By leveraging AI and machine learning, brands can deliver personalised and effective advertising campaigns that engage and convert consumers and ensure financial sustainability in an increasingly competitive market.

This article is written by Murali Dharan, Chief Commercial Officer at Xtend.

The insight is published as part of MARKETECH APAC’s thought leadership series under What’s NEXT 2024. What’s NEXT 2024 is a multi-platform industry initiative which features marketing and industry leaders in APAC sharing their marketing insights and predictions for the upcoming year.

United States – Global technology company IBM has announced that is laying off staff across its marketing and communications teams, as previously reported by CNBC.

According to media reports, the IBM layoffs were made by the company’s chief communications officer Jonathan Adashek in a meeting that only lasted seven minutes amongst those in the marketing and communications teams who will be affected.

The layoffs reflect IBM’s rapid move to replace nearly 8,000 jobs in the company with AI, as well as massively upskilling all of its employees on AI. Its CEO, Arvind Krishna, previously told CNBC that it will be cutting 3,900 positions back in January 2023.

“In 4Q earnings earlier this year, IBM disclosed a workforce rebalancing charge that would represent a very low single digit percentage of IBM’s global workforce, and we expect to exit 2024 at roughly the same level of employment as we entered with,” the company stated.

IBM joins a slew of tech companies making significant layoffs this year including Cisco, Amazon, Snap, Okta, PayPal, and Microsoft.

Singapore – AI enterprise customer data platform Amperity has announced two new generative AI capabilities, Explore and Assist, to accompany existing AI-powered capabilities, Stitch and Predict, forming a new comprehensive suite known as ‘AmpAi’.

Through AmpAi, Amperity focuses on fixing data quality and access challenges many brands face with traditional CDPs, promising brands the confidence to make decisions based on a trusted data foundation.

Going into detail on AmpAi’s capabilities, Assist supports marketers, analysts, and data operators with creating marketing workflows more quickly. The first product within Assist is ‘Ai Assistant’, which removes the barriers to creating SQL queries and fixing potential errors within those queries.

On the other hand, Explore is all about enabling business users across the organisation to access and use customer data. ‘AmpGPT’, the first product in Explore, empowers marketers to interact with their data using natural language.

Amongst the new additions, Stitch unifies all data sources to create the highest data quality foundation to make business decisions, whilst Predict helps marketers understand what they need to do to keep the customers they have and make them even more profitable.

The company will introduce more products under Assist and Explore in the coming months to help further democratise customer data and make it accessible to all users in a privacy safe way.

Talking about this, Barry Padgett, CEO at Amperity, said, “We’re on the cusp of a transformative shift in how brands interact with their customer data. For too long, the complexity of data queries and segment creation has been a barrier, consuming valuable time that could be spent on strategic initiatives.”

“With Generative AI, we are empowering all users to be data scientists by democratising data usage and making customer insights accessible across the organisation. We’re not just helping brands save time; we’re empowering every team member to drive value and make informed decisions based on a trusted data foundation,” he added.

Hong Kong FWD Group Holdings Limited announced today that it has extended its partnership with Microsoft for a four-year period. This partnership intends to provide access to the most recent generative artificial intelligence technologies while still retaining support for FWD’s cloud-first technology strategy. 

By utilising Microsoft’s Azure OpenAI Service and other enterprise-grade developments, FWD Group aims to enhance its generative AI ambitions. The business expects to gain from Microsoft Azure’s AI models as well as its private networking, security, and monitoring features. 

In a number of areas, including underwriting, claims, channel and agent performance, acquisition, marketing, underwriting, and customer service, FWD Group is actively working to enhance the client experience and streamline its business processes. The business also used Copilot for Microsoft 365 early on, which is an AI assistant that helps staff with daily chores. 

Speaking about the partnership, Ryan Kim, group chief digital officer of FWD Group, said, “Digital innovation has always been core to FWD’s vision of changing the way people feel about insurance. This collaboration marries FWD’s pioneering spirit in Asia in some of the fastest growing insurance markets in the world, with the global scale and skill that Microsoft brings in engineering and AI.” 

He added, “We’re excited to harness more next-generation innovations to develop new industry use cases and standards that we believe will shape the insurance journey of the future.” 

Meanwhile, Bill Borden, corporate vice president of Worldwide Financial Services, Microsoft, stated, “AI is driving transformation across the financial services industry, opening new opportunities for innovation and business growth with agility and at scale. We are thrilled to strengthen our AI partnership with FWD by offering Azure OpenAI Service and Copilot for Microsoft 365 capabilities to enable world-class customer experiences and operations securely and responsibly.” 

“As a pivotal player in the global financial landscape, the Asia Pacific region stands out for its dynamism and adaptability, fostering continuous growth and driving innovation. We are committed to empowering our customers in the region with generative AI capabilities in a responsible way. With Microsoft’s enterprise-grade AI advancements, we are helping the financial services ecosystem accelerate innovation to drive operational efficiency and greater value creation to customers,” Borden added. 

Singapore – Businesses are increasingly integrating customer data platforms with AI and analytics to personalise customer experiences and drive business success, according to a new report from customer engagement platform Twilio.

Data from the report suggests that amidst the widespread adoption of AI, businesses are grappling with an exponential increase in data volume, with Twilio Segment processing a record high of 12.1 trillion API calls in 2023.

This increase is indicative of a larger trend towards more sophisticated, data-centric operations, emphasising the essential role of real-time data processing and seamless AI technology integration.

With this in mind, Twilio emphasises that the ability to quickly harness data insights through CDPs that are open and interoperable with data warehouses is a critical competitive edge, enabling businesses to efficiently collect, unify, and activate data across various platforms.

Going into detail, other key findings of the report claim that data integrity is essential for deriving meaningful customer insights and leveraging AI competitively, as the effectiveness of AI hinges on the data it’s trained on. 

Additionally, data warehouses continued to spike in popularity as one of the most popular destination categories for customer data in 2023, as businesses pave the way for deeper analytics and AI-driven insights.

Talking about these findings, Kathryn Murphy, SVP of product and design at Twilio, said, “In 2024, more and more brands will turn to AI to deliver better, more personalised experiences for their customers. Our report showcases the essential role customer data plays in maximising AI’s effectiveness.”

“At Twilio, we’re seeing a significant trend towards leveraging the interconnectedness of AI, data warehouses, and digital communication channels. The ability to interoperate with data warehouses is essential, ensuring that CDPs act as a pivotal technology for brands eager to leverage AI and data to forge even stronger relationships with their customers,” she added. 

Meanwhile, Chris Hecht, SVP, corporate development and product partnerships, at Databricks, commented, “As businesses look to break down data silos and rely on a unified data platform to power their analytics and AI initiatives, the importance of data sharing and data quality has never been more apparent.”

“Our collaboration with Twilio Segment signals our dedication to ensuring that organisations leverage the full potential of their data – no matter where it lives – and can effectively bridge the gap from data to insights using cleaned and verified event profile data”, he added.