Tag: Artificial Intelligence

Mumbai, India – CleverTap, an all-in-one customer engagement and retention platform, has announced the launch of Clever.AI, its AI-powered customer engagement engine. 

For CleverTap, it aims to provide brands with the next generation of AI capabilities required for building human-like knowledge of customers and efficiently delivering personalised experiences that resonate with them, hence enhancing customer lifetime value.

The three core AI pillars of predictive, generative, and prescriptive AI form the foundation of Clever.AI. ‘Clever.AI’ uses these pillars to transform the way brands communicate with their customers, making their interactions more informed and effective. 

Talking about the launch, Anand Jain, co-founder and chief product officer, CleverTap, said, “We’re thrilled to unveil Clever.AI, a testament of our pursuit over the last several years in leading the way in adopting the latest tech to transform customer engagement. We will continue to innovate CleverTap’s All-in-One engagement platform with Clever.AI enhancing its precision in predictions, its ability to prescribe intelligent customer experiences strengthened by advanced product analytics and deeper persona profiling to ensure brands can build highly personalised experiences, and campaigns more effectively, ensuring every customer interaction is personalised and outcome-driven.”

Imagine a world where every customer interaction is not just a transaction, but a meaningful conversation. A world where businesses can anticipate customer needs, respond with precision, and engage at a personal level that transforms the customer experience.

The next 2024 revolution in customer engagement entails breakthrough features that redefine the standards of online customer interactions, and I will be listing two prominent ones in this article; ‘Automated Workflow Platform’ and  ‘AI Response Feature’. These features are not only incremental improvements, they represent a sea change in how businesses of all sizes—from startups to corporations—can connect with their potential and existing customers on online platforms; WhatsApp, Instagram, Facebook etc.

An Automated Workflow Platform is an automated powerhouse that streamlines customer conversations. The platform offers a visually user-friendly interface and intuitive approach to analyse, understand, and respond to customer queries in real time with pre-determined questions and answers. It delivers a personalised experience 24/7 without the need of constant supervision.

If you’re looking to create dynamic and responsive workflows, most platforms offer custom conditions and branching, which allow you to set up workflows that respond only to certain scenarios: 

  • Custom conditions: You can create workflows that adhere to different scenarios. For example, auto-responses will be sent when you receive messages containing specific keywords. 
  • Branching: Branching allows you to create different paths within a workflow based on a criteria. This enables your desired actions to specified individuals or groups, such as assigning conversations to sales representatives based on inquiry type.

The power of automated workflows results in increased productivity in sales and consistent, efficient and optimised customer interactions, all while saving innumerable hours.

An AI Response Feature takes time- and cost-efficiency to the next level. With great feasibility, businesses can easily generate AI-powered responses in chats within a click. The features aims to solve the pain point of time and costs expended in training and assigning staff for personalised customer engagement, since this feature allows businesses to go beyond the usual responses to customer queries; it enriches each conversation with tailored content, product recommendations, and offers, driven by a deep understanding of customer preferences and past interactions. The tool’s superpower is in human-like digital communication in superhuman speed and efficiency, which has proven to foster loyalty and significantly boost sales conversion rates (by up to 80%).

For businesses that operate in the diverse and rapidly expanding Southeast Asian market, they are left to pivot in the complexity of engaging with a multicultural audience. An Automated Workflow Platform and AI Response Feature emerge as game-changing features that equip businesses to navigate language differences and unique cultural nuances with ease. The AI-powered platform ensures that no message goes unattended, enabling businesses to cultivate robust customer relationships and drive growth in this dynamic landscape.

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

Singapore – ADA, a provider of digital and data-driven business transformation services across Asia, has launched a suite of AI CoPilots engineered to redefine enterprise marketing and commerce functions, driving efficiency and effectiveness, fostering a new era of strategic and operational agility through data and AI democratisation.

Seamlessly integrated with ADA’s suite of digital services, the AI CoPilots tap into diverse datasets and proprietary AI models. Each CoPilot unites disparate information streams, dismantling the silos between different platforms and business units. By doing so, it provides transformative insights with real-time predictive analytics and delivers strategic guidance customised for the unique dynamics of each enterprise. 

ADA’s inaugural lineup of AI CoPilots encompasses four strategically devised tools, each tailored to optimise distinct facets of digital enterprise. They include tools for full-funnel marketing, e-commerce, conversational AI, customer segmentation.

Srinivas Gattamneni, CEO at ADA, said, “Our new AI CoPilots will set a benchmark in the industry by enabling enterprises to execute unprecedented marketing and commerce strategies. This initiative is not just an investment in technology, it’s an investment in our customers’ future, providing them with the tools they need to thrive in a competitive market.”

He added, “This move not only amplifies the reach of ADA’s advanced machine learning models and AI technologies but also reinforces the company’s dedication to fostering a synergistic ecosystem where our client’s strategies are seamlessly executed from insight to action.”

ADA’s commitment to innovation and its expansive presence in 12 countries underscore its role as a transformative force in the industry. As ADA continues to expand its capabilities and reach, it remains dedicated to delivering cutting-edge solutions that drive business success. 

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 2023-2024. What’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.