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This report aims provides insight into the Banking Industry in Vietnam. We look into people's preferences into their customer experience; using either traditional or digital banks, we deep dive into the topics driving social conversations about the banking industry, and the top mentioned brands related to the banking industry in Vietnam.

We have explored the latest trends and unpacked the current situation faced by the digital banking industry in Vietnam.

Download the whitepaper and read more.

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Media Monitoring is more than just a buzz word 

There are many common misconceptions about media monitoring that need to be cleared up sooner rather than later to give your brand the best chance of positive PR. Rather than letting your company succumb to the myths and misinformation being spread around, here are three of the most prevalent misunderstandings and the fact behind the fiction:

There's more to media monitoring than the digital platforms.

Myth #1 - You only need digital

While digital platforms are becoming more important to media monitoring, this is by no means the only area you need to be covering. Tweets, online newspapers and blogs are of course crucial, but so too are traditional media options, like local newspapers, talkback radio and other offline sources.

In fact, the best way to approach your media monitoring strategy is to accept that digital and traditional media are commonly connected, rather than separate features. For instance, social is often used as an extension to broadcast offerings, according to a study from Nielsen.

Here at Isentia, we understand that all platforms are important. No matter how small. 

Myth #2 - Only the big publications matter

For many companies, getting the brand name or products mentioned on a national radio show or published in a country-wide newspaper can mean a big break. Alternatively, a negative story across these major platforms could result in a significant blow to your reputation and profitability.

It is clear, then, that keeping tabs on the big media players is crucial. However, while some media monitoring providers will focus on national newspapers, big brand radio shows and other major publications, these strategies could be missing an important element.

National publications can give you a clear picture of what millions of consumers are reading, thinking and discussing, but this is unlikely to give you much information on what the local people believe.

If your business operates in a rural or remote location, you need to be tracking the local publications.

If your business operates in a rural or remote location, you need to be tracking the local publications - no matter how small. Similarly, even newspapers circulating in smaller parts of big cities can provide a significant level of insight, if only you are aware of their readership and content.

Myth #3 - Listening is the most important part

While media monitoring is critical for business success, listening to the conversations about your brand and industry is far from the be-all and end-all to your strategies.

Once you have uncovered a relevant story or discussion, it's not enough to simply stand idly by and learn from the experience. Taking the next step involves getting an insightful and useable report, deciding on relevant and effective action and getting involved in the discussions.

Of course, this is all easier said than done, but with the right media monitoring tools, you can get started with your best foot forward. Click here to check out some of our services so that you can be on the right track! 

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I crowd-sourced some opinions on ‘how PR has evolved’ via Facebook before this article was penned, and ‘chaotically’, ‘always-on’, ‘unpredictably’ and ‘intense’ were among some of the top keywords surfaced. Exactly how fast is the news-making cycle today? I’ve experienced it first-hand a couple of weeks ago.

9am, on my way to work, I posted on my Facebook page about a new flat fare option launched by local taxi company ComfortDelGro. 

By 11 am on the same day, three interview requests had arrived via Facebook Messenger from three different publications. By 2 pm, all interviews were completed on WhatsApp and my name appeared in the papers on the very next day. The whole event took place in less than 24 hours.

This is a glimpse of how news is made in this day and age. Journalists today are online and on social media; they are following key opinion leaders (KOLs) to get opinions. Gone are the days when they relied solely on press releases and spokesperson soundbites to write news and when public opinion was easier to gauge as people were only accessing a handful of mediums to receive information.

The convergence of digital, social and mobile has added layers of complexity in PR and clearly disrupted the practice, as news today becomes 24×7 and travels across the globe at the speed of the internet. The infamous United Airline incident for example, although happened in Chicago, created an uproar and boycott in China and trended in the top news on Weibo, all because of the power of social media.

The rise of digital and social certainly has benefited PR by creating the direct relationships with people, rather than requiring a media filter. To fully unleash its benefits, the best PR talents should strike the balance between creating content that people actually want to read, listen to or watch, and providing what traditional journalism would consider “news.”

With a good piece of content and story at the core, PR professionals are required to have the ability to navigate an increasingly complex media environment and to embrace the beauty of digital and social to enhance storytelling.

Instead of issuing a formal corporate announcement, why not consider tapping on Facebook Live for product launches and public activations? OCBC Bank recently launched its Stay True campaign via Facebook Live, where the bank’s Head of Consumer Financial Services was put through a lie detector test. The video garnered over 200,000 view to date.

Another example of leveraging digital to innovate traditional PR approach is a revamp of online corporate newsrooms. Dynamic Newsroom is a mash-up of PR, content and digital, which is designed to drive engagement, not simply overload information. It takes the best of everything we know about media relations and hosting content online, to more effectively connect brands with journalists.

Having talked about the benefits and opportunities, I also would like to caution that this trend of digital and social convergence also poses certain threats.

As social media increasingly becomes a main source of news and information and due to the fact that most social media content is user-generated, in order to boost visibility and garner likes and shares, brands and citizen journalists have been noticed to use unethical techniques to make their content exciting or ‘viral’. Such fake news and clickbait headlines are detrimental to brand reputation and consumer trust.

With great power comes great responsibility. The ability to earn credibility becomes even more important in an era of round-the-clock marketing messages. PR is becoming even more important and relevant than ever as the most reliable voice.

Originally published on Digital Marketing Asia 

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A customer evaluating a brand, a journalist researching a CEO, and a policymaker looking up a public agency are all doing the same thing — discovering organisations through AI. The difference is that they may each receive a different description, supporting evidence, and overall impression.

This shift represents a major change in modern communications.

Large language models (LLMs) now interpret information for audiences, rather than just helping them find it. Instead of listing search results, they retrieve content, select authoritative sources, and generate a single synthesised answer that many users accept without reviewing the original articles.

For PR, communications, and marketing professionals, this presents a new reputation challenge. Your organisation now has multiple AI-generated reputations, each shaped by the model your stakeholders use. Understanding these differences is becoming as important as understanding media coverage – in fact, the two are often closely linked.

There isn't one AI version of your organisation

A common misconception is that ChatGPT, Gemini, Claude, and Perplexity all access the same information. In reality, they  don’t. 

At a high level, all leading LLMs are built on similar foundations. They're trained on vast collections of books, websites, news articles, public documents and licensed datasets that help them understand language and generate human-like responses. Increasingly, they're also capable of retrieving live web information, allowing answers to incorporate recent events rather than relying solely on historical training data.

However, their similarities end there.

Each LLM uses a unique combination of training data, retrieval architecture, and ranking logic. As a result, each model answers three key questions differently before generating a response:

• What information should I retrieve?
• Which sources should I trust most?
• What deserves emphasis in the final answer?

These decisions fundamentally shape how organisations are represented. To explain this better, we summarise how different AI models consult different publications and finally cite them in AI answers:

LLMs are not impartial, but come with their own weights and balances. In a sense, it reflects dynamics we already see at play across PR & Comms. 

If four experienced journalists had to write a profile of the same CEO after attending the same press conference and have access to the same reports, one may write about leadership, another  financial performance, another governance and another might frame the story around innovation. None are necessarily wrong—they're simply viewing the event through a lens of individual expertise, and therefore, making different editorial decisions.

LLMs behave in similar ways. As a result, organisations are now represented by multiple AI-generated narratives, rather than a single authoritative digital narrative.

The same prompt can produce four different narratives

These differences are most apparent when users ask AI questions that require judgment rather than simple factual recall.

Consider a prompt like: "Which are the leading banks in Southeast Asia?" Across ChatGPT, Gemini, Claude, and Perplexity, you will likely see many of the same names — DBS, UOB, OCBC, and Maybank. This overlap occurs because all four models recognise these institutions as major regional banks.

However, the models differ in their explanations of why these banks are considered leaders.

ChatGPT may highlight DBS's digital banking leadership and customer experience. Claude is potentially more likely to discuss governance, regional strategy, and long-term institutional strength. Gemini may emphasise recent awards and publicly available web information, while Perplexity often presents answers as research comparisons supported by multiple citations. These changes are not necessarily so big as to be immediately noticeable, but over time the results accumulate. 

An organisation may not meaningfully change between two users asking two different LLMs , but the reputation narrative shifts accordingly. This distinction matters because stakeholders rarely ask AI for isolated facts. They ask questions like:

• Should I work with this company?
• Which university is most innovative?
• Has this government agency delivered on its commitments?
• Who are the market leaders in this sector?

AI responds by interpreting credibility, authority, and context, rather than simply retrieving documents.

For communications teams, this means your organisation is increasingly evaluated through comparative prompts, where competitors, industry peers, and institutional benchmarks appear alongside your organisation by default.

Every LLM has its own citation fingerprint

When ChatGPT, Gemini, Claude, or Perplexity answer a question, they first retrieve documents from distinct information ecosystems. A recent study analysed 17.2 million AI citations across ChatGPT, Gemini, Claude and Perplexity and found that each model retrieves from substantially different source ecosystems rather than a shared pool of webpages. Two LLMs can answer the same question accurately while relying on entirely different publications.

Instead of viewing these as technical differences, it is more useful to consider them as citation behaviours. Each model consistently references different types of publications when explaining organisations. 

• ChatGPT – Building consensus from multiple sources

ChatGPT functions more like an executive briefing writer than a traditional search engine.

Rather than listing multiple links, ChatGPT typically combines information from several credible publications into a coherent narrative. If mainstream media, company information, and industry commentary consistently describe an organisation as a market leader, ChatGPT is likely to reinforce that positioning, regardless of the original source.

For a prompt like "Tell me about Singapore Airlines”, a typical ChatGPT response integrates its history, customer experience, awards it has won over time, its financial performance, etc., into a cohesive description. Individual articles become almost invisible, as the model prioritises a coherent narrative in its answer over transparently showcasing all citations. 

For brands, this means consistency across publications is very valuable. ChatGPT values credible signals that repeat across multiple publications over one off news moments.

• Gemini – Reading the living web

Gemini approaches organisations differently, as its retrieval is closely linked to Google's broader information ecosystem.

When we ask  "What has Enterprise Singapore done to support AI businesses?", Gemini is more likely to include recent programme announcements, official government webpages, and newer online reporting. Its responses often feel more current because they draw from a web ecosystem designed to reflect continuously updated information.

For government agencies, this has practical implications. Official announcements and well-structured public information become machine-readable assets that help AI explain policy more accurately. These become important information touchpoints into how audiences understand government through AI.

• Claude — explains the ‘why’, in addition to the ‘what’

Claude's defining feature is its emphasis on context.

Other models often prioritise concise answers but Claude frequently elaborates on why an organisation is respected. Questions about leadership, governance, ethics, and institutional reputation tend to produce more detailed explanations rather than brief summaries.

For a prompt like "Why is DBS considered one of Asia's leading banks?", Claude is more likely to discuss how the bank has regionally expanded, its digital transformation, what’s unique about its leadership etc. Its responses resemble analyst reports more than search summaries, as it favours high-authority editorial and institutional content.

For executive communications, this makes Claude particularly influential. Leadership narratives and corporate values often receive more contextual treatment than with other models.

• Perplexity — makes your media strategy visible

Perplexity changes the experience by treating citations as a central feature rather than a supporting detail.

A question comparing two sustainability leaders, for example, typically returns numerous linked sources from business media and research publications. Users can immediately review the origin of each claim.

For PR teams, this creates greater transparency. Publication quality becomes visible within the user experience and is not hidden behind an AI summary. This means media strategy influences the evidence presented alongside the perception of the organisation. 

How commercial brands and government agencies are represented differently

Although LLMs behave similarly across sectors, the questions users ask differ fundamentally.

• For brands, AI compares before customers get the chance 

Consumers rarely ask AI what a brand does. They definitely do ask which brand is better.

Questions like:

1. Which airline has the best customer experience?
2. Is Salesforce better than HubSpot?
3. Which bank is most innovative?
4. Who are the leaders in cloud computing?

These questions are inherently comparative.

As a result, your organisation is often introduced alongside competitors before stakeholders visit your website.

During a recent Isentia webinar on Measuring your brand’s visibility in AI answers, we analysed airlines across multiple LLMs with Lumina AI View. Using identical prompts, different models highlighted different airlines and emphasised varying strengths, such as premium service, operational performance, innovation, and customer experience. Competitive positioning shifted depending on the model, even though the underlying organisations remained unchanged.

For commercial communicators, competitor association is becoming as important as share of voice. AI evaluates brands in context, not in isolation.

• AI becomes the interpreter of policy for government agencies

Public sector organisations encounter a different reputational challenge. Audiences increasingly ask questions that begin with how, why and can I trust:

1. What support does this agency provide?
2. Has this ministry achieved its policy goals?
3. What AI initiatives has this department introduced?
4. Has this programme faced criticism?

LLMs then synthesise official publications and institutional information into a single accessible explanation. This changes the role of public communications. The objective is not to ensure AI models have sufficient credible, authoritative context to explain complex policy accurately.

As AI becomes the primary interpreter of government information, communications teams are responsible for managing both visibility and understanding.

Measuring AI reputation with Lumina AI View

Reputation has been shaped across three familiar environments: media, search and social. AI introduces a fourth environment.

LLMs condense dozens of publications into a single response, reducing the traditional discovery process between a question and an opinion.

During the recent webinar, Prashant Saxena, VP of Revenue and Insights says, “We used to Google it. Now we're ChatGPT-ing it.”

This behavioural shift is significant. Reuters Institute research found that only a small proportion of AI chatbot users regularly click through to original articles. 

Practically, every piece of earned coverage now has two audiences:

• The people reading it.
• The AI models learning from it.

Both shape reputation.

This is the challenge Lumina AI View was designed to solve.

Instead of measuring whether an organisation appears in ChatGPT, AI View measures how different LLMs represent the organisation. Using consistent question sets across ChatGPT, Gemini, Claude, Perplexity, Grok, and Google AI Overviews, it compares narratives, citations, and competitive positioning through a unified reputation methodology.

The framework is built around four familiar dimensions: Direction, Performance, Integrity, and Innovation. Rather than tracking keywords alone, AI View identifies which reputation pillars dominate AI responses, which publications influence those narratives, and how representation differs across models. It also gives the organisation an AI score based on all the above factors.

The tool also shows organisations which publications are covered by different AI models. If the organisation’s AI score on the tool is high for ChatGPT, but struggles with or is a lower score on Gemini or Perplexity, the tool allows you to see where you’re underperforming. If there are publications that are covering the wrong information, or the important ones just don’t show up, then that’s an indication to why the AI score is low on that platform. 

What this means for PR and communications professionals

The communications profession has always adapted to new discovery channels, from newspapers to search engines, and from social media to digital news.

AI is different because it does not simply help audiences find information, but it increasingly determines how organisations are introduced.

For communications leaders, this changes media strategy in three key ways. 

• First, publication quality becomes more important than quantity, as different LLMs repeatedly reference authoritative sources when constructing narratives around your organisation.
• Second, visibility for the organisation’s spokespeople now extends beyond interviews and articles. The expertise attributed to leaders increasingly shapes how AI explains an organisation's direction and credibility. 
• Finally, competitor positioning is now continuous rather than campaign-based, as AI naturally compares organisations whenever users ask for recommendations or leadership insights.

Organisations that succeed in this new environment are those that have leadership, performance, integrity, and innovation represented consistently across every major AI model, regardless of where audiences and stakeholders begin their search.

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Want to see which sources are shaping how AI describes your organisation? Get in touch about Lumina AI View.

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