From proof to persuasion: The evolving art and science of modern communications

Evidence-based communications, which emphasises building strategies based on solid data, have long been a cornerstone of client consulting in the communications and advertising industry. The rise of data analytics and related technologies has further strengthened this approach.

However, as data-driven methodologies become the norm, we are faced with a new dilemma: Is data all you need to help clients build reputation as a competitive advantage?

In today's complex and ever-changing environment, reputation is built not only on truth and fact, but also on belief. So how do we ensure that the evidence we base our strategy on is also persuasive enough to impact belief?

Evidence-based communications rely on understanding the audience's perception and key factors influencing it.

This process often starts with public opinion. In the past, analysing public perception provided valuable insights into the audience’s cognitive state. However, now, with the evidence increasingly "polluted” by platform algorithms and various social media effects, there may be a significant gap between what we infer from a perception analysis report and the actual public perception.

The Pitfalls of Evidence Pollution

• Fake data interference: Advancement in AI technologies have made it easier to create convincing fake content, leading to widespread manipulation of comments, the growth of bot accounts and paid commentators. This distorts public opinion data and creates misleading sentiment and public opinion analysis.

• Algorithmic and commercial bias: Algorithms that curate content on social media can introduce bias, skewing public opinion data and influencing user perceptions in unintended ways. Similarly, social media trends and rankings are often driven by commercial interests, which may not reflect the audience's true cognitive state.

• Manipulation of trending topics: The fast turnover of trending topics can lead organisations to focus on short-lived issues, diverting attention from long-term strategic goals. This can cause organisations to prioritise the wrong issues and overlook more significant, longer-term trends.

• Dynamic perception shifts: Social media users are increasingly drawn to dramatic and sensational stories. The emotional and immediate nature of these discussions can change perceptions within hours or even minutes, making it difficult to track and respond effectively.

• Silent majority ignored: Current sentiment monitoring tools only capture the voices of active online users, neglecting the opinions of the silent majority. This can lead to a distorted understanding of the real perception of the target audience.

• Echo chambers: Online communities and social networks often create echo chambers where similar viewpoints are reinforced. This can lead to a skewed perception of public opinion and make it harder to assess broader public sentiment.

• Complexity of video content analysis: Video content is now mainstream, but monitoring and analysing it is much more complex than text or static visuals. The cost of acquiring videos and the challenges of navigating restrictions on video platforms adds to the difficulty.

• Short-lived trends and rapid content turnover: The fast pace of social media creates short-lived trends and rapid content turnover, making it hard for organisations to maintain a consistent and long-term communication strategy.

Bridging the Gap with AI

The development of AI has opened new possibilities for analysing audience cognition and behavior. This helps marketers better understand audience conversations, analyse behavior behind public opinion data, and develop strategies that influence perception and belief.

While data-driven communication is common, ensuring the accuracy and reliability of ‘evidence’ is increasingly challenging. To address these, Burson’s Innovation Portfolio offers cognitive AI models like The Sonar Suite and The Decipher Suite. These tools help us understand the ‘why’ behind the ‘what’ in public opinion data.

Sonar, with its advanced AI and machine learning capabilities provides deep insights into risk detection and mitigation. It goes beyond mainstream channels, including video channels, to uncover harmful narratives and misinformation that could impact an organisation's reputation. On the other hand, Decipher offers predictive analytics to forecast the impact of communications, ensuring that messages are both credible and engaging.

This empowers communications professionals to craft more targeted and persuasive messages and to navigate the changing landscape of public opinion by anticipating shifts in audience perception. Integrating cognitive AI into communication strategies marks a significant step towards a more robust evidence-based approach to public engagement.

Practical Strategies for Modern Communication

By bridging the gap between data and actionable insights, AI helps communication professionals navigate the complexities of modern media and achieve precise messaging. However, simply adopting these tools isn’t enough. Communicators need to adopt a content operations model approach. This approach, used by companies that monetise content, helps professionals effectively leverage AI tools.

A few recommendations include:

• Stakeholder-centric content creation: Focus on creating content that meets your audience’s needs and expectations. This involves continuously adapting and optimising content based on user feedback and behaviour data. Treat stakeholders as consumers of your content and apply user-centered thinking and best practices.

• Engagement and community building: Encourage and facilitate user participation through comments, shares, and other forms of interaction. Building a strong community around your brand can enhance trust and loyalty. Look beyond content, individuals, and influencers. Consider communities and social circles. Tools can provide this perspective, and it’s up to us to learn how to use them effectively

• Comprehensive content strategy: Go beyond content creation and distribution. Using data to understand market supply and demand can help identify new topic opportunities. AI tools can help uncover trends, emerging topics, and narratives, helping guide content decisions and discover opportunities.

• Data-driven: Use data to monitor content performance and make necessary adjustments. This ensures that your communication strategy remains relevant and effective. While this may seem obvious, it is becoming increasingly important. Understanding the principles of forecasting and prediction tools and applying them correctly—combined with our human wisdom and judgment—will be more critical than ever.

Despite the challenges of evidence pollution, adopting a content operations mindset to leveraging AI tools can help communication professionals build strategies that effectively navigate the fluid nature of reputation in today’s complex environment.

Looking forward, it is crucial for professionals to integrate these advanced tools into their daily practices, fostering innovation and maintaining a competitive edge in the dynamic world of modern communications. By focusing on stakeholder-centric content, fostering engagement and community, developing comprehensive content strategies, and optimising with data, we can navigate the complexities of the modern media.

This article is written by Joe Peng, APAC & Greater China head, digital & intelligence, Burson.