The world of data and analytics has never been so important to businesses, yet is still often cloaked in mystery. Venkata Bhonagiri, senior partner at Mindshare, explores the crucial role of human intelligence in protecting reputation and reaching audiences.

How can data be used to strengthen reputation management?

In today’s digital landscape - with access to data, social media platforms and other online channels - brands, organisations and individuals can shape their presence and maintain their reputation in a multitude of ways.

Reputation has become increasingly important. Brands use data and analytics tools to monitor and engage in online conversations, social media platforms and reviews to gain insights into consumer sentiment that allow them to craft strategies to drive consumer experience and engagement. Steve Jobs said it best: “Start with the customer experience and work backward to the technology, not the other way round”. For example, Amazon leverages customer data to personalise recommendations, enhance user experience and improve customer satisfaction.

Once you have a strong insights capability, you can then develop and implement a content strategy that focuses on promoting positive narratives and showcasing the achievements, expertise and values of your brand to shape public perception and build a strong reputation over time. Nike does this well by encouraging their customers to create and share content related to their products and experiences using branded hashtags. They analyse the UGC (user-generated content) to identify trends, stories and user testimonials that align with their brand values.

How can communicators most effectively integrate data into campaigns and strategies?

While there are numerous ways of working and integrating data into campaigns and strategies, in my experience these fives phases or stages to do this effectively:

• Defining objectives: everything starts with clearly defining the north star and the objective of the campaign. This is because each objective warrants the usage of data and the ability to set it up and integrate it. For example, increasing brand awareness is vastly different than driving web traffic or improving customer experience.
• Data strategy: this stage encompasses a few different components, including identifying relevant data, data partners, tools and approaches to acquire data, store it, and leverage it to design and activate. We must ensure we implement a solid data strategy to support data collection, management and usage. 
• Defining key metrics and KPIs: no process and - or - integration can be effectively managed without tracking and measuring it. This requires identifying the key metrics and data points that align with the overarching objectives. For example, if the objective is to increase customer engagement, relevant metrics could include social media engagement rates, website click-through rates or email open rates. Selecting the right metrics ensures that the collected data will provide insights directly related to your campaign goals.
• Design audiences: understanding audiences is key for any campaign and data is required to segment the target audience based on their attitudes, preferences and behaviours. This allows us to create more personalised and targeted campaigns that resonate with specific segments of the overarching growth audience. Data can be used to take this one step further i.e., to develop a data-driven messaging. That means tailoring communication to address the specific needs, interests and pain points of different audience segments: using data to create compelling stories that resonate with the target audience.
• Testing and learning: data can be used to run experiments and analytics to refine and optimise campaign performance.

When done well, brands can make informed decisions, enhance targeting and personalisation, optimise performance and ultimately achieve their objectives more effectively.

Do you find many people feel intimidated by analytics and, if so, why?

The perception of analytics can vary among individuals, leading to a mix of sentiments. While some people may indeed feel intimidated by analytics, there are also those who find it incredibly valuable and are passionate about it. The intimidation factor often stems from the perceived complexity and technical nature of analytics, along with a lack of familiarity or expertise in the field.

However, for others, analytics represents an exciting opportunity to gain valuable insights from data, make informed decisions, and uncover patterns or trends that can drive success in various domains. These individuals may have a natural affinity for numbers, a curiosity to explore data, and a genuine interest in leveraging analytics to solve problems and improve outcomes. Their enthusiasm and passion for analytics can stem from the empowerment it brings, the ability to make data-driven decisions and the potential for uncovering valuable insights that can lead to growth and innovation.

Overall, the perception of analytics as intimidating or valuable often depends on an individual's background, experience and personal inclinations.

Do you think human intelligence has become more important post-pandemic?

Yes, human intelligence has become even more important in the post-pandemic era, especially in the realm of media and advertising. The COVID-19 pandemic drastically altered consumer behaviours, preferences and priorities. As a result, advertisers needed to swiftly adapt their strategies to effectively reach and engage their target audiences.

Human intelligence played a key role in understanding and interpreting the evolving consumer landscape, enabling advertisers to make informed decisions about messaging, channel selection and campaign optimisation. For example, advertisers used data analysis and market research to identify shifts in consumer needs and tailor their advertising messages accordingly. Within Mindshare, we had an ongoing COVID research tracker for exactly those purposes, as well as leveraging research from our NeuroLab, to analyse subconscious responses to different parts of the pandemic. As a result, we were able to work with our clients to understand the emotional and psychological impact of the pandemic on consumers, crafting empathetic and relatable campaigns that resonated with their audiences.

How can data and analytics be used to improve engagement at companies?

Data and analytics can play a significant role in improving engagement at companies. First, by analysing employee data such as performance metrics, feedback surveys, and communication patterns, companies can identify key drivers of engagement and develop targeted strategies to address them. For example, if data analysis reveals that a particular team has lower engagement scores, the company can investigate the underlying causes, such as a lack of communication or unclear goals and implement interventions accordingly.

Second, data and analytics can be used to personalise employee experiences. By leveraging data on individual preferences, work styles and career aspirations, companies can tailor learning and development opportunities, recognition programs, and career paths to suit employees' specific needs and interests. This personalisation fosters a sense of individual value and fulfilment, leading to increased engagement and satisfaction among employees. Ultimately, data and analytics provide valuable insights and enable evidence-based decision-making to create a more engaging work environment.

How do you think AI will impact stakeholder engagement strategies?

AI is expected to have a transformative impact on stakeholder engagement strategies, particularly within the advertising industry. With AI-powered technologies, organisations can harness vast amounts of data to gain deeper insights into consumer preferences, behaviours and buying patterns. This enables more targeted and personalised advertising campaigns, delivering relevant content to specific segments of stakeholders.

AI can optimise advertising efforts by analysing real-time data and making data-driven decisions, such as identifying the most effective channels, ad placements and messaging. AI-powered chatbots and virtual assistants can enhance customer service and engagement by providing instant responses and personalised recommendations. Imagine a customer visiting an e-commerce website looking for a specific product. As they browse through the website, they encounter a chatbot powered by AI. The chatbot utilises NLP (natural language processing) and ML (machine learning) algorithms to understand customer queries and provide instant responses. In this scenario, the customer can ask questions about the product's features, availability, pricing or even seek recommendations based on their preferences. The AI-powered chatbot can quickly analyse the customer's query, search through product databases, and provide personalised recommendations based on the customer's browsing history, purchase history and other relevant data.

In the future advertisers have an opportunity to leverage AI to continue to create more impactful and tailored experiences, fostering stronger connections with stakeholders and driving better engagement and conversion rates at a much faster rate than ever before.