The AI Agent Advantage: Addressing Marketing’s Core Challenges 

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5/5 (2)

For marketers, the “golden goal” has always been to deeply understand customers, enabling more effective cross-selling, upselling, and targeted campaigns. The promise of maximising wallet share hinges on this fundamental principle. Imagine having technologies that can analyse customer journeys deeply, uncovering meaningful, real-time insights into customer behaviour and sentiment. This rich, dynamic data could empower marketing teams to move beyond static profiles, gaining immediate visibility into how customers react to campaigns, messages, and interactions across all channels. 

Data Fragmentation: The CMO’s Blind Spot 

However, achieving this goal has become increasingly difficult. The modern marketing stack, built upon CRM, content marketing platforms, retargeting ad solutions, social listening tools, and countless other applications, often operates in silos. This wide, disconnected array of tools creates a significant challenge: making sense of the fragmented data. Efforts to truly understand customers and identify valuable prospects frequently fall short of desired outcomes. The lack of integration and the sheer volume of disparate data leave marketers struggling to connect the dots and extract actionable insights. 

Unified Customer Vision: AI Agents for Intelligent Marketing 

The solution lies in leveraging AI agents that operate seamlessly in the background. By implementing AI agents, CMOs can gain a comprehensive understanding of their customers, enabling them to run more effective campaigns, drive greater wallet share, and build stronger, more meaningful customer relationships. 

These intelligent agents can bridge the gaps in customer data, sentiment, and campaign perception by accessing and processing information across the entire marketing stack. By learning from metadata, successful and failed campaigns, and a broad range of customer insights – including conversational and digital contact centre data – these agents can provide a unified view of the customer. 

What They Bring to the Table 

  • Unified Data Access. AI agents can traverse siloed marketing applications, extracting and correlating data from various sources. 
  • Real-Time Insight Generation. They can analyse customer interactions, including social media sentiment, conversational AI data, and voice bot interactions, to provide dynamic, real-time insights. 
  • Autonomous Action & Adaptation. Agentic workflows can adapt to campaigns, email blasts, and lead generation activities autonomously, refining strategies and messaging on the fly. 
  • Content Curation & Optimisation. Content curation agents can tailor content based on real-time customer feedback and preferences. 
  • Proactive Opportunity Identification. By identifying gaps in customer understanding and campaign performance, AI agents can empower marketers to uncover new opportunities for engagement and growth. 

Extending AI Agent Value: Practical Applications for the Modern CMO 

Beyond unified data access and autonomous action, AI agents offer a wealth of practical applications that can revolutionise marketing operations. Consider the following scenarios: 

  • Automating Time-Consuming Tasks. Identify and offload repetitive, manual tasks associated with campaign execution and lead generation to a team of AI agents, freeing up valuable human resources for strategic initiatives. 
  • Enhancing Sales Pipeline Intelligence. Leverage AI agents to extract insights from sales pipelines and customer feedback, enabling data-driven campaign adjustments and improved sales alignment. 
  • Real-Time Sentiment Analysis. Deploy multiple AI agents to monitor customer sentiment across conversations and social media platforms, providing immediate feedback on campaign effectiveness and brand perception. 
  • Strategic Scenario Planning. Use AI agents to formulate and evaluate various marketing spend scenarios across different channels and agencies, optimising resource allocation and maximising ROI. 
  • Dynamic Campaign Monitoring. Implement AI agents to track campaign performance in real-time, allowing for immediate adjustments and optimisation. 
  • Event Sentiment Analysis. Employ AI to monitor customer sentiment during live events, providing immediate insights into audience reactions and engagement. 
  • Unlocking Conversational Intelligence. Extract valuable insights from sales conversations and contact centre interactions, feeding them into future sales strategies and upselling opportunities. This extends beyond relying solely on CRM data, providing a richer, more nuanced understanding of customer interactions. 

By implementing these capabilities, CMOs can transform their marketing operations, moving from reactive to proactive, and ultimately driving greater customer engagement and business success. 

The “Wow” Factor: Agentic AI and Unified Data 

Ultimately, the pursuit of a seamless customer journey and deeper conversational engagement hinges on bridging the persistent departmental disconnect. Despite each team interacting with the same customer, data remains siloed, hindering a holistic understanding and unified approach. 

The missing link lies in fostering a dynamic, interconnected data ecosystem where insights from campaigns, social listening, contact centre conversations, chatbot interactions, VoC programs, marketing applications, and CRM flow freely and mutually reinforce each other.  

This is where Agentic AI steps in. By empowering AI agents to adapt and act autonomously across these diverse data sources, we create a symphony of customer intelligence. These agents, working in harmony, unlock the potential for real-time, actionable insights, enabling marketers to craft truly exceptional, “wow” moments that resonate deeply with customers. In essence, Agentic AI transforms fragmented data into a unified, powerful force, driving unparalleled customer experiences and forging lasting brand loyalty. 

The Experience Economy
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Feedback Disruption: Break Down Silos With GenAI

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Customer feedback is at the heart of Customer Experience (CX). But it’s changing. What we consider customer feedback, how we collect and analyse it, and how we act on it is changing. Today, an estimated 80-90% of customer data is unstructured. Are you able and ready to leverage insights from that vast amount of customer feedback data?

Let’s begin with the basics: What is VoC and why is there so much buzz around it now?

Voice of the Customer (VoC) traditionally refers to customer feedback programs. In its most basic form that means organisations are sending surveys to customers to ask for feedback. And for a long time that really was the only way for organisations to understand what their customers thought about their brand, products, and services.

But that was way back then. Over the last few years, we’ve seen the market (organisations and vendors) dipping their toes into the world of unsolicited feedback.

What’s unsolicited feedback, you ask?

Unsolicited feedback simply means organisations didn’t actually ask for it and they’re often not in control over it, but the customer provides feedback in some way, shape, or form. That’s quite a change to the traditional survey approach, where they got answers to questions they specifically asked (solicited feedback).

Unsolicited feedback is important for many reasons:

  • Organisations can tap into a much wider range of feedback sources, from surveys to contact centre phone calls, chats, emails, complaints, social media conversations, online reviews, CRM notes – the list is long.
  • Surveys have many advantages, but also many disadvantages. From only hearing from a very specific customer type (those who respond and are typically at the extreme ends of the feedback sentiment), getting feedback on the questions they ask, and hearing from a very small portion of the customer base (think email open rates and survey fatigue).
  • With unsolicited feedback organisations hear from 100% of the customers who interact with the brand. They hear what customers have to say, and not just how they answer predefined questions.

It is a huge step up, especially from the traditional post-call survey. Imagine a customer just spent 30 min on the line with an agent explaining their problem and frustration, just to receive a survey post call, to tell the organisation what they just told the agent, and how they felt about the experience. Organisations should already know that. In fact, they probably do – they just haven’t started tapping into that data yet. At least not for CX and customer insights purposes.

When does GenAI feature?

We can now tap into those raw feedback sources and analyse the unstructured data in a way never seen before. Long gone are the days of manual excel survey verbatim read-throughs or coding (although I’m well aware that that’s still happening!). Tech, in particular GenAI and Large Language Models (LLMs), are now assisting organisations in decluttering all the messy conversations and unstructured data. Not only is the quality of the analysis greatly enhanced, but the insights are also presented in user-friendly formats. Customer teams ask for the insights they need, and the tools spit it out in text form, graphs, tables, and so on.

The time from raw data to insights has reduced drastically, from hours and days down to seconds. Not only has the speed, quality, and ease of analysis improved, but many vendors are now integrating recommendations into their offerings. The tools can provide “basic” recommendations to help customer teams to act on the feedback, based on the insights uncovered.

Think of all the productivity gains and spare time organisations now have to act on the insights and drive positive CX improvements.

What does that mean for CX Teams and Organisations?  

Including unsolicited feedback into the analysis to gain customer insights also changes how organisations set up and run CX and insights programs.

It’s important to understand that feedback doesn’t belong to a single person or team. CX is a team sport and particularly when it comes to acting on insights. It’s essential to share these insights with the right people, at the right time.

Some common misperceptions:

  • Surveys have “owners” and only the owners can see that feedback.
  • Feedback that comes through a specific channel, is specific to that channel or product.
  • Contact centre feedback is only collected to coach staff.

If that’s how organisations have built their programs, they’ll have to rethink what they’re doing.

If organisations think about some of the more commonly used unstructured feedback, such as that from the contact centre or social media, it’s important to note that this feedback isn’t solely about the contact centre or social media teams. It’s about something else. In fact, it’s usually about something that created friction in the customer experience, that was generated by another team in the organisation. For example: An incorrect bill can lead to a grumpy social media post or a faulty product can lead to a disgruntled call to the contact centre. If the feedback is only shared with the social media or contact centre team, how will the underlying issues be resolved? The frontline teams service customers, but organisations also need to fix the underlying root causes that created the friction in the first place.

And that’s why organisations need to start consolidating the feedback data and democratise it.

It’s time to break down data and organisational silos and truly start thinking about the customer. No more silos. Instead, organisations must focus on a centralised customer data repository and data democratisation to share insights with the right people at the right time.

In my next Ecosystm Insights, I will discuss some of the tech options that CX teams have. Stay tuned!

The Experience Economy
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