When OpenAI released ChatGPT, it became obvious – and very fast – that we were entering a new era of AI. Every tech company scrambled to release a comparable service or to infuse their products with some form of GenAI. Microsoft, piggybacking on its investment in OpenAI was the fastest to market with impressive text and image generation for the mainstream. Copilot is now embedded across its software, including Microsoft 365, Teams, GitHub, and Dynamics to supercharge the productivity of developers and knowledge workers. However, the race is on – AWS and Google are actively developing their own GenAI capabilities.
AWS Catches Up as Enterprise Gains Importance
Without a consumer-facing AI assistant, AWS was less visible during the early stages of the GenAI boom. They have since rectified this with a USD 4B investment into Anthropic, the makers of Claude. This partnership will benefit both Amazon and Anthropic, bringing the Claude 3 family of models to enterprise customers, hosted on AWS infrastructure.
As GenAI quickly emerges from shadow IT to an enterprise-grade tool, AWS is catching up by capitalising on their position as cloud leader. Many organisations view AWS as a strategic partner, already housing their data, powering critical applications, and providing an environment that developers are accustomed to. The ability to augment models with private data already residing in AWS data repositories will make it an attractive GenAI partner.
AWS has announced the general availability of Amazon Q, their suite of GenAI tools aimed at developers and businesses. Amazon Q Developer expands on what was launched as Code Whisperer last year. It helps developers accelerate the process of building, testing, and troubleshooting code, allowing them to focus on higher-value work. The tool, which can directly integrate with a developer’s chosen IDE, uses NLP to develop new functions, modernise legacy code, write security tests, and explain code.
Amazon Q Business is an AI assistant that can safely ingest an organisation’s internal data and connect with popular applications, such as Amazon S3, Salesforce, Microsoft Exchange, Slack, ServiceNow, and Jira. Access controls can be implemented to ensure data is only shared with authorised users. It leverages AWS’s visualisation tool, QuickSight, to summarise findings. It also integrates directly with applications like Slack, allowing users to query it directly.
Going a step further, Amazon Q Apps (in preview) allows employees to build their own lightweight GenAI apps using natural language. These employee-created apps can then be published to an enterprise’s app library for broader use. This no-code approach to development and deployment is part of a drive to use AI to increase productivity across lines of business.
AWS continues to expand on Bedrock, their managed service providing access to foundational models from companies like Mistral AI, Stability AI, Meta, and Anthropic. The service also allows customers to bring their own model in cases where they have already pre-trained their own LLM. Once a model is selected, organisations can extend its knowledge base using Retrieval-Augmented Generation (RAG) to privately access proprietary data. Models can also be refined over time to improve results and offer personalised experiences for users. Another feature, Agents for Amazon Bedrock, allows multi-step tasks to be performed by invoking APIs or searching knowledge bases.
To address AI safety concerns, Guardrails for Amazon Bedrock is now available to minimise harmful content generation and avoid negative outcomes for users and brands. Contentious topics can be filtered by varying thresholds, and Personally Identifiable Information (PII) can be masked. Enterprise-wide policies can be defined centrally and enforced across multiple Bedrock models.
Google Targeting Creators
Due to the potential impact on their core search business, Google took a measured approach to entering the GenAI field, compared to newer players like OpenAI and Perplexity. The useability of Google’s chatbot, Gemini, has improved significantly since its initial launch under the moniker Bard. Its image generator, however, was pulled earlier this year while it works out how to carefully tread the line between creativity and sensitivity. Based on recent demos though, it plans to target content creators with images (Imagen 3), video generation (Veo), and music (Lyria).
Like Microsoft, Google has seen that GenAI is a natural fit for collaboration and office productivity. Gemini can now assist the sidebar of Workspace apps, like Docs, Sheets, Slides, Drive, Gmail, and Meet. With Google Search already a critical productivity tool for most knowledge workers, it is determined to remain a leader in the GenAI era.
At their recent Cloud Next event, Google announced the Gemini Code Assist, a GenAI-powered development tool that is more robust than its previous offering. Using RAG, it can customise suggestions for developers by accessing an organisation’s private codebase. With a one-million-token large context window, it also has full codebase awareness making it possible to make extensive changes at once.
The Hardware Problem of AI
The demands that GenAI places on compute and memory have created a shortage of AI chips, causing the valuation of GPU giant, NVIDIA, to skyrocket into the trillions of dollars. Though the initial training is most hardware-intensive, its importance will only rise as organisations leverage proprietary data for custom model development. Inferencing is less compute-heavy for early use cases, such as text generation and coding, but will be dwarfed by the needs of image, video, and audio creation.
Realising compute and memory will be a bottleneck, the hyperscalers are looking to solve this constraint by innovating with new chip designs of their own. AWS has custom-built specialised chips – Trainium2 and Inferentia2 – to bring down costs compared to traditional compute instances. Similarly, Microsoft announced the Maia 100, which it developed in conjunction with OpenAI. Google also revealed its 6th-generation tensor processing unit (TPU), Trillium, with significant increase in power efficiency, high bandwidth memory capacity, and peak compute performance.
The Future of the GenAI Landscape
As enterprises gain experience with GenAI, they will look to partner with providers that they can trust. Challenges around data security, governance, lineage, model transparency, and hallucination management will all need to be resolved. Additionally, controlling compute costs will begin to matter as GenAI initiatives start to scale. Enterprises should explore a multi-provider approach and leverage specialised data management vendors to ensure a successful GenAI journey.
In today’s competitive business landscape, delivering exceptional customer experiences is crucial to winning new clients and fostering long-lasting customer loyalty. Research has shown that poor customer service can cost businesses around USD 75 billion in a year and that 1 in 3 customers is likely to abandon a brand after a single negative experience. Organisations excelling at personalised customer interactions across channels have a significant market edge.
In a recent webinar with Shivram Chandrasekhar, Solutions Architect at Twilio, we delved into strategies for creating this edge. How can contact centres optimise interactions to boost cost efficiency and customer satisfaction? We discussed the pivotal role of voice in providing personalised customer experiences, the importance of balancing AI and human interaction for enhanced satisfaction, and the operational advantages of voice intelligence in streamlining operations and improving agent efficiency.
The Voice Advantage
Despite the rise of digital channels, voice interactions remain crucial for organisations seeking to deliver exceptional customer experiences. Voice calls offer nuanced insights and strategic advantages, allowing businesses to address issues effectively and proactively meet customer needs, fostering loyalty and driving growth.
There are multiple reasons why voice will remain relevant including:
- In many countries it is mandatory in several industries such as Financial Services, Healthcare, & Government & Emergency Services.
- There are customers who simply favour it over other channels – the human touch is important to them.
- It proves to be the most effective when it comes to handling complex and recurrent issues, including facilitating effective negotiations and better sales closures; Digital and AI channels cannot do it alone yet.
- Analysing voice data reveals valuable patterns and customer sentiments, aiding in pinpointing areas for improvement. Unlike static metrics, voice data offers dynamic feedback, helping in proactive strategies and personalised opportunities.
AI vs the Human Agent
There has been a growing trend towards ‘agentless contact centres’, where businesses aim to pivot away from human agents – but there has also been increasing customer dissatisfaction with purely automated interactions. A balanced approach that empowers human agents with AI-driven insights and conversational AI can yield better results. In fact, the conversation should not be about one or the other, but rather about a combination of an AI + Human Agent.
Where organisations rely on conversational AI, there must be a seamless transitioning between automated and live agent interactions, maintaining a cohesive customer experience. Ultimately, the goal should be to avoid disruptions to customer journeys and ensure a smooth, integrated approach to customer engagement across different channels.
Exploring AI Opportunities in Voice Interactions
Contact centres in Asia Pacific are looking to deploy AI capabilities to enhance both employee and customer experiences.
Using predictive AI algorithms on customer data helps organisations forecast market trends and optimise resource allocation. Additionally, AI-driven identity validation swiftly confirms customer identities, mitigating fraud risks. By automating transactional tasks, particularly FAQs, contact centre operations are streamlined, ensuring that critical calls receive prompt attention. AI-powered quality assurance processes provide insights into all voice calls, facilitating continuous improvement, while AI-driven IVR systems enhance the customer experience by simplifying menu navigation.
Agent Assist solutions, integrated with GenAI, offer real-time insights before customer interactions, streamlining service delivery and saving valuable time. These solutions automate mundane tasks like call summaries, enabling agents to focus on high-value activities such as sales collaboration, proactive feedback management, and personalised outbound calls.
Actionable Data
Organisations possess a wealth of customer data from various touchpoints, including voice interactions. Accessing real-time, accurate data is essential for effective customer and agent engagement. Advanced analytics techniques can uncover hidden patterns and correlations, informing product development, marketing strategies, and operational improvements. However, organisations often face challenges with data silos and lack of interconnected data, hindering omnichannel experiences.
Integrating customer data with other organisational sources provides a holistic view of the customer journey, enabling personalised experiences and proactive problem-solving. A Customer Data Platform (CDP) breaks down data silos, providing insights to personalise interactions, address real-time issues, identify compliance gaps, and exceed customer expectations throughout their journeys.
Considerations for AI Transformation in Contact Centres
- Prioritise the availability of live agents and voice channels within Conversational AI deployments to prevent potential issues and ensure immediate human assistance when needed.
- Listen extensively to call recordings to ensure AI solutions sound authentic and emulate human conversations to enhance user adoption.
- Start with data you can trust – the quality of data fed into AI systems significantly impacts their effectiveness.
- Test continually during the solution testing phase for seamless orchestration across all communication channels and to ensure the right guardrails to manage risks effectively.
- Above all, re-think every aspect of your CX strategy – the engagement channels, agent roles, and contact centres – through an AI lens.
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CX leaders in Australia are actively refining their customer and employee strategies. Due to high contact centre operational costs, outsourcing to countries like the Philippines, Fiji, and South Africa has gained popularity. However, compliance issues restrict some organisations from outsourcing. Despite cost constraints, elevating customer experience (CX) through AI, self-service, and digital channels remains crucial. High agent attrition also highlights the need to enhance employee experience (EX).
Meeting these challenges has prompted organisations to assess AI and automation solutions to enhance efficiency, cut costs, and improve EX. Australian CX teams hold extensive data from diverse applications, underscoring the need for a robust data strategy – that can provide deeper insights into customer journeys, proactive service, improved self-service options, and innovative customer engagement.
Here are 5 ways organisations in Australia can achieve their CX objectives.
Download ‘Australian CX Dynamics: Balancing Cost, Compliance, and Employee Experience‘ as a PDF.
#1 Prioritise Omnichannel Orcheshtration
Customers want the flexibility to select a channel that aligns with their preferences – often switching between channels – prompting organisations to offer more engagement channels.
Aim for unified customer context across channels for deeper customer engagement.
Coordinating all channels ensures consistent experiences for customers, with CX teams and agents accessing real-time information across channels. This boosts key metrics like First Call Resolution (FCR) and reduces Average Handle Time (AHT).
It is important not to overlook voice when crafting an omnichannel strategy. Despite digital growth, human interaction remains crucial for complex inquiries and persistent challenges. Context is vital for understanding customer needs, and without it, experiences suffer. This contributes to long waiting times, a common customer complaint in Australia.
#2 Eliminate Data Silos
Despite having access to customer information from multiple interactions, organisations often struggle to construct a comprehensive customer data profile capable of transforming all available data into actionable intelligence.
A Customer Data Platform (CDP) can eliminate data silos and provide actionable insights.
- Identify behavioural trends by understanding patterns to personalise interactions.
- Spot real-time customer issues across channels.
- Uncover compliance gaps and missed sales opportunities from unstructured data.
- Look at customer journeys to proactively address their needs and exceed expectations.
#3 Embed AI into CX Strategies
The emergence of GenAI and Large Language Models (LLMs) has thrust AI into the spotlight, promising to humanise its capabilities. However, there’s untapped potential for AI and automation beyond this.
Australian organisations are primarily considering AI to address key CX priorities: enhancing efficiency, cutting costs, and improving EX.
Agent Assist solutions offer real-time insights before customer interactions, improving CX and saving time. Integrated with GenAI, these solutions automate tasks like call summaries, freeing agents to focus on high-value activities such as sales collaboration, proactive feedback management, personalised outbound calls, and skill development. Predictive AI algorithms go beyond chatbots and Agent Assist solutions, leveraging customer data to forecast trends and optimise resource allocation.
#4 Keep a Firm Eye on Compliance
Compliance in contact centres is more than just a legal requirement; it is core to maintaining customer trust and safeguarding brand’s reputation.
Maintaining compliance in contact centres is challenging due to factors such as the need to follow different industry guidelines, constantly changing regulatory environment, and the shift to hybrid work.
Organisations should focus on:
- Limiting individual stored data
- Segregating data from core business applications
- Encrypting sensitive customer data
- Employing access controls
- Using multi-factor authentication and single sign-on systems
- Updating security protocols consistently
- Providing ongoing training to agents
#5 Implement New Technologies with Ease
Organisations often struggle to modernise legacy systems and integrate newer technologies, hindering CX transformation.
Delivering CX transformation while managing multiple disparate systems requires a platform that can integrate desired capabilities for holistic CX and EX experiences.
A unified platform streamlines application management, ensuring cohesion, unified KPIs, enhanced security, simplified maintenance, and single sign-on for agents. This approach offers consistent experiences across channels and early issue detection, eliminating the need to navigate multiple applications or projects.
Capabilities that a platform should have:
- Programmable APIs to deliver messages across preferred social and messaging channels.
- Modernisation of outdated IVRs with self-service automation.
- Transformation of static mobile apps into engaging experience tools.
- Fraud prevention across channels through immediate phone number verification APIs.
Ecosystm Opinion
Organisations in Australia must pivot to meet customers on their terms, and it will require a comprehensive re-evaluation of their CX strategy.
This includes transforming the contact centre into an “Intelligent” Data Hub, leveraging intelligent APIs for seamless customer interaction management; evolving agents into AI-powered brand ambassadors, armed with real-time insights and decision-making capabilities; and redesigning channels and brand experiences for consistency and personalisation, using innovative technologies.
In my last Ecosystm Insights, I spoke about why organisations need to think about the Voice of the Customer (VoC) quite literally. Organisations need to listen to what their customers are telling them – not just to the survey questions they responded to, answering pre-defined questions that the organisations want to hear about.
The concept of customer feedback is evolving, and how organisations design and manage VoC programs must also change. Technology is now capable of enabling customer teams to tap into all those unsolicited, and often unstructured, raw feedback sources. Think contact centre conversations (calls, chats, chatbots, emails, complaints, call notes), CRM notes, online reviews, social media, etc. Those are all sources of raw customer feedback, waiting to be converted into customer insights.
Organisations can now find the capability of extracting customer insight from raw data across a wide range of solutions, from VoC platforms, data management platforms, contact centre solutions, text analytics players, etc. The expanding tech ecosystem presents opportunities for organisations to enhance their programs. However, navigating this breadth of options can also be confusing as they strive to identify the most suitable tools for their requirements.
As CX programs mature and shift from survey feedback to truly listening to customers, the demand for tech solutions tailored to various needs increases.
Where are tech vendors headed?
As part of my job as CX Consultant & Tech Advisor, I spend a lot of time working with my clients. But I also spend a lot of time speaking with technology vendors, who provide the solutions my clients need. Over the last few weeks and months there’s been a flurry of activity across the CX technology market with lots of product announcements around one specific topic. You guessed it, GenAI.
So, I invested some time in finding out how tech vendors are evolving their offerings. From Medallia, InMoment, Thematic, LiquidVoice, Concentrix, Snowflake, Nice, to Tethr – a broad variety of different vendors, but all with one thing in common; they help analyse customer feedback data.
And I like what I hear. The conversation has not been about GenAI because of GenAI, but about use cases and real-life applications for CX practitioners, including Insights & Research team, Contact Centre, CX, VoC, Digital teams, and so on. The list is long when we include everyone who has a role to play in creating, maintaining, and improving customer experiences.
It’s no wonder that many different vendors have started to embed those capabilities into their solutions and launch new products or features. The tech landscape is becoming increasingly fragmented at this stage.
What are an organisation’s tech options?
- The traditional VoC platform providers typically offer some text analytics capabilities (although not always included in the base price) and have started to tap into the contact centre solutions as well. Some also offer some social media or online review analysis, leaving organisations with a relatively good understanding of customer sentiment and a better understanding of their CX.
- Contact centre solutions are traditionally focused on analysing calls for Quality Assurance (QA) purposes and use surveys for agent coaching. Many contact centre players have evolved their portfolios to include text analytics or conversational intelligence to extract broader customer insights. Although at this stage they’re not always shared with the rest of the organisation (one step at a time…).
- Conversational analytics/intelligence providers have emerged over the last few years and are a powerhouse for contact centre and chatbot conversations. The contact centre really is the treasure trove of customer insights, although vastly underutilised for it so far!
- CRMs are the backbone of the customer experience management toolkit as they hold a vast amount of metadata. They’ve also been able to send surveys for a while now. Analysing unstructured data however (whether survey verbatim or otherwise) isn’t one of their strengths. This leaves organisations with a lot of data but not necessarily insights.
- Social media listening tools are often standalone tools used by the social media teams. There are not many instances of them being used for the analysis of other unstructured feedback.
- Digital/website feedback tools, in line with some of the above, are centred around collecting feedback, not necessarily analysing the unstructured feedback.
- Pure text analytics players are traditionally focused on analysing surveys verbatim. As this is their core offering, they tend to be proficient in it and have started to broaden their portfolios to include other unstructured feedback sources.
- Customer Data Platforms (CDP)/ Data Management Platforms (DMP) are more focused on quantitative data about customers and their experiences. Although many speak about their ability to analyse unstructured feedback as well, it doesn’t appear to be their strengths.
Conclusion
But what does that leave organisations with? Apart from very confused tech users trying to find the right solution for their organisation.
At this stage, there is immense market fragmentation, with many vendors from different core capabilities starting to incorporate capabilities to analyse unstructured data in the wake of the GenAI boom. However, a market convergence is expected.
While we watch how the market unfolds, one thing is certain. Organisations and customer teams will need to adjust – and that includes the tech stack as well as the CX program set up. With customer feedback now coming from anywhere within or outside the organisation, there is a need for a consolidated source of truth to make sense of it all and move from raw data to customer insights. While organisations will benefit immensely from a consolidated customer data repository, it’s also crucial to break down organisational silos at the same time and democratise insights as widely as possible to enable informed decision-making.
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!