In the push to keep clients coming back, AI’s smart solutions are leading the charge—merging innovation with that human touch.

Thanks to artificial intelligence, the game has changed for how companies talk to you and me – it’s all about getting personal in a smart way. Thanks to AI’s power, sifting through massive chunks of information helps businesses get up close and personal with customer trends and necessities. With this smart tech, businesses aren’t just shooting in the dark anymore; they’re pretty spot-on at figuring out what you’ll likely do next.

This means communications tailored to fit you perfectly and experiences that feel deeply personal.

Personalization at Scale

One of the most significant advantages of AI is its ability to personalize interactions at scale. Old-school marketing often blasts the same message to everyone, but with AI, companies can now chat you up with deals that feel like they were made just for you. Imagine an AI so smart it learns what you love to shop for. Now picture getting perfect offers and custom content that makes shopping feel like it’s tailored just for you.

Smart data use lets us get ahead by truly engaging with people first.

Diving into past purchases and behaviors, AI cleverly forecasts what customers might buy next and spots those who may soon wave goodbye. Companies that look forward can quickly reach out to address customer worries and propose solutions right away making it more likely for them to hold onto their clientele.

Enhancing Customer Support with AI

Customer support is a critical touchpoint in the customer journey. AI-powered chatbots and virtual assistants can provide instant, 24/7 assistance to address customer queries and resolve issues promptly.

With these AI solutions working overtime, a flood of customer inquiries gets handled all at once – making sure everyone’s sorted out quickly and nobody’s left hanging. With AI on board, it feels like your support team knows you personally by pulling up your past interactions and preferences – streamlining solutions to keep customers happy.

Continuous Learning for Ongoing Improvement

AI systems are designed to learn and improve over time. Imagine having a backstage pass to every customer’s thought. That’s kind of what it feels like when AI sifts through feedback and chats – it lets businesses shape up their strategies in real time to keep pace with changing desires. Companies that keep learning stay nimble, always crafting experiences that evolve as quickly as customer tastes do.

Implementing AI for Customer Retention

To harness the full potential of AI in increasing customer retention, businesses should consider the following steps:

1. Data Integration: Consolidate customer data from various sources to create a comprehensive view of your customers. To get those spot-on insights, AI must gobble up plenty of good quality data first.

2. Choose the Right Tools: Invest in AI tools and platforms that align with your business objectives and customer retention goals. Picking the perfect tech tool, whether it’s a chatbot powered by AI, software that predicts trends, or systems that offer tailored experiences, makes all the difference.

3. Keep your eyes on the prize – a stellar customer journey. From first click to final purchase, smart use of AI can turn ordinary interactions into memorable experiences that keep people coming back. Let every interaction count – from custom-tailored ads to quick and helpful support that keeps them coming back for more.

4. Monitor and Optimize: Continuously monitor the performance of your AI initiatives and be prepared to iterate and optimize. While AI dishes out the insights, we can’t forget that keeping those strategies in line with our business objectives and what our customers want is a job for humans.

5. Ethical Considerations: Always prioritize customer privacy and ethical considerations when implementing AI. Transparently communicate how customer data is used and ensure compliance with data protection regulations.

Imagine an AI that not only gets your needs before you voice them but also wraps it all up with top-notch help – now that’s how lasting bonds are formed between brands and their fans. With each leap forward in AI, strategies to make sure shoppers stick around are also leveling up. Imagine a future where your favorite brands know you so well; they anticipate what you want before even you do.

As advancements in artificial intelligence proceed at a breakneck pace, attitudes toward the use of AI are changing just as quickly.

While fear and uncertainty over its effect on jobs and the future of societies persist, a growing number of organizations are intrigued by the idea that AI could allow them to offer consistent, always-on customer service more cost effectively.

In fact, a recent survey by Metrigy found that more than 80% of companies are increasing their AI spending in 2024; the top reason, cited by 75.4% of those companies, is that they recognize the efficiencies AI brings.

But while many businesses are exploring how automation can help them reduce the costs associated with delivering customer service, leaders are still trying to understand the implications that outsourcing customer support to AI will have on their brand, reputation, and customer experience.

The Benefits—and Limitations—of AI in Delivering Great CX

One of the biggest selling points for many who are considering AI as part of their CX strategy in their contact center is cost savings, both in automating activities previously handled by people and in helping agents work more efficiently.

Of course, cost is a critical concern in the contact center, where labor costs account for up to 70% of total costs. AI can help automate routine processes, and it is starting to play a key role in supporting the work performed by human agents and in streamlining backend functions associated with customer service—for example, by helping to streamline quality assurance practices as well as agent coaching and training by delivering aggregate data and insights. The possibilities of utilizing AI as a tool to supplement CX practices seem plentiful.

But let’s be clear: AI can’t replace the human characteristics that are so critical to delivering great CX, such as empathy, decision-making, critical thinking, and flexibility. AI can’t build an authentic relationship with a customer, and it relies on massive datasets that can expose sensitive customer data if breached. And while businesses with a strong customer focus are committed to providing great service, AI doesn’t care about doing what’s right for the business or for the customer; you have to program in when AI is allowed to make an exception in order to deliver a remarkable experience to a client, whereas a human can understand when making a policy exception is the right thing to do.

How to Optimize Your Use of AI in Customer Service

Here’s what you need to consider as you evaluate which areas of customer service might be a good fit for AI.

• Start by understanding your brand. How would your customers describe what it’s like to do business with you? If your organization is like most, the customer experience you offer goes beyond your product or service to also include your brand and how you handle interactions with customers. If your value proposition is focused on efficiency and expediency, you may have more leeway to rely on bots to help resolve very simple customer issues; if, however, your value proposition is more focused on building a connection with customers, you need to take care in how and where you implement AI across the customer experience.
• Don’t erase human expertise from the equation. Using AI-enabled tools to enhance your contact center operations can be a win, but handing over the reins to the thinking or the magic of your brand without human interaction or oversight is a big risk. For example, if you’re automating your quality assurance processes, ensure that a human is reviewing the evaluations generated by AI. AI can digest large datasets, but it may not be able to translate nuance, rapport, or real connection, which all matter! It can be helpful to think of AI as a tool—after all, you wouldn’t hand over all of your thinking and decision-making to a tool.
• Use AI to support, not replace, the customer service agents on the front line in driving customer satisfaction. The real opportunity for AI in customer service lies in using AI-enabled solutions to support your agents with things like real-time guidance or automated notetaking. Having bots handle simple customer inquiries can free agents to do what they do best: building a rapport with customers while solving more complex issues requiring human judgment.
• Create policies on the use of AI within your organization. Do you want to allow your teams to use generative AI to write emails to customers or draft annual reviews for their direct reports? Ensure that you have clarity on where you are using AI versus where you are relying on people across all customer service-related activities. Communicating those policies across the organization can help remove some of the fear, risks, and ambiguity surrounding AI.
• Ensure that your customers know when they are interacting with AI. Set up your AI-powered tools so customers or prospects know when they are not engaging with humans; research has found that there are significant risks when bots aren’t properly labeled and identified as AI and can, therefore, be mistaken for a real person. Passing off an AI as a human is not only misleading but also dangerous for all who engage with the AI and for the businesses that choose to deceive their prospects and clients.
• Think critically about the future you want to build. Apply critical thinking to your brand, your business, and how you want to take care of your customers. Check-in to see whether your employees and customers are satisfied with how AI is being used within your organization, and stay up to date as AI advances to ensure that you’re using the technology not only effectively but also responsibly.

As AI continues to evolve, you may be surprised at all of the ways it can supplement what you are doing, but you may also be surprised by how strongly your customers value the human touch that human agents offer.  Connection matters, and only humans can authentically build rapport and connection with your customers. If you decide you want to explore AI within your customer service functions, proceed with intention and with caution. The best use of AI is as a tool to support the humans who are the face, voice, thoughts, and heart behind the customer experience you provide. Connections matter to your brand, your organization, and your very important clients—don’t sacrifice your organization’s humanity for AI-driven efficiency.

About the Author

Natalie Ruiz is the CEO of AnswerConnect, a leader in providing innovative human-powered customer experience solutions for small and mid-sized businesses. She has helped drive the company’s growth and expansion through strategic initiatives and a focus on customer-centric services. Under her leadership, AnswerConnect has flourished, setting new standards in the industry for service excellence and technological advancement. Natalie is known for her dynamic leadership style and commitment to empowering teams to achieve peak performance.

Imagine a world where artificial intelligence (AI) and human expertise seamlessly intertwine, transforming the contact centre landscape as we know it?

As we embark on a journey towards 2030, the once futuristic notion of AI-powered contact centres is no longer a distant dream but a rapidly unfolding reality.

At Sabio Group, we have witnessed first-hand the evolution of AI and its transformative impact on customer service. From the early days of speech analytics and intent-based AI, which we successfully harnessed to enhance customer interactions, to the rise of conversational AI that redefined the way businesses engage with customers, we have been at the forefront of AI’s evolution in the contact centre.

However, this landscape is not a static one; it is relentless and ever-changing and continuously challenges us to adapt and innovate. Just as we began to grasp the full potential of conversational AI, the emergence of generative AI has once again shifted the goalposts; presenting new opportunities but naturally bringing with it further complexities. In this new world, the traditional roles of agents and AI are not merely evolving; they are undergoing a profound metamorphosis that will redefine the very essence of customer service.

What Today Is, Tomorrow Is Not

Today, agents stand as the pillars of customer support, with AI serving as a powerful ally in enhancing their capabilities. However, in the coming years, we will witness a remarkable inversion of roles. AI will emerge as the primary touchpoint for customers, taking centre stage in handling inquiries, resolving issues, and delivering personalised experiences. Behind the scenes, however, a new breed of highly skilled human agents will act as the guiding force, overseeing and fine-tuning the AI’s performance to ensure unparalleled customer satisfaction.

This shift will demand a cultural revolution within the contact centre industry, with the agents of 2030 becoming a far cry from their present-day counterparts. They will be the elite, armed with university qualifications and a deep understanding of AI’s intricacies. These “super agents” will possess a rare blend of technical expertise, emotional intelligence, and problem-solving prowess, enabling them to tackle the most complex customer challenges with finesse.

A Helping Hand

Sabio set up our AI practice more than 20 years ago and are – arguably – regarded as a true visionary in the space that has already begun paving the way for the contact centres of the future. With groundbreaking projects for clients like loveholidays and HomeServe, we’ve already demonstrated the transformative potential of AI in action.

For loveholidays, our support in helping them implement Twilio Flex, an AI-infused contact centre platform, resulted in a remarkable 20% boost in agent productivity. By harnessing the power of AI automation, agents were liberated from mundane tasks, allowing them to focus on delivering exceptional, personalised customer experiences.

Similarly, HomeServe UK’s partnership with Sabio unveiled the true potential of AI and automation in transforming customer journeys. Through intelligent self-service options and smart routing using Google’s CCAI tech, HomeServe UK witnessed a significant reduction in call volumes while simultaneously elevating customer satisfaction to new heights. The speech analytics project also identified those calls that were made as the result of customers being unable to complete digital journeys – thus helping the HomeServe digital team to focus on specific areas that were proving too complex for customers. This helped to unlock £1m plus annual return on its Speech Analytics investment.

As we stand on the edge of a new contact centre tech landscape driven by Generative AI, businesses must embrace this revolution or risk being left behind.

However, navigating the complexities of AI integration requires a steady hand and a visionary partner. This is where – in my opinion – Sabio’s expertise shines bright, and has proven itself over the years.

What will your AI strategy look like?

Our team are ready to guide businesses through the uncharted territories of AI-powered contact centres. By collaborating with Sabio, organisations can chart a course towards a future where AI and human talent work hand in hand, creating a customer service experience that breaks the boundaries of what was once thought possible.

The contact centres of 2030 will be a symphony of AI and human brilliance, delivering unparalleled efficiency, personalisation, and empathy. As customer expectations continue to soar, businesses that harness AI’s power will not only survive but thrive in this new era.

But, to realise this potential, the time to act is now.

Engage with myself or my team of experts at Sabio today and embark on a journey towards the contact centre of tomorrow.

The question is, will you be a pioneer or a spectator?

About the Author

Kevin McGachy is Head of AI Solutions at Sabio.

About Sabio

Sabio Group is a global digital customer experience (CX) transformation specialist with major operations in the UK (England and Scotland), Spain, France, Netherlands, Malaysia, Singapore, South Africa and India.

The Group, which includes ‘makepositive’, delivers solutions and services that seamlessly combine digital and human interactions to support exceptional customer experiences.

Through its own technology, and that of world-class technology leaders such as Avaya, Genesys, Verint, Twilio, Google, Amazon and Salesforce, Sabio helps organisations optimise their customer journeys by making better decisions across their multiple contact channels.

The Group works with major brands worldwide, including Aegon, AXA Assistance, Bankia, BBVA, BGL, Caixabank, DHL, loveholidays, Marks & Spencer, Rentokil, Essent, GovTech, HomeServe, Sainsbury’s Argos, Telefónica, Think Money and Transcom Worldwide.

With the advent of advanced technology, the marketplace is witnessing the emergence of machine customers: non-human entities that autonomously engage in transactions much like humans do, from purchasing goods to utilizing services.

Characteristics of Machine Customers

Machine customers are distinguished by their ability to make decisions based on real-time data and adjust their actions over time, rather than simply following a set of preprogrammed instructions.

This evolution prompts a pressing question for businesses and service providers alike: how to adapt and cater to these new customers whose behaviors and decision-making processes are ruled by algorithms?

As machine customers differ profoundly from their human counterparts in how they make decisions and interact, businesses face the challenge of understanding and orchestrating customer experiences that cater to these automated entities.

The integration of machine customers necessitates a shift from traditional customer service paradigms that prioritize human emotions and relationships toward an approach that emphasizes seamless, efficient, and unambiguously logical interactions.

Providers must also become adept at navigating the unique demands of serving machine customers, recognizing the commercial and operational impacts that they bring. Understanding these implications is crucial, as the presence of machine customers is growing and will potentially reshape markets and how businesses approach customer service and experience.

Machine customers possess the ability to autonomously engage in transactions, even when these actions are not predefined by human operators. For example, they can autonomously purchase goods and services, reflecting an evolving understanding of customer preferences and market dynamics.

Their decision-making is based on analysing a large spectrum of data inputs and machine learning models which allow them to adapt their behavior as they accumulate experiences over time.

Evolution from Traditional Customers

The transition from traditional to machine customers represents a significant shift in how transactions are handled in a business context.

Traditional customers, or human customers, based their purchasing decisions on a mixture of emotional and rational considerations. By contrast, machine customers operate on a basis of logic, often with clear objectives such as cost reduction or efficiency maximization.

Their evolution signifies a move towards transactional processes that emphasize problem-solving capabilities and objectives-driven actions.

CX Rockstar James Dodkins on the Rise of Machine Customers

Technological Infrastructure for Serving Machine Customers

The rise of machine customers necessitates a robust technological infrastructure that ensures seamless transactions, security, and communication compatibility. Below are specific components that facilitate service to this new type of clientele.

Connectivity Standards

To effectively serve machine customers, connectivity standards must be established to ensure that machines can communicate with business services without interruption.

For instance, 5G networks and IoT protocols such as MQTT (Message Queuing Telemetry Transport) are critical for real-time data exchange. These technologies enable machine customers to perform transactions and interact with services at high speeds and with low latency.

Data Security Protocols

Machine customers will be handling sensitive tasks, requiring strict data security protocols.

Encryption techniques like TLS (Transport Layer Security) and robust authentication methods are essential to protect the integrity of transactions. Data privacy regulations, such as GDPR, must also be adhered to, ensuring that machines handle personal data with the same level of protection as human-operated systems.

Interoperability Requirements

Finally, interoperability requirements are crucial for machine customers to function across various platforms and services.

Standards such as OpenAPI Specification (OAS) allow for clear communication between different systems, while universal APIs enable machines to access a wide range of services without the need for custom integration efforts. Interoperability ensures that machine customers can be versatile participants in the economy, engaging with numerous providers and services.

Service Framework for Machine Customers

The transition to serving machine customers necessitates a reimagining of service processes. These entities demand interfaces and support systems distinctly tailored to their non-human methods of operation and communication.

Automated Service Interfaces

Automated service interfaces are paramount for machine customers as they provide clear, logic-based access points for machines to engage with services.

These interfaces must be capable of handling transactions autonomously and allow for seamless integrations. The use of APIs is a core component, enabling machines to retrieve information, execute actions, and update statuses without human intervention.

Customization and Adaptability

Machine customers require a level of customization and adaptability in services that can accommodate a variety of protocols and learning algorithms.

Services must be designed to adapt dynamically to a machine’s evolving preferences and its operational environment. For example, services that cater to machine-driven decisions should factor in the machine’s ability to process large data sets rapidly and apply changes to its algorithms in real-time.

Real-Time Support Systems

Finally, real-time support systems are essential in a framework designed for machine customers.

Given the constant and instantaneous nature of machine processing, support mechanisms must provide immediate responses to queries or issues, utilizing transparent decision-making processes. Leveraging machine learning to predict potential problems before they occur can greatly enhance the efficiency and reliability of these systems.

Challenges and Considerations

As businesses prepare for the integration of machine customers, they face multifaceted challenges and considerations that are critical to a sustainable and ethical engagement with these non-human entities.

Ethical Implications

The rise of machine customers reshapes the ethical landscape of business practices.

These entities operate autonomously, making decisions based on logic and predefined rules. Businesses must ensure that the algorithms guiding them align with societal values and ethical norms to prevent biases and unfair outcomes. The use of AI in customer interactions demands a high degree of transparency.

Legal and Regulatory Compliance

Adhering to legal standards is a significant concern when serving machine customers.

It involves navigating a complex web of laws that may not be fully developed to address the nuances of AI-based transactions. Businesses must focus on compliance with existing regulations while also anticipating future legal shifts that might necessitate adjustments in how machine customers operate and engage in commerce.

Privacy Concerns in Machine Interactions

Handling data security and privacy is a paramount concern in transactions with machine customers.

The vast amount of data processed by AI systems must be protected to maintain customer trust and comply with privacy laws. The integration of machine learning must be managed responsibly to uphold data integrity and confidentiality.

Some Industry Examples

Machine customers are reshaping how businesses interact with transactional processes. This section explores concrete examples across various industries where machine-driven purchasing is becoming increasingly prevalent.

Retail Sector Innovations

In the retail industry, machine customers are revolutionizing inventory management through systems like the Staples Easy System, which allows for automatic reordering of office supplies.

These innovations ensure that stock levels are maintained efficiently, reducing the risk of overstocking and shortages.

Manufacturing and Predictive Maintenance

Manufacturing is being transformed by IoT and AI, which enable machines to predict when maintenance is needed. This predictive maintenance avoids downtime and saves costs.

For instance, sensors in manufacturing equipment can detect wear and tear and autonomously order replacement parts, as part of a self-healing manufacturing model.

Automotive Industry Advancements

Autonomous vehicle systems from companies like Google, Tesla, and Toyota represent a significant leap forward.

These vehicles themselves can act as customers, deciding on maintenance schedules, route optimization, and even entertainment subscriptions. These advancements not only increase vehicle efficiency but also open up new revenue streams for related services.

Future Perspectives in Serving Machine Customers

As businesses anticipate substantial transformation, they must adapt to the emergence of machine customers, focusing on the expansion of new markets, the evolution of customer service roles, and a redefined approach to long-term strategic planning.

Potential Market Growth

The advent of machine customers has opened up a significant growth sector.

By 2030, it’s predicted that a considerable proportion of purchasing decisions will be made by automated systems. Companies are thus preparing to cater to these non-human entities that make choices based on algorithms and preset criteria.

Evolving Customer Service Roles

Customer service must evolve in tandem with these new purchasers.

Service professionals will likely shift toward roles that support and maintain AI decision-makers, focusing on technical troubleshooting, system optimization, and ensuring transparency in machine-driven decisions. The depth of their knowledge must expand to include an understanding of artificial intelligence, data analysis, and machine learning processes.

Long-Term Strategic Planning

Strategic planning must now incorporate scenarios involving machine customers. Businesses will need to forecast and strategize for a future where AI-powered systems influence customer behavior.

This will necessitate a balance between human preferences and machine efficiency. Planning will emphasize data privacy, ethical considerations, and the integration of machine-friendly interfaces into business models.

Today, online shopping is booming like never before, driven by technological advancements and consumer behaviors. Two big players in this area are Artificial Intelligence (AI) and Machine Learning (ML).

In this article, we’ll look at how AI and ML are shaking up online shopping and making it better for everyone involved.

Key Aspects of E-commerce Changed by AI and ML

AI and ML technologies are making big changes in online shopping, spawning numerous e-commerce development solutions. Here’s how they’re doing it:

Personalized Product Recommendations

Personalized product recommendations are perhaps the largest area highly impacted by AI and ML algorithms. These technologies analyze user data like browsing history, purchases, and preferences to suggest products tailored to each person’s interests.

There are different methods for making these recommendations. One popular way is called collaborative filtering, where the system looks at what similar users have liked or bought to suggest items.

Another method, content-based filtering, focuses on suggesting items similar to ones a user has interacted with before. Often, a combination of these methods is used to provide more accurate recommendations.

What’s impressive is that these systems can adjust recommendations in real time as users browse the website or app. This means you’re more likely to see suggestions that are relevant to you right when you need them.

Predictive Analytics for Customer Behavior

Predictive analytics for customer behavior is like having a crystal ball for online retailers. It uses advanced algorithms and data analysis to forecast what customers might do next based on their past actions.

Imagine you’re an e-commerce business owner. Predictive analytics would look at all the data you have on your customers: what they’ve bought in the past, how often they visit your site, how long they stay, what pages they look at, and so on.

Then, using machine learning algorithms, it identifies patterns and trends in this data to predict future behavior.

For example, it might notice that customers who buy certain types of products are more likely to come back and make another purchase within a certain time frame.

Or it might find that customers who spend more time on your site tend to spend more money. Armed with these insights, you can tailor your marketing efforts to target specific customer segments more effectively.

Natural Language Processing (NLP) for Customer Support

Natural Language Processing (NLP) is similar to having a team of agents who can understand and respond to customer inquiries instantly, 24/7.

When a customer reaches out for support, whether through chat, email, or social media, NLP algorithms kick into action.

One of the most common applications of NLP in customer support is chatbots. These virtual assistants can engage in real-time conversations with customers, answering questions, providing information, and even assisting with purchases.

Another thing NLP is good at is sentiment analysis, which allows it to perceive the mood and emotions behind customer messages.

By analyzing the tone and language used in customer interactions, businesses can identify issues and address them before they escalate.

Visual Search and Image Recognition

With visual search and image recognition technology, users can search for products using images instead of relying solely on text-based queries.

When a customer uploads an image or takes a photo of a product they’re interested in, visual search technology studies the visual features of the image, such as shape, color, and texture.

Then, using image recognition algorithms, it compares these features to the products in the e-commerce database to find visually similar items.

Visual search offers several benefits for both customers and e-commerce businesses. For customers, it provides a more intuitive and convenient way to find products, especially when they’re not sure how to describe what they’re looking for in words.

For e-commerce businesses, visual search technology helps drive engagement and conversions by making it easier for customers to find and purchase goods.

Fraud Detection and Risk Management

Detecting fraud and managing risks are crucial tasks for e-commerce businesses to keep their operations secure. AI plays a vital role in this process by analyzing various data points to identify suspicious activities.

When someone makes a purchase online, AI algorithms instantly examine different factors like the user’s past transactions, behavior patterns, device details, and location. By looking at all this information together, AI can spot unusual behavior that might indicate fraud.

For instance, if a purchase is much larger than usual for a user, or if it’s from a location they’ve never bought from before, AI could flag it as suspicious or ban it.

Dynamic Pricing and Demand Forecasting

Dynamic pricing and demand forecasting use data and smart algorithms to adjust prices and predict what customers will want.

Dynamic pricing means that prices change based on factors like demand, competition, and even the time of day.

For example, if a product is selling quickly, the price might go up to take advantage of high demand. Conversely, if sales are slow, the price might drop to attract more customers.

Demand forecasting uses data and algorithms to predict future demand for products. It considers factors like past sales, seasonality, trends, and even external factors like the weather.

By analyzing all this information, businesses can anticipate how much of a product they’ll need and adjust their pricing and inventory accordingly.

Customer Experience Enhancement

In addition to all aspects mentioned above, AI and ML are also used to make the customer experience better.

They help businesses talk to customers in a way that feels personal, show ads that are just right, and understand what customers think in real time.

By looking at how customers behave and what they like, businesses can send emails, deals, and suggestions that match each person’s interests. ML also helps businesses see what people are saying about them online and spot any problems early.

Data Privacy and Ethical Considerations

Despite all the good AI and ML bring, there are some big things to think about when using these technologies.

One issue is keeping people’s information safe. AI and ML need lots of data to learn from, like what you buy and look at online. But it’s highly important that this data is kept safe and not used in the wrong way.

Another big concern is doing things ethically. Companies need to ask permission before collecting data and be clear about how they’ll use it. They also need to make sure their systems aren’t unfairly treating different groups of people based on things like their race or gender.

Conclusion

AI and ML technologies have greatly influenced online shopping, bringing many advantages to both businesses and customers alike. By using them responsibly and creatively, online shops can stay ahead and make customers happy, which leads to more success online.

Now more than ever, delivering exceptional customer service is vital to a business’ survival as consumers look to cut back on spending in light of the ongoing cost-of-living crisis and wider economic uncertainty. 

Often, customer service professionals are the unsung heroes in building and maintaining a sustainable business, so ensuring that they are sufficiently equipped to meet consumers growing demands has moved beyond a nice to have. It’s become a matter of business survival.

We spoke to Matt Bunn, co-founder of Scaling Partner, and Chibeza Agley, co-founder and CEO of OBRIZUM, about the value of AI-powered adaptive learning, and the role it will play in driving customer service beyond basic survival and towards more sustainable business growth. 

1.   What are the challenges of retaining talented staff?  

Matt: “Contact centres are facing the pressing issue of rising operational costs adding to the fact that customer service roles continue to experience high attrition rates.

“As agents feel the frustration of lengthy, repetitive onboarding processes, organisations are finding it increasingly difficult to find and retain top customer service talent to manage customer relations and protect brand loyalty.”

Chibeza: “Additionally, for the agents that remain, once they are deemed skilled enough to deal with customers, the work that greets them is often unfulfilling and stressful, requiring them to work long hours to meet incoming demands.

“Given that, with time, agents become more familiar with the company and develop a deeper level of understanding around the types of incoming queries and how best to deal with them, organisations would greatly benefit from retaining these experienced individuals.

“The key is to become more productive and efficient.”

Matt: “For example, contact centres are returning to onshoring as the need to focus on quality over cost effectiveness becomes ever more apparent.”

“It’s during times of intense stress and pressure that the true value of these individuals is realised. It’s unsurprising that we’ve seen contact centres double down on their commitments to agent wellbeing in the face of the economic challenges.” 

2.   How does adaptive learning help? 

Chibeza: “When it comes to training customer service professionals, one of the key metrics that managers should focus on are speed-to-competency and total call handling time.”

“Analysis shows that around 17% of customer service agents leave the company without answering a single customer call due to how long it takes for them to be deemed ‘competent’ enough for client-facing environments. The problem is, organisations still use long-winded, laborious, linear training programmes where each person, regardless of prior experience, is taken down the same learning pathway.

“But in reality, we know people learn in different ways and so linear learning fails to cater for unique needs.”

Matt: “This is where adaptive learning comes in. It provides a far more effective way of upskilling an individual to the point that they can demonstrate expertise in a specific subject area. By taking each learner through a tailored learning journey, these non-linear models fast track those with more baseline capabilities while providing the necessary levels of granular detail for those that need it.”

Chibeza: “Non-linear adaptive learning has already  increased  the confidence and capability of agents and this has been translated into significant improvements in call quality . Not only are calls dealt with more quickly, but customers are also left far more satisfied with the outcome. Additionally, an effective learning model will make onboarding agents far more cost effective.”

Matt: “People still want to speak to people, especially when it comes to more emotive situations. Whilst artificial intelligence and its conversational capabilities are incredibly powerful tools, there are certain aspects of the customer service role that stand well outside its capabilities. It’s therefore vital that agents are trained to the highest level to manage the situations that cannot be automated by technology.” 

3.   What is the value of data-driven decision making? 

Chibeza: “The best business practice is to make decisions that are based on strategically collected, relevant data.”

“OBRIZUM’S adaptive learning has a unique feature called the ‘confidence matrix.’ It requires learners to rate how confident they are in their responses, avoiding the ‘quick click’ approach that finishes assessments at a faster rate. In turn, the confidence statistics provide the management team with a true reflection of an individual’s knowledge in a particular area of expertise.”

“It’s also designed to give the individual a greater sense of self-awareness which is directly linked to being a better-quality learner. By encouraging a more thoughtful approach to learning programmes, an accurate picture is painted of a workforce’s skill levels. For customer service professionals, organisations can assign agents to their priority customers based on their skill levels, further supporting brand loyalty and general retention.”

Matt: “In a nutshell, an effective learning programme reduces business risk and improves overall quality and output.”

“Through the partnership with OBRIZUM, Scaling Partner is proud to have presented the business case of non-linear adaptive learning to several large corporate and public sector organisations. As a result, these companies have reaped the benefit from accelerated time-to-competency and reduced the call handling times of their customer service teams.”

“In a time where every penny counts, non-linear adaptive learning holds the key to transforming how customer service is learnt, harnessed, and delivered.”

Chibeza: “We were able to approach larger corporations with our solution with the help of the Scaling Partner team who had the experience and knowledge of working with large customer service teams.

“With so many startups creating solutions to help solve problems in the customer service sector it’s hard for enterprises to know which new AI technology to go with. Through mutual partnerships and a strong focus on data-driven decision making, we’ve been able to ensure our solution is the best market fit so customer service managers know they are getting the best solution out there.”

About the Authors

Matt Bunn is Co-Founder at Scaling Partner and Chibeza Agley is Co-Founder and CEO at OBRIZUM.

You may have heard the term generative AI before, but what does it actually mean? In short, generative AI is a type of artificial intelligence that is used to create new things. This could be anything from new products and services to unique customer experiences. According to Gartner, Generative AI is one of the top 5 impactful technologies in 2022.

How does Generative AI work?

Generative AI leverages the growing computing power of machines to generate content that is as close as possible to human-authored content. Generative AI relies on a variety of techniques such as Natural Language Processing and Generative Adversarial Networks (GANs) in order to effectively learn patterns and algorithms from existing data. Generative AI then uses this knowledge to automate the creation of new and unique content without requiring direct input from the user.

Generative AI can be used for anything from creating auto-generated music or text, to automatically producing models in various fields like customer service, healthcare or finance. Generative AI is set to enhance the creative process in many different areas, opening up unprecedented opportunities for automation.

How is ChatGPT changing the customer experience?

Here we look at some ways Generative AI and ChatGPT is revolutionizing the customer experience (CX).

1. Automated customer support

Generative AI offers a powerful method for businesses to automate their customer support. It combines natural language processing with machine learning to generate custom responses in real-time, allowing customer service teams to address inquiries quickly and reliably. Generative AI can be trained from existing ticket data, as well as any other customer messages or feedback, giving it comprehensive understanding of customers’ needs and preferences. It can also be configured so that complex questions are directed to the right departments, thus allowing automated customer service to work more effectively than ever before.

2. Personalized product recommendations

Generative AI has enabled organizations to understand customers in deeper and more meaningful ways. It can use this understanding to make helpful, personalized product recommendations. This innovative technology works by processing customer input and contextual data to produce insights which it uses to tailor the product offerings presented. Generative AI can create intelligent, creative solutions adapted precisely to an individual or group’s needs with an accuracy that was never before possible. This is empowering businesses to generate value from their customer relationships that has previously not been attainable. Generative AI will be instrumental in increasing customer engagement with products tailored precisely towards their needs resulting in increased user satisfaction and brand loyalty.

3. Improved customer segmentation

Generative AI can improve the way businesses segment their customers. It provides companies with a powerful tool for quickly identifying and understanding customer segments. It is capable of combining and analyzing data from multiple sources to create more targeted customer segments than traditional methods. Generative AI can uncover distinct patterns in customer behavior, allowing companies to build up more meaningful rules for each segment and create more tailored marketing campaigns. It can also be used to gain insights into emerging trends in customer segments over time, enabling companies to adjust their strategies accordingly and ensure they reach their target audiences effectively.

4. Automated customer surveys

Generative AI is changing the way customer surveys are done. It allows companies to automate data collection and analysis on a much larger scale than ever before. It uses its advanced capabilities to learn patterns in customer interactions that can be used to uncover insights into how customers feel about products or services. Furthermore, it can also generate new questions based on customer behavior and responses, enabling companies to get even more detailed information from their customers. Generative AI makes it possible for companies to quickly interpret feedback from customer surveys with greater accuracy and precision, allowing them to make better decisions faster.

5. Improved customer engagement

Generative AI is at the forefront of customer engagement. By introducing Generative AI tools, businesses can dramatically increase how they interact with their customers. Generative AI can automate data-driven conversations, allowing businesses to learn more about what a customer wants and needs while also creating personalized experiences that enhance customer engagement. The technology can even provide insights into why certain customers engage more than others by predicting patterns in their interactions with companies. Generative AI has the potential to revolutionize how businesses engage with their customers and ensure that all users are provided with the most efficient and effective experience possible.

6. Automated marketing campaigns

Generative AI can provide a great boost to any marketing campaigns by automating various tasks that go into it, such as content creation and optimization. It is powered by machine learning algorithms which are trained to mimic certain processes such as writing articles, creating images, optimizing online content and more. Generative AI also has the ability to identify patterns in customer behavior, allowing marketers to create more targeted and customized campaigns. With this advanced technology, businesses have access to the data needed to make better decisions on what topics or content areas to focus their marketing efforts on, which could ultimately lead to higher ROI for the business in terms of sales.

7. Enhanced customer analytics

Generative AI offers incredible potential to enhance customer analytics capabilities. Generative AI models can detect even complex patterns and draw meaningful insights from data that are otherwise difficult to access or interpret. It enables companies to gain a deeper understanding of their customer profiles and identify groups of customers who share common preferences, desires, and behaviors. As customers’ needs evolve over time, it provides the ability to anticipate changes in these trends, allowing organizations to stay ahead of the curve. Generative AI also makes it possible for businesses to analyze the effectiveness of marketing campaigns and segment customers quickly and accurately in new and innovative ways, enabling them to run better campaigns at a lower cost.

8. Better Product Development

Generative AI can be used to enhance product development by automating the design process—allowing creativity to be unlocked without human labor. It also has the potential to create virtual experimentation and prototyping, which helps reduce research and development costs of new products. Generative AI can generate ideas for product improvements faster than other methods, leading to faster delivery to markets. It allows companies of all sizes, from small startups to big corporations, to make better-informed decisions when it comes to their product designs.

9. Improved customer retention

When applied to customer retention Generative AI can provide a host of benefits such as being able to quickly discover user patterns, identify churning customers in advance, or find new trends in purchase habits. Generative AI also enables companies to deliver personalized recommendations tailored for each individual customer’s needs that can increase loyalty and help to cement lasting relationships with them. Through its consistent use businesses are able to offer loyal customers always targeted offers while keeping their data secure. Generative AI allows companies to build trust between their brand and the customer through relevant content tailored specifically for them.

10. Cost saving & productivity

Generative AI is the new frontier of cost savings. As we have discussed, it allows businesses to save time and money by automating mundane tasks, such as data analysis and pattern recognition. The technology can also help lower costs associated with machine learning, including the processing power required to run complex computations. By leveraging Generative AI, businesses can achieve superior results more quickly and affordably than ever before.

In summary

Generative AI and ChatGPT can help businesses quickly identify customer needs and provide tailored solutions. By taking advantage of generative AI, businesses hope to build better relationships with their customers and create more engaging experiences. Expect to see more ways Generative AI will revolutionize the customer experience in the future!

Great customer support is all about simplicity. When a customer has a problem, often the best experiences are when they can solve it themselves.

In fact, according to a recent study, 69% of customers want to resolve as many issues as possible on their own, and 63% of customers always or almost always start with a search on a company’s online resources when they have an issue. However, the same research also shows that most customer experience teams aren’t offering channels beyond phone and email, meaning they aren’t living up to customer expectations.

If this sounds familiar to you, you might want to look into a little thing called semantic AI.

What is semantic AI? Simply put, semantic AI allows you to tag and classify your customer support content in a way that makes it easy for customers to find the answers they’re looking for. It also enables this content to be delivered via AI-powered chatbots, smart virtual assistants and other self-service applications that can identify what your customer needs in the same way a human would, without the need for any actual human interaction. So, what should you have in place to transform customer support in this way?

Create an interconnected network of knowledge

It all starts with laying a solid foundation for managing knowledge across your organization. Too often, customer service information is unstructured, scattered across various internal systems, and formatted in several different ways. It’s important that the environment you manage your content in can render your content in a consistent way that is easily understood by machines and is organized centrally for easy access.

The best tool for this sort of knowledge management is a component content management system (CCMS). CCMS’ make information easy to find, manage, and disseminate by means of componentized content. This means that content is structured into small modules, often called ‘topics’, and stored in a central repository. And because it has been componentized, content classification and tagging becomes much more granular, since it happens at the component level, not the document level. Information becomes much more findable when you can search for a keyword and get the precise section you’re looking for, as opposed to the whole gamut.

And that’s not all – the beauty of a CCMS is that content is separated from its format, meaning it becomes much more flexible and can be moulded to fit any channel or format. Your customer service agents won’t have to plough through hundreds of pages of documents to find answers, they can simply search for the relevant ‘topic’ and share it with the customer. Or the content can be published to an online knowledge base, mobile app, or self-service hub. And when you combine a CCMS with semantic AI you create ‘intelligent content,’ which is where your ability to deliver contextual information to customers comes into its own.

Semantic AI – the smart solution to self-service

By turning traditional documents into intelligent content, your customers’ self-service experience really comes to life. Searching for information becomes a smooth, Google-like experience. Their searches are auto completed. They don‘t have to find an exact match but get results based on synonyms and context. They can see suggestions for related content. This type of intuitive, responsive search experience is the best way to satisfy the expectation of customers being able to solve their issues independently.

Considering that 90% of Americans use customer service to decide whether to do business with a company – it’s absolutely critical that you get your customer service right. That’s where the combination of semantic AI and a CCMS can really help, and transform one-off customers into life-long loyal fans.

If you would like to learn more about Semantic AI and how it can power your customer support strategies, click here.

About the Author

Fraser Doig is Associate Product Marketing Manager at RWS.

About RWS

RWS is the global leader in content management and translation technology and services. 90 of the top 100 global companies work with RWS. Tridion Docs provides streamlined end-to-end component content management. It includes easy web-based authoring, reviewing, versioning, translation, and publication management, underpinned by the DITA XML standard. As a true collaborative environment with a familiar Microsoft Word-style interface, subject matter experts (SMEs) in your organization can contribute their knowledge.

Authors and reviewers can work simultaneously in the same document providing comments to each other, tracking and merging changes. Tridion Docs supports global enterprise use cases including single sourcing, product documentation, learning and training, policies and procedures, and efficient translations with delivery to multiple end points such as documents, PDFs, knowledge portals, Intranet, customer facing websites, apps, chatbots, and IoT devices.

Contact RWS here or visit their website at rws.com. Twitter:@rwsgroup, Linkedin: RWSGroup.

Cora AI Coach, powered by Zenarate, helps Genpact accelerate digital transformation.

Zenarate, the leading AI Conversation Simulation solution, today announced that Genpact, a global professional services firm focused on delivering digital transformation, is leveraging its award-winning AI Coach to improve agent performance and customer experiences throughout its global client contact centers. AI Coach enables Genpact to develop confident top-performing contact center agents through voice and chat simulations that provide highly realistic immersive learning experiences. Integrating the AI technology with Genpact’s Cora Banking ecosystem allows Genpact to elevate its performance for more than 700 global clients.

AI Coach is transforming how contact centers develop confident, prepared new hires before their first call and close skill gaps for experienced agents. The simulation training platform creates hyper-realistic simulations of any voice or chat scenario, allowing agents to learn through practicing, solving problems and navigating errors. By providing a platform to build proficiencies, risks and costs are minimized in the short and long term for clients.

“Genpact is helping freshly hired agents improve their confidence before their first call. At the same time, they are driving up the average performance of existing agents by engaging their personal AI Coach from home or the office,” said Brian Tuite, Zenarate CEO and founder. “Helping support Genpact’s purpose – the ‘relentless pursuit of a world that works better for people’ – perfectly aligns with our vision to power human connections moving people, ideas, and businesses forward.”

“Cora AI Coach, powered by Zenarate, is one of the fastest, most effective ways to develop top-performing agents across use cases, from customer service and retention to collections and sales,” said Sachin Pai, Global Leader for Customer Support at Genpact. “The days of passive learning through reviewing content, watching videos and taking tests are over. With Cora AI Coach conversation simulation, our agents can quickly adapt to and better serve clients and their customers with complete confidence.”

Simulation Training will continue to evolve, making experiential learning more efficient, scalable, and realistic to help customer service agents prepare for and adapt to dynamic client needs. To learn how Zenarate is helping contact center and training leaders develop top-performing agents using AI Conversation Simulation, please visit www.Zenarate.com.

About Zenarate             

Zenarate’s AI Coach helps leading brands develop confident top-performing agents through AI Conversation Simulation training. Zenarate’s AI Coach is used worldwide every day in over a dozen countries, including the U.S., Canada, Mexico, Philippines, India, and Europe in 13 languages. Zenarate customers include 8 of the top 10 US financial institutions, and leading companies in the healthcare, telecommunications, technology, and services industries. For more information, visit www.zenarate.com.

No matter how brilliant a business idea, inevitably, the greatest challenge to building a successful business is finding the right employees to run it.

Talk to any level of business owner, whether seasoned or just starting out, and most will agree that hiring effective team members can make or break a business.

For entrepreneurs, hiring can be particularly challenging. When you are building an organization from scratch on a shoestring budget with tightly competing priorities, finding the right teammates may seem as likely as winning the lottery.

Despite the multitude of online platforms that streamline the hiring process, hiring as we know it is still broken. Finding that perfect employee is critical but the process is very time-consuming – from creating a job description, screening candidates, rounds of interviews and checking references – and time is an entrepreneur’s most precious commodity. Once you have narrowed it down to the right candidate and negotiated terms, developing and implementing a thorough training (while imperative) is labor-intensive.

Of course, supervising a new employee requires hours of dedication, coaching, and support, as well as quality control. And despite your best efforts, results still may vary. Some employees will still leave after you’ve invested in building them up.

Our company faced these hurdles too, but then we found a way to make it work for ourselves that was so successful we made it our core offering going forward. Enter Wing Assistant. What makes Wing Assistant different? Wing leverages technology and combines it with irreplaceable human capabilities, resulting in a superior customer experience as a remote labor supplier.

Wing alleviates an entrepreneur’s main employment challenges by addressing the trifecta of finance, time, and quality control constraints. Our business model is similar to hiring a human resources team, training department, on-going supervisor, and quality assurance division in one 45 minute interview, for a low monthly subscription fee and based on your needs. With Wing’s AI technology, a business owner can ensure ongoing employee and output quality without applying the intense time or micromanagement often required otherwise.

In a typical employment setting, a manager would be hard-pressed to monitor the minute details of their employees’ interactions – and even if they had the time or tools to do so, that level of micro-management would create an overwhelmingly stressful work environment. Wing’s tech team developed a proprietary algorithm that identifies quality control concerns during interactions between the customers and their virtual assistants. The concerns are documented and rated and Wing flags the issues for the quality control team and supervisors. Supervisors can address the concerns in real time with the virtual assistants, providing instant feedback and corrective action plans for improved output. Studies show that lack of trust at work are a serious issue and will likely result in high turnover rates (and an article from Entrepreneur.com uncovered a link between micromanagement and higher employee mortality rates).

Wing understands the value of human interaction, even as technology tremendously speeds up identifying quality issues. If virtual assistants were simply informed of their errors by a chatbot, there would be no opportunities for learning or even discussion. However, occasionally the algorithm over-identifies issues that the virtual assistant is able to explain. Supervisors are trained to instruct, coach, and support their virtual assistants to ensure they understand and continue to improve in their roles. With Wing, AI plus personalized review and supervision create a highly effective solution for employers.

Wing’s advances have turned hiring for the better. With a low overhead cost and a team dedicated to producing quality results, entrepreneurs can focus on the elements of their business that only they can spearhead, helping them to head towards exceptional growth.

About the Author

Diane Meehan is currently the Head of Customer Success at Wing, where she recently joined after several years guiding mom entrepreneurs toward their business goals as the Director of Customer Success at Pepperlane. Previously, she was consistently ranked in the top 5% of Paint Nite licensees across the country and grew her social painting business to over
$6 million in sales in less than 5 years. As a mom to three daughters, she is particularly skilled in time management, organization, and eye-roll interpretation.