But beyond the buzzwords and hype, what does AI really mean for you, the executive tasked with not just meeting but exceeding customer expectations in an increasingly competitive market? Let’s demystify AI in CX together and explore how this powerful technology can address your most pressing challenges while opening up new avenues for growth and innovation.

The Balancing Act of AI in CX

As a CX leader, you’re no stranger to the delicate balancing act of modern CX. It’s a dance of delivering personalized, high-touch interactions while managing cost efficiency and scalability. This is where AI steps in, not as a replacement but as an invaluable partner.

With its capacity to analyze vast datasets, AI identifies patterns and preferences, predicting customer needs before they even arise. This proactive approach ensures that your team is always a step ahead, delighting customers with timely, relevant interactions.

Enhancing Human Connection

One of the greatest myths about AI is that it diminishes the human touch. On the contrary, AI amplifies our ability to connect on a deeper level. By handling routine inquiries through chatbots and automated responses, AI frees your team to focus on what truly matters: the human connection.

Consider the scenario where a customer is navigating a challenging issue. Instead of being stuck in the quagmire of basic troubleshooting, your CX team can devote their energy to empathetic, meaningful conversations. AI handles the mundane, allowing human agents to shine in moments requiring compassion and creativity.

Data-Driven Insights: The Secret Sauce

Let’s talk about data. In the C-suite, you know its power. AI transforms raw data into actionable insights with a precision that’s nothing short of magical. Sentiment analysis, for instance, scans customer interactions across channels, providing a pulse on customer feelings and perceptions. These insights are your secret weapon, guiding strategy and innovation.

Imagine launching a new product or service. AI’s data-driven insights can predict customer reception, highlight potential pitfalls, and suggest optimizations that are grounded in hard data and real-time analysis.

Scalability Without Compromise

Scaling personalized CX efforts is often a Herculean task. But with AI, scalability doesn’t mean compromising on quality. AI-driven tools can handle an influx of customer interactions with grace, ensuring consistency in service delivery, day in and day out.

Imagine every customer interaction, from the mundane to the extraordinary, being met with the same level of precision and care. AI is your ever-vigilant ally, working tirelessly behind the scenes. Whether it’s a regular customer interaction or the busiest season of the year, AI’s ability to learn and adapt means it can anticipate needs, streamline processes, and personalize interactions on a massive scale.

Consider the everyday challenges: a customer needs assistance with a complex issue, another is looking for product recommendations, while yet another has a simple query. AI seamlessly navigates these varying demands, providing accurate, contextually relevant responses instantly. This consistency in service fosters trust and loyalty, making every interaction a testament to your brand’s commitment to excellence.

But it doesn’t stop there. AI’s adaptability means it can continually refine its approach based on real-time data and feedback. This dynamic capability ensures that your CX strategy remains agile and responsive, always one step ahead of customer expectations. In essence, AI doesn’t just keep up with the pace of your business—it sets the pace, ensuring your brand stays ahead of the curve without ever compromising on the quality of customer interactions.

The Path Forward: Embrace the Future of AI in CX

The benefits of AI in CX are not just theoretical—they’re tangible, transformative, and within your grasp. Embracing AI isn’t about replacing the human element; it’s about enhancing it and making your CX team more effective, empathetic, and empowered.

We invite you to explore these possibilities further with our Foundations in AI for CX Certification Program. In partnership with COPC, the program is aimed at senior executives, business leaders, and C-suite professionals who want to effectively tap into the explosion of AI capabilities. This is not just a 101-beginner certification. It is also for leaders with experience in AI programs who want to achieve higher CSAT, better profitability, and more stakeholder buy-in.

Top Five Reasons to Get CX for AI Certified:

1. AI capabilities and uses are expanding exponentially every day.

2. Almost a quarter of global jobs are expected to change within the next five years.

3. Few organizations have implemented GenAI at a scale where its impact can be effectively measured.

4. To be an early adopter and add value to your resume. No prior AI background is required.

5. The curriculum is expertly crafted and hands-on.

Don’t get left behind. Seize this opportunity to lead with confidence and innovation. Learn more and enroll in our Foundations in AI for CX Certification Program today.

Embrace the future. Empower your team. Elevate your customer experience.

The post The Benefits of AI in CX appeared first on Execs In The Know.

As technology capabilities advance, business strategies evolve. This paradigm has never been more evident than right now. Brands are looking to harness AI for many different use cases and applications. AI is rapidly reshaping environments and even industries. The unbridled possibilities that AI brings are very appealing, and the desire to implement AI quickly and reap its benefits sooner rather than later is understandable. However, rushed initiatives to leverage the power of AI do not usually deliver the change businesses are expecting. The reason that this approach falls short is because it tends to focus on solving for technology needs rather than business needs. Consequently, many AI initiatives fail to get completed, or fail to have lasting and holistic impact. Modern technology, including AI, is a business enabler, not a business solution.

Five Reasons AI Projects Often Fail

AI efforts usually fail due to numerous issues collectively working against them.

1. Unrealistic Expectations
   Many AI projects start off overly ambitious, creating a discrepancy between expected and actual performance, resulting in disappointment when outcomes don’t live up to expectations. This is often the case when AI initiatives are not aligned to business objectives and designed to generate value. The goal should be to use AI to generate value by enabling your people, processes, or products to deliver value.
2. Unclean Data
   Unstructured and outdated data produces unclean data. The power of AI is harnessed from the data the technology can access. No matter how you plan to use AI, its intelligence is artificial, and it will only ever be as good as the data it pulls from. Producing clean and reliable data can seem like an unbearable task, and many brands try to bypass this step without realizing its significance. This is an essential step and needs to be one of the highest priorities for any organization looking to deploy AI solutions.
3. Lack of Experience
   Limited experience with the different types of AI and their use cases, coupled with unfamiliarity around the complexities of AI, often produce incomplete project plans, unobtainable timelines, and unpredictable costs, undermining its chances of success.
4. Integration into Legacy Systems
   Many legacy systems are not built to work with the latest AI technologies. This causes compatibility issues that often lead to ill-constructed integrations resulting in a faulty or fragmented solution delivering subpar performance. Knowing what is and isn’t capable with legacy systems and how to integrate is imperative, otherwise you may limit AI’s ability to be revolutionary for your business.
5. Inadequate Day 2 Support
   AI solutions need to be tweaked and monitored continuously. Brands that have continued success with their AI solutions have an entire teams dedicated to maintaining and correcting the various solutions, or they have a strategic partnership with a service provider to manage it collaboratively with them.

The DIY or SI Crossroad

Businesses frequently debate whether to use a Do-It-Yourself (DIY) strategy or to work with an AI solutions integrator (SI) provider. Each option carries a unique set of hurdles that can have an impact on how AI implementations are delivered and continued.

The choice of whether to take a DIY approach or work with an AI solutions integrator is complicated and depends on an organization’s willingness to take on risk, level of knowledge, and long-term objectives.

DIY choices provide flexibility, but they also carry a high chance of failure and underperformance. Working with a trustworthy AI solutions integrator (particularly an SI that provides managed services and offers bundled solutions) reduces risk and produces a smoother, more fruitful AI deployment process.

Benefits of the SI Route

Here are some of the top reasons why you should choose to partner with a solution integrator firm rather than going for DIY:

Access to Cutting-Edge Technology and Specialized Knowledge

When you work with a solutions integrator, you have access to a group of skilled AI experts who have a thorough understanding of the technology and its uses.

Industry Knowledge

A partner who is an expert in the technology and knowledgeable about your industry will be aware of specific nuances to consider and is better equipped to deliver AI solutions that are in line with CX requirements unique to your company and industry.

Faster Deployment and Implementation

Solutions integrators can streamline project execution with their existing procedures and methods. Their expertise shortens the time to market and speeds up the realization of AI-driven CX value.

Scalability and Adaptive Flexibility

Scalable solutions are available from SIs and are integrated to meet your company’s current demands while enabling you to quickly adapt for future needs and expansion. Good integrators operate under Change Management best practices and governance, so upgrades and tweaks to your solution are as seamless as possible. This is especially important as your business strategies change with technology evolutions.

Collaborative Approach

Select a partner who appreciates candid communication and teamwork to make sure your company’s objectives are at the center of the AI strategy.

Anticipated Costs

DIY ventures necessitate hefty upfront recruiting, training, and infrastructure setup costs. AI tools may require continuous costs to operate and maintain that outweigh any immediate benefits.

Customization

DIY methods frequently fall short of being able to deliver the same level of customization that is possible through specialized partners. Customization is often needed to address specific CX needs, enhance agent experiences, and increase customer satisfaction.

Day 2 and Continuous Evolution

After implementation, working together with an AI managed services provider materializes immediate value. Security, billing, customer experience, and marketing issues are quickly and expertly handled by their diverse staff. An AI partner’s involvement becomes crucial in the post-implementation and support period as the initial excitement of new AI functionality wanes. They maintain success by keeping an eye on systems, responding quickly to problems, and maximizing algorithms for a higher level of efficiency.

Cornerstones of their services include smooth scalability, strong security measures, and flexibility to react to shifting business needs. A trusted AI partner delivers efficiency and alignment of evolving objectives with dedicated teams for troubleshooting and customized assistance. This stage establishes the partnership’s value, allowing continuous AI improvement and a simple path to innovation.

Overall, working with a solution integrator to incorporate AI in your CX practice offers several benefits over a do-it-yourself strategy. The disadvantages of DIY, such as unplanned cost structures, time-to-value, and less customization, highlight the strategic benefits of working with knowledgeable solution integrators.

This kind of trusted partnership removes the need for specialized employees, assists in optimizing business processes, and produces seamless AI integrations with proactive issue resolution and thorough optimization. The result is enhanced productivity and long-term growth.

Choosing the Right AI Services Partner

A thorough assessment is necessary when choosing the right AI services partner for your organization. The following aspects should be considered when choosing the best agency to partner with for your business:

A Direct Path to Value

When it comes to the field of artificial intelligence, the decision between a DIY approach and working with an AI partner is a complex one that greatly impacts an organization’s progress. This decision is influenced by several factors, such as risk, current competency levels, and strategic goals.

Collaborating with a reputable AI solution integrator offers numerous benefits that reduce risks and improve outcomes. Solution integration specialists, especially those offering managed services, can streamline implementations, overcome obstacles, and optimize AI solutions for a smoother journey that delivers maximum business value.

Guest blog post written by Black Box.

The post Navigating the AI Journey: DIY or Partner-Led Crossroad appeared first on Execs In The Know.

This skill is a critical component of a well-rounded well-being strategy, as it empowers individuals to navigate stress, build resilience, and foster healthy relationships – both personally and professionally.

Value and Work Efficiency

When employees feel valued and supported, and their well-being is prioritized, they are more likely to remain engaged and committed to their roles. This translates to reduced turnover rates, saving organizations both time and resources that would otherwise be spent on recruitment and onboarding. A well-balanced workforce is a productive workforce. Employees who are physically and mentally well are better equipped to focus on their tasks, make sound decisions, and collaborate effectively with colleagues.

In their 2022 global Work Trend Index, Microsoft found that 53% of over 31,000 global employees said they’re more likely to prioritize health and well-being over work than before the pandemic [Forbes].

By investing in well-being, organizations create an environment where employees thrive, leading to improved job satisfaction and enhanced performance.

The Path to Emotional Intelligence

Cultivating emotional intelligence requires intentional learning and development strategies that focus on mindfulness and self-awareness. As CX  leaders, fostering these skills in your workforce can drive substantial well-being improvements.

Here are some effective strategies:

• Mindfulness Practices: Integrate mindfulness practices into the workplace. Encourage short mindfulness breaks, meditation sessions, or even dedicated spaces for relaxation. Mindfulness enhances self-awareness and self-regulation, leading to reduced stress and improved decision-making.
• Emotional Literacy Training: Offer training sessions that teach employees to identify and label emotions accurately. Emotional literacy enhances empathy and communication skills, creating a more emotionally intelligent workforce.
• Feedback Culture: Promote a culture of constructive feedback. Encourage employees to give and receive feedback with empathy and openness. This practice fosters self-regulation and social skills while promoting continuous growth.

Leveraging AI Technology to Amplify Well-Being Strategies

The integration of AI technology into well-being strategies offers unprecedented opportunities for personalization and effectiveness. AI-powered platforms can analyze employee data, behavior patterns, and emotional responses to offer tailored interventions and support. Here’s how AI technology complements emotional intelligence and mindfulness strategies:

• Personalized Well-Being Interventions: AI algorithms can analyze employee data to recommend personalized well-being interventions. This could include mindfulness exercises, stress-reduction techniques, or resources for developing emotional intelligence.
• Real-Time Feedback: AI-driven tools can provide real-time feedback on emotional states and stress levels. This enables employees to become more self-aware and take proactive steps to manage their emotions.
• Learning Enhancements: AI can adapt learning materials based on individual preferences and emotional responses, enhancing the effectiveness of emotional intelligence training programs.

Shaping a Well-Being-Centric Future

As leaders, your role in shaping a well-being-centric corporate culture is pivotal. By intentionally cultivating emotional intelligence through mindfulness practices and leveraging the power of dynamic neuroinsights and AI technology, you’re equipping your workforce with essential skills to thrive. Empathy-driven leaders, emotionally intelligent employees, and AI-enhanced strategies collectively contribute to a resilient, empowered, and thriving corporate environment.

This approach transcends traditional isolated initiatives, weaving well-being into the fabric of company culture and systems. From onboarding processes that instill well-being values to ongoing training programs that develop emotional intelligence and stress management skills, holistic system design ensures that well-being is ingrained at every stage of an employee’s journey. By offering ongoing education, resources, and a supportive environment, organizations empower employees to consistently prioritize their well-being. This not only enhances individual lives but also contributes to a harmonious and engaged workforce that drives sustainable business success.

Guest blog post written by IntouchCX.

Join their panel at Customer Response Summit (CRS) Nashville on Wednesday, September 20th at 2:15pm, led by Chris Harrison SVP, Well-Being and Organizational Development at IntouchCX

The post Cultivating a Culture of Employee Well-Being appeared first on Execs In The Know.

Gone are the days of long wait times, repetitive processes, and frustrating customer experiences. Today, AI is paving the way for personalized, efficient, and engaging interactions that leave customers delighted and businesses thriving. According to a Gartner report, one in 10 agent interactions will be automated by 2026, an increase from an estimated 1.6% of interactions today that are automated using AI.

Let’s explore the transformative power of AI in call centers, uncovering how it is reshaping the way businesses interact with their customers.

AI Is a Top Destination for Investment

In our CX Leaders Trends & Insights: 2022 Corporate Edition, we discovered that 61% of organizations have either applied the use of AI to their contact center organizations or expect to soon. Although this number is up from 57% in 2021, it is still down substantially from 2020 when 71% of organizations had deployed (or planned to deploy) AI. AI continues to be a top destination for investment (page 70), but brands are also investing in technologies that enable AI, like Text/Voice Analytics, Big Data Utilization, and Predictive Analytics.

AI Application and Channel Deployment

Customer-facing solutions remain the most popular use of AI, occurring among 69% of those organizations currently deploying AI. This is followed by Agent-Assisting AI solutions, which occurs in an area (Agent-Assist Technologies) that saw a modest bump in investment year-over-year, as indicated on page 70. It remains to be seen how budgetary pressures might affect investments in AI in the near-term, yet it is certain investment will continue to flow toward AI in the future, especially as the possible applications of AI continue to broaden.

Objectives for AI

Several trends continue to offer promise when CX leaders were asked to identify which objectives they are primarily trying to accomplish with their use of AI. First, “Improve the Customer Experience” has further stretched its lead against other objectives, while use of AI to reduce costs and divert contacts continues to fall. AI best serves customers when it is used to simplify, expedite, or in some other way enhance the customer care experience, particularly in its application toward things like unassisted solutions. On the flip side, reducing cost and reducing human-assisted contacts are not customer-centric objectives and shouldn’t be the primary drivers of AI’s use.

Enhancing Customer Interactions

AI-powered chatbots and virtual assistants are taking customer interactions to new heights. These intelligent systems are equipped with Natural Language Processing (NLP) capabilities, allowing them to understand and respond to customer queries in real time. Say goodbye to scripted conversations and hello to dynamic, contextual interactions. With AI, call center agents can focus on complex issues that require a human touch, while AI handles routine inquiries with precision and efficiency.

Empowering Call Center Self-Service

Customers no longer need to rely solely on call center agents for support. AI has empowered self-service options, enabling customers to find solutions to their problems quickly and independently. Through intelligent chatbots integrated into websites and mobile apps, customers can access instant support, receive product recommendations, and resolve common issues on their own terms. This self-service capability not only boosts customer satisfaction but also reduces call volumes, allowing call center agents to dedicate their time to more complex and value-added tasks.

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Predictive Analytics for Personalized Experiences

AI-driven analytics is unlocking a wealth of customer insights, enabling call centers to deliver personalized experiences. By analyzing vast amounts of data, AI algorithms can predict customer behavior, preferences, and needs. Armed with this knowledge, call center agents can tailor their interactions, offering relevant recommendations and solutions that resonate with each customer. Through AI-powered analytics, businesses can foster stronger customer relationships, boost loyalty, and drive long-term success.

Streamlining Call Routing and Prioritization

Gone are the days of customers being passed from one agent to another, frustratingly repeating their issue multiple times. AI algorithms now analyze and classify customer queries in real-time, ensuring they are routed to the most suitable agent with the necessary expertise to handle the specific issue. This intelligent call routing saves time, reduces customer frustration, and increases first-call resolution rates. It empowers agents to focus on their strengths, leading to improved efficiency and overall customer satisfaction.

Continuous Learning and Improvement

AI never stops learning. Call centers are leveraging AI technologies to capture and analyze customer interactions, gaining valuable insights for ongoing improvement. Through sentiment analysis, AI can understand customer emotions and detect potential issues or areas of improvement. This data-driven feedback loop allows call centers to optimize their processes, refine agent training programs, and proactively address customer pain points. By harnessing the power of AI, call centers can evolve into customer-centric hubs of excellence.

From a Reactive to a Proactive Call Center 

The call center landscape is undergoing a remarkable transformation powered by AI. With intelligent chatbots, personalized experiences, efficient call routing, and continuous learning, businesses can provide unparalleled customer experiences that set them apart from the competition. The days of long wait times and frustrating interactions are becoming a thing of the past. It’s time to embrace AI and leverage its potential to elevate your call center into a customer-centric powerhouse.

Looking to gain more insights into CX technology and AI? Check out our CX Leaders Trends & Insights: 2022 Corporate Edition report (pages 72-77) or you can watch our recent CX Leaders Webinar, “How Generative AI, Gig, and Traditional Support Are Shaping the Future of the Call Center” on demand.

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In an era where customers hold the power, their experience with a brand can make or break its success.

With advancements in technology and changing customer expectations, the landscape has shifted, giving rise to innovative outsourcing solutions that prioritize personalized, seamless, and meaningful customer experiences. As brand leaders, it’s crucial to understand the latest trends, opportunities, and challenges in customer experience outsourcing to stay ahead of the game and ensure your brand thrives.

Let’s dive in.

Key CX Outsourcing Considerations

In a recent discussion with our community of CX leaders, they shared their insights and concerns regarding CX outsourcing. This post will explore why brand leaders should be paying close attention to this rapidly evolving industry and how businesses can effectively navigate the outsourcing landscape while maintaining a strong brand experience.

Importance of Generative AI
Many CX leaders acknowledged the potential of generative artificial intelligence (AI) in driving efficiencies. However, concerns were raised about governance and compliance in implementing AI solutions. Organizations are actively exploring how AI can be integrated into their CX operations while ensuring that the human touch and brand experience remain at the forefront. The soft skills of agents and their ability to understand and respond to emotional cues from customers are essential in creating lasting impressions.

A Focus on Efficiency and Cost Control
With the pressure of cost control from stakeholders, businesses are shifting their goals towards efficiency.

Cultural Alignment
The importance of cultural sensitivity and alignment when outsourcing CX functions is a hot topic right now. Outsourcers represent the brand, so it is crucial to ensure that they understand and connect with an organization’s target market.

Protecting the Brand
Protecting the brand has been a recurring theme throughout discussions. CX leaders stress the significance of driving the right customer experience with outsourcing partners and viewing cost as the price of admission. Capability and strategic fit have been identified as key differentiating factors when selecting outsourcing partners.

Outcome-Based Contracts
Some organizations leverage outcome-based contracts to ensure that outsourcing partners align with the brand and deliver the desired results. These contracts tie outcomes to incentives and credits, providing suppliers with the flexibility to manage day-to-day operations and provide strategic feedback.

Engaged Partnership
Successful outsourcing partnerships require active engagement from both parties. Clients who were most satisfied with outsourcing services were highly engaged and maintained clear communication with their partners, discussing expectations, and addressing business-related matters beyond key performance indicators (KPIs).

Challenges of Business Model Disruption
Business process outsourcing (BPO) providers also face challenges in monetizing their services due to disruptions in the business landscape. Friction between revenue goals and clients’ cost control can arise, necessitating innovative approaches to pricing and service delivery.

Change Management and Leadership
Change management has been highlighted as a critical aspect of successful outsourcing relationships. Leaders should focus not only on implementation but also on bringing people along for the journey, gaining commitment, buy-in, and ensuring follow-through.

Tools for Data Security
Discussions with CX leaders have touched upon the importance of data security and the need for tools that mask data. The significance of finding solutions that safeguard sensitive customer information and maintain compliance with data protection regulations is something that has been top of mind.

Business Continuity Planning (BCP) for Work-from-Home (WFH)
Many organizations are expressing concerns about the lack of internet and power redundancies in work-at-home setups, limiting their ability to ensure uninterrupted service during crises.

Remote Work Privilege
The privilege of remote work has been emphasized as something employees need to earn by demonstrating productivity on par with in-office peers.

Virtual Rewards and Recognition
Coordinating virtual rewards and recognition initiatives has posed challenges, such as organizing virtual pizza parties, which are easier to manage in physical office settings.

Embracing Hybrid Flexibility
AppFolio, an industry-leading company, advocated for creating an environment that enables, tracks, and encourages success and engagement for all employees, regardless of their location. Hybrid flexibility has been shown to boost productivity and employee satisfaction.

Gamification and Engagement
Implementing gamification techniques within a virtual community platform, as suggested by TTEC, can be an effective way to drive engagement and performance among remote teams.

3 Common Factors Influencing CX Outsourcing Decisions

CX leaders must carefully consider various factors that can significantly impact the success of their outsourcing initiatives.

From cost considerations to operational efficiency, cultural compatibility, and technological capabilities, the decision to outsource is influenced by a complex interplay of factors that require careful analysis and planning.

1. RFP criteria: When evaluating outsourcing partners, organizations should consider factors such as the capability to deliver, pricing, infrastructure and security, strategic fit, economic and political stability, and availability of talent.
2. Cultural alignment: Cultural fit, change tolerance, and alignment of values between the outsourcing partner and the client organization play vital roles in ensuring a successful collaboration.
3. Security and Compliance: Organizations should assess whether potential partners can meet security requirements and compliance regulations to safeguard sensitive customer data.

Common Challenges

The road to successful outsourcing can be challenging and it demands your attention, insight, and strategic decision-making skills.

These challenges can arise due to various factors such as communication, cultural differences, quality control, and maintaining a consistent customer experience.

Internal Resistance
Some organizations face internal resistance, distrust, or pressure when considering outsourcing. Educating stakeholders about the potential benefits and dispelling myths is crucial.

Labor Cost vs. Performance
Higher labor costs from outsourcing partners may be justified by superior performance, leading to improved first-call resolution (FCR) and customer satisfaction (CSAT) metrics, resulting in lower overall incident costs.

Complexity and In-House Expertise
Organizations often retain complex or specialized contact types in-house due to concerns about the ability of outsourcing partners to handle them with the same level of quality.

Compliance and Regulatory Considerations
In emerging fields like generative artificial intelligence (AI), organizations should ensure they understand compliance and regulatory limitations to avoid potential legal or ethical issues.

Customer experience outsourcing offers numerous advantages for companies striving to deliver exceptional service. By addressing challenges such as BCP for WAH, embracing hybrid flexibility, and leveraging virtual rewards and recognition, organizations can optimize CX outsourcing.

Additionally, careful consideration of RFP criteria, cultural alignment, and security requirements are key factors.

Actionable Takeaways for CX Leaders Who Manage Outsourced Operations

Here’s some actionable, practical advice for CX leaders from CX leaders who are responsible for managing outsourced operations.

Prioritize Customer Feedback
Regularly collect and analyze customer feedback to identify pain points and areas for improvement. Use this feedback to guide decision-making and ensure that customer needs are at the forefront of all actions.

Create a Seamless Customer Journey
Optimize the customer journey by ensuring that every touchpoint is consistent, intuitive, and easy to navigate. This includes everything from website design to customer service interactions.

Foster a Customer-Centric Culture
Ensure that everyone in the organization is aligned around a customer-first mentality. This can be achieved through training, communication, and recognition programs.

Leverage Technology
Utilize technology to enhance the customer experience, whether it’s through chatbots, personalized marketing, or data analytics. However, ensure that technology is not prioritized over human interaction and empathy.

Empower Employees
Give employees the tools and autonomy they need to deliver exceptional customer service. This includes training, access to information, and the ability to make decisions that benefit the customer. 

Continuously Iterate and Improve
Customer experience is not a one-time project, but an ongoing process. Continuously gather feedback, analyze results, and make improvements to ensure that the customer experience is always evolving and improving.

Deliver Exceptional Experiences

In summary, CX outsourcing can be a valuable strategy for businesses seeking to enhance their customer interactions and drive growth. By leveraging the expertise and resources of outsourcing partners, companies can tap into a global talent pool, gain access to advanced technologies, and achieve operational efficiencies.

However, it is crucial for organizations to carefully consider the key factors that impact CX outsourcing, such as cultural alignment, security measures, and change tolerance. With proper planning, effective vendor selection, and ongoing collaboration, organizations can successfully outsource CX functions and deliver exceptional experiences to their customers.

Interested in the topic of Outsourcing? We’re thrilled to announce that we have added two In-Person Outsourcing Briefings to our schedule for 2023. These informative and collaborative events will be hosted in New York, New York and Tempe, Arizona.

You’ll gain invaluable insights from industry experts and connect with top executives who are shaping the future. It’s the perfect chance to learn from expert panelists and your peers on strategies, best practices, and insider knowledge that can transform the way you approach Outsourcing.

Mark your calendars and register register today to secure your spot. Space is limited, so don’t miss out on this incredible chance to learn, connect, and grow.

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Here are four examples of problems that occur when automation goes wrong and how to avoid them.

1. Automation goes wrong when you lack human involvement.
   Sometimes, customers need to speak to a real person who can empathize with their situation, provide personalized solutions, or escalate their issue to a higher authority. You risk losing their trust if your automation system does not allow customers to opt for human assistance or transfer to an agent when needed.
2. Automation goes wrong when your AI solution can’t determine intent.
   Customers may have complex or ambiguous queries that require more than a simple yes/no answer. Automation systems that force customers to repeat themselves multiple times to understand their intent can lead to frustration and lower CSAT and NPS scores.
3. Automation goes wrong when a human can’t listen in on a call.
   Automation systems may encounter technical glitches, errors, or unexpected situations that require human intervention. If your automation system does not have a monitoring mechanism that allows human agents to listen in and intervene, when necessary, you may miss opportunities to resolve issues quickly and effectively.
4. Automation goes wrong when used in place of a conversation with a human agent.
   A 2020 NICE inContact survey found that 91% of consumers want to speak with a live agent when they have a complex issue. Disrespecting their preference can alienate customers and damage your brand reputation.

To avoid these pitfalls, design your automation system with the customer in mind. That means playing by the rules of conversation and building trust. It also means balancing your automation system with human agents who can provide customers the personal touch and support they expect and deserve.

The post What to Do When Contact Center Automation Goes Wrong appeared first on Execs In The Know.

1. Capture customer sentiment and learn from it

Contact centers are an organization’s window into customer feedback, trends, and sentiment. Is a caller content, confused, or upset? Is there a recurring issue with a specific product? Gathering this data and making it actionable for CX leaders and contact center associates can be a challenge. Asking an agent to assess a customer’s mood across hundreds of calls and report those consolidated impressions makes their job even harder than it already is.

But when an AI/ML-powered contact center can automatically flag phrases like “not happy” or “cancel my subscription”—or even pick up on tone of voice—an organization can gather a rich vein of data while agents stay focused on the matter at hand. AI/ML can transcribe calls, track customer sentiment, detect common issues and customer trends, or even pinpoint discrepancies—such as a price promotion in an email that doesn’t match the promotion on the website. The result is an up-to-the-minute picture not just of what people are calling about, but also of how they feel about your company and its service. These insights reports can also chart agents’ performance to uncover coaching and training opportunities.

Ring is a smart home security company that provides support to their customers, who they call “neighbors.” Using the ML features of Amazon Connect, Ring’s customer support team can understand neighbor sentiment, needs, and safety issues in real time. When a neighbor calls in with an issue that requires additional support, agents have context about their account so they can help resolve the customer issue quickly. This helps Ring deliver faster, more personalized service right from the start of a conversation.

2. Eliminate friction with personalized self-service

AI/ML can also streamline customer self-service so they get answers quickly without having to spend lengthy time searching out of date websites, navigating frustrating menus of “Press 1 for sales or 2 for support,” or waiting for a human agent. Today, customers can naturally explain in their own voice why they are calling and where they need help. AI/ML understands customer intent, makes sense of the request, and formulates a response, providing specific information to the customer that is timely, accurate, and personalized.

For example, early in the COVID-19 pandemic, MetroPlus Health, part of the largest municipal health system in the United States, needed to reach thousands of members quickly. This scale was impossible to accomplish without automation, but they didn’t want to lose the personal touch. MetroPlus Health used an Amazon Connect interactive chatbot powered by ML to quickly understand people’s health needs, then reached out to as many as 10,000 people per day with proactive, personalized messages that connected people with care, resources, and support.

3. Streamline the agent experience to better serve customers

People reach out to contact centers to get answers to complex and sometimes urgent problems. They want accurate answers as fast as possible. They don’t want to repeat information a company should know about them, like their name or what product they purchased, or wait as an agent digs through overwhelming amounts of information to find the answer to what they are calling about.

Today, AI/ML can augment agent work to simplify a large range of tasks like automating real-time caller authentication to making voice interactions faster and more secure. AI/ML can automatically assemble relevant customer information from multiple applications for the agent as soon as the support call or text interaction begins. It can provide agents relevant recommendations and answers across knowledge repositories, applications, internal wikis, and FAQs. And when a call ends, AI/ML can automatically create post call summaries to save agents time and deliver more insights to agents and supervisors the next time the customer may call. All these and more help agents better know who they are speaking with, assist them in better understanding the customer’s issue, and expediting answers and satisfactory resolution.

One organization realizing benefits today is Traeger Grills, the outdoor cooking choice of food enthusiasts. As Lizzy Mitchell, Head of Customer Experience Analytics at Traeger Grill, shares, “At Traeger, our mission is to help create a more flavorful world. Our customers are passionate about their grills. We handle tens of thousands of contact center contacts every month where people have questions on everything from grill care to WiFi connectivity. Our Traeger Techs used to spend a ton of time navigating multiple systems to find customer data. Now, when agents are connected to our customers, Amazon Connect automatically surfaces a unified customer profile that shows who is calling, their contact history, purchase history and grill type. Our agents no longer have to toggle between applications to find information and that helps provide a world-class customer experience. Since implementing Amazon Connect Customer Profiles, we’ve seen a ~25% reduction in handle time and a ~10% increase in CSAT.”

AI/ML: Buzzwords or a practical reality?

These are just three areas which we’ve seen organizations benefit from AI/ML-powered contact centers. If you are interested in exploring how businesses across industries are benefiting from cloud and AI technologies in their contact centers, check out our featured customer stories for Amazon Connect!

Author: Cory Glover, Senior Manager Product Marketing, Amazon

Guest blog post written by AWS. To learn more about this topic and others, visit the events page to check out all of our upcoming events.

The post 3 Ways AI and ML Can Take Your Customer Experience to the Next Level  appeared first on Execs In The Know.

Difference Between AI and Cognitive Technology

At the outset, Vishal stated the differences between artificial intelligence and cognitive technology. Often used interchangeably, a general air of confusion around the two terminologies continues to prevail. As defined by Rodney Brooks, “artificial intelligence doesn’t mean one thing. It’s a collection of practices and pieces that people put together”. Therefore, there’s no accepted, standard definition of what artificial intelligence really is.

At the most abstract level, AI mimics human behavior, solves complex problems, and can make decisions on its own. On the other hand, cognitive technology, which leverages a subset of technologies that AI uses, augments human thinking, and adapts to human reasoning. To laymanize it, cognitive technology lends you a hand whereas artificial intelligence can do things for you. Although AI is an umbrella term for cognitive technology and is much higher in the scope, organizations still leverage cognitive solutions to automate and enable a wide range of problem areas that demand some sort of cognition. The reason being that building, adopting, and applying AI to the specific needs of a business is highly challenging as the scope isn’t predefined.

Customer Journey in the Modern Era

Ideally, the customer experience can be divided into three stages: Pre-sales, onboarding, and post-sales. Each stage brings along its own set of challenges that can be dealt with by deploying cognitive technology. Let’s see how.

Stage 1: Pre-Sales
Since customers take stock of multiple options before selecting the best possible solution at this stage, one cannot afford to mess with the basics and leave the wrong impression of the brand. After all, a good first impression paves the way to many other good opportunities.

Pre-sales is all about discovering exactly what the customer is looking for and what their unique challenges are. Customers leave digital footprints in the form of information they access to evaluate the product or services. It is crucial for the sales team to tap into this information in order to position their products and services in a way that best addresses the customer’s challenges.

On that account, a unified brand experience is critical to the success of each opportunity and the overall sales organization. Cognitive technology unifies data from across disparate data silos and bridges the gap between multiple channels to ensure real-time personalization. After all, content personalization is the stepping stone to building a unified brand experience.

Stage 2: Customer Onboarding
The top two reasons for customer churn are 1) lack of understanding of the product, and 2) customers don’t obtain any value from the product. Therefore, it is imperative to provide a positive onboarding experience in order to convince your customers that they made the right choice. Needless to say that you’ve created an amazing portfolio of products and services for your audience. Just a little hand-holding and your customers stay loyal towards you forever.

Acquainting customers with product features is of paramount importance to get them started. However, bombarding them with hundreds of features in one go, especially when it’s a SaaS-based product, can be dangerous. Information overload can lead to a paralysis of analysis, making it harder for them to make the best decisions; thus, risking your brand image.

This is where cognitive search comes into play. It unifies interactions from multiple touchpoints to identify customers’ pain points and assist them with feature adoption by recommending the right set of features at the right time. At the same time, cognitive technology can empower your employees by assisting them in building hyper-personalized launch packs. Additionally, it can help employees increase self-service adoption amongst their customers by providing contextual and relevant information across channels like phone, email, communities, and more. Thus, laying the groundwork for a smoother onboarding experience and in turn, enabling stronger reinforcement of your brand image.

Stage 3: Customer Support
Once a customer is on board and starts to spend more time getting the knack of the product, they need further support. This is where support teams come into the picture. With new customers joining the family, support reps are likely to be swamped with hundreds and thousands of queries. To take the burden off customer agents, every organization creates multiple support channels like community, support portals, help center, agent interaction, etc. However, disconnected support channels often make the customer repeatedly narrate their problems that is no less than adding insult to injury.

With cognitive search, you can power a connected support experience across all these channels by not only unifying enterprise information but also taking into account the user behavior on every channel. Additionally, the cognitive solution that offers an AI-powered chatbot takes the employee and customer experience to a whole new level. Unlike the standalone bot that operates on predefined dialogues, search-powered chatbots leverage NLP and text-analytics to understand the intent and sentiments of a customer.

For an in-depth understanding of how cognitive technology helps you delight your customers as well as employees at each stage of the customer journey, watch this webinar.

The post Webinar Recap: Double Win – Transforming Customer & Employee Experience with Artificial Intelligence (AI) appeared first on Execs In The Know.

Because the use of bots across digital channels is skyrocketing, there’s a pretty good chance consumers have had multiple bot experiences. Good, bad, or indifferent. Why settle for anything less than a great bot experience for your customer though. In this post, we’ll show you what the interaction looks like for your customers, but first, let’s take a closer look at why it’s important to create a great bot interaction.

Why Develop A Great Bot Interaction

Even before COVID, bots were growing in popularity. Salesforce predicts a 136% growth rate in the use of AI chatbots and according to Gartner, by 2020, around 85% of our interactions will be handled by bots instead of humans. This is not because they have replaced humans entirely, but because they have augmented humans by taking care of the tasks that don’t require a human to complete.

More companies are turning to bots to facilitate a variety of common or repetitive tasks, to free up agents to handle more complex interactions. Bots can be integrated into a spectrum of workflows across an organization, like customer referral programs or promotional offers. Whether it’s a bot that greets your customers and gathers data to route them to the best suited agent or queue, or a bot that presents offers based on past purchases, bots can do a lot of heavy lifting.

In short, as bots become more integrated into your processes and workflows, interactions with your bots become intertwined with your brand. Just like training any new employee, you’ll want your bot to deliver against your brand values and voice. Every company is different, but there are a few key characteristics that are consistent with a great bot experience.

Characteristics Of A Great Bot Experience: Make It Purpose-Built

It’s important to remember that your bot will only be as good as your configuration of it. Thoughtfully planning out your bot flow before you jump into any bot designer is critical. Here are a few considerations you should keep top of mind when designing your bot.

• Conversational
  While the customer should know they are chatting with a bot, it shouldn’t feel like they are interacting with a machine. If your agents would normally say “Hey! Glad you’re here. How can I help you?” configure your bot to do the same. Ensure the bot mirrors your brand voice. Keep questions clear and simple with short suggested answers to make it easy for your customer to understand and respond.
• Efficient
  A great bot experience will usher the customer through a frictionless workflow with the shortest path possible to the answer they’re looking for. Design a bot that can transfer conversations to other queues, agents, or even other bots if necessary.
• Intelligent
  Just because it’s artificial intelligence doesn’t mean you can’t make your bot genuinely smart. Teach your bot industry terms, synonyms for common words, and alternate ways a customer might ask a question. A bot can deliver a great experience by being emotionally intelligent too. Enable sentiment analysis to understand your customers’ tone and general attitude during an interaction to deliver a personalized experience, or to automatically escalate to a live agent when necessary.
• Integrated
  Integrate your bot with other backend systems and/or your CRM. Make it easy for your bot to retrieve information that your customer needs to resolve their inquiry. Bots shine when it comes to retrieving information quickly. Finding an order number, sending a link to track the status of a shipment, or helping a customer reset a password are the easy, low effort requests that can be frustrating for customers to call or email about.
• Omni-capable
  Your customers aren’t using just one digital platform. Design a bot that can converse across multiple digital platforms. Keep it consistent by building one bot and enabling across multiple digital channels such as web chat, SMS/text, and social media.

Set The Chatbot Bar

Each experience your customer has with a chatbot creates their expectations of what the next experience should look like. Designing with the features above in mind can help you set the bar high for chatbot experiences.

If you’re ready to implement chatbots, or would like to learn more about this topic, register for the REPLAY of the June 9, 2020 webinar, “Embrace and Activate Digital Customer Engagement for Future Success.”

­­­­­­­­­­­­­­­­­­­­­­­­Blog post, written by Quiq
Quiq helps brands present their customers with consistently jaw-dropping customer experiences across SMS/text messaging, rich messaging, web chat, and social channels.

The post 5 Key Traits Of A Great Bot Design appeared first on Execs In The Know.

At our recent Customer Response Summit session on AI, a show of hands revealed a surprising number of senior CX executives are still stuck at square one or two on the path to full implementation. Here are the five biggest stumbling blocks they cited (hint: it all comes down to #5).

1. Absence of a clear long-term strategic plan

In their enthusiasm for new tech, many brands plowed ahead on small-scale applications without creating a long-term blueprint for success. “Our approach started out very piecemeal,” said one executive. “We’re looking to bring various elements into a more cohesive strategy.”

2. Lack of ownership and internal collaboration

Too often, the teams tasked with developing AI products are working at loggerheads with the departments their programs are supposedly designed to support. “Sometimes our IT group doesn’t bring us in to ask us how something should work because they don’t like what we have to say,” said another attendee. “They assume, ‘customer service will just figure it out.’”

“We went to chatbots to drive our costs down, but it hasn’t worked out as we’d hoped,” said another. “It was a result of too many cooks in the kitchen.”

3. High levels of complexity within the existing organization

Many brands cited concerns that AI could further muddy some already highly complex organizational structures. “When you’re a Tier-3 vendor, you have 40 different systems to navigate,” said a vendor in the CX space. “We need AI to have these systems talk to each other, but we’re afraid to exacerbate existing problems.”

Said another, “In many cases AI seems to be adding to the complexity, not reducing it.”

4. Predicating resource allocation on achieving short-term goals

“How do you get your organization to be patient with AI?” an attendee asked. “Implementation is a long-term process.”

“The leadership obviously wants a return on its investment sooner rather than later, so it’s a question of managing expectations and providing clear and specific metrics to capture incremental improvement,” another suggested. “But that’s easier said than done.”

“It’s been hard for us to secure the funding to clean and integrate our data without a near-term ROI,” another agreed.

5. Poor foundational data and data governance practices

“How do we get from here to there?” one attendee asked. “Our challenge is the capacity – systems in the support organization were not in place. We’re tasked with laying the data foundation system we need to implement AI.”

“Our customer service organization has a pretty good data foundation but not all departments do,” said another senior executive. “There needs to be more integration. Who ‘owns’ the data?”

“You need to be thinking about your underlying data sources,” another added. “Without that piece, your AI will never function as you envisioned. You must have a strong foundational data element to build on.”

The Bottom Line

Given the importance of building on a strong data foundation for any AI strategy, square one isn’t necessarily a bad place to be. It’s far easier to begin your AI journey by implementing a data governance framework than it is to go back and clean your data and data-storage structures after AI products are already in place.

No single CX Leader can go at it alone. If armed with a long-term strategic plan, a solid support network of cross-functional stakeholders, and clear expectations for incremental improvement, you can gain buy-in for taking that critical first step.

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