Understanding the Modern Data Stack: Architecture, Tools, and Benefits

Data is the cornerstone of today’s digital enterprises, enabling solutions to complex challenges and driving innovation. Yet, many organizations struggle to fully capitalize on their data. A staggering 73% of data goes unused, hindered by silos, poor quality, and outdated systems. To address these challenges, businesses are leveraging a revised, modern data stack.

The modern data stack is an ecosystem of cutting-edge tools designed to manage the growing volume, velocity, and variety of data while meeting the increasing demands for scalability, flexibility, and actionable insights. By leveraging a modern data stack, organizations can align people, processes, and technology to collect and store data, analyze it, automate workflows, and scale to support the growing number of analytics use cases over time.

In this guide, we explore the architecture, tools, and benefits of the modern data stack, highlighting how it outperforms traditional approaches.

What Sets the Modern Data Stack Apart?

The transition from traditional to modern data stacks represents a paradigm shift in how data is managed and leveraged. Traditional data stacks, built for static and siloed environments, are ill-equipped to handle today’s complex data landscapes. Here’s how modern data stacks stand out:

Key Differentiators Between Traditional and Modern Data Stacks:

Traditional data stacks were built for an outmoded approach to data. They were highly customized and required extensive in-house resources to maintain. Because of these limitations, traditional data stacks could not handle the complexity or volume of data needed to support modern data and analytics.

Enter the modern data stack. Modern data stacks set themselves apart by being built for today’s data challenges and future data demands. There are several factors that separate traditional and modern data stacks

Cloud-Native Architecture:
- Traditional stacks often rely on on-premise infrastructure, leading to inefficiencies and scalability issues. Modern data stack architecture leverages cloud platforms, offering cost efficiency, flexibility, and near-limitless scalability.
Decentralized Data Ownership:
- Within the traditional approach to data, centralized teams often controlled all aspects of the data lifecycle from sourcing, transformation, and distribution. This approach often led to bottlenecks, preventing users from obtaining their core data assets efficiently.
- Modern data teams, however, are focused on curating high-quality, standardized data outputs which are readily available to business users. Business users will work in conjunction with the data team to produce high-value data products
Streamlined Integration:
- Modern data stacks incorporate sophisticated ETL/ELT tools with pre-built connectors, simplifying data ingestion and reducing the need for costly, time-consuming development.
Advanced Analytics and AI Integration:
- While traditional stacks struggle with advanced technologies, modern data stacks are purpose-built to support machine learning, AI, and predictive analytics.
Proactive Data Governance:
- Modern data stacks embed governance at every stage, ensuring data integrity, compliance, and security from the ground up.
Storage
- Modern data stacks leverage data lakes and/or lake houses, which provide greater flexibility in data storage and analysis. Data lakes allow you to store raw data, and are built to provide flexible, scalable storage as your data volumes grow.
Security and Compliance
- Modern stacks often include advanced, integrated tools for data access, security, and compliance.

Benefits of the Modern Data Stack

The modern data stack enables businesses to keep up with the velocity and scale of modern data analytics while also reducing costs and the burden on internal resources.

1. Enhanced Data Capabilities: Modern data analytics requires real-time insights. With modern data stacks, users can make timely, on-the-spot decisions with real-time data. In situations where speed and accuracy are critical, such as customer-facing interactions, these capabilities can be a key competitive differentiator.

As companies incorporate advanced analytics tools like machine learning and artificial intelligence, modern data analytics enables them to implement and use those tools effectively. This supports more accurate predictions, better understanding of customer behaviors, and enhanced operational efficiency.

2. Refined Processes: Modern data stacks incorporate data governance and support compliance measures to ensure that data is accessible, accurate, consistent, and reliable. These practices also reduce risk by supporting processes to ensure compliance with regulations like HIPAA and General Data Protection Regulations (GDPR).

3. Empowered Data-Driven Processes: A modern data analytics stack can support self-service tools so users can self-serve without needing to rely heavily on IT teams or waste valuable time collecting and normalizing data.

Data-driven collaboration is also increasingly important for compliance. With the Securities Exchange Commission (SEC) and the European Union making progress to enact standards for Environmental, Sustainability, and Governance (ESG) compliance, companies need to ensure that they have the right data to plan, execute, and report on a wide variety of activities. Modern data analytics tools incorporate governance practices that allow companies to make data-driven decisions and share their results with stakeholders and regulators with confidence.

Unlock the Potential of Your Modern Data Stack

As data continues to grow in importance, adopting a modern data stack is no longer optional—it’s essential. The right modern data stack architecture and tools empower organizations to innovate, scale, and stay competitive in a data-driven world.

At Kenway, we specialize in helping businesses design and implement modern data stacks tailored to their unique needs. From optimizing data pipelines to enhancing governance and enabling advanced analytics, we ensure your data ecosystem is ready for today’s challenges and tomorrow’s opportunities.

Overcome data silos and inefficiencies. See how we helped an asset management firm overcome data silos and inefficiencies to achieve a 360-degree customer view. With our expertise, the firm built a modern data warehouse and platform that delivers real-time insights and seamless access to critical information. Today, the firm’s employees can easily access all the information they need on prospective and current clients from a single source.

Ready to transform your data strategy? Contact us today to explore how a modern data stack can revolutionize your business.

Modern Data Stack FAQs

What is the modern data stack?

The modern data stack is a cloud-based ecosystem of tools designed to simplify data integration, storage, governance, and analysis. It enables businesses to manage data efficiently, scale seamlessly, and extract meaningful insights to drive decision-making

How to build a modern data stack

Building a modern data stack involves:

Defining Goals: Identify your business objectives and data needs.
Selecting Tools: Choose scalable, cloud-based tools for ingestion (ETL/ELT), storage (data lakes/warehouses), and analytics.
Integrating Governance: Incorporate data governance, security, and compliance from the start.
Enabling Collaboration: Use self-service analytics tools to empower teams.
Testing & Scaling: Continuously test performance and scale as your needs grow.

What should I look for in each component of the modern data stack?

Here’s what to prioritize for each component of the modern data stack:

Data Sources: Broad compatibility with structured and unstructured data formats.
Data Ingestion Tools: Pre-built connectors, scalability, and support for ETL/ELT processes.
Data Storage: Flexible, cloud-based solutions (e.g., data lakes/warehouses) with scalability.
Governance & Security Tools: Built-in access controls, data lineage, and compliance features.
Analytics Tools: User-friendly, self-service options for advanced analytics and real-time insights.

What is the modern data stack architecture?

Modern data stack architecture leverages cloud-native platforms to ensure scalability, flexibility, and efficiency. It incorporates tools for data ingestion, storage, governance, and analytics, enabling seamless end-to-end workflows.

What are modern data stack tools?

Modern data stack tools include ETL/ELT solutions (e.g., Fivetran), cloud data warehouses (e.g., Snowflake), BI tools (e.g., Looker), and governance platforms (e.g., Alation).

How does a modern data warehouse fit into the stack?

A modern data warehouse serves as the central repository, enabling fast, scalable analytics and real-time data accessibility. It supports structured and semi-structured data, making it a cornerstone of the modern data stack.

What challenges arise when building a modern data stack?

Common challenges include selecting the right tools from a crowded market, ensuring interoperability, and aligning people and processes for maximum efficiency.

How can AI and ML enhance a modern data stack?

AI and ML tools automate analytics, generate insights, and enable predictive modeling, enhancing decision-making capabilities and operational efficiency.

Unveiling the Power of Intelligent Virtual Agents in Contact Center Solutions

In the rapidly evolving landscape of contact centers, businesses need to meet the expectation of seamless, personalized, and efficient customer service interactions. Intelligent Virtual Agents (IVAs) have emerged as a powerful solution for meeting these demands. IVA systems are AI-driven solutions that enable businesses to automate responses and provide human-like, intelligent interactions that align with customer expectations.

IVA vs. IVR: The Journey from Traditional to Intelligent

Contact center organizations are transitioning from rule-based, static Interactive Voice Response (IVR) systems to the dynamic capabilities offered by Intelligent Virtual Agents.

Traditional IVRs operate within predefined rules, provide limited assistance, and struggle to grasp the nuances of natural language. These platforms are a great start for contact centers seeking to serve customers through automation, but they do not allow the flexibility that adjusting to free-speech conversations requires.

IVA systems represent a leap forward, incorporating AI technologies such as Natural Language Understanding (NLU) and Natural Language Processing (NLP) to comprehend and respond to customers with contextual intelligence. These technologies empower IVAs to understand the intricacies of human language, allowing for more nuanced and context-aware interactions.

Additionally, IVAs emphasize omnichannel support for an optimized omnichannel strategy that ensures a consistent user experience regardless of whether customers engage through text-based chat, voice interactions, or SMS.

IVAs also represent the move from traditional data connectivity to a modern data view, as they can analyze vast amounts of customer interaction data and learn from past engagements to enhance future interactions.

All of these IVA features illustrate just how personalized and innovative IVA systems are when comparing IVAs vs IVRs.

The Crucial Role of IVAs in CCaaS Platforms

Like nearly every other industry, contact centers have evolved rapidly towards cloud-based platforms in recent years. Contact Center as a Service (CCaaS) platforms increase flexibility, scalability, and operational efficiency.

However, the main driver for organizations to move towards CCaaS solutions is that it eliminates the need to maintain on-premises infrastructure, which reduces cost and increases flexibility for remote workers.

IVAs are a core component of the CCaaS ecosystem. There are many vendors in the market today that offer this service across all channels. The 2023 Gartner CCaaS Magic Quadrant highlights the following vendors as standout leaders:

Whether voice, chat, text, or a combination of the above, CCaaS providers offer solutions to fulfill client’s needs for more responsive, data-driven, and efficient IVA systems.

IVA Implementation in Contact Centers

The IVA vs IVR conversation marks a significant evolution for contact centers. For companies seeking to stay ahead of the pack in contact center innovation, here are some tips when implementing IVA systems:

Develop Use Cases

Using IVA systems like a catch-all for your contact center’s potential inefficiencies will not serve your organization or your customers well in the long run. Before implementing your IVA, utilize data analytics to determine where your contact center may be falling behind, and whether an IVA could be the best solution for filling that gap.

Evaluate IVA Options

Once use cases are identified, it’s time to identify the right IVA provider for your organization. Factors to consider include cost, capabilities, and user-friendliness.

Test and Monitor Regularly

Testing your IVA system upon implementation is a crucial element of setting your contact center up for success as it grows and modernizes. Testing early and often will aid in identifying inefficiencies and pain points within the system. Continuous monitoring after the initial testing period is also important to ensure the standards for your IVA system always remain high.

Contact Center Solutions with Kenway Consulting

Organizations are embracing IVAs with advanced NLU and NLP capabilities, omnichannel support, and a data-driven approach. The flexibility and cost-efficiency of IVA systems position them as indispensable assets in modern contact center frameworks, offering a pathway to deliver exceptional and consistent customer experiences across diverse channels at a reasonable cost.

If you’re looking to take the next step in your contact center journey, have questions about IVAs vs IVRs, or aren’t sure how to get started, connect with our Contact Center Solutions experts to begin strategizing for a more streamlined and efficient contact center.

FAQs:

What are the differences when comparing IVA vs IVR?

IVAs use virtual agents to provide personalized conversations and solutions for contact center callers. IVRs are useful but less advanced tools, which use a prerecorded menu with limited navigation options.

How do businesses use IVR and IVA?

Businesses use IVR and IVA services for contact center optimization. IVRs are often used for automated menu offerings while IVAs can carry on full conversations with customers.

How to choose between IVA vs IVR?

Many businesses already have IVR capability in their contact center system and may decide that now is a good time to upgrade to an IVA system. This decision depends on the level of optimization and customer service your company desires to provide, but Kenway recommends utilizing IVR systems in order to stay competitive within your industry.

Master Contact Center Management: Key Principles for Success

Effective contact center management goes far beyond answering customer calls. Call center managers tend to a variety of needs day in and day out. Some of these needs include prioritizing customer satisfaction, agent-well being, and keeping your technology and workflows up-to-date.

Today, CCaaS (Contact Center as a Service) Solutions involve more than just fielding calls. CCaaS solutions integrate technology, data analysis, and customer-centric approaches that can unlock significant improvements in customer satisfaction and operational effectiveness.

In order to better understand the complex world of CCaaS, we will be discussing contact center management best practices below:

Managing Customers

Every industry wants to serve its customers well, but contact center customers are particularly unique stakeholders because the core of contact center operations is based on customer interaction.

At Kenway, we spend a lot of time talking about contact center improvement ideas through technology, but your technology can only go as far as the foundation laid by the people in your organization. Managing customers also refers to managing the people and processes in your company, because maintaining healthy employees leads to great customer service, and great customer service leads to satisfied customers.

Tips for increasing contact center efficiency for customers and employees include:

1. Agent training

Contact center work requires a lot of person-to-person interaction, and those first impressions can make or break a call center interaction. Ensuring that your agents are thoroughly trained and given the tools they need to succeed is the best way to set your entire contact center up for success.

2. Focus on mindset

A customer-first mindset will help your agents approach every interaction with a positive, solutions-focused attitude.

3. Proactive problem-solving

The more your agents become familiar with the solutions your organization offers, the more they will be able to anticipate customer needs and address issues before they arise, resulting in fewer calls and happier customers.

Managing Technology

Now that we’ve covered the important interpersonal element of contact center management, it's time to explore the ins and outs of leveraging technology for contact center improvement.

1. Harnessing the Power of Intelligent Virtual Assistants (IVAs)

IVAs have revolutionized contact center management by offering automated yet personalized interactions. These AI-driven assistants handle routine questions, freeing up human agents to focus on complex issues. The strategic implementation of IVAs can significantly reduce call volumes, lower costs, and elevate the overall customer experience.

2. Comprehensive Reporting for Informed Decision Making

Data is the lifeblood of effective contact center management. Business intelligence reporting, machine learning models, and predictive analytics provide insights into call patterns, agent performance, and customer behaviors. When this reporting is utilized, contact center managers can make data-driven decisions that lead to improved service quality and operational efficiency.

3. The Crucial Role of Routing Architecture

Efficient routing architecture ensures customer questions are directed to the most appropriate agent or channel every time. Dynamic routing considers factors like account data, agent skills, customer history, and intent. Routing architecture in contact center management can minimize wait times, enhance first-call resolution rates, and ultimately aid in customer satisfaction.

4. Synergy with Computer Telephony Integration (CTI)

CTI integration seamlessly connects call center technologies, enabling agents to access customer data instantly. Agents can then view past interactions and caller details in real time. Integrating CTI with IVAs and routing systems creates a unified ecosystem, resulting in smoother customer journeys.

5. Continuous Improvement and Training

Effective contact center management is an ongoing process. Regular performance analysis and strategic adaptation are necessary for managing both people and technology. Utilizing AI for continuous improvement is a great option. AI driven agent assist tools like Google’s Agent Assist or Five9’s Agent Assist are great places to start.

Next Steps in Transforming Contact Center Operations

Contact center management has evolved into a critical cost driver for organizations and demands a strategic approach. Embracing the steps outlined in this blog will help your organization enhance customer satisfaction, increase call capacity, and save on costs.

If you’re looking to improve contact center operations, streamline reporting, or find self-service opportunities, connect with our Contact Center Solutions experts today.

FAQs

How can you improve call center customer service?

To improve customer service in a call center, focus on enhancing agent training, implementing customer-centric technologies such as IVAs, and continually analyzing performance metrics to refine workflows and address inefficiencies.

How do you improve call center agent performance?

Improving agent performance requires a combination of regular training, performance-based incentives, and even AI-driven agent assist tools for faster and more accurate responses.

What is contact center management?

Contact center management involves overseeing the day-to-day operations of a contact center, ensuring customer satisfaction, agent well-being, and operational efficiency through technology, data analysis, and continuous improvement strategies.

What does a contact center manager do?

A contact center manager is responsible for managing the team of agents, overseeing technology implementations, analyzing performance data, and ensuring that customer service standards are met. They also focus on optimizing operational efficiency and driving overall improvement in contact center performance.

Testing Automation: What You Need to Know

With a long history in Quality Assurance, Kenway has been a trusted partner in supporting the implementation of automation solutions for our clients, specifically in the IVR (Interactive Voice Response) and Contact Center spaces. Testing automation is a game changer for any company that can implement it; however, many do not consider the investments needed to ensure consistent success and meaningful ROI. Without common dependencies being proactively governed and appropriately managed, automation becomes impossible to implement effectively. Kenway works with our clients to address these extremely complex challenges while guiding them successfully through their automation adoption journey.

The Fundamentals of Testing Automation

Strategy & Process

A well planned and thorough implementation strategy is fundamental to the success of an automation initiative. By establishing specific goals and desired outcomes, teams can prioritize areas in need of automation. This clarity enables efficient resource allocation and ensures that automation efforts are targeted towards areas that will yield the greatest benefits.

Well-defined testing processes guided through comprehensive test plans help mold effective automation certification practices. These plans outline the scope, coverage, and test cases to be automated, ensuring that all critical scenarios and functions are adequately tested. With a solid test plan in place, an organization can effectively prioritize its automation efforts and ensure comprehensive coverage, minimizing the risk of overlooking critical aspects of the application.

Furthermore, strong testing processes provide standardized procedures for test case design, execution, and result analysis. This consistency ensures that the testing automation team follows best practices, adheres to established guidelines, and maintains a high level of quality and accuracy in their testing activities. By following a systematic approach, firms can confidently rely on automation results to make informed decisions about the application's quality and readiness for deployment.

Dependency Management

There are several dependencies which require proactive mapping, with either direct resolution or meaningful mitigation, to support a healthy automated quality assurance experience. Some of the more common and high priority dependencies are associated with data and environments. Clean and reliable data is a fundamental dependency for implementing automation successfully. It is the fuel that drives the automation engine and plays a crucial role in producing accurate and meaningful test results.

Test data configured to mimic real-world scenarios replicates realistic user interactions with the developed applications. With compliant data, automated tests can be executed without encountering false failures, and/or false positives. When working with flawed or unreliable data, automation scripts will likely encounter unexpected issues or fail to produce accurate results. Quality data eliminates inconsistencies, errors, and duplicates that can hinder the effectiveness of automation.

Furthermore, clean and reliable data enables better analysis and troubleshooting during the testing process. When automation scripts produce results that are based on accurate data, it becomes easier to identify and isolate issues or defects within the application. This expedites the debugging and resolution process, saving valuable time and resources. Data management practices, including version control and documentation, contribute to the overall traceability and repeatability of automated tests. Teams can track changes in test data, compare results between different test runs, and reproduce specific test scenarios with confidence. This traceability fosters transparency and enables effective collaboration between testers, developers, and stakeholders involved in the automation process.

Available, stable, and release compliant environments are also fundamental to supporting effective automation implementations. Overall, environments which are managed effectively provide the foundation for consistent, reliable, and efficient QA processes. They help ensure that test results are accurate, defects can be reproduced, and certification processes can be optimized, and eventually automated. Therefore, organizations should strive to establish and maintain available, stable, and release compliant environments to enhance the effectiveness of their quality assurance and automation practices.

Tool Selection and Maintenance

The process of adopting testing automation includes the critical task of selecting the right automation tools which align with project requirements, technology stack, and testing objectives. The choice of tools directly impacts the efficiency and effectiveness of the automation process. By evaluating different options and identifying the tool that best suits a company’s needs, organizations can ensure a seamless and successful implementation of automation.

There is a wide range of automation tools available in the market. Some of the leading choices include Cyara, Selenium, and Hammer. Kenway recently partnered with Cyara, a leading automated CX assurance platform. As Kenway is tool agnostic, we do not recommend one vendor vs another based on our own relationships or partnerships; we help our clients choose the right solution based on their unique paradigm and requirements. In this case we worked with the client to build requirements and validate that it was the right choice for their needs and would recommend that all organizations take the same steps to ensure the tool selected is the best fit.

Once the tools are selected, ongoing maintenance becomes essential for the long-term sustainability of the automation effort. Regular updates, patches, and enhancements keep the tools compatible with the ever-evolving technology landscape.

Maintenance activities also involve monitoring tool performance, addressing any issues or bugs, and ensuring the tools remain reliable and efficient in supporting the automated testing process. Proper tool maintenance also includes staying informed about the latest advancements in automation technology, allowing teams to leverage new features and capabilities to enhance their automated testing efforts.

Moreover, tool maintenance encompasses providing training and skill development opportunities for the testing team. Ensuring that testers are proficient in utilizing the selected automation tools is crucial for maximizing their potential. By equipping the team with the necessary skills, teams can effectively design and execute test cases, analyze test results, and fully leverage the capabilities of the testing automation tools.

Testing Automation Governance

Governance establishes and ensures the maintenance of standardized methodologies, procedures, and guidelines for testing automation, test case design, execution, and result analysis across different projects and teams. By providing a framework for standardization, governance facilitates collaboration, reduces errors, and improves the overall quality of testing – automated or not. Without a foundation in good governance, the ability to reap the benefit of automation becomes extremely difficult.

Governance plays a crucial role in risk management. A well governed program identifies and mitigates risks associated with autonomous certification practices (e.g., data privacy and security, test environment stability, tool selection, regulatory requirements, and industry standards). By implementing controls and protocols, governance functions proactively address potential risks and minimize disruptions or failures in the testing process.

Governance promotes accountability and transparency in testing activities. It establishes clear roles and responsibilities for all stakeholders. By defining and enforcing these roles, governance fosters transparency, trust, and effective collaboration among team members, leading to better outcomes in automated testing. It also facilitates knowledge management by capturing and sharing best practices, lessons learned, and automation artifacts. Managers are better equipped to ensure that valuable insights, tools, and resources are documented, stored, and made accessible to relevant stakeholders. This knowledge management aspect of governance promotes learning, innovation, and efficiency in testing, especially when automated.

Change Management

A well targeted and streamlined change management approach will help guarantee adoption of automation. Each team impacted by the enhanced processes to support automation will need to understand their roles and responsibilities to ensure the organization achieves the highest ROI. Each stakeholder must have a clear understanding of how their role impacts the success of automation. Governance must be equipped to validate that automation processes are being consistently leveraged, with any associated risks and issues being quickly resolved or mitigated.

Kenway understands that automation is an iterative process. As automation initiatives progress, it's common for organizations to revisit and refine their testing strategy, governance, dependency management, and change management practices. Continuous evaluation, adaptation, and improvement are also crucial to optimize the benefits of automation over time.

How Kenway Can Help

When engaging with a client interested in automation, Kenway will assess processes, dependencies, and governance prior to conducting an analysis on automation tools. By identifying gaps in the fundamentals early, we can create an effective plan to prioritize and address issues which ultimately support the delivery of a highly functional automated testing framework. We guide our clients through automation orchestration, model adoption options, and tool selection processes, ensuring that the client’s unique needs are met. Throughout this journey, we focus on the impacts to the existing teams, unique personas, and processes to ensure a smooth transition via a well-defined change management strategy. Automation is a powerful tool, but only when organizations make the proper investments to effectively integrate and nurture it. At Kenway, we pride ourselves on having helped clients maximize the return on their automation investment.

If you are struggling with your manual testing, having difficulties implementing automated testing, or simply have interest in automated testing, we would like to hear from you at [email protected].

Improving Patient Engagement: Healthcare Contact Center Innovations

In today's dynamic healthcare environment, the pursuit of exceptional patient experience in healthcare has become more than just a goal; it's a fundamental imperative. As the industry continues its transition towards value-based care models, healthcare leaders are increasingly turning to advanced contact center solutions to drive patient engagement, satisfaction, and ultimately, better outcomes. At the forefront of this transformation is Kenway Consulting's Contact Center Solutions practice, which offers a strategic blend of innovation, technology, and patient-centricity.

Patient Engagement

Patient engagement is not a static concept but rather a living, breathing capability that demands continuous refinement and adaptation. Healthcare leaders must orchestrate the proliferation of patient journeys, ensuring that interactions are personalized, relevant, and seamless across all touchpoints. This entails treating engagement as a dynamic process rather than rigid patient journeys, allowing for flexibility and responsiveness to evolving patient needs and preferences.

One of the key advancements in contact center solutions is the integration of artificial intelligence (AI) into patient engagement processes. By embedding AI capabilities such as predictive analytics, natural language understanding, and chatbots, healthcare providers can deliver more personalized and proactive experiences to patients. However, striking the right balance between human intervention and automation is crucial to maintaining trust and empathy in patient interactions. While automation can streamline routine tasks and improve efficiency, human touchpoints remain essential for delivering compassionate care and addressing complex patient needs.

Every industry is different, and every company is unique, but callers want to have their needs met as quickly and as efficiently as possible. Good IVA design blends the desires of the business with the technology from IT while keeping the experience front and center.

Critical Considerations for Healthcare Leaders

Sensitive patient data is a paramount concern in healthcare, and protecting it requires robust security measures and adherence to strict compliance standards such as HIPAA. Contact center solutions must employ encryption, access controls, and other security protocols to safeguard patient information and maintain trust.

Before launching communication initiatives, healthcare leaders should conduct rigorous testing to ensure effectiveness and mitigate potential risks. This includes testing communication channels, messaging strategies, and response mechanisms to optimize engagement and minimize errors.

Handling Structured and Unstructured Responses in Patient Interactions

Structured vs. unstructured responses pose a unique challenge in patient interactions. While structured responses provide clear data points for analysis and decision-making, unstructured responses require more nuanced interpretation and understanding. Contact center solutions must be equipped to handle both types of responses effectively, leveraging AI and natural language processing to extract valuable insights from unstructured data. With the proliferation of Generative AI solutions in the Contact Center, ensure your team is ready to identify and correct hallucinations from your bots.

Implementing Contact Center Solutions for Patient Engagement

Healthcare leaders should consider several factors when implementing contact center solutions for patient engagement:

Treat engagement as a living, breathing capability, adapting patient journeys to meet evolving needs and preferences.
Utilize cloud-based Software-as-a-Service (SaaS) platforms that integrate seamlessly with existing technology stacks and support high-volume communications.
Minimize reliance on custom code but recognize when it may be necessary for implementing rules engines or tracking touchpoints.
Develop iteratively, starting with small-scale initiatives before expanding to more complex patient journeys.
Define decision-making authority and documentation strategies for patient journey logic upfront to ensure clarity and consistency.
Evaluate which components of contact center solutions to build internally versus sourcing from external vendors based on organizational capabilities and strategic priorities.

Transform Patient Engagement with Kenway

Our Contact Center Solutions practice works closely with our healthcare experts to support healthcare leaders on their transformative journey, driving excellence in patient engagement and care delivery. By embracing innovation, patient-centricity, and strategic integration of contact center solutions, healthcare providers can elevate patient experiences, improve outcomes, and thrive in the era of value-based care.

Our Contact Center Solutions team has supported clients with Digital Customer Experience Consulting for nearly 20 years. We partner with our clients and apply best practices in concert with their culture, skills, and methodologies. We do not force-fit practices simply because they work elsewhere. Connect with us to give your patients and members the best experience possible.

Maximizing IVR Performance: Comprehensive Metrics and Executive Dashboards

In the ever-evolving landscape of customer service, the voice channel remains a cornerstone of engagement, providing a vital link between organizations and their clientele. At Kenway Consulting we believe that maximizing your IVR performance is crucial in ensuring seamless interactions with customers. By harnessing your Contact Center data and effectively channeling it into a Business Intelligence solution, we empower businesses to understand what’s happening in your Interactive Voice Response (IVR) or Intelligent Virtual Assistant (IVA) systems.

Leveraging Data Insights for Enhanced IVR Performance

The phrase “Data is the New Oil”, became a hot topic because people understand just how valuable data can be. However, a mistake we see time and time again across so many implementations is that leveraging data to assess the success of your customer experience comes as an afterthought.

Over the years we’ve shared our ideas about Mastering CCaaS & IVR Reporting and discussed Improving Your IVR with Customer Journey Reporting and Analytics on our blog. Our Contact Center Solutions practice works hard with our Data & Analytics practice to meticulously assess the health and efficacy of IVAs to ensure they are working as intended, customers are happy, and the business is seeing the value in their investment.

Evaluating IVR and IVA Performance

When evaluating how well an IVR or IVA is performing, it is important to adopt a holistic approach that encompasses a diverse array of Key Performance Indicators (KPIs). While individual metrics offer valuable insights, it is the combination of these metrics that provides a nuanced understanding of system performance and user experience. I divide these KPIs into two categories, General Metrics and Experience Metrics.

General Metrics

Total Calls: A foundational metric providing insights into overall call volume and demand patterns.
Total Transfers: An indicator of the frequency with which calls are redirected to live agents, influencing operational efficiency.
Agent Request Rate: A measure of customer preference for human assistance, guiding resource allocation strategies.
Intent Capture Rate: A metric assessing the system's proficiency in accurately discerning and addressing user intents.
Call Length: Providing insights into user engagement levels and system efficiency.
Hang-up Rate: Highlighting potential usability issues or customer dissatisfaction leading to premature call terminations.
Containment Rate: Reflecting the system's capacity to autonomously resolve queries, thereby enhancing self-service capabilities.

Total Self-Service Attempts: Tracking user engagement with self-service options, a critical aspect of IVR functionality.
SMS Sent: Monitoring the adoption of alternative communication channels, reflecting evolving customer preferences.

Experience Metrics

Total Calls to X Prompt: Identifying critical junctures in call flows, essential for optimizing user journeys.
Total Attempts: Offering granular insights into user interactions across various stages of engagement.
Prompt Retry Counts / No Match: Signifying instances where users encounter challenges in communicating with the system, thereby pinpointing potential usability concerns.
Prompt Timeouts / No Input: Measuring user responsiveness to prompts, aiding in the refinement of prompt durations.
Total Questions/Prompts Heard: Providing a comprehensive view of user engagement, facilitating iterative content enhancements.
Self-Service Attempts per Call: Evaluating user reliance on self-service options, guiding system design refinements.
Total SMS Sent per Call: Reflecting user preferences for communication channels, informing strategic channel prioritization decisions.

Executive Dashboards for Real-time Insights

Integrating these KPIs into an Executive Dashboard should be a cornerstone of daily operations, offering real-time insights into system performance. Contact Center leaders should work with their business and technology partners to establish baselines for each metric. Once baselines are established, leveraging the native alarming capability in a Business Intelligence tool provides instant notifications that an established baseline has fallen too low or high.

How Kenway Can Help Your Customer Service Strategies with Data-driven Solutions

At Kenway Consulting, we advocate for a culture of data-driven decision-making, where these KPIs are shared across the organization to inform strategic initiatives and foster a culture of continuous improvement. Utilizing comprehensive metrics and executive dashboards is instrumental in unlocking the full potential of IVR and IVA systems. By embracing this analytical approach, organizations can not only optimize current performance but also lay the foundation for future advancements in customer service excellence. If you're interested in learning more about how our solutions can enhance your customer service strategies, contact us today.

Unlocking Efficiency: Generative AI's Role in Contact Center Innovation

Introduction

Throughout the past year, Kenway Consultants have been deeply involved in orchestrating the buildout of a Contact Center as a Service (CCaaS) implementation at a major telecom provider in the United States. Our team has played critical roles on the program such as defining the customer experience for each unique self-service offering and expanding on the functionality of their existing IVA. Kenway continues to successfully bridge the gap to the business while working closely with AI Architects, Developers, and technical teams. In this blog, we will share our insights, key challenges faced during the implementation of Generative AI, and the steps taken to overcome these obstacles.

In 2023, more than 25% of all investment dollars in American startups were channeled into AI-focused companies. Global spending on AI for 2024 is projected to exceed $110 billion. Much of this investment is geared towards Generative AI, which saw unprecedented innovation in the last year. Implementation in the real world has spanned across industries, including Technology, Media, Telecom, and Financial Services, due to the clear alignment of use cases in those industries.

The Time-Intensive Problem

Before diving into the insights gained by implementing Generative AI in the Contact Center space, we will expand on some of the problems with traditional prompt creation and routing to further emphasize the importance of Generative AI. The first piece is the time intensive nature of the traditional flow or prompt creation. Before Developers can build a self-service experience, a myriad of requirements needs to be discussed and documented. Business Analysts define the requirements, conversational architects will take those requirements and build a visual flow representing the experience, content writers adjust and approve all language that a customer will hear, technical teams conduct data mapping to configure the technical solution, and so on.

Despite this level of attention to detail, certain use cases and edge cases will fall through the gaps and will only be discovered once the experience is live in production. This feeds into the second piece that all callers hear the same generically constructed content, offering a less personal, more robotic experience. Furthermore, customer utterances deemed a “No-Match”, or a “No-Input” have no path forward other than endless retries and ultimately speaking to a live agent. Traditional implementation fails to capture these use cases, as it is extremely time-consuming, and expensive, to build handling and routing for all potential utterances a real customer may provide.

Generative AI in Contact Centers

The introduction of Generative AI helps ease this pain on all fronts. Leveraging Gen AI solutions can reduce time spent throughout the Software Development Lifecycle (SDLC) by reducing the number of prompts and routing needed to deliver a particular enhancement and reducing the time spent solutioning edge cases. Technical teams can spend their time more efficiently, and superior experiences are also provided to the customer. Generative AI enables companies to provide personalized prompting for each individual caller’s needs. With the ability to leverage content from the companies’ website, existing forums, corporate databases, and more – Generative AI can offer dynamic informational prompting once trained on these materials.

Secondly, Generative AI can mimic the conversational style of the caller as it starts to use words similar to the caller’s verbiage as the call goes on, repeating it back if the system is having trouble matching the customers’ utterance to an established route or intent. This powerful feature emulates a human-to-human connection, allowing the IVA to respond as a human would.

Finally, the IVA can keep callers engaged longer with the use of Generative AI, which ultimately improves containment rates, a key metric used to gauge the performance of self-service experiences. In short, an IVA containment rate refers to the percentage of inbound calls or chats that are successfully handled without having to speak to a human agent. Higher containment rates equate to a lower volume of live agent transfers, improved agent workloads, and in parallel, a potential reduction in labor costs for the organization.

Limitations and Challenges of Generative AI Implementation

While acknowledging the benefits, the use of Generative AI poses potential drawbacks and challenges. As programs navigate from the traditional SDLC to leveraging Generative AI, the first concern is that testing (Quality Assurance, IST, and User Acceptance Testing) can be significantly more cumbersome. Generative AI provides dynamic prompting to different callers, this requires more test cases to verify the application is functioning correctly. To alleviate this stress, organizations can use automated testing platforms such as Botium, by Cyara, to test AI with AI. A powerful tool such as Botium can go through thousands of test cases in a matter of minutes. What better way to test AI, than with AI?

In the AI world, made-up values or incorrect facts are called hallucinations. These hallucinations can be caused by a variety of factors, including insufficient training data, incorrect assumptions made by the model, or biases in the data used to train the model. Due to this, there are concerns that stem from a legal and content standpoint. Generative AI may lead to the usage of verbiage or language that does not necessarily have signoff from all involved parties. In the traditional model, content writers meticulously groom each user facing prompt with legal considerations top of mind. Due to the dynamic model of Generative AI, customers may receive content that misrepresents the business and their normal communication standards. Even more concerning, there is the risk that something being communicated is inaccurate or misleading to the user, potentially opening the organization up to legal penalties. For instance, a recent article by Forbes discusses the legal ramifications of these hallucinations within the airline industry. Additionally, the Guardian outlines a recent use case in the court of law that resulted in severe repercussions for a Canadian lawyer.

Mitigation through Design

However, there are tools to mitigate these risks. The first being the appropriate selection of the Large Language Model (LLM), or AI toolset, and the data used to train the model. Establishing robust guidelines during the model training timeframe can help limit this risk downstream. The data upon which the model is trained will need to be selected carefully to avoid bias. It is important to also continuously tune and deploy the model with parameters that strike a balance between generating a human-like customer experience and one that does not stray from the confines of its knowledge base (i.e. creating hallucinations). The perpetual monitoring of the model’s performance after it has been deployed to production will further refine the output.

Secondly, leveraging automated testing tools to cover thousands of use cases and edge cases will allow for teams to identify defects further up in the DevOps lifecycle, before those defects reach production, reducing costs down the road.

Lastly, keeping the concept of Responsible AI at the forefront when designing the model guidelines will ensure these risks are further tamed. It is worth noting that the selection process for Generative AI will vary depending on the business or industry it intends to support. For example, the AI model deployed in a Contact Center at a telecom provider will vary significantly from a model used to serve an internal purpose at a Private Equity firm, or one used to send out targeted marketing campaigns to potential customers. These models can be seen as highly customizable, where the concept of ‘one size fits all’ does not apply.

Above all, the most impactful mitigation tool for AI risks is choosing the right use cases. Specifying all-encompassing use cases, creating KPIs around engagement volume, evaluating the potential severity of defects, and where the highest ROI would come from utilizing generative AI, can all be very difficult. However, by selecting appropriate use cases, most generative AI limitations can be avoided based on these decisions being made further upfront in the design process.

Moving Forward with AI in Contact Centers

It is important to reiterate that the model must have access to reliable and germane data on which it can be trained. Think of a chatbot trained on semi-biased data, potentially perpetuating stereotypes or generating offensive hallucinations. Acquiring and curating high-quality, contextually relevant data is paramount. No business function will be successful without the iterative process of testing, investigating, enhancing, and reassessing the performance of Generative or Conversational AI. This iterative approach ensures that these models not only function effectively, but also continually adapt and grow, becoming invaluable assets for businesses with aims to stay ahead of the curve.

AI is continuing to evolve and advance, the future of Generative AI is a bright, and an ever-changing horizon. At Kenway, we have built expertise over the last few years in the AI realm, and we have launched our newly formed Artificial Intelligence Practice. Our services include:

Conversational AI
Generative AI
Computer Vision (Automation)
AI Enablement & Strategy (where Responsible AI methodologies reside)

As Kenway takes on more Contact Center Implementations and seeks to expand into other industries that are now ready to realize the ROI of AI, the firm continues to design intelligently for the future, ensuring the latest methodologies and tools are deployed at our clients. To read more about our Contact Center Practice, please visit: Contact Center Solutions.

FAQs:

How important is data to being able to build an AI tool?

The need for quality and integrity of data underpinning an AI tool cannot be overstated. Across all industries, AI, especially Generative AI, requires a robust, compliant, application-specific data set without bias to produce high-quality, ROI-driving responses regardless of the specific application. Selecting the right data set can be the most important decision a firm makes when making implementation decisions.

What guidelines or frameworks should I consider when looking to implement Generative AI in my organization?

Consider AI Ethics guidelines that cover principles such as fairness, accountability, transparency, privacy, and security.

Consider AI Risk Management frameworks, such as NIST, that manage risks associated with AI systems throughout their lifecycle, from design to deployment and monitoring.

Consider AI Governance frameworks that define governance structures, policies, and processes for AI systems, ensuring alignment with organizational values, risk management, and compliance.

Consider Responsible AI Frameworks that allow AI systems to respect human rights and promote inclusiveness.

Consider Data Privacy and Retention Regulations to ensure you continue to remain compliant with these mandates.

Our business is doing great, and we are consistently beating our revenue targets. Why do I need to invest in AI?

Making a data-driven decision on the implementation of artificial intelligence for a specific use-case can not only reduce time spent on specific workstreams, but even open doors to new ways to interact with customers, more efficient ways to manage tasks, and ensure your organization stays ahead of the curve.

How do I scale?

Before delivering an AI solution at scale across an enterprise, ensure the following criteria are met. The model must be maintainable, robust with the ability to avoid hallucinations, and executed efficiently throughout the continuous deployment cycle. The model should have the ability to integrate with core systems and provide performance monitoring to avoid any detrimental impacts on internal operations.

Critical Considerations When Selecting a CCaaS Provider

Introduction

Selecting the right Contact Center as a Service (CCaaS) provider is a crucial decision for businesses aiming to enhance their customer service. We often find our clients seeking to understand how to approach the selection of the vendor to enable them to realize their Digital Strategy.

A CCaaS solution integrates a range of components, including omnichannel support, workforce management, quality management, routing, telephony, AI, reporting, and security. This guide will delve into the essential considerations within each of these categories, as well as integrations, configurability, and fees, to help you make an informed choice.

I. Core Components of a CCaaS Provider

Omnichannel Support

A CCaaS provider must excel at supporting your required communication channels. Depending on the products and services you provide, you will need to find a CCaaS provider that can support the right combination of channels, such as an Interactive Voice Response (IVR), Automatic Call Distribution (ACD), Chat, SMS, Voicemail, social media, etcetera. This allows customers to reach you through their preferred channels and ensures consistent customer engagement. The provider should enable efficient handling of questions, offering a seamless experience no matter how a customer chooses to get in touch.

Workforce Management (WFM)

Workforce Management tools are essential for optimizing agent scheduling, forecasting, and performance monitoring. The right CCaaS solution should offer robust WFM capabilities, ensuring that your team is adequately staffed, reducing idle time, and increasing overall efficiency.

Quality Management (QM)

Quality Management is about maintaining service quality through monitoring, evaluation, and coaching. A good CCaaS provider should offer QM tools that help you ensure that your agents consistently meet quality standards in customer interactions.

Routing

Efficient routing is vital for ensuring that customer inquiries are directed to the right agents. A CCaaS solution should have advanced routing capabilities that allow for intelligent and dynamic distribution of customer interactions to the most appropriate agents. To delve further into this topic, explore our blog on call routing benefits and best practices.

Telephony

The telephony component encompasses call handling, recording, voicemail, and more. It's crucial to evaluate the telephony features provided by the CCaaS provider to meet the specific telecommunications needs of your business.

AI (Artificial Intelligence)

AI integration is becoming increasingly important in the world of customer service. A robust CCaaS solution should offer AI capabilities, including chatbots, virtual agents, (human) agent assist, and data-driven insights. These tools can significantly enhance the customer experience and improve operational efficiency.

Moreover, AI can also be extremely helpful once the customer gets past any self-service functionality and reaches an agent. Some CCaaS providers have developed AI services that can deliver near-real-time note taking, post-interaction summarization capabilities, or real-time guidelines, links, and articles to assist the agent to effectively serve the customer’s needs. This allows to reduce the average time spent within an interaction by decreasing the average hold time (AHT), which leads to higher volume management.

Reporting and Analytics

Data-driven decision-making is at the core of effective customer service. The CCaaS provider should offer comprehensive reporting and analytics tools that provide insights into agent performance, customer behavior, and overall operational efficiency. In addition to out-of-the-box reporting, best-in-class CCaaS vendors provide easy and cost-effective access to the data created within the platform via APIs. This allows your team to access the data, store it in your own cloud environment, and use your enterprise Business Intelligence tool for driving insights out of your Contact Center data.

For deeper insights and additional information, explore our blog posts on mastering CCaaS & IVR reporting, customer journey reporting and analytics and IVR reporting best practices.

Security

Ensuring the security and compliance of customer data is of utmost importance. When choosing a CCaaS provider, make sure they adhere to industry regulations (HIPAA, CCPA, GDPR, etc.) and have robust security measures in place to protect sensitive customer information.

II. Integrations and Configurability

Another critical consideration is the provider's ability to seamlessly integrate with your existing tools. This includes CRM software, helpdesk applications, and analytics platforms. The ability to integrate with your current systems ensures a smooth transition and continued efficiency in your customer service operations.

Given the uniqueness of each business, companies might require complex ad-hoc solutions. Depending on the complexity and custom needs a firm may have, some may opt in for an out-of-the-box solution, while others may prefer the ability to configure certain areas. Your CCaaS provider should offer a balanced approach between a highly customizable solution and a more straightforward, plug-and-play one, always in alignment with your specific requirements. To be successful in choosing the platform and the integration, it is crucial not only to find the right solution, but also partner with a team that can help create a seamless integration from the beginning.

III. Fees and Total Cost of Ownership (TCO)

The final critical consideration revolves around the financial aspect. You should carefully evaluate the total cost of ownership (TCO), which includes not only upfront costs but also ongoing maintenance expenses. These expenses encompass licensing, usage fees, and any hidden charges. A transparent pricing model will help you budget effectively and make a well-informed decision.

While not all, some CCaaS vendors employ a pay-by-usage model which may offer more attractive and cheaper solutions in the beginning. However, this requires understanding your needs based on usage estimations to further understand the cost of selecting their platform. Furthermore, configurable solutions are also tied to the need for technical resources, which can also ebb up your TCO given the potential increase of implementation costs.

Disclaimer: the chart above is merely theoretic and discloses information for graphical purposes.

Conclusion

Selecting the right CCaaS provider is essential for achieving your business objectives and delivering outstanding customer service. By thoroughly assessing each component, considering integrations, finding the right balance between configurability and out-of-the-box features, and understanding the total cost of ownership, you can make an informed decision that aligns with your unique business requirements. The right CCaaS provider will set the stage for exceptional customer experiences, helping your business thrive in today's competitive landscape.

For any further information you or your company may need, do not hesitate to contact us at [email protected] for further help and assistance. At Kenway Consulting, we help and guide our clients towards selecting the correct CCaaS provider that fits their needs and requirements.

Cyara Xchange - This Year's Highlights

Kenway Consulting's team recently had the privilege of attending Cyara's Xchange '24 conference in sunny San Diego, where we delved into the latest advancements shaping the world of Customer Experience, GenAI and Quality Assurance. We enjoyed engaging with leading brands and industry experts from around the world to help improve the performance of CX teams and technologies. Here are a few of the key highlights and insights we gathered and found particularly useful.

Top 3 Highlights

Cyara is rolling out some unique solutions to assist Quality Assurance Teams validating Artificial Intelligence (AI) functionality. You can get an early start with Fact Checker today which is in a Beta release. Fact Checker can reveal the accuracy of a Large Language Model (LLM) powered bot against a source of truth. Discover reasons why an LLM powered bot may provide an inaccurate answer.
Integration with Jira: The integration capabilities of Botium and Velocity with Jira offer immense value to organizations. Automatic ticket creation for bugs found during testing streamlines the defect management process, enhancing efficiency and ensuring issues are promptly addressed.
CentraCX Acquisition: Max Lipovetsky, Cyara’s Chief Product Officer, shed light on Cyara’s roadmap and future product innovations, such as Complex Journey Assurance, Dashboards & Advanced Analytics, and the launch of their newest solution Number Trust which provides Call Line Identification (CLI) Assurance to validate company numbers are being presented on mobile devices as intended.

General Insights

Our discussions at Xchange '24 touched upon various crucial topics, including best practices in leveraging and scaling test automation. We emphasized the importance of integrating tools effectively to optimize automated QA models and stabilize QA test dependencies. Additionally, the challenges of test data and environment stability were highlighted across industries.

A highlight of the conference was the fireside chat featuring Navrina Singh, Founder and CEO of Credo AI, and Alok Kulkarni, Co-Founder and CEO of Cyara. The discussion centered on the pivotal role of generative AI in CX practices, emphasizing the need for governance to ensure scalability and trust in AI platforms. Navrina Singh's insights resonated deeply, particularly her emphasis on governance for scalable AI implementations. Kenway's Sean Rabago says that "We continue to see, first-hand, the negative impacts of attempting to implement GenAI without an effective governance and management framework defined. The need to ensure a more proactive approach to aligning GenAI against more appropriate, less risky use cases, while using effective governance to support a healthy implementation model is paramount to implementing value consistently here."

Cyara's Xchange '24 was a rich source of insights, reaffirming the critical role of cutting-edge technologies in shaping the future of customer experience testing. We're excited to apply these learnings to drive innovation and excellence in our own consulting endeavors.

If you have questions about what we learned at Xchange ’24, Cyara’s products, or anything else related to testing automation and GenAI, email us at [email protected].

Unveiling Customer Experience Dynamics: Leveraging CSAT and NPS Scores to Find Areas for Improvement

Companies commonly use Net Promoter Scores (NPS) and Customer Satisfaction (CSAT) as a loyalty barometer. It comes as no surprise that when talking about Contact Center, CSAT and NPS scores become the center of conversations with our clients. CSAT and NPS play a pivotal role in unveiling the nuances of customer experience to a Contact Center organization.

At Kenway, we treat each client uniquely and bring a tailored approach to evaluating these metrics. Whether you’re a large corporation or a small regional business, CSAT and NPS scores might have different meanings for you. But with the right approach, you can leverage CSAT and NPS scores to pinpoint the highs and lows of your customer’s journey.

Our Contact Center experience has taught us that while valuable, CSAT scores offer a selective view. For instance, attributing a negative CSAT to a seamless IVA experience doesn't account for a, potentially, subsequent unfavorable interaction with an agent or vice versa. It would be incorrect to say the IVA experience was bad when the IVA did everything correctly. This is one simple example, but it illustrates the point that these are diagnostic tools, illuminating segments of excellence and areas that demand refinement.

Let’s go beyond a simple analysis of CSAT and NPS scores and look deeper at the story behind each metric. How do you define and calculate them? Why do you use these metrics and not something else? Starting with these answers helps us understand your mindset and strategies.

Understanding CSAT Calculation

IVA (Voice or Chat) CSAT

If you’re measuring your customer’s satisfaction based on their experience with your bot (voice or chat) you need to consider the experience. Did the customer need to provide a reason for contacting you or did you already know and anticipate that need? Did the customer need to provide some method of authentication (phone or account number, password, etc.)? Did the customer have to repeat any of these questions or go into a circular flow? Did you need to refine or disambiguate the customer’s intent? Was that disambiguation to support the customer’s need or was it to support your company’s routing? Yeses and Nos to the questions above aren’t necessarily bad on their own. The point is that customers will, in general, play along when they feel like they are making progress toward their goal. A smart flow that gives customers confidence will typically result in higher CSAT numbers.

Agent CSAT

When focused solely on human-agent interaction, this score becomes a beacon highlighting the role of agents in shaping the customer experience. Delving into the agent's experience unveils potential areas for improvement. Organizations should consider external factors when using CSAT and NPS scores. Is their product a commodity where switching to a competitor is easy for a customer? Is the issue the customer is facing beyond the scope of the agent? An agent can only do so much to help a customer if the company has turned off their service and charged them 4x their standard bill.

The Nexus Between CSAT and NPS scores

CSAT and NPS scores are not solitary indicators but are deeply intertwined with CSAT, jointly acting as compasses in the customer experience landscape. The focus extends beyond the Contact Center, with brand loyalty, technological preferences, and customer-centric practices influencing CSAT and NPS scores. Organizations that prioritize customer experience see higher CSAT and NPS.

In the broader context of Voice of the Customer (VoC) metrics, NPS and CSAT become powerful tools for identifying good and bad segments of the customer journey. When used correctly, these metrics not only contribute to higher customer retention or increased sales but serve as catalysts for continuous improvement.

The Kenway Approach to Evaluating CSAT and NPS Scores

Our approach is not a one size fits all when it comes to evaluating NPS and CSAT scores. We don’t force a framework for understanding and utilizing VoC scores to drive your business. We blend industry expertise, and your unique business needs to understand the significance of NPS and CSAT in discerning the intricacies of customer experience.

Connect with us to learn more about how our tailored approach can help your business navigate the complexities of customer experience management and drive measurable results.