AI Customer Service Architecture: The Foundation of Exceptional Customer Experience

September 9, 2026 Leave a comment

Unified customer systems are the foundation of successful AI. Learn how to build the architecture that powers exceptional customer service and customer experience. This article explores core principles of AI Customer Service Architecture to help you succeed.

Why AI Customer Service Still Frustrates Customers

AI Customer Service Architecture is rapidly becoming a competitive differentiator, yet despite billions of dollars invested in AI customer service technologies, many organizations continue to deliver fragmented customer experiences. Customers are routed between systems, asked to repeat information, and often receive inconsistent responses across channels. The problem is not AI itself. The problem is that most organizations lack the unified customer architecture required to support AI-powered customer experience at scale.

Organizations have spent years investing in customer-facing technologies, yet customers still encounter disconnected journeys, inconsistent service experiences, and AI interactions that lack context. While companies have built impressive collections of capabilities, very few have built integrated customer systems capable of supporting intelligent, end-to-end experiences.

As a result, AI is often deployed on top of fragmented operational environments rather than unified customer architectures. And when the underlying foundation is fragmented, AI simply exposes and accelerates the fragmentation that already exists.

Most organizations don’t operate a unified customer system or a mature AI customer service architecture.

Most organizations don’t operate a unified customer system.

Instead, they operate a collection of functional systems designed to solve specific business problems. Over time, these systems become increasingly specialized, increasingly valuable, and increasingly disconnected from one another.

The Modern Customer Experience Stack

A typical enterprise customer environment includes:

  • Customer Success platforms such as Gainsight and Totango
  • CRM platforms such as Salesforce and HubSpot
  • Customer support platforms such as Zendesk and ServiceNow
  • Contact center platforms such as Genesys and Five9
  • Product and behavioral analytics platforms such as Pendo and Mixpanel

Each of these platforms performs its intended function effectively.

The challenge is that they were rarely designed to function as a single coordinated customer system. They are optimized for departmental objectives rather than customer outcomes.

The customer, however, experiences all of them as a single company.

When these systems fail to operate together, customers experience the gaps.

What this looks like in practice:

AI customer service architecture compared to fragmented customer systems

As shown above, most organizations operate through disconnected customer-facing systems rather than a unified customer architecture. Customers experience the gaps between them.

A customer contacts support and discovers that service has no visibility into recent sales conversations. A customer success manager is unaware of unresolved support issues. Product usage signals never reach teams responsible for retention or expansion. Every system contains valuable information, but no system owns the complete story.

This challenge existed before AI.

AI does not create fragmentation. It simply exposes and accelerates the consequences of fragmentation that already exists.

What AI Customer Service Architecture Requires to Succeed

Many organizations assume that AI can compensate for fragmented systems and disconnected processes.

In reality, a successful AI customer service architecture depends on capabilities that must already exist before intelligent automation can consistently create value.

Research from McKinsey on AI in customer operationshas similarly shown that organizations achieve the greatest impact when AI is embedded within broader operational and customer experience transformations rather than deployed as a standalone technology initiative.

There are three capabilities that are especially critical.

Unified Context

AI requires a complete and current understanding of the customer.

That includes historical interactions, support activity, product usage, account health, lifecycle stage, transaction history, and organizational relationships. When information remains fragmented across multiple platforms, AI operates with an incomplete understanding of the customer.

The result is predictable. Recommendations become less accurate. Personalization becomes inconsistent. Customer interactions feel disconnected.

Decision Authority

AI can identify issues.

AI can recommend actions.

AI can generate responses.

What AI often cannot do is act across disconnected operating environments.

Resolving a customer problem may require coordination across support systems, CRM platforms, customer success tools, operational workflows, and product teams. Without integrated processes and execution capabilities, AI becomes another recommendation engine rather than a driver of outcomes.

Lifecycle Awareness

Most AI solutions are event driven.

Customers, however, experience journeys.

Customers move through onboarding, adoption, value realization, support, expansion, renewal, and advocacy. Understanding a single interaction is useful. Understanding where that interaction fits within the broader customer lifecycle is what creates exceptional experiences.

Without lifecycle awareness, AI reacts.

With lifecycle awareness, AI orchestrates.

The Customer Impact

When organizations lack unified context, decision authority, and lifecycle awareness, the customer experience suffers.

Customers repeat information across channels.

AI delivers incomplete or contradictory responses.

Issues are transferred rather than resolved.

Accountability becomes unclear.

Continuity disappears.

These outcomes are often described as AI failures.

In reality, they are architecture failures.

AI is operating exactly as the surrounding systems allow it to operate.

The Root Cause: Function-Led Rather Than Customer-Led Design

Most organizations are structured around functions.

Each team makes rational decisions within its own area of responsibility.

The result, however, is an ecosystem optimized around departmental effectiveness rather than customer experience.

No one owns the customer system as a whole.

This is not primarily a technology problem.

It is a governance problem.

It is an operating model problem.

And it is one of the primary reasons organizations struggle to scale AI successfully.

Many organizations expect AI to solve operational fragmentation. In reality, AI depends on organizational alignment, process consistency, and system integration that must already exist before intelligent automation can achieve meaningful outcomes.

The Foundations Required for AI Customer Service Architecture

The Foundations Required for AI Success

For years, organizations have approached AI as a technology initiative.

The organizations achieving the strongest results are approaching it as an enterprise capability.

AI requires far more than a model, chatbot, assistant, or copilot. It requires a foundation built across both technology and organizational capabilities.

Successful AI initiatives depend on both.

The relationship between these foundations is illustrated below:

Technology and organizational foundations required for successful AI

Success depends on both technical foundations and organizational readiness. Weakness in either area limits the value AI can deliver.

Technology Foundations

  • Data governance and quality management
  • Access to historical outcomes and decisions
  • Centralized knowledge management platform
  • Enterprise integration and API architecture
  • Security, privacy, and compliance controls

Organizational Foundations

  • Enterprise AI strategy
  • Executive sponsorship
  • Process standardization
  • Prioritized use cases
  • AI literacy and workforce readiness
  • Change management
  • Responsible AI governance aligned with the NIST AI Risk Management Framework.
  • NIST AI Risk Management Framework.
  • Success metrics and accountability

These capabilities are not optional.

They determine whether AI creates measurable business value or simply magnifies existing organizational weaknesses.

The better the foundation, the more effective the AI.

A Real-World Example: Foundations Matter

A useful example comes from Ford.

Company leaders publicly discussed the limitations of relying on AI and automation without sufficient supporting expertise, institutional knowledge, and operational rigor. After increasing reliance on automated approaches, Ford ultimately brought back experienced engineers and technical specialists to strengthen the quality systems, processes, and expertise supporting product development.

One executive summarized the lesson directly:

“Mistakenly, we thought that by just introducing artificial intelligence and ingesting the design requirements that we had, that that would produce a high-quality product.”

The lesson is not that AI failed.

The lesson is that foundations matter.

Before pursuing large-scale AI initiatives, executive teams should ask several critical questions:

  • Have we standardized and governed our data?
  • Do we have a unified view of customers, products, or operations?
  • Have we captured critical institutional knowledge?
  • Are our processes consistent enough to automate?
  • Do employees understand how to work effectively with AI?
  • Is leadership aligned around measurable business outcomes?
  • Have we established governance and accountability?

Or are we expecting AI to compensate for problems that already exist within the organization?

Ford’s experience serves as a reminder that AI does not create operational excellence.

AI amplifies the quality of the foundation beneath it.

Organizations interested in the Ford example can read more here:

If AI Needs Foundations, What Should Organizations Build?

The answer is not another application, another database, or another AI tool.

The answer is a unified customer architecture that connects customer data, lifecycle orchestration, decisioning, and execution into a single coordinated system.

This is what a unified customer system actually looks like:

Unified customer architecture for AI customer service

A unified customer architecture connects data, orchestration, AI decisioning, and execution into a coordinated customer operating model.

AI only works when built on a unified customer architecture, not layered on top of fragmented systems.

A Reference Architecture for AI-Powered Customer Experience

Most organizations seeking to scale AI-driven customer experiences discover that success depends on the strength of their underlying AI customer service architecture.

AI Customer Service Architecture Model

The Unified Customer System Model

Organizations must move from a collection of tools to a coordinated system built across five layers:

1. Data Layer — Unified Customer Model

  • Identity resolution across systems
  • Event-level data standardization
  • Real-time synchronization

The Data Layer establishes a single source of truth for customer information across the enterprise. By connecting identities, interactions, and events across systems, organizations create the unified context that both AI and customer-facing teams required to make informed decisions. Without this foundation, every downstream capability operates on incomplete or inconsistent information.

2. Orchestration Layer — Lifecycle Management

  • Onboarding → Adoption → Value Realization → Expansion → Renewal
  • Cross-system triggers and workflows

The Orchestration Layer connects customer activities across the lifecycle and ensures that customer experiences are coordinated rather than isolated. Instead of departments responding independently to events, workflows span systems and functions, enabling proactive engagement, consistent handoffs, and customer journey continuity. Link to my thought leadership article on the quantifiable customer journey map.

3. AI Layer — Decisioning and Intelligence

AI should not sit inside tools.

It should sit above them.

AI becomes:

  • Decision engine
  • Orchestration trigger
  • Experience coordinator

In a mature architecture, AI serves as a cross-functional intelligence layer rather than a standalone feature inside an individual platform. With access to complete customer context and orchestrated workflows, AI can identify opportunities, recommend actions, automate decisions, and coordinate experiences across the customer lifecycle.

4. Execution Layer — System Activation

  • CRM
  • Support
  • Customer Success
  • Product Systems

These systems execute—but do not define—the experience.

The Execution Layer contains the operational systems that perform the actual work. CRM, support, customer success, contact center, and product platforms remain essential, but they function as execution points within a larger customer architecture rather than isolated systems operating independently.

5. Experience Layer — Customer Outcomes

  • Resolution without repetition
  • Proactive engagement
  • Seamless cross-channel journeys

The Experience Layer is where customers ultimately judge the effectiveness of the entire architecture. Customers do not evaluate data models, integrations, or AI algorithms. They evaluate whether interactions feel connected, consistent, relevant, and easy across every stage of their relationship with the organization.

What World-Class AI Customer Service Architecture Looks Like

When this architecture is in place, the experience changes fundamentally:

Unified customer intelligence platform for customer experience

The shift is not incremental. It’s from reactive service to orchestrated experience.

  • Customers no longer repeat themselves
  • AI understands full context immediately
  • Issues are resolved across systems in one interaction
  • Outreach happens before problems escalate
  • Digital and human interactions feel seamless

This is not better customer service.

This is a fundamentally different operating model.

Organizations with unified customer architectures move beyond isolated transactions and disconnected interactions. Instead, they create a coordinated customer experience where data, intelligence, automation, and human engagement work together across the entire lifecycle.

Final Thought on AI Customer Service Architecture

AI has reached a point where its success is determined less by the technology itself and more by the quality of the foundation beneath it. Organizations that invest in unified customer architecture, governance, and orchestration are best positioned to realize its full potential.

The organizations that create lasting advantage with AI will not be those that simply deploy the most tools.

They will be the organizations that first establish the foundations required for AI to succeed.

  1. Data.
  2. Knowledge.
  3. Governance.
  4. Process.
  5. Integration.
  6. Leadership.
  7. Change.
  8. Architecture.

Because when AI is finally sitting on top of a truly unified customer system—

It doesn’t just respond faster.

It doesn’t just automate.

It delivers something most companies still cannot:

A complete, consistent, and intelligent customer experience.

It becomes clear why leading organizations are investing in unified customer architectures as the foundation for the next generation of customer experience.

Frequently Asked Questions

What is AI customer service architecture?

AI customer service architecture is the combination of customer data, operational systems, governance, workflows, and orchestration capabilities that enable artificial intelligence to deliver consistent experiences across channels.

Why do AI customer service initiatives fail?

Many fail because customer data, processes, and systems remain fragmented, preventing AI from accessing complete customer context.

What is a unified customer system?

A unified customer system connects customer data, workflows, and technology platforms into a coordinated architecture that supports the entire customer lifecycle.

Why is a 360-degree customer view important for AI?

AI performs best when it can access a complete view of customer interactions, history, behavior, and lifecycle status.

What is customer lifecycle orchestration?

Customer lifecycle orchestration coordinates activities across onboarding, adoption, support, expansion, and renewal to create a seamless customer experience.

Author Bio

Steven Jeffes is a Customer Experience, Customer Success, CRM, and AI transformation leader who helps organizations build scalable customer operating models that improve growth, retention, and operational performance.

Ready to Build a Customer System AI Can Actually Power?

If your customers are repeating themselves across channels, your teams are working from fragmented data, or your AI initiatives aren’t delivering the outcomes you expected, it may be time to rethink the underlying foundation. The organizations realizing the greatest value from AI aren’t simply deploying new technologies—they’re creating unified customer systems that connect data, processes, people, and intelligence across the entire customer lifecycle.

At LegendaryCX we help organizations transform Customer Experience, Customer Success, Customer Service, CRM, and AI strategies into scalable operating models that drive measurable business outcomes. Whether you’re evaluating your customer architecture, developing an AI roadmap, redesigning customer journeys, improving operational performance, or building a unified customer experience strategy, we can help.

Explore more insights: LegendaryCX – https://www.legendarycx.com/
Read additional thought leadership articles: https://stevenjeffes.com/
Connect with us: https://www.linkedin.com/in/stevenjeffes
Schedule a conversation: 518-339-5857 or stevenjeffes@gmail.com

The future of customer experience isn’t about adding more technology. It’s about building the foundation that allows technology—and AI—to actually deliver on its promise.

Let’s start that conversation.

Filed under Customer Experience, customer service, Customer Service AI Tagged with , , , , , , , , , , , , , , , , , ,

Best Practices in Customer Experience (CX) Measurement and Analytics

February 27, 2020 1 Comment

Top 10 customer experience measurement metrics

The following are the top 10 customer experience measurement & Metric concepts you will learn in this blog article:

  1. What are the most common set of metrics used to measure customer experience quality and effectiveness.
  2. What these common customer experience metrics are used for
  3. When are these best practice customer experience metrics best measured
  4. What a customer journey (a.k.a. customer life-cycle) is and how it related to customer experience metrics
  5. Why a balanced scorecard is better than any one single customer experience metric
  6. Why NPS is not sufficient to provide a comprehensive picture of your customer experience quality and effectiveness
  7. The top 10 best practices in developing a world-class customer experience measurement program and balanced scorecard
  8. Sample of what a customer journey looks like as well the customer experience analytics collected at each journey phase
  9. Examples of embedded detailed customer journey phase analytics paired with summary & executive level customer experience analytics
  10. How to develop customer experience analytics that also drive the development and support of a customer first, surprise and delight culture.

Peter Drucker once said “If you can’t measure it, you can’t manage it”. This ageless and famous quote applies to almost all situations and customer experience is no exception. There is virtually no way to determine how effectively your customers are being treated without a robust set of measures to gauge how well you are fulfilling their needs, wants, desires, etc. In this blog article, we will cover the specific metrics that best practice companies use to measure their customer experience delivery along with it is done.

Customer Experience Measurement is the discipline that allows organizations to objectively assess customer satisfaction, loyalty, retention, and overall experience effectiveness.

 

Peter Drucker's Famous Measurement Quote
Peter Drucker’s Famous Measurement Quote

Most commonly used Customer Experience Measurment Metrics

The Chart below illustrates some of the more commonly used customer experience (CX) metrics and how/where they are used in the customer journey continuum.

These measures form the foundation of any effective Customer Experience Measurement program.

Commonly Used Best Practice Customer Experience Measurement (CX) Metrics
Customer Experience Measurement Metrics
  • Customer satisfaction (CSAT) – one of the most common uses of customer satisfaction ratings is on ratings websites like Yelp, TripAdvisor, Facebook, Google, etc. using the now famous five star rating system seen below. Other customer satisfaction feedback mechanisms are more sophisticated, querying customers on an array of customer experience topics that are multi-dimensional in nature.
Customer Experience Measurment Score Example
Customer Satisfaction Score Example

  • Customer Churn Rate (CCR): Customer churn rate is almost always expressed in terms of a percentage and is a product of the number of lost customers divided by the number of retained customers for any given period (day, week, month, Quarter, Year).

Customer Churn Rate Example Calculation
Customer Churn Rate Example Calculatio
  • Customer Effort Score: Customer Effort Score is recorded to keep a pulse on how easy it is for a customer to accomplish certain transactions with your company (e.g. return a product, handle an issue, inquire about upgrades, etc.). It is obtained via surveying customers following a major interaction and is expressed in terms of a numeric, typically on a 1-10 or 1 to 7 scale. Here is a sample I developed for a client where the score is translated into a 1 to 7 scale (from “Strongly Disagree”=1 to “Strongly Agree”=7).
Customer Experience Measurement, Customer Effort Score Example Quantification
Customer Effort Score Quantification Example 
  • Customer Average Time to Resolution (CATTR): This metric is a measure the average time it takes to resolve categories of customer interactions (inquiry, product issue, service issue, contract renewal, return, etc.). This is expressed in average time per interaction category as shown in this example
Customer Experience Measurement -Customer Average Time to Resolution (CATTR) Example Calculation
Customer Average Time to Resolution (CATTR) Example Calculation
  • First Contact Resolution (FCR): All companies should strive for what is called “one and done” customer service, enabling the customer to handle any need with one short effort. The benefits of achieving this are endless including the following: Research I have read has indicated that a 1% increase in FCR rates translate into decreasing operating costs by 1%, increases of both customer satisfaction and employee scores by 1-3% as well as increasing customer loyalty (up to 20%). How companies measure FCR vastly differs including surveying customers, tracking it in a CRM system, tracking it in a contact center database or querying the customer at the end of a call. Many companies sadly do not track this metric and lose out on the visibility and resulting benefits this provides.
Customer Experience Measurement - One & Done Customer Service
One & Done Customer Service Creates Elated Customers
  • Contract Renewal Rates (CRR): This metric is more company specific but, when applicable and used in conjunction with the other metrics, provides a great barometer on the health of the contract oriented business. For example, you might be experiencing great FCR and customer average time to resolution, but contract renewal rates might be lagging due to a perceived lack of value by the customer for the price paid. By using this metric in a balanced scorecard along with CSAT, FCR, CATTR you have a much more comprehensive view of total customer satisfaction than with just a few measures, allowing you to reduce business risk and potential revenue surprises.
Customer Experience Measurement -High Contract Renewals = High Customer Satisfaction
High Contract Renewals = High Customer Satisfaction
  • Net Promoter Score (NPS): Net Promoter Score (NPS) is the most commonly used and simplest customer experience metric that exists.  NPS is typically measured by asking the following question:

How likely are you to recommend [business, service, product] to a friend or colleague?

Customers rate your company, service, product, etc. on a scale of 0 to 10. Respondents are grouped in the following categories:

  • Customer Promoters (Score 9-10)
  • Customer Passives (Score 7-8)
  • Customer Detractors (Score 0-6)

Calculate Net Promoter Score is typically calculated by subtracting the percentage of net detractors from net promoters. Here is a great illustration on how this is determined, calculated:

Customer Experience Measurement - Net Promoter Score Example Calculation
Net Promoter Score Example Calculation

It has been found that only those customers who provide a rating of a 9 or 10 on the NPS scale are those who will truly become adjunct volunteer company sales and marketing agents and are a result of experiencing surprise and delight levels of customer service. These same elated customers are the ones who tell everyone they meet about your exceptional company and your amazing, services, products, customer service, etc. More on this in a future blog that will address the topic of “Delivering Consistent Surprise and Delight Customer Service”.

On this last point of NPS, there exist many misnomers about what to measure for customer experience effectiveness. Many professionals I have met in my consulting travels have the misconception that measuring one metric like Net Promoter Score (NPS) is sufficient to measure the quality of the customer experience you are delivering to their customers.  This is equivalent to believing that taking your body temperature is sufficient to determine your overall health when in actuality there are many measures taken together that help make this healthy/not healthy determination. The same is true for measuring the quality of your customer experience. While NPS is a good measure for helping to determine the quality of your customer experience effectiveness when used correctly, similar to body temperature, it must be augmented with many other measures to determine its overall effectiveness.

Other customer experience metrics include employee turnover (a leading indicator of customer satisfaction), year-over-year same customer spend, customer loyalty and average longevity, customer acquisition rates over time, etc. I will go more into this when I cover the topic of customer journeys.

Customer Experience Measurement - Customer Experience, Satisfaction Humor, Joke
Customer Experience, Satisfaction Humor

Top 10 best practices for Customer Experience Measurement

  1. Monitor Customer Experience Metrics in Real Time and continuously improve customer experience programs based on actual CX metrics/program performance.
  2. Track top level Customer Experience (CX) Metrics for all customers (i.e. average customer satisfaction) and for individual customer segments (i.e. price sensitive customers or high value customers).
  3. Request both customer qualitative and quantitative ratings throughout the Customer Life-cycle during critical customer interactions. Accomplish this my providing a conduit for your customers to become brand partners who are invited to participate in providing program feedback prior to full launch, provide detailed focus group feedback on selected topics and for most valuable customers to participate in exclusive customer advisory boards.
  4. Ensure group appropriate customer experience metrics are being delivered to each layer of the organization (highest importance summary level for CEO – Chief Customer Experience officer, more granular metrics for tactical managers and line staff).
  5. Cultivate and measure your own internal customer metrics and calibrate against externally measured CX like the American Customer Satisfaction index or metrics collected by firms like the Service Management Group (Kansas City), Direct Opinions (Beachwood Ohio), C-Space (Boston), Engine Group (NYC), etc. For additional benchmarking information, refer to the American Customer Satisfaction Index (ACSI): https://www.theacsi.org/
  6. Track customer experience effectiveness via a balanced scorecard of Customer Experience Metrics including customer satisfaction, NPS, Customer Churn and renewal rates, customer spend per year and employee turnover (a proven leading indicator of customer satisfaction).
  7. Ensure the collection and dissemination of Customer Experience metrics meet the golden rules of being seamless to your customers, easy to obtain and are ingrained as part of normal business operations.
  8. Review customer experience metrics during key management reviews like operational reviews, leadership team reviews and financial reviews. Ensure action plans are developed for metrics above and below expected performance levels.
  9. Ensure that the company culture and training is supported and in-line with customer experience metrics by making everyone’s KPIs metrics align to the performance of key customer metrics.
  10. Develop customer journeys (a.k.a. customer life-cycles) and develop customer experience metrics for each major step in the customer journey.

Advanced Customer Experience Measurement programs integrate journey analytics directly into customer lifecycle management.

The last best practice is to identify key end-to-end customer journeys or paths of customer progression when engaging your company and then attach appropriate customer experience journey analytics along those customer paths. Once you understand the different touch-points and how they impact the overall customer journey, you will be in a far better position to pick the most appropriate metric to use at each touch-point. The best metric is company determined based on a developed set of customer experience standards and goals.

In my example in the introduction, Net Promoter Score (NPS – which answers the question, “How likely are you to recommend [business, service, product] to a friend or colleague?” and is rated on a 0 to 10 score), is not a total customer experience solution metric. The reason is that NPS works best when measured at the end of a customer journey (a.k.a. customer life-cycle), such as at contract renewal time. For example, if a customer is getting frustrated returning a product or trying to resolve a service issue, then they will likely defect long before they are queried on NPS. It is better to measure customer satisfaction right after an interaction to have real-time insights into a customer’s experience satisfaction and not wait until NPS query time

The following example demonstrates how Customer Experience Measurement can be deployed across multiple customer journey phases.

Here is a sample customer journey I developed from a recent client consulting engagement along with the metrics they decided to collect at an aggregate level as well as along this customer journey. Some of the metrics and customer journey names have been changed to protect my client’s identity. In addition, this client wanted to err on the side of measuring many metric points frequently and not all clients are this exhaustive in measuring their program. Some of these metrics were already in place before we added many others.

Customer Experience Measurement - Customer Journey Analytics Illustration
Customer Journey Analytics Illustration

The above illustrates one of the main customer journeys (discover to renewal) in the life of a customer along with the Macro customer phases in that journey (i.e. 1-customer discovery, 2-customer sales & on-boarding, 3-customer support, 4-customer renewals) as well as the micro phases in that journey (product, service credibility evaluation).

One best practice embedded in the above is to report on the number of customer stars (in the 1st and 3rd phases above) per period whereby employees who have delivered exceptional “surprise and delight customer service” are recognized and rewarded. Customers of this company as well as executives from the company are provided incentives to recognize employees who went above and beyond in delivering exceptional customer service. This company tracks this via reports and recognizes top employee customer stars quarterly and annually with top company customer stars getting recognized, rewarded, etc. This helps build a culture of support for being customer exceptional with top stories being told over and over to teach employees what it means to be customer exceptional  and encourage others to emulate this valued behavior

Summary of Customer Experience Measurment Metrics

In summary, effective Customer Experience Measurement must be guided by a set of best practices. The use of customer journeys as well as customer experience journey analytics, balanced by summary customer experience metrics comprises a customer experience balanced scorecard.  By not measuring or under-measuring your customer experience delivery effectiveness, you are flying blind and having to take guesses as to whether your program is delivering exceptional customer service to your customers or not. Only when you reach the level of consistently delivering exceptional “surprise and delight” customer service will you reap bottom line benefits of accelerated customer acquisition, reduced sales and marketing costs, increased customer loyalty and increased employee and customer satisfaction.

With all this being true, there is no excuse to not actively work on creating the best customer experience program possible!!

If your organization is seeking experienced assistance in measuring and improving your customer service and customer experience, then give me a call or e-mail me at 518-339-5857 or stevenjeffes@gmail.com

Lastly, this is just one article of nearly 50 articles I have written on Customer strategy, customer experience, CRM, marketing, product management, competitive intelligence, corporate innovation, change management – all of which I have significant experience in delivering for Fortune 500 companies. 

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