AI and Customer Service: What Are Companies Actually Achieving?

A research-led look at the evidence behind the claims

Artificial intelligence has moved rapidly from being an interesting experiment in customer service to becoming part of the operating model of some of the world's largest companies.

Customer-service teams are using AI to answer customers directly, help agents find information, draft responses, summarise conversations and reduce the amount of routine work handled by people.

And the numbers being reported can be impressive.

Companies are reporting large reductions in response times, improvements in first-time resolution, millions of customer conversations handled by AI and significant cost savings.

But there is an important question behind all of these numbers:

What do they actually tell us about customer service?

To explore this, we looked at publicly available evidence from companies that have implemented AI in customer-service operations.

Rather than simply collecting impressive-sounding statistics, we have tried to look at what each measure actually means, where the evidence comes from and what it does — and does not — demonstrate.

How we researched this

We looked primarily for publicly available information from the companies themselves, including regulatory filings, investor presentations and published customer case studies from technology providers.

We have prioritised original sources where possible.

That distinction matters.

A result published in a company's regulatory filing is different from an independently audited study. A technology provider's customer case study is different again. And a company's own measurement of customer satisfaction should not automatically be treated as independent evidence that customers prefer AI.

Throughout this article, we therefore identify the source of the claims.

The figures below should be read as reported results, rather than as a controlled scientific comparison of AI and human customer service.

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1. Klarna: AI handling a large share of customer conversations

Klarna is probably one of the most widely discussed examples of AI being used in customer service.

In February 2024, the company launched an AI assistant developed in partnership with OpenAI.

OpenAI initially reported that, during the assistant's first month, it handled 2.3 million conversations — approximately two-thirds of Klarna's customer-service chats at the time. Klarna said the assistant was doing work equivalent to around 700 full-time agents, resolving customer issues in less than two minutes compared with 11 minutes previously, and reducing repeat enquiries by 25%. It also said customer satisfaction was on par with human agents.

The figures continued to develop as Klarna expanded the system.

In its third-quarter 2025 results, Klarna reported that its AI assistant was doing the equivalent work of 853 full-time agents. The company's investor presentation said the assistant was handling around 28 million annualised conversations, solving 81% of customer-service chats, and delivering approximately $58 million in annualised cost savings as of 30 September 2025. Klarna also said customer satisfaction was on par with human agents.

That was a snapshot of the programme at the end of September 2025.

Klarna's subsequent 2025 Annual Report, covering the full year to 31 December 2025, reported that the AI assistant had handled 80% of customer-service chats during the year, with no drop in consumer satisfaction levels. The company's detailed filing also reported work equivalent to more than 850 full-time agents and approximately $59 million in cost savings during 2025.

What does this tell us?

This is unusually strong evidence of scale.

The AI isn't being used simply as a small experimental chatbot. Klarna's own reported figures indicate that it became responsible for a substantial proportion of customer-service interactions.

The figures also show why the date of a statistic matters.

The Q3 results provide a snapshot of the programme as it stood at September 2025. The subsequent annual report covers the entire 2025 calendar year. The percentages should therefore not be treated as contradictory measurements of exactly the same period.

There is also evidence of an operational benefit. Klarna reports substantial cost savings, shorter resolution times and fewer repeat enquiries.

What should we be careful about?

The phrase "equivalent to 853 full-time agents" is particularly important to understand.

Klarna describes this as an estimate based on reductions in chat and telephone conversations handled by full-time agents following the launch of its AI assistant.

That is a calculation of workload capacity — not necessarily a statement that 853 individual employees were made redundant.

There is a similar qualification around customer satisfaction.

Klarna says its AI-handled chats rank on par with human agents in consumer satisfaction, based on its service-chat data and consumer satisfaction surveys. That is useful evidence, but it remains company-reported evidence, rather than an independent controlled comparison.

The Klarna example therefore gives us a fairly clear picture of what AI can achieve operationally at scale.

It gives us less certainty about the broader question of whether customers generally prefer AI to human service.

And that distinction — between operational efficiency and customer experience — is one we see repeatedly throughout the other case studies.

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2. Vodafone: measuring first-time resolution

Vodafone provides an interesting contrast because one of its headline measures is not primarily about cost.

The company has been developing SuperTOBi, a generative-AI version of its digital assistant, alongside SuperAgent, an AI tool designed to support customer-service employees.

Vodafone reported that initial testing of SuperTOBi produced an approximately 50% improvement in first-time resolution for critical customer journeys such as complex billing enquiries. In its FY25 H1 results presentation, Vodafone also reported a roughly 50% improvement in first-time resolution based on interactions with 7 million customers.

Why is this interesting?

First-time resolution is much closer to the customer experience than a simple measure of AI adoption.

A customer generally doesn't care how sophisticated the underlying technology is.

They care whether their problem is solved.

If a customer can get their problem resolved during the first interaction, that potentially represents a meaningful improvement in service.

But there is a catch

"First-time resolution" is a useful metric, but it needs context.

A higher first-time-resolution rate doesn't automatically tell us whether the underlying answer was better, whether the customer was happier or whether the issue stayed resolved.

And Vodafone's figures are company-reported rather than the result of an independent controlled study.

Still, this is a good example of an AI programme being measured against an outcome that customers actually experience.

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3. Simplyhealth: AI helping people rather than replacing them

Not all of the interesting examples involve AI talking directly to customers.

Simplyhealth has used generative AI to help customer-service employees respond to emails.

According to Salesforce, Simplyhealth initially trained its system to create knowledge-based email replies to frequently asked questions. The AI-generated response is reviewed and edited by an employee before being sent to the customer.

The process that previously took around 12 minutes was reduced to approximately one minute. Salesforce reported that Simplyhealth was handling more than 600 such emails each week, saving more than 90 hours of staff time, while the company reported productivity improvements of up to 90%.

More recent Salesforce reporting says Simplyhealth has since expanded its use of AI, including autonomous handling of routine enquiries, and now reports around 120 hours of weekly time savings.

Why does this matter?

It demonstrates that "AI customer service" doesn't necessarily mean replacing the customer-service agent.

There is another possibility:

AI can remove some of the work around the conversation so that the human has more time for the conversation itself.

For relatively straightforward enquiries, drafting an answer can be repetitive.

If AI can retrieve the relevant information and produce a useful first draft, the employee can spend more time checking the answer, understanding the customer's circumstances and dealing with the parts of the interaction that require judgement.

What should we be careful about?

These figures come from Salesforce's customer case study and Simplyhealth's own reported results.

They are therefore useful evidence of what the company says it achieved, but they are not independent experimental results.

And "up to 90%" is not the same thing as saying that every customer-service interaction became 90% more productive.

The more specific and useful finding is that Simplyhealth reported substantial time savings from using AI for particular categories of customer communication.

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4. Sicredi: a small pilot with measurable results

One of the more useful examples we found comes from Brazil.

Sicredi worked with IBM to develop a generative-AI assistant designed to help customer-support representatives find answers in the organisation's support documentation.

The company spent three weeks co-creating the assistant and then tested it for 20 days.

During the pilot, support representatives served 6,500 members asking questions about Sicredi's consortium product.

Compared with the previous month's customer-support data for that product, Sicredi reported:

• a 10–12% improvement in queries resolved without involving a product specialist;

• a 1% improvement in Net Promoter Score;

• an 8% reduction in support-call abandonment caused by waiting times; and

• a reduction in average time to resolve customer queries.

Why this example matters

It is a relatively small experiment, but it illustrates something important about evaluating AI.

The system was not judged simply on how many questions it could answer.

The company looked at several measures:

resolution without escalation, customer advocacy, abandonment and resolution time.

That gives us a more rounded picture of what the technology was doing.

It also shows why pilot projects can be valuable.

Rather than making a broad claim that "AI improves customer service", the company can compare a defined group, over a defined period, against previous performance.

What should we be careful about?

This was a 20-day pilot, and the results were published by IBM.

It therefore shouldn't be treated as proof that the same improvements will occur across every customer-service operation.

But it is a useful example of how AI projects can be evaluated more meaningfully.

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5. Lenovo: AI as an agent assistant

Lenovo provides another example of the human-plus-AI model.

Its Premier Support operation uses Microsoft Dynamics 365 Contact Center and Customer Service with Copilot.

When a customer engages with support, AI helps the service representative identify possible solutions using historical service interactions. The system can also create a summary after the interaction.

Microsoft's published customer story says Lenovo achieved:

• 15% higher agent productivity

• 20% lower average handling time

• record-high customer satisfaction.

What does this tell us?

This is another example where AI isn't necessarily replacing the person dealing with the customer.

Instead, it is reducing the amount of time the employee spends searching for information and documenting the interaction.

A customer may never know that AI was involved.

The visible result is simply that the employee can potentially deal with the problem more quickly.

And again, the measurement matters

"Agent productivity" is an operational metric.

It isn't automatically the same thing as better customer service.

However, Lenovo also reports higher customer-satisfaction ratings, which at least begins to connect the efficiency improvement with a customer outcome.

The evidence is still based on a customer story published by Microsoft, so it should be understood as a reported business result rather than an independently verified experiment.

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6. Nationwide: perhaps the most revealing use of AI is behind the scenes

Nationwide offers another useful example because its AI implementation has been positioned as a copilot rather than an autopilot.

Microsoft reports that Nationwide is using GPT-4 through Azure OpenAI to help with customer correspondence.

According to Microsoft, AI-assisted letters reduced response times from approximately 45 minutes to around 10–15 minutes.

The customer doesn't necessarily interact with an AI system at all.

Instead, the employee uses AI to help prepare the response.

This changes the question

When we talk about AI and customer service, it is easy to focus on chatbots.

But there is a much larger opportunity behind the scenes.

Customer-service organisations spend enormous amounts of time:

• searching knowledge bases;

• reading previous interactions;

• summarising calls;

• drafting emails;

• categorising enquiries;

• finding relevant policies;

• updating case notes.

These activities are largely invisible to the customer.

If AI can reduce that workload, the customer may experience the benefit as a faster or more informed response — without ever knowing AI was involved.

This may be one of the most important areas of AI adoption in customer service.

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7. NatWest: when the headline number needs careful reading

NatWest provides a particularly interesting example of why we need to read AI claims carefully.

The bank told a UK Parliament inquiry that its Cora digital assistant handled more than 11 million customer interactions in 2024.

NatWest also reported that its generative-AI upgrade, Cora+, delivered up to a 150% increase in customer satisfaction, while a pilot had halved the number of query cases requiring colleagues to intervene.

At first glance, a 150% increase in customer satisfaction sounds extraordinary.

But this is precisely where research-style reporting needs to slow down.

NatWest's evidence does not mean that customers became "150% happier" in some universal measure of satisfaction.

The statement relates to the bank's own measurement of Cora+ and describes an increase in customer satisfaction associated with the system.

Without knowing the precise baseline, sample, methodology and calculation behind the percentage, the number cannot sensibly be compared with another company's customer-satisfaction figure.

The lesson

A percentage is not automatically a comparable metric.

"50% improvement in first-time resolution", "20% reduction in handling time" and "150% increase in customer satisfaction" sound like three numbers that could sit neatly beside each other.

They can't.

They measure different things, using different methodologies, in different organisations.

That is one of the biggest problems with trying to create a simple league table of AI customer service.

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What do these case studies have in common?

Despite the differences between the companies, several patterns appear repeatedly.

1. AI is producing measurable operational improvements

Across the examples, companies report:

• shorter response times;

• shorter handling times;

• more enquiries resolved without escalation;

• fewer repeat enquiries;

• higher agent productivity;

• fewer abandoned interactions;

• significant volumes of conversations handled automatically;

• lower service costs.

This is probably the clearest area of evidence so far.

Companies can measure operational performance relatively easily.

Time is measurable.

Volume is measurable.

Cost is measurable.

The number of interactions handled by a system is measurable.

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2. Customer outcomes are harder to measure

Customer service is ultimately about the customer.

But customer outcomes are more complicated.

A shorter interaction isn't necessarily a better interaction.

A conversation resolved in two minutes may be excellent if the customer's problem is genuinely solved.

It may be terrible if the customer has to contact the company again tomorrow.

This is why metrics such as repeat contact, first-time resolution, customer satisfaction, abandonment and escalation are potentially more interesting than raw AI adoption numbers.

Klarna's reported reduction in repeat enquiries is therefore more informative than simply knowing how many conversations its AI handles.

Likewise, Vodafone's first-time-resolution measurement addresses a customer-service outcome rather than simply reporting chatbot usage.

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3. "AI handled X% of conversations" doesn't tell the whole story

This is perhaps the easiest metric to misunderstand.

Suppose an AI system handles 80% of conversations.

That sounds like an enormous achievement.

But there are several questions underneath it:

What kinds of conversations are included?

How difficult are they?

How many are actually resolved?

How many customers contact the company again?

How many are eventually transferred to a human?

What happens to the remaining 20%?

And perhaps most importantly:

What does the customer experience?

Conversation volume tells us about the scale of automation.

It doesn't, on its own, tell us about service quality.

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4. "Equivalent to X agents" needs particularly careful interpretation

The Klarna example illustrates this well.

Klarna says its AI does work equivalent to more than 850 full-time agents.

That is a useful way of communicating the scale of the workload being automated.

But it shouldn't automatically be interpreted as more than 850 jobs being eliminated.

An FTE-equivalent calculation measures workload capacity.

A company can use that capacity in several ways:

• reducing staffing requirements;

• handling a growing customer base without proportional hiring;

• reducing outsourced service costs;

• moving employees to more complex work;

• extending service hours;

• or some combination of these.

The number tells us something important about capacity.

It doesn't tell us by itself what happened to the people who previously performed the work.

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5. The most interesting implementations may be the least visible

The examples from Simplyhealth, Lenovo and Nationwide all point towards the same idea.

AI doesn't have to sit between the customer and the employee.

It can sit beside the employee.

That means:

Customer → Human → AI assistance

rather than:

Customer → AI → Human if necessary

The first model may be particularly important for complex customer service.

AI can search, summarise, draft and recommend.

The human can interpret, empathise, make judgements and take responsibility for the final response.

That is a very different proposition from simply trying to automate the entire conversation.

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So, is AI actually improving customer service?

The evidence we found suggests that companies are achieving real and measurable improvements in customer-service operations.

There is public evidence of faster responses, shorter handling times, greater automation, fewer repeat enquiries, more first-time resolution and significant reported cost savings.

But the evidence is much less straightforward when the question becomes:

Does AI provide a better customer experience than a human?

There isn't a single answer in the data we examined.

Some companies report customer satisfaction that is comparable with human service.

Others report improvements in satisfaction.

Some are measuring operational outcomes rather than satisfaction at all.

And many of the published figures come from the companies themselves or from the technology providers supplying the AI.

That doesn't make the results meaningless.

It simply means we should understand what kind of evidence we are looking at.

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The customer-service AI scorecard

Perhaps the most useful way to evaluate future claims is not to ask:

"How much AI is this company using?"

Instead, ask five questions:

1. What has actually been automated?

Is AI answering customers directly, or helping employees?

2. What is being measured?

Is the headline number about cost, speed, volume, productivity, resolution or customer satisfaction?

3. Is the result company-reported?

If so, has the methodology been independently verified?

4. What happened to repeat contact?

A fast answer is not necessarily a successful answer.

5. What happened to the customer?

Ultimately, the most important question is whether the customer got what they needed, with less effort and less frustration.

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The bigger question for customer service

The most interesting conclusion from this research may be that AI customer service is not really one thing.

There are at least three different developments happening at once.

AI as the agent:
The technology communicates directly with the customer and attempts to resolve the enquiry.

AI as the assistant:
The technology helps a human agent find information, draft responses and complete administrative work.

AI as the infrastructure:
The technology works behind the scenes to route, classify, summarise and analyse customer interactions.

Companies are already reporting measurable benefits from all three.

But they are not interchangeable.

And as more businesses publish increasingly impressive AI statistics, the ability to distinguish between automation, efficiency and genuine customer-service improvement will become increasingly important.

The next stage of AI in customer service may therefore be less about asking whether companies are using AI.

They clearly are.

The more useful question is:

Are they using it to make customer service more efficient — or to make customer service better?

Those are not necessarily the same thing.

And the evidence, so far, suggests we should keep measuring both.

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Sources and methodology

This article is based on publicly available information published by the companies and technology providers discussed.

Primary sources used include Klarna's SEC filings and investor presentations, Vodafone's investor materials, the UK Parliament's published evidence from NatWest, and customer case studies published by IBM, Salesforce and Microsoft.

Where a result is company-reported or appears in a technology-provider case study, it is described as such. We have not treated those figures as independently verified research.

That distinction is important because AI customer-service metrics are still developing, and apparently similar percentages can represent very different things.

For this reason, the figures in this article are intended to illustrate what companies are reporting — not to create a league table of AI customer-service performance.

Prepared by ChatGPT and prompted on 25th September 2026

The Evolution of Excellence: Customer Service Trends Over the Last Decade

 Customer service trends 2016-2026

The landscape of customer service has undergone a seismic shift in the past ten years. What was once a reactive function has transformed into a proactive, personalized, and pivotal aspect of brand success. From the rise of digital interactions to the demand for instant gratification, understanding these trends is crucial for any business aiming to thrive in today's competitive market.

Let's dive into the most significant transformations:

1. The Digital Revolution: From Phone Calls to Omnichannel Engagement

A decade ago, the phone was king for customer service. Today, customers expect to connect with brands across a multitude of channels – often seamlessly. This era has seen the explosion of:

• Social Media Support: Customers now air grievances and seek solutions on platforms like Twitter, Facebook, and Instagram, demanding quick and public responses.

• Live Chat & Messaging Apps: For immediate queries, live chat on websites and support via apps like WhatsApp and Apple Business Chat have become indispensable, offering real-time text-based assistance.

• Email Continues its Reign: While not as instant, email remains a critical channel for detailed inquiries and documentation.

The key takeaway? An omnichannel strategy isn't a luxury; it's a necessity, ensuring a consistent and connected experience regardless of the touchpoint.

2. The Rise of Self-Service: Empowering the Customer

Customers increasingly prefer to find answers themselves. This decade has seen significant investment in:

• Comprehensive Knowledge Bases: Rich FAQs, help centers, and articles allow customers to troubleshoot issues independently, reducing the load on support agents.

• AI-Powered Chatbots: Early chatbots were clunky, but modern iterations are sophisticated, capable of handling a wide range of common queries, guiding users, and even performing basic transactions. This frees up human agents for more complex issues.

Self-service offers convenience for customers and cost savings for businesses, proving to be a win-win.

3. Personalization as the New Standard

Generic interactions are a thing of the past. Customers expect brands to know them, understand their history, and anticipate their needs. This has been driven by:

• CRM Integration: Advanced Customer Relationship Management (CRM) systems now provide agents with a 360-degree view of the customer, enabling personalized and informed interactions.

• Proactive Service: Leveraging data, companies can now predict potential issues and reach out to customers before they even realize there's a problem, turning potential frustration into loyalty.

• AI-Driven Recommendations: From product suggestions to personalized support content, AI is tailoring experiences to individual preferences.

Personalization builds stronger relationships and significantly impacts customer satisfaction and loyalty.

4. The Data-Driven Approach: Analytics and Insights

The ability to collect, analyze, and act on customer service data has transformed operations. Businesses are now using analytics to:

• Identify Pain Points: Spotting recurring issues and bottlenecks helps improve products, services, and processes.

• Measure Agent Performance: Tracking metrics like resolution time, customer satisfaction (CSAT), and first-contact resolution (FCR) helps optimize team efficiency and training.

• Predict Customer Behavior: Advanced analytics can even help predict churn or identify opportunities for upselling.

Data is no longer just numbers; it's a strategic asset for continuous improvement.

5. Empathy and Emotional Intelligence Take Center Stage

In an increasingly automated world, the human touch remains invaluable. This decade has highlighted the importance of:

• Empathetic Communication: Training agents to genuinely understand and acknowledge customer emotions, even in difficult situations.

• Soft Skills over Hard Skills: While product knowledge is crucial, the ability to listen, de-escalate, and connect on a human level is becoming equally, if not more, important.

• Agent Well-being: Recognizing the demanding nature of customer service, companies are increasingly focusing on supporting their agents to prevent burnout and ensure they can deliver their best.

Ultimately, behind every digital interaction, there's a human being, and treating them with respect and understanding is paramount.

Looking Ahead

The past decade has set a high bar for customer service. As we move forward, we can expect further advancements in AI, even deeper personalization, and an unwavering focus on creating truly effortless and enjoyable customer journeys. Businesses that embrace these trends and adapt quickly will be the ones that win customer hearts and secure lasting success.

This post was prepared with the assistance of Gemini - prompted on 29/01/2026

For more customer service resources

Very Good Service Dogs

What can Very Good Service Dogs teach us about good customer service

Very good service dogs demonstrate traits and behaviours that can provide valuable insights for improving human-centered customer service. Drawing from the characteristics and training principles of top-performing service dogs, we can extract actionable lessons for businesses:

1. Attentiveness and Responsiveness

Service dogs are trained to sense subtle cues from their handlers and respond promptly to their needs. Similarly, good customer service requires actively listening to customer cues, anticipating their needs, and providing timely solutions.

Application: Train staff to observe and respond promptly to verbal and nonverbal feedback from customers, creating a sense of personalized care.

2. Consistency and Reliability

Service dogs perform tasks consistently under varying conditions, maintaining calm and focus regardless of distractions. Customers value reliability; inconsistency can lead to frustration and unmet expectations.

Application: Establish standardized procedures and quality controls that ensure customers receive reliable service every interaction.

3. Patience and Composure

An exceptional service dog remains patient, tolerating long waits or repetitive activities without frustration. Patience is equally vital in customer service, particularly when dealing with complex inquiries or dissatisfied clients.

Application: Foster a culture of composure where employees handle challenging situations calmly and empathetically.

4. Adaptability

Service dogs are adaptable to new environments and varying tasks, seamlessly switching between contexts while maintaining performance. Excellent customer service similarly requires flexibility to adjust to unique customer profiles and dynamic market conditions.

Application: Encourage staff to be versatile, train in multiple service scenarios, and empower them to tailor solutions to individual customer needs.

5. Proactive and Anticipatory Behaviour

Service dogs often anticipate needs before being explicitly directed, e.g., alerting to seizures or other hazards. In customer service, proactively identifying and addressing needs enhances satisfaction and can prevent issues from escalating.

Application: Use predictive analytics, customer feedback, and attentive observation to anticipate customer requirements and offer solutions before being asked.

6. Training and Continuous Improvement

Service dogs undergo extensive, progressive training to refine and expand their capabilities. Similarly, employees benefit from ongoing professional development to maintain high service standards.

Application: Implement structured training programs, workshops, and feedback loops to continuously improve service skills.

7. Building Trust and Reliability

A service dog forms a strong, loyal bond with its handler, reinforcing trust. Good customer service builds similar trust through dependability and ethical interactions.

Application: Encourage transparency, keep promises, follow through on commitments, and develop long-term relationships with customers.

8. Positive Engagement and Enthusiasm

Service dogs approach tasks with focus and enthusiasm, enhancing their effectiveness. Greeting customers warmly and conveying genuine care can similarly elevate the customer experience.

Application: Foster a service culture where employees display positivity, warmth, and attentiveness, making each interaction memorable.

Conclusion

The traits of highly effective service dogs—attentiveness, patience, reliability, adaptability, proactive behaviour, continuous skill development, trust-building, and positive engagement—translate seamlessly into principles for exemplary customer service. Businesses that emulate these qualities can enhance customer satisfaction, loyalty, and overall service excellence.

To learn more about Very Good Service:

 

This post was prepared with the assistance of Copilot and prompted on 12th January 2026.

The Self-Service Revolution: Why Customers Are Taking Control of Customer Service?

 

The Self-Service Revolution: Why Customers Are Taking Control

There's a quiet revolution happening in customer service, and it's being led by customers themselves. They're bypassing phone calls, skipping the "contact us" button, and solving their own problems at 2 AM in their pyjamas. Welcome to the age of self-service—and it's growing faster than anyone predicted.

Self-service isn't just a nice-to-have anymore; it's become the preferred method of support for most customers. Recent data shows that over 70% of customers expect a company's website to include a self-service option, and that number climbs even higher among younger demographics. More striking still, self-service interactions have grown by over 40% year-over-year across industries, with some tech-forward companies reporting that 85% of their support queries never reach a human agent.

This isn't about companies forcing automation on reluctant customers. It's about customers demanding it.

Why Self-Service Is Winning

Speed Trumps Everything

In an instant-gratification economy, waiting on hold for 15 minutes feels like digital torture. Self-service offers immediate answers. No queue. No elevator music. No repeating account numbers to three different people. Customers can find their answer in the time it would take just to reach a human agent.

The 24/7 Expectation

Modern customers don't operate on business hours, and they don't want their problems to either. Whether it's a shipping question at midnight or a password reset at dawn, self-service portals don't clock out. This around-the-clock availability has become table stakes in customer expectations.

Autonomy and Control

Many customers simply prefer to help themselves. There's something empowering about navigating a knowledge base, watching a tutorial video, and solving your own problem. It's faster, less socially awkward than explaining your issue to a stranger, and gives customers a sense of mastery over the products they use.

The Privacy Factor

Not everyone wants to discuss their account issues, billing questions, or product confusion with another person. Self-service offers a private, judgment-free zone where customers can learn at their own pace without feeling embarrassed about "basic" questions.

What's Fuelling the Exponential Growth?

Better Technology, Better Experience

Early self-service was often frustrating—clunky search functions, outdated FAQs, and no real answers. Today's self-service tools leverage AI-powered search, natural language processing, and intelligent routing to actually understand what customers are asking. The experience has dramatically improved, which drives adoption.

Mobile-First Design

With more than 60% of web traffic coming from mobile devices, self-service has adapted. Modern knowledge bases, video tutorials, and troubleshooting guides are designed for thumb-scrolling and quick scanning, making it easier than ever to get help on the go.

The YouTube Effect

An entire generation has learned to solve problems by watching videos. From fixing a leaky faucet to understanding cryptocurrency, video has become the universal instruction manual. Smart companies are meeting this expectation with comprehensive video libraries, screen recordings, and visual guides that feel native to how people already learn.

AI and Chatbots That Don't Suck

Let's be honest—early chatbots were terrible. They misunderstood questions, provided irrelevant answers, and sent customers into frustration spirals. But modern AI-powered assistants have crossed a threshold of usefulness. They understand context, provide relevant answers, and gracefully hand off to humans when needed. This improved performance has rehabilitated the reputation of automated support.

The Business Case Is Undeniable

For companies, the math is straightforward. A phone support interaction might cost $8-15 per contact, while a self-service interaction costs pennies. When thousands or millions of customers shift to self-service, the savings compound quickly—not just in direct support costs, but in reduced wait times, improved customer satisfaction, and freed-up human agents who can handle more complex, high-value interactions.

But the real magic happens when self-service becomes a competitive advantage. Companies with exceptional self-service experiences see higher customer retention, increased lifetime value, and better word-of-mouth marketing. Customers remember when finding an answer was easy, and they punish brands where it's difficult.

The Dark Side of Self-Service

Not everything is rosy in the self-service boom. Done poorly, it becomes a frustrating maze where customers feel abandoned and companies hide behind automation to cut costs rather than improve experience.

The common pitfalls:

• Search functions that don't work - Nothing is more frustrating than knowing the answer exists somewhere but being unable to find it
• Outdated information - A knowledge base that hasn't been updated in two years is worse than no knowledge base at all
• No escape hatch - When customers can't find what they need, they should be able to easily reach a human—not get trapped in an automated loop
• One-size-fits-all content - Different customers need different levels of detail and various content formats

Building Self-Service That Actually Works

The companies winning at self-service share several key practices:

Start with search. If customers can't find answers quickly, nothing else matters. Invest in powerful search functionality that understands synonyms, common misspellings, and natural language questions.

Create content in multiple formats. Some people want quick bullet points. Others need detailed step-by-step guides. Many prefer video. Offer all three.

Use real customer language. Your knowledge base should be written in the words customers actually use, not internal jargon or corporate speak. Mine your support tickets for the exact phrases customers search for.

Make it easy to escalate. Every self-service article should include a clear path to human help. Customers should never feel trapped or abandoned.

Measure and improve constantly. Track which articles get the most views but don't resolve issues (high views, high escalations). These are your improvement opportunities.

Leverage AI thoughtfully. Use AI to power search, suggest relevant articles, and provide instant answers—but always with a human safety net.

The Future: Self-Service Gets Smarter

We're only in the early innings of the self-service revolution. The next wave will bring even more sophisticated capabilities:

• Predictive support that anticipates problems before customers encounter them
• Personalized knowledge bases that adapt content based on your product, subscription level, and past behaviour
  
• Visual AI that can watch you use a product and provide real-time guidance
• Voice-activated support that lets you solve problems hands-free
• Community-powered solutions where customers help each other in real-time

The Bottom Line

Self-service isn't replacing human customer service—it's elevating it. By handling routine questions efficiently, self-service frees human agents to focus on complex problems, emotional situations, and relationship-building. It's not about eliminating the human touch; it's about deploying it where it matters most.

Companies that recognize this shift and invest in truly excellent self-service will build deeper customer relationships and more efficient operations. Those that view self-service as a cost-cutting measure or a way to avoid talking to customers will create frustration and drive churn.

The exponential growth of self-service isn't a trend—it's a fundamental shift in how customers want to interact with brands. The question isn't whether to embrace it, but how quickly you can make your self-service experience exceptional.

Because in the end, the best customer service is the service customers can deliver to themselves—quickly, easily, and on their own terms.

This post was prepared with the help of Claude and ChatGPT and prompted on 12th November 2025.

Top TikTok & YouTube Trends for Service-Oriented Brands

 

Top TikTok & YouTube Trends for Service-Oriented Brands

In the fast-paced world of digital content, TikTok and YouTube have emerged as dominant platforms for brands looking to connect with audiences. For service-oriented businesses, leveraging these platforms strategically can lead to higher engagement, brand loyalty, and customer trust. Let’s explore the top trends reshaping customer service in the digital landscape.

1. Short-Form Videos Rule

Both platforms prioritize quick, digestible content, making short-form videos a must for service brands. On TikTok, snappy tutorials, explainer videos, and "day-in-the-life" clips perform well. YouTube Shorts follows a similar formula, offering a space for bite-sized customer service hacks and brand storytelling.

💡 Tip: Showcase quick customer success stories, troubleshooting guides, or behind-the-scenes service excellence.

2. Interactive & Engaging Content

Customers love interactive experiences. Brands now leverage Q&A sessions, live streams, and polls to engage their audiences. TikTok's interactive stickers and YouTube’s Community Tab allow service-oriented brands to keep their followers involved.

💡 Tip: Host live Q&A sessions addressing customer concerns or feature a weekly service tip.

3. User-Generated Content (UGC)

Authenticity drives engagement. Encouraging customers to share their positive experiences through reviews, testimonials, or service reactions boosts credibility. TikTok challenges and YouTube reaction videos have become powerful organic marketing tools.

💡 Tip: Launch a UGC campaign where customers share their best service experiences with a branded hashtag.

4. AI-Powered Personalization

Brands now use AI to personalize content, offering customer-specific recommendations, responses, and interactions. TikTok’s algorithm-driven For You Page (FYP) and YouTube’s data-driven recommendations help service brands stay visible to their audience.

💡 Tip: Use AI-driven captions and targeted messaging to enhance engagement.

5. Trends-Based & Viral Content

Jumping on trending audio, memes, and challenges can significantly boost visibility. Service brands now use relatable humor, viral service moments, and trending sounds to connect with users.

💡 Tip: Monitor trending hashtags and create fun, relatable content tied to your service industry.

Final Thoughts

Service-oriented brands have an incredible opportunity to elevate customer engagement using TikTok and YouTube trends. By focusing on short-form content, interaction, personalization, UGC, and viral trends, businesses can build stronger connections with their audiences and enhance brand loyalty.

Are you ready to implement these trends into your service-oriented strategy? Let’s turn engagement into excellence!

Share your thoughts with us by connecting with @verygoodservice on your favorite platform and using  hashtag #verygoodservice to flag when you receive a very good service.

https://www.tiktok.com/@verygoodservice Very Good Service on TikTok

https://www.youtube.com/verygoodservice Very Good Service on YouTube

This post was prepared with the assistance of Copilot - prompted 18/6/2025

What Animals Teach Us About Great Customer Service

 

What Animals Teach Us About Great Customer Service

Over 10 years ago we published an original post about animals and customer service . Things have moved on clearly so we thought an update would be timely. Please do add more animals and attributes in the comments below.

Customer service is an art that requires patience, adaptability, speed, and problem-solving skills. Interestingly, the animal kingdom is full of creatures that embody these essential qualities. By drawing inspiration from them, we can become better at handling customers, solving problems, and creating meaningful connections.

Take the tortoise, for example. Known for its slow and steady pace, it teaches us the importance of patience. In customer service, some interactions take longer than others, and rushing through them can lead to mistakes or dissatisfaction. A patient approach ensures that customers feel heard and valued, ultimately leading to stronger relationships.

On the opposite end of the spectrum is the cheetah—a master of speed. Some situations require fast action, whether it’s processing an order, troubleshooting a tech issue, or responding to urgent inquiries. Customers appreciate quick and efficient service, and those who can balance speed with accuracy stand out.

Friendliness is another key trait, and few animals embody this better than the golden retriever. These loyal, affectionate dogs naturally make people feel welcome and comfortable. In customer service, a warm and friendly attitude can instantly put customers at ease, creating a positive experience even in challenging situations.

But not every interaction is the same, and that’s where the octopus comes in. Highly intelligent and adaptable, it changes color and shape to blend into different environments. A great customer service professional does the same—adjusting their communication style based on the situation and the customer’s personality, whether they’re dealing with an angry caller, a confused shopper, or a long-time client.

Of course, attentiveness is just as important as adaptability. The owl, with its sharp vision and focus, reminds us of the power of active listening. Customers often drop subtle hints about their needs, and those who pay close attention can catch these details and offer more effective solutions. The best customer service professionals listen deeply, ask thoughtful questions, and respond with care.

Handling customers isn’t always smooth sailing, though. Some situations require resilience, just like the camel, which survives extreme conditions with remarkable endurance. Difficult customers, high-pressure environments, and long hours are part of the job, but staying professional and composed helps turn tough interactions into successful outcomes.

Then there’s the dolphin, a creature known for its intelligence and ability to solve complex problems. Customer service isn’t just about following a script—it’s about thinking on your feet, finding creative solutions, and making decisions that benefit both the customer and the business. The best professionals don’t just provide answers; they provide solutions.

In leadership roles, the lion stands as a symbol of strength, confidence, and decisiveness. Customer service managers and senior agents must lead by example, inspiring their teams and setting a high standard for service excellence. Great leadership fosters teamwork, encourages growth, and ensures that every customer interaction meets the highest standards.

Speaking of teamwork, the ant is a perfect representation of collaboration. Customer service teams thrive when they support each other, share knowledge, and work together to provide seamless service. No single agent can handle everything alone, but a well-coordinated team ensures that customers receive the best possible care.

Finally, the elephant reminds us of the power of loyalty. Elephants form deep bonds and never forget those who have helped them. In customer service, building long-term relationships with customers creates trust and brand loyalty. A customer who feels valued will return again and again, strengthening the company’s reputation and success.

The next time you find yourself helping a customer, take a moment to think—what animal do you feel like in that moment? Are you the patient tortoise, the adaptable octopus, or the problem-solving dolphin? Share your thoughts in the comments! We'd love to hear which animal best represents your customer service style.

This post was prepared with the help of Chat GPT. Prompted in February 2025.

Industries Excelling in Social Customer Service

 In today's digital age, social media has become a pivotal platform for customer engagement and support. Certain industries have harnessed the power of social customer service more effectively than others, leveraging it to enhance customer satisfaction and loyalty. This article delves into industries particularly well-suited for social customer service, exploring the reasons behind their success and highlighting best practices that set the standard.

Industries Excelling in Social Customer Service

1. Telecommunications

   Telecom companies manage a vast customer base, leading to frequent inquiries about service disruptions, billing, and technical support. Social media offers a real-time platform to address these concerns efficiently. By actively monitoring social channels, telecom providers can swiftly identify and resolve issues, thereby reducing customer frustration and enhancing satisfaction. This proactive approach not only addresses immediate concerns but also fosters customer loyalty in a highly competitive market.

2. Travel and Hospitality

   The travel and hospitality sector thrives on timely communication. Travelers often turn to social media for assistance with bookings, cancellations, and real-time updates. By providing prompt responses on these platforms, companies can improve the customer experience, leading to positive reviews and repeat business. This immediacy in communication is crucial in managing customer expectations and ensuring satisfaction.

3. Retail

   Retailers utilize social media to showcase products, announce promotions, and engage with customers. By addressing customer inquiries and complaints on these platforms, retailers can enhance the shopping experience and build brand loyalty. This direct line of communication allows for personalized interactions, making customers feel valued and heard. 

4. Financial Services

   Banks and financial institutions have embraced social media to provide support for account inquiries, fraud alerts, and service updates. Given the sensitive nature of financial information, these institutions must balance responsiveness with security, often directing customers to secure channels when necessary. This approach ensures that customer concerns are addressed promptly while maintaining confidentiality. 

5. Healthcare

   Healthcare providers are increasingly using social media to manage appointments, answer insurance queries, and offer patient education. By engaging with patients on these platforms, healthcare organizations can improve accessibility and patient satisfaction. However, it's essential to handle interactions with care, ensuring patient privacy and adhering to regulatory standards. 

Best Practices in Social Customer Service

To excel in social customer service, businesses across these industries implement several best practices:

• Responsiveness: Timely replies to customer inquiries demonstrate that the company values its customers and is committed to addressing their needs. 

• Dedicated Support Channels: Establishing specific social media handles for customer support helps streamline inquiries and ensures that support teams can focus on resolving issues efficiently. 

• Monitoring and Engagement: Regularly monitoring social media mentions allows companies to proactively address potential issues and engage with customers, turning negative experiences into positive ones. 

• Professional Tone: Maintaining a consistent and professional tone across all interactions helps build trust and reflects the company's brand values. 

• Data-Driven Improvements: Collecting and analysing customer feedback from social media interactions can provide valuable insights for service enhancements and product development. 

By adopting these best practices, companies can effectively leverage social media as a powerful tool for customer service, leading to increased satisfaction and loyalty.

This blog post has been prepared with the help of Chat GPT - prompted on 8th February 2025.

Sentiment Analysis of Customer Reviews: A Powerful Tool for Improving Customer Service

 In today's hyper-connected world, customer feedback is more valuable than ever. Businesses that can effectively analyse and understand this feedback can gain a significant competitive advantage. One powerful tool for achieving this is sentiment analysis.

What is Sentiment Analysis?

Sentiment analysis, also known as opinion mining, is a natural language processing (NLP) technique that automatically extracts subjective information from text. It aims to understand the underlying emotional tone behind customer reviews, social media posts, and other forms of textual data.  

How Can Sentiment Analysis Improve Customer Service?

1. Proactive Issue Resolution:

   • Early Identification of Problems: By analysing customer reviews, businesses can quickly identify recurring issues and pain points. This allows them to proactively address these problems before they escalate and damage customer satisfaction.  
   • Targeted Support: Sentiment analysis can help identify customers who are experiencing negative emotions or have specific concerns. This allows customer service teams to reach out to these customers proactively and provide personalized support.  

2. Enhanced Customer Experience:

   • Personalized Interactions: By understanding customer sentiments, businesses can tailor their interactions to individual needs and preferences. This can lead to more personalized and satisfying customer experiences.  
   • Improved Product and Service Development: Customer feedback can be used to identify areas for improvement in products and services.
     Sentiment analysis can help pinpoint specific features or aspects that are causing dissatisfaction, allowing businesses to make targeted changes.  

3. Increased Customer Loyalty:

   • Demonstrating Empathy and Understanding: By responding to customer feedback with empathy and understanding, businesses can build stronger relationships with their customers. This can lead to increased customer loyalty and repeat business.  
   • Building a Positive Brand Image: By proactively addressing customer concerns and improving the overall customer experience, businesses can build a positive brand image and reputation.  

4. Data-Driven Decision Making:

   • Measuring the Impact of Customer Service Initiatives: Sentiment analysis can be used to measure the effectiveness of customer service initiatives. By tracking changes in customer sentiment over time, businesses can determine which strategies are working and which need improvement.
   • Identifying Key Performance Indicators (KPIs): Sentiment analysis can help identify key performance indicators (KPIs) that are most relevant to customer satisfaction. This allows businesses to focus their efforts on the metrics that matter most.  

Conclusion

Sentiment analysis is a powerful tool that can help businesses gain valuable insights from customer feedback. By understanding the emotional tone behind customer reviews, businesses can improve their customer service, enhance the customer experience, and build stronger relationships with their customers. As the volume of customer data continues to grow, sentiment analysis will become an increasingly important tool for businesses of all sizes.

Prepared with the help of Gemini - prompted 9th January 2025

 Making Marketing and Customer Service Work Together

Image by DALL·E 3

Imagine this: your marketing team crafts a beautiful campaign, promising the world to potential customers. But when those excited prospects turn into actual customers, they hit a brick wall at customer service. Confused, frustrated, and frankly, disappointed, they walk away with a sour taste in their mouths.

That is the painful reality when marketing and customer service operate in silos. It is like pouring sugar in your coffee with one hand while adding vinegar with the other. The result? A bitter mess that nobody enjoys.

But it doesn't have to be this way. In fact, when marketing and customer service work together, it's a match made in brand heaven. They become two sides of the same coin, each amplifying the other's success. Here's how:

1. United Voice, Strong Brand: Marketing tells the story, and customer service makes it real. By sharing customer insights and feedback with marketing, you ensure your messaging resonates with real needs and pain points. This creates a consistent brand voice across all touchpoints, building trust and loyalty.

2. Targeted Content, Satisfied Customers: Customer service is a treasure trove of data on what customers love (and hate) about your product. Leverage this knowledge to create targeted content that addresses their concerns and highlights the value you offer. Think FAQs, tutorials, and educational blog posts – content that empowers customers and reduces support tickets.

3. Seamless Onboarding, Happy Advocates: Marketing gets customers through the door, but customer service ensures they stay. By collaborating on onboarding experiences, you can eliminate friction and confusion, turning first-time users into lifelong fans. Think personalized welcome emails, helpful product demos, and proactive outreach from the customer service team.

4. Real-Time Feedback Loop, Constant Improvement: Marketing campaigns live and die by data. But what if you could see how they actually impact the customer experience? By integrating customer support data into your marketing analytics, you get a real-time feedback loop. This allows you to refine your campaigns, optimize messaging, and ensure you're attracting the right audience.

5. Brand Ambassadors Arise: Happy customers don't just stay around, they sing your praises to the world. Customer service interactions are golden opportunities to turn customers into brand ambassadors. Empower your support team to go the extra mile, exceed expectations, and create stories that marketing can amplify.

Making marketing and customer service work together is not just about improving efficiency; it is about creating a seamless customer journey that fosters trust, loyalty, and advocacy. It is about breaking down the silos and fostering a culture of collaboration. And the rewards? Stronger brand identity, happier customers, and ultimately, a business that thrives on the synergy of its efforts.

So, what are you waiting for? Go forth, break down the walls, and unleash the power of your united marketing and customer service team. Your brand (and your customers) will thank you for it.

AI powered customer service

How AI can improve customer service

Customer service by Bing Image creator

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In today's digital age, customers expect fast, efficient, and personalized customer service. Artificial intelligence (AI) can help businesses meet these expectations by automating tasks, providing personalized recommendations, and resolving customer issues more quickly. Here are some of the ways that AI can improve customer service:

• Automate tasks: AI can be used to automate many of the tasks that are traditionally performed by human customer service agents, such as answering FAQs, providing order status updates, and troubleshooting common problems. This frees up human agents to focus on more complex issues, resulting in faster resolution times and improved customer satisfaction.
• Provide personalized recommendations: AI can be used to analyse customer data to identify their needs and preferences. This information can then be used to provide personalized recommendations for products, services, and content. For example, an AI-powered recommendation engine could suggest new products to customers based on their past purchases or browsing history.
• Resolve customer issues more quickly: AI can be used to quickly identify and resolve customer issues. For example, an AI-powered chatbot could be used to answer customer questions, troubleshoot problems, and escalate issues to human agents as needed. This can help to reduce customer wait times and improve overall satisfaction.

In addition to these specific benefits, AI can also help to improve customer service in a number of other ways. For example, AI can be used to:

• Improve the accuracy of customer data: AI can be used to analyse customer data to identify errors and inconsistencies. This information can then be used to improve the accuracy of customer records, which can lead to better customer service.
• Identify customer pain points: AI can be used to analyse customer data to identify areas where customers are having problems. This information can then be used to improve the customer experience by addressing these pain points.
• Personalize the customer journey: AI can be used to personalize the customer journey by providing customers with relevant information and offers at the right time. This can help to improve customer satisfaction and loyalty.

Overall, AI has the potential to revolutionize customer service. By automating tasks, providing personalized recommendations, and resolving customer issues more quickly, AI can help businesses to deliver a better customer experience.

How to Get Started with AI for Customer Service

If you're interested in getting started with AI for customer service, there are a few things you need to do:

1. Identify your goals: What do you hope to achieve by using AI for customer service? Do you want to reduce wait times, improve accuracy, or personalize the customer experience? Once you know your goals, you can start to look for AI solutions that can help you achieve them.
2. Gather your data: AI solutions need data to work effectively. This data can include customer contact information, purchase history, and product usage data. The more data you have, the better the AI solution will be able to understand your customers and provide them with the best possible service.
3. Choose the right AI solution: There are a number of AI solutions available for customer service. Some of these solutions are more complex than others, so it's important to choose one that is right for your business. You'll also need to consider your budget and your technical capabilities.
4. Roll out the solution: Once you've chosen an AI solution, you need to roll it out to your customer service team. This may involve training your team on how to use the solution and how to interact with customers who are using it.
5. Measure the results: After you've rolled out the solution, you need to measure the results. This will help you to determine whether the solution is meeting your goals. You can measure things like wait times, customer satisfaction, and customer retention.

Conclusion

AI has the potential to revolutionize customer service. By automating tasks, providing personalized recommendations, and resolving customer issues more quickly, AI can help businesses to deliver a better customer experience. If you're interested in getting started with AI for customer service, there are a few things you need to do: identify your goals, gather your data, choose the right AI solution, roll out the solution, and measure the results.

Fishing for customers or delivering a great customer service?

Fishing for customers or delivering a good customer service?

Most of us already know the answer. Trying to catch new customers:

- is time consuming and sometimes requires extreme patience, 

- can be lonely and frustrating if you are on the road knocking on doors, 

- is not always rewarding as you often come home empty handed,

- does not necessarily bring success as you can catch the wrong type of customer,

- requires innovation as the competition comes trawling over your patch,

- often necessitate expensive baits such as promotion and discounts...

and the list goes on... So much that one wonders why there are still so many enthusiasts?

Most of us already know the alternative, provide an excellent customer service. Customers will come back, buy more, tell their friends, give you ideas for new products, do your marketing and negotiate your prices a little less. Maybe the time has come to do a little less fishing and a little more servicing.