Ever heard the phrase, "Customer service is broken?" 

It's melodramatic, right? —something a Southern lawyer might declaim with a raised finger. Regardless, there’s some truth to it, and the reason is a deadly combination of interaction volume and staffing issues. Too many inbound interactions, too few people to handle them. The demands of scale do, in fact, break customer service. 

This challenge of scaling up is a natural phenomenon. You find it everywhere, from customer service to pizza parlors.

Too much appetite, too little dough

If you want to scale a pizza, you have to stretch the dough, but you can't stretch it infinitely. There’s a limit. Stretch it too far, and it breaks. 

Customer service isn't exactly physical, but physical beings deliver it— the kind who have bad days, sickness, and fatigue. When you stretch physical things too far (like balloons, hamstrings, or contact center agents), they break. In contact centers, broken agents lead to broken customer service.

Contact centers are currently stretched pretty thin. Sixty-three percent of them face staffing shortages. Why are they struggling? Some cite rising churn rates year after year. Others note shrinking agent workforces in North America and Europe. While workers flee agent jobs for coding classes, pastry school, and duck farming, customer request volumes are up. In 2022, McKinsey reported that 61% of customer care leaders claimed a growth in total calls. 

To put it in pizza terms (because why not?), your agent dough ball is shrinking even as your customers' insatiable pizza appetite expands.

What’s a contact center to do? There are two predominant strategies right now: 

• reduce request volumes (shrink the appetite)
• stretch your contact center’s service capacity (expand the dough)

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Contact centers seem intent on squeezing more out of their digital self-service capabilities in an attempt to contain interactions and shrink agent queues. At the same time, they’re feverishly investing in technology to expand capacity with performance management, process automation and real-time agent support. 

But even with both of these strategies working at full force, contact centers are struggling to keep up. Interaction volume continues to increase, while agent turnover carries on unabated. Too much appetite. Not enough dough to go around. 

How do we make more dough?

Here’s the harsh reality – interaction volume isn’t going to slow down. Customers will always need support and service, and traditional self-service options can’t handle the scope and complexity of their needs. We’ll never reduce the appetite for customer service enough to solve the problem.

We need more dough. And that means we need to understand the recipe for customer service and find a way to scale it. The recipe is no secret. It’s what your best agents do every day:

1. Listen to the customer
2. Understand their needs
3. Propose helpful solutions
4. Take action to resolve the issue

The real question is, how do we scale the recipe up when staffing is already a serious challenge?

Scaling up the recipe for customer service

We need to scale up capacity in the contact center without scaling up the workforce. Until recently, that idea was little more than a pipe dream. But the emergence of generative AI agents has created new opportunities to solve the long-running problem of agent attrition and its impact on CX.

Generative AI agents are a perfect match for the task. Like your best human agents, they can and should listen, understand, propose solutions, and take action to resolve customers’ issues. When you combine these foundational capabilities into a generative AI agent to automate customer interactions, you expand your contact center’s capacity – without having to hire additional agents. 

Here’s how generative AI tools can and should help you scale up the recipe for customer service:

1. Generative AI should listen to the customer
   Great customer service starts with listening. Your best agents engage in active listening to ensure that they take in every word the customer is saying. Transcription solutions powered by generative AI should do the same. The most advanced solutions combine speed and exceptional accuracy to capture conversations in the moment, even in challenging acoustic environments. 

2. Generative AI should understand the customer’s needs
   Your best agents figure out what the customer wants by listening and interpreting what the customer says. An insights and summarization solution powered by generative AI can also determine customer intent, needs, and sentiment. The best ones don’t wait until after the conversation to generate the summary and related data. They do it in real time.

3. Generative AI should propose helpful solutions
   With effective listening and understanding capabilities in place, generative AI can provide real-time contextual guidance for agents. Throughout a customer interaction, agents perform a wide range of tasks – listening to the customer, interpreting their needs, accessing relevant information, problem-solving, and crafting responses that move the interaction toward resolution. It’s a lot to juggle. Generative AI that proposes helpful solutions at the right time can ease both the cognitive load and manual typing burden on agents, allowing them to focus more effectively on the customer.

4. Generative AI should take action to resolve customers’ issues
   This is where generative AI combines all of its capabilities to improve customer service. It can integrate the ingredients of customer care—listening, understanding, and proposing—to safely and autonomously act on complex customer interactions. More than a conversational bot, it can resolve customers’ issues by proposing and executing the right actions, and autonomously determining which backend systems to use to retrieve information and securely perform issue-resolving tasks.

Service with a stretch: Expanding your ball of dough

Many contact centers are already using generative AI to listen, understand, and propose. But it’s generative AI’s ability to take action based on those other qualities that dramatically stretches contact center capacity (without breaking your agents). 

A growing number of brands have already rolled out fully capable generative AI agents that handle Tier 1 customer interactions autonomously from start to finish. That does more than augment your agents’ capabilities or increase efficiency in your workflows. It expands your frontline team without the endless drain of recruiting, onboarding, and training caused by high agent turnover. 

A single generative AI agent can handle multiple interactions at the same time. And when paired with a human agent who provides direct oversight, a generative AI agent can achieve one-to-many concurrency even with voice interactions. So when inbound volume spikes, your generative AI agent scales up to handle it. 

More dough. More capacity. All without stretching your employees to the breaking point. For contact center leaders, that really might be as good as pizza for life. 

Want more? Read our eBook on the impact of agent churn

Agent churn rates are historically high, and the problem persists no matter what we throw at it — greater schedule flexibility, gamified performance dashboards, and even higher pay.

Instead of incremental changes to timeworn tools, what if we could bypass the problem altogether?

Download the Agent Churn: Go Through It or Around It? eBook to learn why traditional strategies for agent retention aren't working, and how generative AI enables a radical new paradigm.

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At ASAPP we develop AI models to improve agent performance in the contact center. Many of these models directly assist contact center agents by automating parts of their workflow. For example, the automated responses generated by AutoCompose - part of our ASAPPMessaging platform - suggest to an agent what to say at a given point during a customer conversation. Agents often use our suggestions by clicking and sending them.

How we measure performance matters

While usage of the suggestions is a great indicator of whether the agents like the features, we’re even more interested in the impact the automation has on performance metrics like agent handle time, concurrency, and throughput. These metrics are ultimately how we measure agent performance when evaluating the impact of a product like the AutoCompose capabilities of ASAPPMessaging, but these metrics can be affected by things beyond AutoCompose usage, like changes in customer intents or poorly-planned workforce management.

To isolate the impact of AutoCompose usage on agent efficiency, we prefer to measure the specific performance gains from each individual usage of AutoCompose. We do this by measuring the impact of automated responses on agent response time, because response time is more invariant to intent shifts and organizational effects than handle time, concurrency and throughput.

By doing this, we can further analyze:

• The types of agent utterances that are most often automated
• The impact of the automated responses when different types of messages are used (in terms of time savings)

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Altogether, this enables us to be data-driven about how we improve models and develop new features to have maximum impact.

Going beyond greeting and closing messages

When we train AI models to automate responses for agents, the models look for patterns in the data that can predict what to say next based on past conversation language. So the easiest things for models to learn well are the types of messages that occur often and without much variation across different types of conversations, e.g. greetings and closings. Agents typically greet and end a conversation with a customer the same way, perhaps with some specificity based on the customer’s intent.

Most AI-driven automated response products will correctly suggest greeting and closing messages at the correct time in the conversation. This typically accounts for the first 10-20% of automated response usage rates. But when we evaluate the impact of automating those types of messages, we see that it’s minimal.

To understand this, let’s look at how we measure impact. We compare agents’ response times when using automated responses against their response times when not using automated responses. The difference in time is the impact—it’s the time savings we can credit to the automation.

Without automation, agents are not manually typing greeting and closing messages for every conversation. Rather they’re copying and pasting from notepad or word documents containing their favorite messages. Agents are effective at this because they do it several times per conversation. They know exactly where their favorite messages are located, and they can quickly copy and paste them into their chat window. Each greeting or closing message might take an agent 2 seconds. When we automate those types of messages, all we are actually automating is the 2-second copy/paste. So when we see automation rates of 10-20%, we are likely only seeing a minimal impact on agent performance.

The impact lies in automating the middle of the conversation.

If automating the beginnings and endings of conversations is not that impactful, what is?

Automating the middle of the conversation is where response times are naturally slowest and where automation can yield the most agent performance impact.

- Heather Reed, Product Manager, ASAPP

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The agent may not know exactly what to say next, requiring time to think or look up the right answers. It’s unlikely that the agent has a script readily available for copying or pasting. If they do, they are not nearly as efficient as they are with their frequently used greetings and closings.

Where it was easy for AI models to learn the beginnings and endings of conversations, because they most often occur the same way, the exact opposite is true of the middle parts of conversations. Often, this is where the most diversity in dialog occurs. Agents handle a variety of customer problems, and they solve them in a variety of ways. This results in extremely varied language throughout the middle parts of conversations, making it hard for AI models to predict what to say at the right time.

ASAPP’s research delivers the biggest improvements

Whole interaction models are exactly what the research team at ASAPP specializes in developing. And it’s the reason that AutoCompose is so effective. If we look at AutoCompose usage rates throughout a conversation, we see that while there is a higher usage at the beginnings and endings of conversations, AutoCompose still automates over half of agent responses in between.

The low response times in the middle of conversations are where we see the biggest improvements in agent response time. It’s also where the biggest opportunities for improvements are realized.

Whole interaction automation shows widespread improvements

ASAPP’s current automated response rate is about 80%. It has taken a lot of model improvements, new features, and user-tested designs to get there. But now agents effectively use our automated responses in the messaging platform to reduce handle times by 25%, enabling an additional 15% of agent concurrency, for a combined improvement in throughput of 53%. The AI model continues to get better with use, improving suggestions, and becoming more useful to agents and customers.

Want more? Learn to choose the right digital platform for your contact center

Customers expect a rich digital experience that’s efficient, convenient and easy – and most importantly, resolves their issues. To deliver the kind of experience customers expect and to reap the business benefits of digital CX, you have to get beyond the basics.

Download the eBook to learn how to choose a digital platform that will equip you to create digital experiences that satisfy your customers, boost efficiency in your contact center and lower your costs.

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A bridge to the future

Talk of Generative AI transforming contact centers is everywhere. From boardrooms of the world’s largest enterprises to middle management and frontline agents, there’s a near-universal consensus: the impact will be massive. Yet, the path from today’s reality to that future vision is full of challenges.

The key to bringing customer-facing Gen AI to production today lies in a human-in-the-loop workflow, where AI agents consult human advisors whenever needed—just as frontline agents might seek guidance from a tier 2 agent or supervisor. This approach both unlocks benefit today while providing a self-learning mechanism driving greater automation in the future.

Challenges today

We've spilled our fair share of digital ink on how Generative AI marks a step change in automation and how it will revolutionize the contact center. But let's take a moment to acknowledge the key challenges that must be overcome to realize this vision.

• Safety: The stochastic nature of AI means it can "hallucinate"—producing incorrect or misleading outputs.
• Authority: Not every customer interaction is ready for full automation; certain decisions still require human judgment.
• Customer request: Regardless of how advanced an AI agent is, some customers will insist on speaking with a human agent.
• Access: Many systems used by agents today to resolve customer issues lack APIs and are thus inaccessible to Gen AI agents.

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Safety: Constant vigilance

AI's ability to generate human-like responses makes it powerful, but this capability comes with risks. Gen AI systems are inherently probabilistic, meaning they can occasionally hallucinate—providing incorrect or misleading information. These mistakes, if unchecked, can erode customer trust at best and damage a brand at worst.

When evaluating a Gen AI system, the critical question to ask is not “will the system hallucinate?” It will. The critical question is, “will hallucinated output get sent to my customers?”

With GenerativeAgent every outgoing message is evaluated by an output safety module that, among other things, checks for hallucinations.  If a possible hallucination is detected, the message is assigned to a human-in-the-loop advisor for review. This safeguard prevents the dissemination of erroneous information while allowing the virtual agent to maintain control of the interaction.

In systems without this kind of human oversight, mistakes could lead to escalations or, worse, unresolved customer issues, or worse yet, damaged customer relationships or brand. By integrating a human-in-the-loop workflow, businesses can mitigate the risks inherent to this technology while maximizing the business benefits it offers.

Authority: Responsible delegation

Deploying customer-facing Gen AI in a contact center doesn’t mean relinquishing all decision-making power to the AI. There are situations where human judgment is crucial, particularly when the stakes are high for both the business and the customer.

Take, for example, a customer requesting a payment extension or a final review of their loan application. These are pivotal decisions that involve weighing factors like risk, compliance, and customer loyalty. While a Gen AI agent can automate much of the interaction leading up to these moments, the final decision still requires human judgment.

With a human-in-the-loop framework, this is no longer a limitation. The AI agent can seamlessly consult an advisor for approval while maintaining control of the customer interaction, much like a Tier 1 agent consulting a supervisor. This approach provides flexibility—automating what can be automated while reserving human intervention for critical decisions. For the business, this boosts automation rates, reduces escalations, and empowers advisors to focus on high-impact judgments where their expertise is essential.

Customer request: Last ditch containment

No matter how advanced Gen AI virtual agents become, some customers will still ask to speak with a live agent—at least for now. Of all the challenges outlined, this one is the most intractable, driven by customer habits and expectations rather than technology or process.

Human-in-the-loop offers a smart alternative. Rather than instantly escalating to a live agent when requested, the Gen AI agent could inform the customer of the wait time and propose a review by a human advisor in the meantime. If the advisor can help the Gen AI agent resolve the issue quickly, the escalation can be avoided entirely—delivering a faster resolution without disrupting the flow of the interaction.

While not every customer will accept this deflection, it can significantly reduce this type of escalation and address one of the toughest barriers to full automation.

Access: Ready for today, better tomorrow

Many systems that agents rely on today lack APIs, putting a large volume of customer issues beyond the reach of automation.

Rather than dismissing these cases, a human-in-the-loop advisor can bridge this gap—handling tasks on behalf of the Gen AI agent in systems that lack APIs.

The value extends beyond expanding automation. It highlights areas where developing APIs or streamlining access to legacy systems could deliver significant ROI. What was once hard to justify becomes clear: introducing new APIs directly reduces the advisor hours spent compensating for their absence.

Conclusion

Generative AI is poised to reshape the contact center, but unlocking its full potential requires a tightly integrated human-in-the-loop workflow. This framework ensures that customer-facing Gen AI is ready for production today while providing a self-learning mechanism driving to greater automation gains tomorrow.

While AI technology continues to advance, the role of human advisors is not diminishing—it’s evolving. By embracing this collaboration, businesses can strike the right balance between automation and human judgment, leading to more efficient operations and better customer experiences.

In future posts on human-in-the-loop, we’ll dive deeper into how to enhance collaboration between AI and human agents, and explore the evolving role of humans in AI-driven contact centers.

Want more? Discover how GenerativeAgent ends bad customer service - for good

The modern contact center is 40 years old. That's decades of bad customer service. Customer happiness is at a 17-year low, agents hate their jobs, and companies are investing billions to run their contact centers. It's time for a change.

Watch the GenerativeAgent launch video to see how it handles complex customer queries with human-in-the-loop escalation when necessary, all while maintaining seamless interactions with the customer.

AI security & AI safety

In the rapidly evolving landscape of generative AI, the terms "security" and "safety" often crop up. While they might sound synonymous, they represent two distinct aspects of AI that demand attention for a comprehensive and trustworthy AI system. Let's dive into these concepts and explore how they shape the development and deployment of generative AI, using real-world examples from contact centers to make sense of these crucial elements. To start, here is a quick overview video on AI security and AI safety:

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AI security: The shield against malicious threats

When we think about AI security, it's crucial to differentiate between novel AI-specific risks and security risks that are common across all types of applications, not just AI.

‍The reality is that over 90% of AI security efforts are dedicated to addressing critical basics and foundational security controls. These include data protection, encryption, data retention, PII redaction, authorization, and secure APIs. It’s important to understand that while novel AI-specific threats like prompt injection - where a malicious actor manipulates input to retrieve unauthorized data or inject system commands - do exist, they represent a smaller portion of the overall security landscape.

Let's consider a contact center chatbot powered by AI. A user might attempt to embed harmful scripts within their query, aiming to manipulate the AI into disclosing sensitive customer information, like social security numbers or credit card details. While this novel threat is significant, the primary defense lies in robust foundational security measures. These include input validation, strong data protection, employing encryption for sensitive information, and implementing strict authorization and data access controls.

Secure API access is another essential cornerstone. Ensuring that all API endpoints are authenticated and authorized prevents unauthorized access and data breaches. In addition to these basics, implementing multiple layers of defense helps mitigate novel threats. Input safety mechanisms can detect and block exploit attempts, preventing abuse like prompt leaks and code injections. Advanced Web Application Firewalls (WAFs) also play a vital role in defending against injection attacks, similar to defending against common application threats like SQL injection.

Continuous monitoring and logging of all interactions with the AI system is very important in detecting any suspicious activities. For example, an alert system can flag unusual API access patterns or data requests by an AI system, enabling rapid response to potential threats. Furthermore, a solid incident response plan is indispensable. It allows the security team to swiftly contain and mitigate the impact of any security events or breaches.

So while novel AI-specific risks do pose a threat, the lion’s share of AI security focuses on foundational security measures that are universal across all applications. By getting the basics right we build a robust shield around our AI systems, ensuring they remain resilient against both traditional and emerging threats.

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AI safety: The guardrails for ethical and reliable AI

While AI security acts as a shield, AI safety functions like guardrails, ensuring the AI operates ethically and reliably. This involves measures to prevent unintended harm, ensure fairness, and adhere to ethical guidelines.

Imagine a scenario where an AI Agent in a contact center is tasked with prioritizing customer support tickets. Without proper safety measures, the AI could inadvertently favor tickets from specific types of customers, perhaps due to biased training data that inadvertently emphasizes certain demographics or issues. This could result in longer wait times and dissatisfaction for overlooked customers. To combat this, organizations should implement bias mitigation techniques, such as diverse training datasets. Regular audits and red teaming are essential to identify and rectify any inherent biases, promoting fair and just AI outputs. Establishing and adhering to ethical guidelines further ensures that the AI does not produce unfair or misleading prioritization decisions.

An important aspect of AI safety is addressing AI hallucinations, where the AI generates outputs that aren't grounded in reality or intended context. This can result in the AI fabricating information or providing incorrect responses. For instance, a customer service AI Agent might confidently present incorrect policy details if it isn't properly trained and grounded. Output safety layers and content filters play a crucial role here, monitoring outputs to catch and block any harmful or inappropriate content.

Implementing a human-in-the-loop process adds another layer of protection. Human operators can be called on to intervene when necessary, ensuring critical decisions are accurate and ethical. For example, contact center human agents can be the final step of authorization before performing a critical task, or providing additional insight when the AI system produces incorrect output or does not have enough information to support a user.

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The intersection of security and safety

Though AI security and AI safety address different aspects of AI operation, they often overlap. A breach in AI security can lead to safety concerns if malicious actors manage to manipulate the AI's outputs. Conversely, inadequate safety measures can expose the system to security threats by allowing the AI to make incorrect or dangerous decisions.

Consider a scenario where a breach allows unauthorized access to the contact center’s AI system. The attackers could manipulate the AI to route calls improperly, causing delays and customer frustration. Conversely, if the AI's safety protocols are weak, it might inaccurately redirect emergency calls to non-critical queues, posing serious risks. Therefore, a balanced approach that addresses both security and safety is essential for developing a trustworthy generative AI solution.

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Balanced approach for trustworthy AI

Understanding the distinction between AI security and AI safety is pivotal for building robust AI systems. Security measures protect the AI system from external threats, ensuring the integrity, confidentiality, and availability of data. Meanwhile, safety measures ensure that the AI operates ethically, producing accurate outputs.

By focusing on both security and safety, organizations can mitigate risks, enhance user trust, and responsibly unlock the full potential of generative AI. This dual focus ensures not only the operational integrity of AI systems but also their ethical and fair use, paving the road for a future where AI technologies are secure, reliable, and trustworthy.

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Want more? Learn what to watch out for before trusting an AI vendor with your data and your brand

Alisher Yuldashev, ASAPP expert in AI safety and trust, will walk you through the key considerations for eliminating unreliable providers and identifying the trustworthy partners. You’ll discover how a trustworthy provider should manage your data (including usage in ML training), have AI-specific safety measures, manage model development with robust quality assurance processes, and much more.

Are you not getting the value that you were hoping for from your digital chat platform? 

If so, this blog and our recent on-demand webinar are for you.

We recently had a great webinar featuring Melissa Price, VP, CX Digital Self-Service at Altice USA and the experts at ASAPP. In it, we talked about what is holding people back from seeing the value they want out of their existing digital chat platform, what they should look for in a new one, and how they can make that migration as painless as possible.

The conversation is ripe with solutions for some of the widespread pain points CX leaders are experiencing with their digital chat platforms.
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Here are some key takeaways from the webinar:

3 Underlying Challenges Holding Platforms Back 

In the webinar, we discussed three critical issues that often prevent customer engagement platforms from succeeding:

1. Fragmented Customer Journeys: Disconnected touchpoints lead to inconsistent customer experiences. Automation on many platforms is not customer-friendly or efficient, often leading to frustration rather than satisfaction.
2. Lack of Adaptability: Many platforms suffer from a lack of ongoing innovation and adaptability. Without continuous improvement loops and the support to keep up with evolving needs, platforms become outdated quickly and fail to deliver the optimized experiences necessary for customer retention and satisfaction. This static approach that many platforms offer contrasts sharply with the dynamic nature of customer service needs.
3. Limited Effectiveness of Bolted-on AI: Many legacy chat providers rely on bolted-on AI solutions rather than being AI-native, resulting in subpar suggested responses, ultimately failing to deliver the additional efficiencies, predictive insights, and automation that AI should provide.
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How Modern Chat Platforms, Like ASAPP, Address These Problems 

Moder, AI-Native® digital chat platforms, like ASAPPMessaging tackles these challenges head-on:

• Seamless customer experience Integration: ASAPP Messaging unifies customer interactions, ensuring a cohesive journey.
• Continuously improving AI-Enhanced Workflows: By automating routine tasks, AI frees agents to focus on complex issues, enhancing both efficiency and satisfaction. The AI continually learns and adapts with use, leading to smarter workflows and ongoing improvements.
• Advanced AI Capabilities: The platform’s AI not only predicts customer needs but also provides real-time support to agents, enhancing decision-making.

What to Look for in a Digital Chat Platform

Choosing the right digital chat platform can significantly impact your operational efficiency and customer satisfaction. Here are some key factors to consider:

1. Driving Digital Adoption:
   • Encourage Digital Shift: Prioritize a platform that promotes digital engagement over traditional voice channels, which are typically more expensive.
   • Cost Efficiency: Digital interactions are far more cost-effective than voice calls.
   • Reliability: Ensure the platform has a strong track record with minimal outages.
   • Customer Behavior Insights: Research the factors that drive customers to choose digital channels over voice to tailor your strategy accordingly.
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2. User Experience for Agents and Customers:
   • Customer-Focused Design: A platform with excellent user experience (UX) will make customers more aware of and inclined to use chat services.
   • Agent Empowerment: Good UX for agents is essential to drive adoption and enhance agent productivity through automation.
   • Service-Oriented: The technology should be built to serve both agents and customers, focusing on empowerment rather than control.
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3. AI-Native and Continuous Innovation:
   • Harnessing AI: Choose a platform that is AI-native and continuously innovates, especially given the current surge in AI advancements.
   • Custom Insights with GenAI: Platforms that leverage Generative AI can provide customized insights and maintain a competitive edge.
   • Experimentation and Adaptability: Opt for providers who constantly experiment with both internal and leading external AI models, rather than relying on standard models driven by financial incentives.
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Real Results with Altice 

Melissa Price, VP of Customer Experience for Digital Self Serve at Altice, shared compelling results achieved through the implementation of ASAPP’s digital chat platform. She highlighted results, including:

• Increased Efficiency: Significant reduction in handling times and increased resolution rates.
• Enhanced Customer Satisfaction: Improved experiences leading to higher customer satisfaction scores.
• Value of AI-Native Solutions: The AI-native approach of ASAPP was crucial in achieving these outcomes, providing deep insights and automation that traditional platforms couldn’t match.
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Switching Platforms 

There are short term challenges of switching platforms. But, the long-term benefits—including enhanced operational efficiencies, better customer experiences, and staying ahead of technological advancements—far outweigh the initial transition hurdles.
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How to make Migration as painless as possible?

To ensure a smooth migration to a new digital chat platform, start by focusing on thorough planning, selecting the right platform with excellent UX and reliability, and leveraging AI for continuous innovation. Engage stakeholders, provide comprehensive training, and implement in phases to minimize disruptions. For a more comprehensive guide, watch the on-demand recording.
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Watch the Webinar On-Demand

Curious about how Altice and ASAPP are transforming customer engagement? Watch our on-demand webinar to gain valuable insights into their groundbreaking strategies and real-world successes. It’s a must-watch if your current digital chat platform isn't meeting your expectations.

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Summary

Choosing the right CX software can be difficult. Learn how to think about choosing between a best-of-breed digital channel solution vs. an omnichannel solution.

Choosing the right CX software can be difficult. In this blog series, our goal is to give you helpful frameworks through which to think about how to purchase the right CX software to help your contact center improve customer experience, augment agent productivity, and reduce costs. (For a thorough breakdown of how to choose the right Transcription and Summarization Solutions, read our buyer’s guide here.)

This blog post covers how to think about acquiring the right Digital Customer Interaction Solution (DCIS) for your contact center. Should you choose a best-of-breed solution, specializing in digital channels, or an omnichannel/CCaaS solution, which offers both voice and digital channels?

Let’s dig in.

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The Problem with the “All-in-One Solution”

Omnichannel Solutions, also commonly referred to as Contact Center-as-a-Service Solutions, offer an appealing promise: meet all your CX software needs across channels from just one vendor. The only problem? It’s rare to find an omnichannel solution that offers excellent technology for both voice and digital channels.

Most omnichannel solutions were built to optimize for voice channels and tacked on digital support only as an afterthought. As a result, omnichannel solutions tend to deliver subpar digital experiences, such as poor agent UX not optimized for chat workflows, limited bots, and low chat agent usage, frustrating both customers and agents. 

It’s difficult to encourage customers to transition to digital support when you don’t have the technology to make their digital experience as useful and delightful as possible. 

Of course, the lack of a comprehensive digital solution has significant consequences for your contact center’s operations. 

Digital interactions are 1.5x to 4x cheaper than voice interactions. Contact centers understandably want to shift their mix to increase digital support. But without a digital chat platform that can reliably satisfy customers, it can be difficult to change customer behavior and preferences.

In sum, omnichannel solutions present themselves as an all-around answer, but in reality, their digital support options are severely lacking. What they offer in perceived convenience, they often trade in effectiveness.

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Below, we break down how to think about best-of-breed vs. omnichannel solutions:

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Interested in Learning More About Building a Digital-First Contact Center?

A digital-first contact center is essential to running a modern, efficient, and customer-friendly customer support operation.

Request a demo of ASAPP here to learn how ASAPP customers are:

• Automating Tier 1 interactions
• Experiencing happier, more productive agents
• Improving customer satisfaction
• Increasing revenue with contact center insights
• Drastically lowering operational costs

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Conclusion

It’s natural to desire a single platform that can handle voice and digital contacts in one place. But there’s good reason to be skeptical of omni-channel platforms, too: by definition, omni-channel platforms lack specialization. It’s the classic jack of all trades, master of none problem. 

Worse, because voice is often more profitable to omni-channel vendors than digital, companies that offer omnichannel platforms often invest the bulk of their development resources in voice over digital, allowing their digital technology to lag behind.

As more customers look to digital to interact with companies, it’s more important than ever that you get digital right. Consider choosing a vendor who specializes in digital channels rather than a broader omni-channel approach.

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Who is ASAPP?

ASAPP is the AI-native® software for contact centers.

We help customer service leaders unlock their full value by minimizing costs & inefficiencies, improving agent compliance & productivity, and surfacing actionable insights while helping you deliver a great customer experience.

Our customers are large enterprises who care deeply about leveraging AI to transform CX by delivering unprecedented cost savings and maximizing customer delight.

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What do we make?

We make a full suite of AI-native solutions designed specifically for the needs and nuances of the CX industry.

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Why we are the right partner?

We strive to be the best technology partner you have ever had. 

ASAPP is not new to the AI or the CX space. We have been building AI-native products for the contact center since 2014 and building our own LLMs since 2018. We invest heavily in our products and our workforce to bring our customers the best solutions on the market and the subject matter experts to ensure those customers are getting the maximum benefit. 

We offer white-glove service and insight into contact centers’ best practices across industries, and our consultative nature drives transformative results. ASAPP is laser-focused on business outcomes, data usability, and helping you realize your desired customer experience. 

If you are interested in how best-of-breed digital software for your contact center can benefit your organization, please click below to schedule a consultation:

Speak to an Expert

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Omnichannel solutions present themselves as an all-around answer but in reality their digital support options are severely lacking. What they offer in convenience, they often trade in effectiveness.

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Summary

From understanding customer issues to analyzing agent performance, voice analytics are crucial to operating an efficient, modern contact center.

From understanding customer issues to analyzing agent performance, voice analytics are crucial to operating an efficient, modern contact center.

But what exactly are call center voice analytics, and how can they be utilized to improve your contact center operations?

This blog post defines voice analytics and describes its potential benefits. Then, it walks through various use cases for voice analytics. Finally, it prescribes a framework for thinking through acquiring technology to improve your analytics.

Let's get started.

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What is Call Center Voice Analytics?

Analytics is the process of "discovering, interpreting, and communicating significant patterns in data." In other words, analytics is the art of transforming raw data into usable metrics.

Call Center Voice Analytics, also known as Speech Analytics, is the process of transforming conversational data from voice interactions into meaningful metrics.

Typically, speech analytics software will utilize advanced computing techniques, like machine learning and natural language processing (NLP), to input all conversational data between agents and customers and output digestible metrics.

Voice analytics might include some of the following metrics:

• Average Handle Time
• Average Talk Time
• Average After-Call Work Rate
• Resolution Rate
• Repeat Caller Rate
• Intent Data
• Sentiment Data
• Custom Structured Data Reflecting Contents of Call
• Automated Summaries of Conversations

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Real-time speech analytics enable contact center management to intimately understand how well their agents are servicing customers and accomplishing company objectives. In the next section, we break down the power of voice analytics for contact center management.

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The Power of Voice Analytics

Voice analytics are to a contact center as the instrument panel is to a pilot.

With high-quality voice analytics, contact center management can keep their finger on the pulse of their organization. Below, we break down some of the use cases for live contact center analytics.

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Use Cases for Voice Analytics

Quality Assurance and Performance Management

Voice analytics enable contact center management to understand the quality of the support its agents are providing. Analytics can help create a scorecard for agents, allowing management to see at a glance how agents are performing on a variety of metrics.

For example, a telcom company might need to weigh several variables when determining agents' performance. These variables might include average handle time, resolution rate, call back rate, and truck roll rate (the percentage of time a service truck is dispatched to a customer.) Voice analytics can compile all this data without manual intervention.

In addition, voice analytics can perform automated quality assurance work, like checking to determine if certain promos were offered, if policies around customer ID verification were followed, etc.

In short, voice analytics automates and simplifies quality assurance and performance management, while offering the ability for managers to gain visibility into a wide variety of metrics.

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Compliance and Risk Management

Similar to the above, analytics on customer conversations can provide documentation for compliance and help companies manage risk.

For example, if a company is required by law to provide certain information for specific products (like a disclaimer for a financial product), voice analytics can provide evidence of agents' compliance.

In addition, analytics can serve as a powerful risk management tool. By creating flags that monitor automatically for likely fraud, potential discrimination, and more, a speech analytics tool can help companies proactively manage risk.

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Analyze Contact Center Trends

Voice analytics can provide detailed information about customer issues, sentiment, satisfaction, and more by leveraging AI to analyze customer conversations.

By understanding exactly why customers are calling, companies can be proactive about resolving problems before they occur. In addition, contact centers might consider bolstering their bot flows to address common issues or creating automated internal processes to handle frequent requests more adeptly.

By utilizing the power of voice analytics, contact centers can identify trends as they occur and design intelligent interventions to improve customer experience.

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Identify and Reduce Churn

Voice analytics can do more than report simple metrics; it can also be configured to run complex operations, such as analyzing customer sentiment and intent and categorizing issues.

One of the most powerful ways to configure voice analytics is to leverage the various analyses it provides to automatically identify potential churn.

For example, suppose analysis demonstrates that customers most likely to churn are those who call at least three times in a week, demonstrate negative sentiment, and use profanity in at least one call. Voice analytics software can flag every case with these qualities so management can determine the best way to intervene and reduce potential churn.

Of course, in a real-world application, the analysis can be much more complex. But the power of voice analytics is that it helps to predict problems before they occur, so management can be proactive rather than reactive.

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Automate After-Call Work

The best voice analytics software leverages the power of AI to automatically summarize customer interactions. With powerful automated summaries, contact center agents can significantly reduce the amount of after-call work needed to document interactions.

More, automated summaries can provide both structured and free text data. Structured data is standardized data, typically tabular with rows and columns, that clearly define data attributes. Best-in-class voice analytics software will automatically summarize call into defined, structured data that's relevant to operations (like competitors mentioned, whether a certain promo was offered, which products were discussed, etc.)

In addition, best-in-class voice analytics software can also provide informative free text summaries of conversations, enabling managers to quickly understand conversations without needing to dive through a transcript or rely on hurried agent notes.

With automated summaries, agents can significantly reduce the time spent on after-call work, and contact centers can receive enhanced summaries with the information they need to be effective.

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How to Think About Voice Analytics Software

Voice analytics software is incredibly powerful. It provides management with a bird' s-eye view of the contact center's operations, quality assurance and performance management, helping to ensure compliance, aiding in identifying and reducing churn, and analyzing customer trends. Meanwhile, it can also improve agents' experiences by helping to automate summaries, enabling them to focus fully on the customer.

But how should contact centers think about purchasing the right voice analytics software for their company? Below, we highlight some key qualities speech analytics software should possess to provide as much insight as possible:

High-Quality Auto-Transcription

Low-accuracy transcription will hold back your voice analytics. Transcription is the core of any voice analytics solution. It is the technology that converts speech to text, so the software can accurately analyze the conversation.

But what is low accuracy?

The roughly 80% accuracy that many transcription solutions tout may sound like it is good enough. However, when 80% of the conversation is composed of simple words, it is the last 20% that holds the most critical, company-specific information you need to fill in the blanks.

If the solution you are considering is only capturing common words and phrases, you are going to miss out on the most valuable information, such as specific products, services, or policies mentioned, technical details, order or serial numbers, and more. With transcription, the real story is in the details. Make sure your solution captures the most important ones.

The right transcription solution will even let you identify and extract the most critical entities that you would like to glean from the transcript, such as order number, product name, date of transaction, dollar amount refunded/charged/credited, last four digits of account number, and competitor name.

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Ability to Customize Insights & Dashboards

Most software offers cookie-cutter templates and insights that force companies to adhere to their definitions of what useful information to glean from customer conversations.

But every business is different, with unique use cases, nomenclature, and key issues to track.

The best software should enable your contact center to customize the valuable insights they want to glean from customer interactions.

Custom configurability should also extend to dashboards. Don't settle for pre-configured dashboards that limit your ability to understand what really matters. You should be able to customize dashboards to reflect the particular priorities and needs of management.

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Generate Aggregated and Structured Data for Deep Analysis

Your summary data should guide corporate policymakers, provide insightful analytics, and be used to arm your agents. For it to truly do this, that data needs to be structured in the following categories for optimal use:

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Integrations

Finally, any voice analytics software you choose should be able to integrate with the systems you use every day to access and manage customer data.

At a basic level, the software of your choice should be integrated with your CRM of choice, so it can automatically push automated summary data to the CRM without the need for additional agent actions.

In addition, the software should be compatible with your voice data, including audio via SIP call recording (SIPREC) protocol from contact center session border controllers (SBCs) or from live media stream services offered by major cloud telephony providers such as Amazon Connect (Kinesis), Twilio (Media Streams), and Genesys Cloud CX (Audiohook).

The right software should also be available both on-prem or as software-as-a-service.

In short, the best software is one that can mold itself to your infrastructure instead of forcing you to adapt to it.

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Voice Analytics with ASAPP

Your conversation data is more valuable with ASAPP than any other solution. With our industry-leading structured data and enrichment capabilities, you will have more actionable insights and data discoverability than you thought possible - without any additional work for your agents. All this while dramatically reducing AHT, slashing ACW, and building a solid data foundation for all AI solutions to come.

ASAPP AutoSummary automates 100% of agents' after-call work. By combining human-readable and insights-ready summaries, AutoSummary can offload monotonous tasks for your agents and enable consistent, unbiased data across your contact center.

Beyond automatic and accurate summaries at scale, AutoSummary differentiates itself from other solutions by providing industry-leading capabilities around enrichment, structured data, configurable insights and dashboards, and flexible integrations.

In addition, ASAPP AutoTranscribe is the fastest and most accurate generative AI transcription solution purpose-built for CX. Only ASAPP delivers industry-leading speed and accuracy without compromising either.

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Want more? Read our exhaustive buyer’s guide.

If you are sold on the need for the most powerful and configurable voice analytics software, then you need to know what to look for (and what to look out for) when evaluating solutions. Just “good enough” isn’t good enough when you are setting the foundation of intelligence for your CX organization. 

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This guide will help you understand the features and capabilities you need to keep in mind when evaluating solutions and the reasons why they are mission-critical for your organization. Read on to ensure you aren’t adding to your technical debt and that you are building a solid foundation for your AI journey with the right solutions. 

Read the guide here.

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Introduction

Before diving into the solutions, let's take a moment to understand why ACW (After Call Work) matters in call center operations. ACW plays a crucial role in ensuring accurate record-keeping, compliance with regulations, and follow-up actions after customer interactions. It's the behind-the-scenes work that agents undertake to maintain high-quality customer service standards. 

While these tasks are essential, they pose significant challenges for agents and contact center operations due to their ongoing conflict with the goal of reducing Average Handle Time (AHT). This natural tension between lowering AHT—a critical performance metric—and thoroughly completing all necessary tasks during ACW requires a delicate balance. Managing these competing priorities creates ongoing difficulties in optimizing contact center efficiency and effectiveness.

To address this, let’s explore the unique challenges of ACW, highlight the main contributors to ACW, and showcase how advanced generative AI is alleviating the tension between ACW and AHT.  

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Defining Call Center ACW

When we talk about ACW, we're referring to the tasks that agents must complete post-call. These tasks include updating customer records, logging call details, completing forms, and ensuring compliance with company policies and regulations. 

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Main Contributors to Call Center ACW

Now, let's break down the main factors contributing to ACW:

• Agent Notes: Agent notes are vital for capturing essential details of customer interactions. These concise summaries encompass key information discussed during the call, including customer inquiries, issues addressed, and actions taken. By meticulously recording this information, agents ensure continuity in service and provide valuable insights for future interactions.
• Dispositioning: This involves categorizing customer interactions based on predefined criteria. Agents assign disposition codes to each call, indicating its outcome and resolution status. These codes facilitate performance tracking, trend analysis, and data-driven decision-making, contributing to overall operational efficiency.
• Compliance Measures: Compliance measures ensure adherence to regulatory requirements and internal policies. Agents must comply with data privacy regulations, industry standards, and organizational policies when handling customer information. Adhering to compliance protocols maintains trust and integrity in customer interactions.
• Sending Surveys and Questionnaires: Surveys, questionnaires, and follow-up emails solicit feedback from customers post-interaction, gauging sentiment, measuring satisfaction, and fostering engagement. These responses provide actionable insights for refining service delivery and improving agent performance. But, it's important to note that surveys often don't always provide the most accurate picture. Agents can often choose whether to send a follow-up survey, potentially skewing results, and even when surveys are sent to all customers, only the most pleased or displeased typically respond, which can lead to an inaccurate representation of overall customer sentiment.

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Common Challenges Go Beyond Time Constraints

One common, well-known challenge in ACW is the time-consuming nature of manual tasks, leading to delays in follow-up actions and reduced agent productivity. However, the challenges extend beyond time constraints and encompass various aspects of data management and analysis. Here are some additional challenges:

• Unstructured Handwritten Notes: Illegible or inconsistent handwritten notes hinder insights extraction.
• Data Silos: Disparate systems lead to data inconsistencies and hinder comprehensive analysis.
• Limited Insights: Traditional methods and low-performing summarization solutions may overlook nuances, limiting actionable insights.
• Manual Error and Compliance Risks: Manual processes increase the risk of errors and compliance breaches, impacting data integrity and regulatory compliance.

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Benefits and Applications 

Optimizing ACW processes can lead to significant benefits for call centers, including improved agent efficiency, enhanced customer satisfaction, and better compliance with regulatory requirements. By streamlining ACW tasks, call centers can deliver a more seamless and personalized customer experience.

Alleviating the Tension Between ACW and AHT
Advanced generative AI now has the power and nuance to effectively manage the tension between reducing Average Handle Time and completing essential After-Call Work tasks. These elevated automation capabilities raise the ceiling on what's possible with the majority of customer interactions. Generative AI can efficiently handle more complex customer issues with minimal human oversight, enabling agents to concentrate on addressing more intricate issues and engaging in higher-level tasks.

Generative AI's capacity to streamline processes not only reduces AHT but also ensures the thorough completion of crucial ACW tasks. This balanced solution optimizes contact center efficiency and effectiveness with proper escalation management, empowering agents to operate more efficiently with the support of generative AI.

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Introducing AutoSummary: Slashing the ACW Processes

ASAPP's summarization solution, AutoSummary, is making a big impact in streamlining post-call tasks and creating rich information for analytics and operational enhancements. AutoSummary automates the summarization of customer interactions, reduces ACW processes, provides real-time operational feedback, and empowers agents to work more efficiently while solving customer problems faster.

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Key Benefits of AutoSummary

• Real-time Summarization: By analyzing interactions in real-time, AutoSummary quickly extracts key insights as customer interactions unfold, enabling prompt response and improved service delivery.
• Seamless Integration: Summarized information seamlessly integrates with the call center's CRM system, eliminating manual data entry and ensuring smooth information flow across the organization.
• Customization Options: AutoSummary offers extensive customization options, allowing call centers to tailor the summarization process to their specific needs, whether by prioritizing specific metrics or keywords or customizing summarization criteria based on industry-specific requirements.
• Scalability and Reliability: Designed for scalability, AutoSummary ensures consistent and dependable performance even amidst fluctuating call volumes or evolving business needs, providing uninterrupted support for critical ACW tasks.
• Unprecedented Business Insights: AutoSummary enables you to understand why your customers are calling, giving you the ability to identify automation opportunities, analyze promotion and cross-sell effectiveness, and respond to anomaly detection.

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Conclusion

Call Center ACW (After Call Work) is a critical aspect of call center operations, and understanding it is essential for success. With the right tools and knowledge, call centers can streamline ACW processes, improve agent efficiency, and deliver exceptional customer experiences. 

Ready to dive deeper into the world of ACW and learn how to optimize your call center operations? Check out our eBook, “The Modern CX Guide to Summaries.” In it, we explore advanced strategies for reducing ACW and improving overall efficiency and unpack how modern solutions to summaries and dispositioning can slash ACW.

Read the eBook

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What is AHT and How Does It Impact Your Business?

Author Name: LANE HARTMAN

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Summary

Understand AHT Meaning (Average Handle Time) and its impact on contact center efficiency and customer satisfaction. Learn how to calculate AHT and how to reduce it.

Average Handle Time (AHT) is a key contact center metric. In short, AHT measures the average time of a customer interaction.

AHT provides crucial information about contact center efficiency, staffing needs, and customer experience. And as such, AHT is one of the essential metrics for contact centers to track.

In this blog post, we define AHT and show you how to calculate it. Then, we discuss six impacts reducing AHT can have on your organization. Finally, we provide recommendations for how to reduce AHT while optimizing customer experience.

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What is AHT?

Average Handle Time (AHT) is a core call center KPI that measures the average time for an agent to handle a customer interaction from start to finish.

AHT encompasses the entirety of an agent's interaction with a customer issue, including total talk time, hold time, and after-call work (ACW).

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How to Calculate AHT

The standard formula to calculate AHT is adding total talk time, hold time, and after-call work and dividing by the total number of interactions.

Talk Time: The time an agent spends actively engaging with a customer

Hold Time: The time an agent spends while a customer is on hold

After-Call Work: The time spent on work that needs to be completed after a call or interaction before the next interaction can start.

In formula form, AHT is calculated as:

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Why is AHT Important?

Average Handle Time is one of the most important KPIs for any contact center to track because it impacts both contact center efficiency and customer experience.

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Contact Center Efficiency

Average Handle Time distills the time spent across interactions and averages it by the amount of interactions. This one metric impacts the entire contact center organization. Understand AHT and understand how to do the following:

Determine Staffing Loads

Understand how many agents are necessary to handle expected case loads. The lower your AHT, the higher the total number of calls your agents can handle in a given time period.

Benchmark Within the Industry

Compare your AHT to standards across your industry to understand your relative performance.

Benchmark Internal Improvement

Measure your AHT over time to understand if your contact center is more efficiently handling issues, satisfying customers, and effectively utilizing resources.

Understand Individual Performance

AHT can be helpful for analyzing both overall contact center performance and individual performance. While other call center metrics, like customer satisfaction and containment rate are essential to contextualizing the entire picture, AHT can be helpful to understand an agent's capacity and efficiency.

Analyze Gaps

If your AHT is higher than expected, it can be helpful to analyze the individual components of the metric (talk time, hold time, after-call work) to determine where your contact center might need the most improvement.

With this information in hand, you can invest in additional training or update your technology stack with modern software that improves agent efficiency and reduces hold time and after-call work.

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Customer Experience

Customers want their issues to be resolved quickly and efficiently. AHT is one of several metrics that helps inform management about how quickly customer issues are being handled. A low AHT implies the following for customers:

Shorter Wait Times

Nobody likes waiting on hold. Reducing AHT by reducing the time customers spend on hold or in a transfer improves customer experience.

Faster Issue Resolution

AHT is a proxy for the time it takes to resolve a given issue. Reducing AHT means customer's issues are being resolved more quickly.

Heightened Customer Satisfaction

Shorter wait times and faster issue resolution contribute to greater customer satisfaction. Work to improve AHT to ensure your customers are having their issues meaningfully resolved quickly.

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How to Reduce AHT

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Monitor AHT

What you pay attention to grows.

If you want to reduce AHT, the first step is to monitor AHT.

Then, set realistic goals, supported by process and technology changes, to reduce AHT over time.

Modern CX software, like ASAPP, can help you automatically track metrics like AHT over time.
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Enhance Self-Service with Modern Virtual Agents

The most efficient way to reduce AHT is to help customers resolve an issue without needing an agent’s support.

But true self-service is more than simply putting a manual’s worth of information online and making it searchable through a help function.

Modern self-service design should leverage modern virtual agents that go beyond simplistic chatbots to handle a broader array of issues, provide more realistic responses, and solve more customer issues before an agent ever needs to get involved.

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Augment Agents with the Information They Need

To reduce AHT, contact centers should provide agents with the tools they need to facilitate resolutions quickly and meaningfully.

Digital features that can augment agents include:

• Surfacing Meaningful Information for Agents Automatically
• Automatic Processes Agents Can Leverage to Save Time
• Concurrency Management Tools
• Auto Complete and Auto Suggestion Functions to Help Finish Thoughts Faster
• Automated Workflows to Quicken Common Processes
• Automated Check-Ins to Ensure Clients in the Queue are Still Present

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Implement Auto Summaries to Free Up Time

One of the largest contributors to talk time and after-call work is time spent taking notes and writing call summaries.

The time required to take these notes can often be around 10% of the actual call duration and is one of the greatest contributors to high AHT.

With such a large percentage of time consumed by agents taking notes, contact centers are put between a rock and a hard place. If they press agents to reduce their ACW by taking notes during a phone call, it can often lead to prolonged pauses that reduce customer satisfaction. On the other hand, if they ask agents to reduce overall time spent taking notes, it can lead to gaps in summaries that might impact other agents down the road and make it difficult for supervisors to have good visibility.

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In short, traditionally, AHT has been difficult to reduce without reducing customer satisfaction. In particular, attempts to reduce ACW by shifting notes into phone calls can either increase talk time or reduce customer satisfaction to an extent that can undermine the initiative altogether.

But now, with modern CX technology, like ASAPP, contact centers can both reduce AHT and increase customer satisfaction.

ASAPP AutoSummary leverages AI to automatically take notes and summarize calls, so agents can focus on the clients. Importantly, ASAPP AutoSummary creates accurate, data-rich, and easily accessible summaries that contact centers and their agents can trust.

With AutoSummary, companies can significantly reduce their AHT and ACW while enhancing the customer experience. In fact, in one case study, ASAPP AutoSummary reduced after-call work from 10% of call duration to just 3% of call duration. 

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Interested in Learning More About How New Technology is Impacting CX Summaries?

Struggling with agent notes, lengthy after-call work, questionable questionnaires, cumbersome compliance checklists, lack of context for calls, and a lack of definitive understanding of WHY your customers are calling you?

The answer to all of these struggles is the ability to have good summaries at scale. 

Unfortunately, you are likely not getting all your business needs from them. Worse yet, they distract your agents from customer conversations and add crucial time to your AHT. This eBook is here to help.

This eBook will cover the best practices and technologies that will deliver the value that good summaries promise without undue agent distraction. Readers will learn:

• What good summaries look like and contain.
• Best practices for summarization - regardless of your methods.
• How new technologies, like generative AI, can both eliminate the need for manual entry and enrich summaries with all necessary vital details.
• What to look for when evaluating summarization technologies.
• How to ensure you are collecting valuable business insights from your summarization efforts.

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Download eBook Now

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Consider Leveraging Generative Agents

The future is here.

ASAPP GenerativeAgent™, AI-powered chat and voice agents, can empathize with customers, understand complex problems, and access internal APIs and processes to provide helpful answers or initiate actions to solve user problems (with company approval, of course.)

Use GenerativeAgent™ to resolve issues before an agent needs to become involved. GenerativeAgent™ can substantially reduce AHT by drastically reducing the time agents spend on interactions and minimizing after-call work. By enabling greater concurrency, Generative Agents increase the number of calls handled in a given period.

Interested in how GenerativeAgent™ could transform CX? Read more about GenerativeAgent™ here.

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Reducing AHT with ASAPP

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Who is ASAPP?

ASAPP is the AI-native software for contact centers, and ASAPP exists to end bad customer service. 

We help customer service leaders unlock their full value by minimizing costs & inefficiencies, improving agent compliance & productivity, and surfacing actionable insights while helping you deliver a great customer experience.

Our customers are large enterprises who care deeply about leveraging AI to transform CX by delivering unprecedented cost savings and improving customer satisfaction.

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What do we make?

We make a full suite of AI-native solutions designed specifically to help companies reduce AHT and improve customer experience.

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Conclusion:

Mastering Average Handle Time (AHT) is essential to operate efficient contact centers and enhance customer experience. By understanding AHT and implementing key strategies to reduce it, contact centers can drive efficiency and delight customers.

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Want to learn more?

Transcription and Summarization solutions are not only the foundation of CX intelligence; they are a foundational capability for your AI strategy as well, so you better choose the right ones.

This guide is here to help.

Not all transcription and summarization solutions are built the same. While some may seem “good enough,” those same products can introduce crippling technological debt, may hamper the effectiveness of your other technology solutions, and may fail to surface the discoverable and actionable insights that you were hoping to achieve.

If you are serious about finding the right transcription and/or summarization solution to reduce AHT, this guide will help you understand:

• Exactly what features and capabilities you need in a transcription and summarization solution, and why they are important
• The specific business value the right solution will bring to your organization - from foundational capabilities to reduced AHT and improved customer satisfaction
• A clear and actionable buyer’s checklist to help you with your search

Download eBook Now

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GenerativeAgent™ can substantially reduce AHT by drastically reducing time agents spend on interactions and minimizing after-call work. 

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