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A common trait of people working in technology is a desire to be able to cleanly categorize information, data, issues, etc. To be able to delineate between one bucket and another. We see this manifest in how companies think about customer conversations—should a conversation be automated? Yes or no? Does the customer need a live engagement for the entirety of the conversation vs. more asynchronous? Yes or no? But customer conversations aren’t actually so clearcut, and the needs don’t stay consistent as conversations and customer journeys go on.

At ASAPP, we have developed a fairly unique way of thinking about conversations. Rather than relying on a single intent to determine how the entire conversation should be handled, let’s look at each turn of the conversation to better inform what the next step should be. Every request has different needs, which change considerably based on various factors.

The above graph provides a nice illustration of how we can think about the issues. Along the y-axis, you have more complex vs. more simple interactions. At the bottom, you have conversations that are well served to be fully automated without any agent intervention. On the top, you have the opposite—conversations that benefit from having a skilled agent along for the ride. But those are extremes, most conversations fall in between the two, they require some human involvement and a bunch of automation. By thinking in very binary terms, automated or not automated, you lose out on all of the opportunities to reduce agent workload on a conversation by 20%, by 50%, by 75%. By treating each piece of a conversation as worthy of its own classification and diagnosis, you bring a lot of efficiency back into your business without risking frustrating your customer.

Now the x-axis, here we’re thinking about how routine vs. how urgent the issue is. It’s easy to think “we can serve customers asynchronously, they send an SMS. We get back to them when we get back to them, just like customers are used to interacting with friends and family.” But that leaves out a very important part of the picture. While many conversations are routine and can benefit from more asynchronous interactions, allowing companies to load balance workload on agents, there are cases where customers need urgent help—make a change to a flight about to take off, help resolve billing issue just before superbowl kick off, and in those cases, you don’t want to risk a customer not getting a response in time, especially not when so many conversions didn’t need that live resolution. Then there are cases just as with complexity vs. simplicity that are in between—an initial response might need help from a live agent, cutting off access to a bank account in the case of fraud, but the follow-ups and resolutions are well-served for asynchronous communication.

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Customer interactions require different levels of attention. From simple routine issues to urgent complex requests, organizations must be able to seamlessly support every type of need, in the most efficient way possible, using the right mix of agent and automation.

Rachel Knaster

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In addition to the content of what the customer is asking about, it’s important to take in every parameter you know about them and the context surrounding their issue. This goes far beyond simple intent classification. In order to determine the type of service customers need, you need to look at the entire weight of their requests. The best way to think about it is along axes of complexity and urgency.

Based on where they fall on this graph, customer interactions require different levels of attention. From simple routine issues (C) to urgent complex requests (A), organizations must be able to seamlessly support every type of need, in the most efficient way possible, using the right mix of agent and automation.

Is the customer’s question simple to solve? Then let’s automate it.

Is it complicated? Then let’s connect them with our frontline and have those agents do what they do best.

Is the issue one that can wait for an answer and more asynchronous by nature? Then let’s treat it that way.

Or is a customer’s flight about to take off and they need help? Let’s immediately connect them with someone.

These are fundamental questions contact centers should consider with every incoming request. There’s “no one size fits all” when it comes to CX strategy. Every interaction requires a different approach. so you can maximize throughput while keeping each customer satisfied.

Consider the graph above. Each quadrant represents a different category of request with its own unique considerations. In each case, the right mixture of live agent and AI, synchronous and asynchronous support can help solve the issue in the most optimal way possible. Here’s the ideal for each:

1. Complex, urgent
2. Agent-based, synchronous
3. Low agent concurrency
4. Automate part of agent workload
5. Opportunity to mix voice and digital in same live conversation for faster resolution
6. Complex, routine
7. Agent-based, asynchronous
8. Automate part of agent workload
9. High agent concurrency
10. Handoff to phone if required
11. Simple, routine
12. Fully automated interaction
13. Low cost to serve
14. Simple, urgent
15. Fully automated, with fast escalation to live agent
16. Complete history (context) of interaction required for agent
17. Medium-high agent concurrency
18. Automate part of agent workload
19. Opportunity to mix voice and digital in same live conversation for faster resolution

While companies might prefer everything be automated or self service, that’s not always the most efficient way to solve an issue. Of course, neither is having your agents occupied addressing routine tasks all day. What’s needed is the right balance between the two—AI enhancing human performance so agents can handle more tasks and fully concentrate on those that need it. This is where more sophisticated machine learning offers incredible value.

There is an opportunity for AI to assist in every interaction, whether it’s handling the entire request or just part of the workload. While typically considered most helpful for automating simple tasks, the right AI models will improve over time, learning from customer interactions to assist with increasingly complex issues.

A single conversation can also become more simple or complex as it evolves, calling for changing levels of agent attention. For instance, now that the primary issue has been resolved, can the rest of this interaction be automated? Or has the issue escalated from automation to the need for an agent? Instant intent analysis provided by machine learning can help identify these occurrences to further optimize agent concurrency.

The truth is, sometimes the best thing is to have an agent live with just one customer, and sometimes it’s to have them handling multiple conversations. What’s important is for each organization to recognize the nuance and to build flexible solutions that adapt for the best outcomes to ensure operational performance is being enhanced, while never compromising on a personalized and connected experience for customers.

AI systems have additional considerations over traditional software. A key difference is in the maintenance cost. Most of the cost of an AI system happens after the code has been deployed. ML models degrade over time without ongoing investment in data and hyperparameter tuning.

The cost structure of AI systems are directly affected by these design decisions; the level of service, and improvement over time are categorically different across different levels. Knowing the level of the AI system can help practitioners and customers predict how the system will change over time – whether it will continuously improve, remain the same, or even degrade.
Levels of AI Systems start at traditional software (Level 0) and progress up to fully Intelligent software (Level 4). Systems at Level 4 essentially maintain and improve on their own – they require negligible work. At ASAPP we call Level 4 AI Native®.

Moving up a level has trade-offs for practitioners and customers. For example, moving from Level 1 to Level 2 reduces ongoing data requirements and customization work, but introduces a self-reinforcing bias problem that could cause the system to degrade over time. Choosing to move up a level requires practitioners to recognize the new challenges, and the actions to take in designing an AI system.

While there are significant benefits in scalability (and typically performance/robustness/etc) in moving up levels, it’s important to say that most systems are best designed at Level 0 or Level 1. These levels are the most predictable: performance should remain roughly stable over time, and there are obvious mechanisms to improve performance (e.g. for Level 1, add more annotated training data).

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AI Levels

Designing AI systems is different from traditional software development, because the behavior of the system is learned – and can potentially change over time once deployed. When practitioners build AI systems, it can be useful to talk about their “level”, just like SAE has levels for self-driving cars.

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Moving up a level has trade-offs for practitioners and customers. This requires practitioners to recognize the new challenges, and the actions to take in designing an AI system

Michael Griffiths

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Level 0: Deterministic

‍No required training data, no required testing data

Algorithms that involve no learning (e.g. adapting parameters to data) are at level zero.
The great benefit of level 0 (traditional algorithms in computer science) is that they are very reliable and, if you solve the problem, can be shown to be the optimal solution. If you can solve a problem at level 0 it’s hard to beat. In some respect, all algorithms–even sorting algorithms (like binary search) – are “adaptive” to the data. We do not generally consider sorting algorithms to be “learning”. Learning involves memory–the system changing how it behaves in the future, based on what it’s learned in the past.

However, some problems defy a pre-specified algorithmic solution. The downside is that for problems that defy human understanding (either once, or in number) it can be difficult to perform well (e.g. speech to text, translation, image recognition, utterance suggestion, etc.).

Examples:

• Luhn Algorithm for credit card validation
• Regex-based systems (e.g. simple redaction systems for credit card numbers).
• Information retrieval algorithms like TFIDF retrieval or BM25.
• Dictionary-based spell correction.

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Note: In some cases, there can be a small number of parameters to tune. For example, ElasticSearch provides the ability to modify BM25 parameters. We can regard these as tuning parameters, i.e. set and forget. This is a blurry line.

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Level 1: Learned‍

Static training data, static testing data

Systems where you train the model in an offline setting and deploy to production with “frozen” weights. There may be an updating cadence to the model (e.g. adding more annotated data), but the environment the model operates in does not affect the model.

The benefit of level 1 is that you can learn and deploy any function at the modest cost of some training data. This is a great place to experiment with different types of solutions. And, for problems with common elements (e.g. speech recognition) you can benefit from diminishing marginal costs.

The downside is that customization to a single use case is linear in their number: you need to curate training data for each use case. And that can change over time, so you need to continuously add annotations to preserve performance. This cost can be hard to bear.

Examples:

• Custom text classification models
• Speech to text (acoustic model)

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Level 2: Self-learning

Dynamic + static training data, static testing data

Systems that use training data generated from the system for the model to improve. In some cases, the data generation is independent of the model (so we expect increasing model performance over time as more data is added); in other cases, the model intervening can reinforce model biases and performance can get worse over time. To eliminate the chance of reinforcing biases, practitioners need to evaluate new models on static (potentially annotated) data sets.

Level 2 is great because performance seems to improve over time for free. The downside is that, left unattended, the system can get worse – it may not be consistent in getting better with more data. The other limitation is that some systems at level two might have limited capacity to improve as they essentially feed on themselves (generating their own training data); addressing this bias can be challenging.

Examples:

• Naive spam filters
• Common speech to text models (language model)

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Level 3: Autonomous (or self-correcting)

Dynamic training data, dynamic test data

Systems that both alter human behavior (e.g. recommend an action and let the user opt-in) and learn directly from that behavior, including how the systems’ choice changes the user behavior. Moving from Level 2 to 3 potentially represents a big increase in system reliability and total achievable performance.

Level 3 is great because it can consistently get better over time. However, it is more complex: it might require truly staggering amounts of data, or a very carefully designed setup, to do better than simpler systems; its ability to adapt to the environment also makes it very hard to debug. It is also possible to have truly catastrophic feedback loops. For example, a human corrects an email spam filter – however, because the human can only ever correct misclassifications that the system made, it learns that all its predictions are wrong and inverts its own predictions.

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Level 4: Intelligent (or globally optimizing)

Dynamic training data, dynamic test data, dynamic goal

Systems that both dynamically interact with an environment and globally optimizes (e.g. towards some set of downstream objectives), e.g. facilitating an agent while optimizing for AHT and CSAT, or optimizing directly for profit. For example, an AutoCompose system that optimizes for the best series of clicks to optimize the conversation.

Level 4 can be very attractive. However, it is not always obvious how to get there, and unless carefully designed, these systems can optimize towards degenerate solutions. Aiming them at the right problem, shaping the reward, and auditing its behavior are large and non-trivial tasks.

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Why consider levels?

Designing and building AI systems is difficult. A core part of that difficulty is understanding how they change over time (or don’t change!): how the performance, and maintenance cost, of the system will develop.

In general, there is increasing value as you move up levels, e.g. one goal might be to move a system operating at Level 1 to be at Level 2 – but complexity (and cost) of system build also increases as levels go up. It can make a lot of sense to start with a novel feature at a “low” level, where the system behavior is well understood, and progressively increase the level – as understanding the failure cases of the system becomes more difficult as the level increases.

The focus should be on learning about the problem and the solution space. Lower levels are more consistent and can be much better avenues to explore possible solutions than higher levels, whose cost and variability in performance can be large hindrances.
This set of levels provides some core breakpoints for how different AI systems can behave. Employing these levels – and making trade-offs between levels – can help provide a shorthand for differences post-deployment.

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Matrix Layout

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In recent years, research efforts in natural language processing and computer vision have worked to improve the efficiency of pre-trained models to avoid the financial and environmental costs associated with training and fine-tuning them. For whatever reason, we have not seen such efforts in speech. In addition to saving costs associated with more efficient training of pre-trained models, for speech, efficiency gains could also mean greater performance for similar inference times.

Today, Wav2vec 2.0 (W2V2) is arguably the most popular approach for using self-supervised training in speech. It has received a lot of attention and follow-up works for applying pre-trained W2V2 models to various downstream applications including speech-to-text translation (Wang et al., 2021) and named entity recognition (Shon et al., 2021). Yet, we hypothesize that there are many sub-optimal design choices in the model architecture that make it relatively inefficient. To justify this hypothesis, we conducted a series of experiments on different components of the W2V2 model architecture and exposed the performance-efficiency tradeoff of the W2V2 model design space. Higher performance (lower word error rate in ASR) requires a large pre-trained model and comes with lower efficiency (inference speed). Can we achieve a better tradeoff (similar performance with higher inference speed)?

What do we propose instead? A more efficient pre-trained model that also achieves better performance through its efficiency gains.

Squeezed and Efficient Wav2vec (SEW)

Based on our observations, we propose SEW (Squeezed and Efficient Wav2vec) and SEW-D (SEW with Disentangled attention) which can achieve a much better performance-efficiency tradeoff—with 1.9x speedup during inference, our smaller SEW-D-mid achieves 13.5% WERR (word error rate reduction) compared to W2V2-base on academic datasets. Our larger SEW-D-base+ model performs close to W2V2-large while operating at the same speed as W2V2-base. It only takes 1/4 of the training epochs to outperform W2V2-base which significantly reduces the pre-training cost.

SEW differs from conventional W2V2 models in three major modifications.

First, we introduce a compact waveform feature extractor which allocates the computation across layers more evenly. This makes the model faster without sacrificing performance.

1. Second, we propose a “squeeze context network” which downsamples the audio sequence and reduces the computation and memory usage.
2. This allows us to use a larger model without sacrificing inference speed.
3. Third, we introduce MLP predictor heads during pre-training which improve the performance without any overhead in the downstream application since they will be discarded after pre-training.

SEW-D further replaces the normal self-attention with disentangled self-attention proposed in DeBERTa (He et al., 2020) which achieves better performance with half of the number of parameters and a significant reduction in both inference time and memory footprint.

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The SEW speech models by ASAPP are faster and require less memory, without sacrificing recognition quality. The architecture improvements proposed by the team are very easy to apply to other existing Wav2Vec-based models – essentially granting performance gains for free in applications such as automatic speech recognition, speaker identification, intent classification, and emotion recognition.

Anton Lozhkov

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Why it matters

These pre-trained models open the door for cost savings and/or performance gains for a number of downstream models in automatic speech recognition, speaker identification, intent classification, emotion recognition, sentiment analysis and named entity recognition. The speedup of a pre-trained model can be directly transferred to the downstream models. Because the pre-trained model is smaller and faster, the fine-tuned downstream model is also smaller and faster. These efficiency gains not only reduce their training/fine-tuning time but also the actual observed latency in products. Conversational AI systems using the SEW pre-trained models will be able to better detect what consumers are saying, who’s saying what, how they feel, and to provide faster response times.

“The SEW speech models by ASAPP are faster and require less memory, without sacrificing recognition quality,” explains Anton Lozhkov, Machine Learning Engineer at Hugging Face. “The architecture improvements proposed by the team are very easy to apply to other existing Wav2Vec-based models – essentially granting performance gains for free in applications such as automatic speech recognition, speaker identification, intent classification, and emotion recognition.”

Want to utilize the pre-trained models from ASAPP? See our paper and open source code for more details. Moreover, our pre-trained models are now available in Hugging Face’s transformers library and model hub. Our paper is accepted and will appear at ICASSP 2022. Please feel free to reach out to the authors in the post-session during the conference.

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Across the globe thousands of customer care agents are starting their jobs today. For most, months of training lay ahead of them as they absorb a parade of policies, procedures, programs, and product details.

The lengthy onboarding process is a costly investment for both agent and employer because it’s typically time spent siphoning away your most seasoned agents from where they’re needed most and instead using them for training. Furthermore, when you consider the fact that agent churn can reach as high as 100%, you’ll soon find that an alarming percentage of an agent’s tenure is training, and not customer care. That’s why getting up to speed fast matters, and the two most-cited pain points are consistently the tools and the subject matter.

Must enterprise mean complicated?

Easily one of the biggest barriers for agents to achieve proficiency is the legacy CRM and chat software that sits in front of them. It’s typically a fossilized enterprise UI with little consideration for agent experience – not to mention customer experience.

Historically, there has been a perceived conflict between designing an enterprise UI that’s both performant and intuitive. The two were thought to be mutually exclusive because in an effort to maximize efficiency, speed, and accuracy, designers would emphasize information density, keyboard commands, hidden shortcuts, and sequences that created a painfully steep learning curve.

You’ll find this trend in professional tools and interfaces across finance, customer care, aviation, and beyond. While these interfaces do emphasize clarity, contrast, predictability, and priority they all require weeks or even months of training to be proficient.

A focus on the familiar

The ASAPP Product Design Team faced similar challenges as our Digital Interactions application grew to support a wide range of augmentation features. The powerful agent desk UI incorporates dozens of ML-driven features designed to help converse, investigate, solve, document—and service multiple customers simultaneously.

On the one hand, we have the opportunity and privilege of designing for a captive audience: a professional user. In a performance-based setting, you’d be correct in assuming that we’d focus on keyboard shortcuts, shortcodes, intelligent search, summarization, minimizing clicks – all of those tricks that, once learned, provide crucial efficiency gains. However, we also have to be careful to not alienate the novice user with a steep learning curve of advanced or hidden features, particularly when we consider the high cost of onboarding due to turnover. That means our application needs to be easy to onboard with a goal of being proficient on day 1 with not just the UI, but also the subject matter.

To minimize agent onboarding time we took inspiration from familiar consumer-grade UI.

Brad Stell

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In an effort to minimize agent onboarding time, the Design team focused on the familiar. We took inspiration from consumer-grade UI and affordances from phones, gaming, dashboards, alarm clocks, and more. The goal was to make new agents who sit down in front of our agent desk feel like they’ve used it before, because in many ways, they had. Not what you’d expect when you think of enterprise software.

Progressive Timers

Phrase AutoComplete

In-app onboarding

Automated workflow

Beyond the UI design, the team also focused on an interactive program of onboarding prompts and tasks that gradually familiarize the agent with the more advanced capabilities. This approach of progressive disclosure takes advantage of engagement-based tool-tips, shortcuts, in-app coaching, and personalization features.

The what, not just the how

Knowing the tools is only half the battle for new agents. They still need to become subject matter experts if they are to become truly proficient. That’s why ASAPP invests heavily in augmentation features that are designed to help even the most novice of agents to become seasoned experts.

For example, ASAPP jumpstarts an agent’s experience with AutoCompose, which recommends responses that are known to be effective in that specific situation – often sourced from the most trusted and successful agents.

In addition, Knowledge Base recommendations provide agents timely reference content to help troubleshoot issues they’re unfamiliar with. It’s an ever-listening assistant, instantly putting resources at their fingertips. Both features draw from machine learning the actions and experience of the very best agents, quickly making new agents as effective as the most tenured.

An onboarding ally

In combining an intuitive user experience with intelligent recommendations, we’ve created an experience that is designed to make agents successful, faster. What’s more, when combined with an interactive, personalized onboarding program, we begin to shift much of the training from in-the-classroom to on-the-job, saving both time and money.

Every day, ASAPP customers handle tens of thousands of inquiries to their contact centers. We have a pretty solid understanding of what the majority of these requests are about. Most are routine issues that agents have experience dealing with. However, on any given day, there is the risk that a customer may be slammed with hundreds or thousands of calls about something out of the ordinary. These events might range from a popular pay-per-view fight, to service outages, to a UI change confusing half the user base. On those days, ASAPP anomaly detection is there to help.

Every conversation that comes into a content center and enters our infrastructure contains a problem statement somewhere within. Problem statements help direct agents towards the caller’s needs, and help contact center managers understand broader analytics for key traffic drivers. In messaging channels, the user is asked to input their problem directly. For voice channels, ASAPP speech-to-text transcription feeds an NLP extraction model that pulls the problem statement out.

We’ve seen a wide variety of problem statements over the years. Most of the time, our augmentation methods help agents swiftly resolve standard issues. But what happens when something totally new appears? When the unexpected causes an influx of inquiries about an unfamiliar issue? How do we quickly identify this new behavior and extract the conversations so our customers can more effectively address them? How do we know if our system is recognizing the right changes in language? And how do we measure the impact of these new behaviors?

The answer we found was to train a model to distinguish the problem statements coming in now from those that have appeared before, comparing the current stream of requests to the history of past requests. On an average day, the data stream coming in looks much like the historical data, full of similar inquiries about common issues. This makes it difficult to train a model to differentiate the current stream of data from the past.

But on an interesting day, the language in our data stream looks significantly different, containing words or phrases that don’t normally appear.

This is where our trained model becomes highly confident it can distinguish the current stream of data from our historical record. Moreover, on these days, the new problem statements typically reflect a single shared issue and include similar language on that topic. This helps explain exactly what issue caused users to hammer a customer’s contact center with traffic in a clearly defined set of words.

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Our models provide actionable intelligence by directly surfacing customer complaints in real time, and even can measure the impact of a problem as it is occurring. All of this is to help contact center teams better recognize and react to new customer behaviors as they develop.

Sam Altschul, PhD

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In the process of training a model to distinguish current behavior from past, we’ve also gotten:

• a list of high confidence problem statements representing the new behavior in the data
• a model for extracting whatever topic set off the alarm

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That new dataset can be used to measure how much traffic is due to the novel problem we discovered. Plus, we can train a classifier to detect the problem the next time it occurs. The trained model can be used to extract historical volumes to see if this issue has happened in the past. It can also be used on incoming data to easily identify this new behavior in the future. Moreover, the models we trained are naturally interpretable, yielding key topic words that can be used in SQL queries for easy analysis.

The system ASAPP built around this new technology operates on a minute time scale. Our models provide actionable intelligence by directly surfacing customer complaints in real time, and even can measure the impact of a problem as it is occuring. All of this is to help contact center teams better recognize and react to new customer behaviors as they develop. Equipped with anomaly detection, CX organizations can more efficiently address unexpected events, then analyze these unique situations to understand and prepare for them.

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The past two years have fundamentally and permanently changed how we think about and approach customer service. Faced with an ongoing pandemic, the shift to remote work, supply chain disruptions, and the “Great Resignation,” companies have been forced to adapt and evolve at an incredible pace, re-examining the role of customer service and the critical link it provides between their customers and their brand.

As we move into the new year, brands will need to continue to advance every aspect of CX to stay competitive, as customer expectations (and frustrations) reach an all-time high. But how to best serve customers, grow revenue, and empower workers, all while keeping costs down? Here are four things forward-thinking CX organizations are doing right, right now.

Elevating the Frontline Agent Experience

Contact center agents represent the voice of your brand. It has always been one of the most difficult and important jobs in any company, and an underserved part of the customer + brand ecosystem. But in the wake of the Great Resignation, there is an even more critical need for companies to focus on agent and employee satisfaction.

It has become increasingly difficult to attract and retain talent in the current environment. With demand at an all-time high, agents are migrating to companies that value them and prioritize employee well-being, flexibility, and engagement. To reduce churn and maintain a motivated workforce, leading companies are actively working to make agents’ jobs better.

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If you want to provide a great customer experience, start with the voice of your brand—your agents. When we look at the fundamental forces at play in evolving CX, it is essential to focus on the people at the center of the conversation.

Michael Lawder

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It starts with focusing attention and resources on training that engages employees from day one and accelerates time to proficiency. Employing AI to improve onboarding and coaching is one way forward-thinking companies are flattening the learning curve. Advanced machine learning can now analyze every word and action taken by top agents in real time, compiling best practices to guide others on how to handle any customer request. With this streamlined approach, new agents get up to speed faster while building competence and confidence.

In addition to training, agents need technology that supports, not overwhelms. Many today are faced with a jumbled stack of applications and processes that can make completing tasks difficult. Companies are starting to be more mindful of the entire journey agents take to fulfill each customer request. With that knowledge, they can identify where to engage automation to reduce tedious tasks and streamline workflows, enabling agents to concentrate on what they actually want to do—help customers.

Making Customer Experience a Feature

There are more ways to engage with customers than ever before. Due to the global pandemic, digital channels more quickly became the norm, and companies are making themselves available on a growing number of platforms (e.g., Apple Messages for Business, Google Business Messaging, SMS, and more). Most consumers now expect unlimited access to their favorite brands through seamless multichannel service. The companies succeeding in CX are those that continue to move aggressively towards these digital and asynchronous experiences. Creating a cohesive cross-channel experience is key to both automated and human-driven modalities, allowing customers to engage wherever and whenever, while supplying agents with critical context to deliver personal and relevant experiences.

To maintain a competitive advantage, brands must provide memorable, elevated service that builds loyalty (and increased lifetime value). This means experiences that bring customers back again and again, making them fans of the company based not only on the product purchased, but how the company took care of them during a “moment of truth.” We are seeing more companies emphasize experience, now fully aware of its potential to differentiate their brand—and possibly turn their cost center into a profit center.

Using Data to Fuel CX Innovation

The growing move to digital, increase in smart products, and evolution of tech to capture customer behavior all mean more data for companies to digest. Faced with an almost overwhelming volume of customer and operational data, leading brands are examining how best to use this information to both improve processes and deliver more effective service. Those at the forefront have figured out how to make the data do the work for them, using AI-powered analytics to inform everything from marketing strategy to agent onboarding.

Customers today expect hyper-personalized interactions with brands, and are more likely to respond to communications that understand their specific interests and history with the company. CX teams can now employ AI to learn from every interaction with an individual, remembering and adapting to their personal preferences to tailor engagement efforts. To truly improve business outcomes, organizations have adopted platforms that make full use of AI to feed analytics and gain actionable insights—in real time. And thanks to the availability of data on past interactions along with advancements in natural language processing, companies can even engage AI services that simulate customer scenarios to train agents in more dynamic, real-world settings before they get on an actual call or chat.

Building a Continuous Cycle of Automation

As noted, one trend increasingly evident in CX is the growing confidence in and adoption of AI. The key here is finding the right balance between automation and humanity. Customers still want human interaction, perhaps more than ever after years of less than optimal chatbot conversations. While bots continue to serve a role, there is a push now to find smarter ways to engage automation for both customers and agents.

What is commonly referred to as “agent assist” has become a core capability. But this basic feature is only the beginning of how CX teams can use AI to achieve significant productivity gains and better customer experiences. The most advanced companies are exploring ways to integrate AI-driven capabilities throughout agent workflows. This unlocks the ability to not only dramatically streamline processes, but power more and more automation using the knowledge gained through machine learning. As the ML models study each interaction, they continually feed improvements to the automated experience, identifying opportunities to both further support agents and enhance self-service for customers.

Looking to the Future Now

Today’s leading companies understand that to compete they must focus on the entire ecosystem of the customer journey—and that starts with frontline agents. Ultimately, we know:

• Happy, engaged agents provide superior customer service
• Elevated experiences are the key to winning customer loyalty
• Experiential data fuels analytics to produce valuable insights
• These insights can help enhance automation, improving engagement

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To succeed in 2022 and beyond, organizations not only need to understand this cycle, but foster each part of it. That is what ASAPP helps our customers do everyday.

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