8 Friction Points in Scaling CX Summaries
Creating effective and insightful summaries of customer interactions is essential for understanding the reasons behind your customer inquiries, the dynamics at play during conversations, and enhancing overall contact center performance. But it’s not easy to do, especially at scale. Here are the top 8 factors impeding contact centers from getting quality, actionable customer information from interactions that move the needle on improving customer experience:
- Manual Processes: Agents are burdened with manually creating summaries, or agent notes, during or after calls, diverting their attention from assisting customers and resulting in inconsistent and incomplete summaries with no useful data for acquiring insights.
- Time Pressure: Agents face pressure to meet aggressive Average Handle Time (AHT) goals, leading to rushed summaries of low quality or skipped summarization altogether.
- Over-reliance on Surveys: Relying solely on surveys for data collection can be problematic due to low response rates and biased feedback from only the most opinionated customers.
- Disruptive to Conversations: Summarization during calls can disrupt the natural flow of conversation, leading to increased friction and potentially lower Customer Satisfaction (CSAT) scores.
- Agent Frustration: Manual, rushed, and frictional summarization processes contribute to agent frustration and burnout, exacerbating already high attrition rates in the industry.
- Inconsistent Quality: The rush to complete summaries results in inconsistent quality and unreliable data, hindering decision-making and organizational performance.
- Under-Reporting: Manual or hurried summarization may lead to under-reporting of critical information, impacting key performance indicators and organizational awareness.
- Multi-Channel Communications: Managing summaries across multiple communication channels adds complexity, making it challenging to aggregate data and provide seamless customer experiences
What does this friction look like?
According to our research, an average call center may be staring down the barrel at these bleak stats regarding summaries:
- 120-300 seconds spent dispositioning per call.
- Less than 25% of notes that are actually of a usable quality.
- No data to aggregate for key business insights.
Overcoming these challenges requires effort, but fortunately, generative AI offers a promising solution to alleviate much of the friction. Implementing a modern AI summarization solution is essential to overcoming these obstacles and unlocking the true value of contemporary CX summaries.
Don’t miss out on summarization value
Without good summaries, your business could miss out on valuable insights and advantages. The absence of crucial historical context hampers agents' ability to assist customers effectively, reducing the personalized touch that builds loyalty. Moreover, lacking necessary compliance data can put your organization at risk of regulatory violations.
Without comprehensive summaries, you lose access to valuable customer and business data vital for strategic decision-making and growth. And the lack of a true reflection of agent performance hinders identifying areas for improvement. Most importantly, without insights into customer satisfaction drivers (or detractors), you're unable to implement targeted improvements, limiting overall service quality and customer experience enhancements.
What’s next? Learn how to get good summaries at scale without adding to your agents’ workflows.
Download the AutoSummary eBook, our exhaustive guide to revolutionizing CX with generative summaries. 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.
CX Summaries 101
Good summaries are essential to a well-run CX organization. They provide a snapshot of customer interactions, distilling essential customer information into concise summaries that give agents quick customer context and contact centers rich analytics data. However, not all summaries are created equal. Below, we’ll explore what summaries are, what makes a good summary, and why they are invaluable for modern CX organizations.
What Are Summaries?
In the context of CX, summaries are condensed representations of longer customer interactions that capture the essence of customer-agent conversations, distilling critical information for reference and analysis. They can be created manually or automatically, and they serve various purposes within a contact center.
Good Summaries Create Value
Not all summaries are created equal. Some are created by agents manually. Some are automated. And the quality of automation can vary drastically. When done right, with precise and context-rich information, good summaries at scale can yield surprising value. Quality summarizations delight customers, empower agents, and give you a clearer picture of what is really driving the successes and areas for improvement in your organization. Here’s how it works:
1. Delight Customers
Customers want to feel recognized and understood quickly. Good summaries ensure that agents can greet customers with appropriate and relevant context and customer information, along with recognition of their past interactions and issues with the brand. This leads to more efficient and personalized customer service, reducing frustration and elevating customer satisfaction.
2. Boost Agent Happiness and Effectiveness
For agents, good summaries provide agents with essential customer background information, allowing them to address issues more effectively. Armed with context, agents can tailor their approach to get to customers’ core problems faster. When summaries are automated, they reduce the time agents spend on manual summarization, leading to a reduction in overall Average Handle Time (AHT). That means more time helping customers and less time doing after-call work. This makes agents happier while solving customer problems more quickly.
3. Generating Valuable Business Data
Summaries serve as a goldmine of data for CX organizations. They help track interactions, identify problem categories, evaluate sales efforts, assess agent strengths and weaknesses, analyze customer sentiment, and monitor high-level initiatives. This summary data is crucial for making informed decisions and holds value for various facets of the business. For example, when a specific problem consistently arises within a product, it can be communicated to the product team to ensure they are aware of and can consider addressing it. It’s a great starting point for automating and improving more of your contact center activities.
Obstacles to Creating Good Summaries
Despite the myriad advantages offered by good summaries, many CX organizations have to overcome various obstacles to get there. Here are some examples:
- Manual Entry Takes Time: Many agents are still required to create manual summarizations, which distracts them from their core responsibility of assisting customers. The result is inconsistent quality and incomplete, often unusable data.
- Rushed Summaries: Agents frequently contend with aggressive AHT goals, leading them to hurriedly complete summarization tasks or even skip them altogether to meet performance targets. This rush compromises summary quality and accuracy.
- Agent Frustration: Agents already dealing with challenging situations find summaries to be an additional burden, extending the time required for each interaction, while trying to adhere to shorter call times. This frustration can contribute to already high agent attrition rates.
- Inconsistent Quality: When hundreds of agents create summaries from scratch, the quality can vary widely, leading to unreliable data and unusable summaries.
- Under-Reporting: Relying on agents to accurately represent both positive and negative aspects of interactions can cause critical key performance indicators to go unnoticed.
Where to Start?
To improve your summarization efforts, consider these high-level best practices:
- Tailor the questions you ask in summaries to what is important for your business.
- Optimize for humans by making questions clear and easy to answer.
- Designate a resource to monitor and extract key insights from summaries.
- Implement a Generative AI solution to automate and enhance your summarization process.
While implementing some best practices can improve summaries, a modern summarization solution can provide accurate, exhaustive, and data-rich summaries at scale. It’s the key to fully realizing summarization value. Here are some high-level points to consider:
- Structured Data and Enrichment: Seek configurable structured data capabilities and ensure availability from the start. Additionally, explore data enrichment options for insights valuable to technical leaders in areas like compliance, service optimization, and new product introductions.
- Handling Multiple Contact Reasons: Verify that your summarization solution effectively supports multiple contact reasons, enabling precise tracking of individual issues within interactions or over a customer's lifetime.
- Generative AI vs Extractive AI: Extractive AI is commonly used but may lack data consistency and flexibility. In contrast, Generative AI offers flexibility, high-quality summaries, and structured data generation.
- CCaaS, Point Solutions, and Key Selection Factors: Be aware that large CCaaS (Contact Center as a Service) vendors may impose limitations on flexibility. Smaller AI point solution vendors might not meet enterprise requirements. And small CCaaS vendors can be costlier and less interoperable. It's crucial to select a partner in line with your strategic objectives and possessing strong AI expertise. Building a DIY solution presents challenges and extended development timelines.
Want more? Read our exhaustive eBook.
Curious to dive deeper into summarizations? Download our summary-focused eBook “The Modern CX Guide to Summaries,” the ultimate guide to revolutionizing CX with generative summaries. Dive in to explore best practices, technologies, and solutions that promise to deliver comprehensive summaries effortlessly. Improve agent efficiency, gain valuable business data without sacrificing agent focus, and harness the full potential of your customer interactions.
Good summaries are the backbone of a well-functioning CX organization. They provide essential context, improve agent efficiency, and offer valuable business insights. However, achieving high-quality summaries at scale can be a challenge. With the advent of modern Generative AI summarization solutions like ASAPP AutoSummary, CX organizations can break the tradeoff between cost and quality, ensuring accurate, data-rich summaries that drive customer satisfaction and business success. Don't miss out on the opportunity to transform your CX organization with the power of great summaries.
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 maximizing customer delight.
Want to learn more about ASAPP and how they can help your team? Request a Demo
The CX AI Trends That Will Shape Contact Centers in 2024
In 2024, generative AI is poised to redefine how businesses engage with their customers and streamline operational efficiency. We expect AI to not only automatically flag particular problems, but also to promptly resolve those specific customer issues. At the core of contact center transformation, generative AI is reshaping the nature of customer interactions, data-driven insights, and how agents are helping customers resolve their issues.
Although we’ve seen AI create incremental changes in service, we’ve arrived at a pivotal moment where commonplace technologies like transcription and free text summaries are evolving into profound generative insights. A large share of agent tasks can now be managed through automation, and for those tasks requiring human intervention, AI stands ready to support and assist agents in their work. Fueled by capabilities now unlocked by generative AI, enterprises can improve contact center efficiency and reduce both direct and indirect costs through nuanced understandings that enrich data and unlock unparalleled value.
To implement AI into their CX stack in 2024, enterprises must establish foundational building blocks, such as high-quality transcription, and get their data house in order before they can really take advantage of the benefits of AI.
– Michael Lawder, Chief Growth Officer, ASAPP, Former CX Leader at Apple, Samsung, and EA
Lawder underscores the importance of laying strong foundations for AI integration. Building these blocks, including high-quality transcription and organized data, is pivotal to reaping the full benefits of AI in contact centers.
Anticipate these impactful trends shaping CX in 2024:
- Evolution of Text Summaries: From Extraction to Generation
- Unveiling Valuable Sentimental Customer Data
- Surge of Generative AI in Bots
- Blending Technology and the Human Touch
- Ending Bad CX: Reconciling Cost vs Quality
Evolution of Text Summaries: From Extraction to Generation
For years, the promise of automating summaries using AI to reduce Average Handling Time (AHT) seemed within reach. But the early AI models, mostly adept at extractive summarization, only provided contextual information from past interactions. While they lightened the post-call workload for agents, these summaries merely offered light context, necessitating manual review for additional insights and a clearer view of the full picture.
So, why will 2024 be the year of AI-driven text summaries? Because the benefit shifts dramatically with the summaries created by generative AI. Unlike extractive methods, generative summarization not only includes pertinent conversation elements but also gauges customer sentiment.
If your CX stack is integrated, then generative AI summaries can be customized based on factors like intent or agent groups. This means automatic categorization of complaint types and severity, identification of key issues, or post-launch focus on new product mentions becomes possible. ASAPP’s AutoSummary stands as a testament to this evolution, showcasing the transformative potential of generative AI in turning data into actionable customer insights.
To learn more about AutoSummary, click here.
Generative AI will continue to deliver incremental innovation across GPUs, LLMs, and Compute frameworks. Data will dominate to be the biggest differentiator, applying LLMs in a hybrid domain focus to achieve accuracy, time to value and scale. These vectors coming together will be the key to unlock exponential value for enterprises.
– Priya Vijayarajendran, President, Technology, ASAPP
Priya sheds light on the pivotal fusion of technology and data. This convergence across computational frameworks not only drives incremental innovation but also highlights the indispensable role of data in achieving accuracy and scalability. It's the synergy between Tech, Data and seamless orchestration connecting enterprise systems for business outcomes that unlocks exponential value for enterprises.
Unveiling Valuable Sentimental Customer Data
Training generative AI on a particular business’s customer conversations can identify unique pain points, understand satisfaction drivers, and strategically enhance the overall customer experience. At the forefront of this transformation is Sentimental Data. This data segment delves into human emotions, pinpointing areas of improvement that customers feel, providing a roadmap to elevate customer experiences to unprecedented levels. Let’s look at a few industry examples.
Using sentiment analysis in the travel industry can uncover customers’ emotions tied to hotel stays, flights, and destinations, guiding improvements. Apparel industry brands can understand customer reactions to their offerings. By decoding feedback from various channels, these companies identify trends and specific customer preferences, enabling targeted product improvements and personalized marketing strategies.
In 2024, more businesses will invest in leveraging AI and advanced analytics in order to create tailor-made offerings at dynamic price points and individualized levels of service.
Sentiment data is also playing a pivotal role in telecommunications CX. Companies gain crucial insights into what delights or frustrates users by dissecting customer sentiments linked to service interactions, network experiences, and support encounters. Ultimately, this information empowers telecommunication providers to address pain points promptly, improve service quality, and tailor offerings to meet specific customer needs.
Surge of Generative AI in Bots
Next year, we’re anticipating a significant rise in the utilization of generative AI in bots, with unprecedented low barriers to accessing this technology. The immediate potential lies in elevating bots to conversational entities and eliminating hurdles in understanding user intent, which allows seamless navigation through customer inquiries.
Looking ahead, the evolution of generative AI involves integration with APIs for tailored responses derived from individual customer data. This could extend to bots understanding unique customer situations, departing from standard responses to offer personalized interactions. Ideally, generative AI bots not only achieve this but also autonomously act on behalf of customers, either independently or after human agent confirmation.
This surge in generative bots represents a transformative shift in customer interactions. Its impact varies based on implementation nuances. Some applications optimize workflows and virtual assistant functionalities, adapting dynamically to interactions and policy changes, substantially reducing manual effort.
But a more advanced, integrated generative bot transcends these efficiencies. It seamlessly handles multifaceted queries, customizing responses by fusing knowledge base insights with specific customer data. It could comprehend customer queries holistically, autonomously accessing APIs, gathering required information, and executing tasks on the customer's behalf, reshaping the customer service landscape.
Blending Tech Investment to Amplify the Human Touch
The transformative integration of generative AI and people in contact centers isn't a distant vision—it's an unfolding reality informing the way businesses empower real human agents. It isn't about replacing human interaction but amplifying the human touch with technology. Generative bots act as indispensable sidekicks to agents, offering nuanced, real-time information, enabling agents to focus on complex calls and foster meaningful connections.
By embracing generative AI, leading contact centers will transcend transactional interactions and foster relationships that are built on understanding, empathy, and personalized attention. It's this blend of technology and human interaction that paves the way for more high quality customer experiences.
In Forrester’s 2024 Planning Guide for CX, 71% of leaders are prioritizing increased budgets to drive deeper customer insights, while 48% are earmarking resources specifically for contact center technologies. This heightened investment shows a serious commitment to leveraging tech advancements to enhance customer experiences. Moreover, a substantial portion of this budget increase is directed at data and research, highlighting the pivotal role of unlocking hidden CX data in the year to come.
Ending Bad CX: Reconciling Cost vs Quality
The struggle to articulate needs and provide seamless experiences often defines poor CX. Miscommunication and missed connections hinder the very relationships businesses aim to build. We all have our bad customer experience stories. Yet, the challenge persists: how to achieve high-quality service while managing costs. It’s been a core CX issue from day one.
Traditionally, this balance led to compromises, where either quality suffered or expenses soared. Now, the emergence of generative AI introduces a new trajectory, bridging the gap between cost-effectiveness and exceptional quality in CX, ensuring each interaction embodies the ethos of service at the heart of every contact center. As it shapes our approach, we move toward a CX era where the convergence of cost and quality through generative AI finally ends bad customer service.
ASAPP is a research-based artificial intelligence cloud provider committed to solving how enterprises and their customers engage. Inspired by large, complex, and data-rich problems, ASAPP creates state-of-the-art AI technology that covers all facets of the contact center. Leading businesses rely on ASAPP's AI Cloud applications and services to multiply Agent productivity, operationalize real-time intelligence, and delight every customer. To learn more about ASAPP innovations, visit www.asapp.com.
ASAPP has significantly improved our efficiency in a very short time. Not only are we moving interactions from phone to digital, we’re doing it in a way that both our customers and our crew members love.
– Ian Deason, SVP Customer Experience, JetBlue
Want to learn more?
Generating New Customer Intelligence
Contact centers are goldmines of market information – from addressing customer issues and gauging their wants and needs, to seeing how they rate you in comparison to your competitors, and more. Customer interactions contain valuable information to improve current products and can provide early warning signals for any potential issues or emerging competition.
Despite the valuable information available, it has been incredibly difficult to access: deciphering phone audio or messaging transcripts is an arduous task. Text analysis has provided some assistance, but more often than not the optimal solution has been to ask the agent or customer on the call to fill out a survey with the data we care about.
Agents can be extremely effective at filling out these surveys, yet at a cost: adding questions is very expensive, and you are only able to acquire future data. Analyzing historical trends is still an onerous task. Requesting feedback from customers proves more difficult as well; sampling bias becomes an issue while other obstacles may occur. Leveraging quality management data for insights quickly runs into sparsity issues, making proactive responses too slow.
At ASAPP, we’ve incorporated Large Language Models (LLMs) to solve this problem for years as part of our Structured AutoSummary product. LLMs are great at understanding the meaning of the text. We can use them to regularize the recording of the interaction. We can represent conversations as a free text summary, and we can pull structured data out of conversations.
Newer LLMs can also perform a facsimile of reasoning. GPT4 and other models can be great at answering questions that require combining pieces of information in a call transcript. That extends the number of questions we can answer with high confidence – and the amount of structured data we can extract from conversations.
Structured data remains essential. Although LLMs can be very good at analyzing a single conversation, it takes a different approach to analyze hundreds or millions of customer interactions. Traditional analytics approaches – e.g. BI tools, Excel, ML models, etc – are the best way to analyze, identify patterns, and understand trends across a large amount of data. Now we can expose customer interaction data in a way those analytical tools understand.
Certainly, there are some complications in relying on AI to convert unstructured conversations to a usable structured format. At ASAPP, we’ve devoted substantial effort to managing hallucinations and reliable data collection by building in dedicated feedback loops and having multiple models working together that tackle different aspects of hallucinations.
Not surprisingly, the quality of data matters too. We’ve benchmarked the quality of our AutoSummary outputs against ASR accuracy (1-WER), and we see that highly accurate transcripts (where our own generative end-to-end ASR system AutoTranscribe sits in the mix) produce materially higher quality data on downstream tasks like extracting structured data out of conversations.
Turning unstructured audio and text into structured data unlocks a wealth of data stored in contact center records. Utilizing existing analysis tools and approaches can make contact center data available to other departments, like Research and Development, Marketing, and Finance, in real-time without purchasing additional IT capabilities for analysis and visualization.
For agents, this provides a massive boost in efficiency, getting quick answers to business questions and freeing up more time to help customers. For customers, it’s even more dramatic. They get answers faster and have a much smoother experience overall – all without survey bias.
LLMs are fantastic tools for language: writing poetry, essays, and code. They are also great at turning natural language into structured data, blurring or eliminating the boundary between “structured data” and “unstructured data.” Leveraging that data, and making it available to all the existing business processes, is where we’re heading with Structured AutoSummary.
Generative AI for Agent Augmentation: Agents Models not Language Models
Generative AI and Large Language Models (LLMs) have made massive waves in the consumer and enterprise technology news due to the remarkable capabilities of tools like ChatGPT and GPT4. The models provide fluent text that integrate reasoning into their responses based on the knowledge they have absorbed from the huge volume of general documents they are trained on. Not surprisingly, their use in chatbots has been one of the most active development areas for enterprise businesses.
While increasing containment of calls can save call centers dollars, the primary cost is still front line agents who must handle the most challenging calls and customers (otherwise a bot would have handled it). At ASAPP, we already have many tools that assist the agent when handling a live call. AutoCompose helps agents craft messages and significantly increases throughput in the call center while increasing CSAT in tandem. AutoSummary helps automate dispositioning steps for agents. Both use Generative AI models, in some cases for approximately five years.
However, agents spend their time doing much more than just writing messages to customers. They must execute actions on the customer’s behalf (e.g., change a seat on a flight or schedule a technician visit) as well as follow flows and instructions in knowledge base articles to be compliant when handling issues with safety or business regulations. To do this agents use a large number of tools. These tools are rarely homogeneous but are a frankenstack of vendors and user interfaces. On top of that, agents handling digital calls are usually managing more than one issue at a time, which leads to a huge number of applications open at once. Any model that focuses only on the text of a conversation and not all the actions the agent is executing is leaving a huge amount of headroom on the floor. For many of our customers agents can spend upwards of 60% of their time on tools outside of the conversation!!
Thus, to truly augment the agent a model must not just be a Language Model – it must be an Agent Model. That is, it needs to be a multimodal model that operates not just on the text of the conversation, but also on all the information the agent is currently interacting with as well as information hidden in business documents and logic that are salient for the issues at hand. At ASAPP, we have already invested in understanding the data stream of all agent actions and have used that data stream to build multimodal models that can improve augmentation for an agent. There is an amazing synergy when using this data. First, conditioning on the agent action data stream allows us to better improve our predictions of what the agent should say and do next. Conversely, information from the conversation feeds into what actions the agent should do, i.e., ‘I need to book a flight from New York to San Fran tomorrow in the morning’ allows the model to predict a flight search action, populate the origin city with ‘New York’, the destination to ‘San Francisco’ and the date as a day from today and execute that command.
Varying levels of experience with internal tools will impact how consistently advisors are solving customer problems. We commonly see less tenured representatives reaching out to their colleagues more often after getting stuck using an internal tool, spending more time searching for knowledge base articles, and switching back and forth more often between screens when handling workflows. Agent models can help newer agents become more comfortable and guide them to more effectively use their tools.
A core aspect of ASAPP’s mission is to ‘multiply agent productivity’. This can only be achieved in its fullest with Agent Models and not just Language Models.
Generative AI: When to Go Wide and When to Go Deep
Generative AI is everywhere, and you might be feeling the pressure from your colleagues or managers to explore how to incorporate Generative AI into your role or business. I’ve been seeing a lot of speculation about ChatGPT’s capabilities and what it can and cannot do. As a research scientist with years of experience in academic and industrial research with large language models, I wanted to dig into some of these notions:
First, ChatGPT is not a product, it’s an engine – and a really good one. However, a valuable solution still needs more in order to make a difference and drive business value in almost every case. This includes the UX (UI, latency, runtime constraints) and critical ML capabilities like data collection, data processing and selection, continuous training frameworks, optimizing models for outcomes (beyond next word prediction) and deployment (measurement, A/B tests, telemetry).
Second, while GPT does amazing things like write poetry, pass medical exams or write code (just to name a few), in CX we need solutions that solve specific problems like improving automated dispositioning or real-time agent augmentation. GPT models can be impressive, but when it comes to user experience and business outcomes, Vertical generative AI models that are trained on human data in a dynamic environment specifically for the task at hand typically outperform larger generic algorithms. In ASAPP’s case, this means solving customer experience pain points and building technology to make agents more productive.
Lastly, while we don’t use ChatGPT at ASAPP, we do train large language models and have deployed them for years. We don’t pre-train them on the web, but we do pre-train them on our customer data, which is quite sizable. From there, we then train them to solve specific tasks optimizing the model for specific KPIs and business outcomes we care about and need to solve for our customers — not just general AI. This includes purpose-built vertical AI technology for contact centers and CX. Vertical AI allows enterprises to transform by automating workflows, multiplying agent productivity and generating customer intelligence to provide optimal CX.
Interested in learning more about ChatGPT or how large language models might benefit your business? Drop us a line.
Automation should help resolve, not deflect
The mission of Solution Design at ASAPP is to help companies identify areas of massive opportunity when it comes to optimizing their contact centers and digital customer experiences. At the highest level, we aim to help our customers provide an extremely personalized and proactive experience at a very low cost by leveraging machine learning and AI. While we’ve seen many different cost saving and personalization strategies when it comes to the contact center, the most common by far is as follows:
- Step 1:
Leverage self service channels (website, mobile apps, etc) built by digital teams and hope customers resolve issues themselves or buy products directly.
- Step 2:
If customers aren’t able to solve issues on their own, offer “assistance” using an IVR or chatbot, with the goal of preventing customers from talking to an agent.
- Step 3:
When these fail, because the question is too complex or there isn’t an easy way to self serve, have an agent handle it as quickly as possible, often starting from scratch.
It’s a strategy that many Fortune 500 companies were convinced would revolutionize the customer experience and bring about significant cost savings. Excited by the promises of chatbot and IVR companies who said they could automate 80% of interactions within a year—which they assumed would reduce the need for agents to handle routine tasks– companies spent millions of dollars on these technologies.
Automation as you know it isn’t working
While some are seeing high containment numbers put forth in business cases, the expected savings haven’t materialized—as evidenced by how much these companies continue to spend on customer service year after year. Furthermore, customers are frustrated by this strategy—with most people (myself included) asking repeatedly for an agent once they interact with an IVR or bot. The fact is, people are calling in about more complex topics, which require knowledgeable and empathetic people on the other end of the line.
We live in a new era where the companies who can provide extremely efficient and personalized interactions at a lower cost than their competitors are winning out.
It’s not surprising that in 2019, executive mentions of chatbots in earnings calls dropped dramatically and chatbot companies struggled to get past seed rounds of investment (cite). These programs cost millions of dollars in software and tooling, and double or triple that for the labor involved with building, maintaining, measuring, and updating logic flows. Beyond NOT increasing contact center efficiency, chatbot technology has reduced customer satisfaction, impeded sales conversion, and has caused the market to missassociate AI with automate everything or nothing.
A better automation strategy
We live in a new era where the companies who can provide extremely efficient and personalized interactions at a lower cost than their competitors are winning out.
There has been a retreat from using bot automation to avoid customer contact. Instead, leading companies are using ML and AI to improve digital customer experiences while simultaneously helping agents become more efficient. Furthermore, by connecting the cross channel experiences and using machine learning across them, conversational data is much more complete and more valuable to the business.
Compared to the earlier strategy, where there were distinct walls between self service, automation and agents, this new strategy looks far more blended. Notice that automation doesn’t stand alone—instead, it’s integrated with the customer experience AND agent workflows. Machine learning provides efficiency gains by enabling automation whenever appropriate, leading to faster resolution regardless of channel.
At ASAPP, we use AI-driven agent augmentation and automation to improve customer experience and increase contact center efficiency. The results have been transformative—saving our customers millions of dollars in opex, generating millions in additional revenue while dramatically improving CSAT/NPS and digital engagement. If you want to learn more about our research, results, or strategy reach out to me at email@example.com.
Are you tapping into the power of personalization?
For decades, one of the biggest consumer complaints has been that companies don’t really know them. Businesses may use segmentation for marketing, yet for inbound customer service, even this level of personalization is nearly non-existent. Now the race is on—because personalized service experiences are quickly becoming a brand differentiator.
When customers reach out to solve a problem, they want to feel reassured and valued. But too often, they’re treated like a number and end up more frustrated. Even if they get good service on one call, the next time they contact customer service it’s basically starting at ground zero because the next agent doesn’t know them.
As more digital customer service channels have emerged, consumers have gained more choices and digital convenience. But that creates a new challenge: people often use different channels at different times, switching between calls, web chat, digital messaging, and social media. And because those channels are often siloed, customers may get a very impersonal and disjointed experience.
The new demand for personalization requires something significantly better. Consumers now expect seamless experiences across their relationship with a company—and without it, brands will struggle to earn repeat business, let alone loyalty. In fact, nearly 60% of consumers say personalized engagement based on past interactions is very important to winning their business.
Increasing value with a unified channel journey
Knowing your customers means providing seamless continuity wherever they engage with your brand. Typically, the experience is fragmented, and consumers have a right to expect better. They provide a considerable amount of data through various channel interactions, and 83% of consumers are willing to share that data to receive more personalized experiences.
When a company barely knows them from one engagement to the next, how do you think that affects their trust in the brand?
It’s no surprise that 89% of digital businesses are investing in personalization. Cutting edge technologies are eliminating the friction and fragmentation of multi-channel journeys, by meeting customers with full context however they make contact. With a unified, AI-powered platform for customer experience, companies can seamlessly integrate voice and digital capabilities—and ensure customers are greeted every time with an understanding of their history with the company, where they’ve been and what happened in previous interactions It gives customers greater flexibility and ease for engaging using their preferred channels, which can dramatically improve satisfaction ratings and NPS scores.
Another powerful benefit of multi-channel integration is that it enables contact centers to think in terms of conversations instead of cases. A unified platform weaves together voice and digital messages into a cohesive thread for a given customer. Any agent can easily step in and join that conversation, having all the right knowledge about the situation and visibility into previous interactions. That continuity enables agents to provide more personalized attention that helps ensure the customer feels known and valued.
Customer service needs to be about conversations, not cases. Creating intelligent, personalized continuity across all engagement channels shows customers you know and value them—and that’s the great CX that wins loyalty.
Improving proactive engagement with personalization
Tapping into a wealth of customer data from many different channels, companies can take customer experience to the next level. Using AI and machine learning, you can build more comprehensive customer histories and serve up predictive, personalized action plans specifically relevant for each customer.
I’m talking about gaining a holistic picture of when, why, and how each customer has engaged over their lifecycle with your company. That opens up significant opportunities, such as:
- Improve customer experience and earn loyalty
- by providing highly personalized support each time someone reaches out.
- Increase customer lifetime value
- with more relevant and timely proactive engagement that is more like personalized conversations, all based on data-driven insights.
- Boost marketing ROI
- using customer data to develop persona-based segmentation strategies and nuanced messaging driven by sentiment analysis and a deeper understanding of intent.
Most consumers now expect companies to know them better and see that reflected in their communications. And the demand for personally relevant experiences isn’t just about marketing—it’s across the journey, including customer service. That’s why ASAPP technology is so compelling.
Support interactions are often the defining moments that dictate how people feel about a brand. The more you can personalize those customer service moments, the more you will earn loyalty, and even word-of-mouth referrals as your happy customers become brand advocates.