Lately, artificial intelligence (AI) is at the core of any customer-centric digital transformation. Strategies not centering around AI and automation are quickly neglected by executive leaders. While being a relatively new type of AI, Generative AI (GenAI) has quickly proven to be of crucial importance to leading organizations due to its ability to drive personalization and workflow efficiency at scale — leading to both cost savings and revenue increases. As explained by Gartner, “By 2026, more than 80% of enterprises will have used generative artificial intelligence (GenAI) application programming interfaces (APIs) or models, and/or deployed GenAI-enabled applications in production environments, up from less than 5% in 2023”. While there’s an extensive list of benefits as it pertains to GenAI, this blog will cover only one of the simpler and yet impactful use cases for GenAI — auto-call summarizations. Continue reading to learn how and why GenAI-powered auto-call summarization helps increase agent productivity, drive process efficiency, and reduce operational costs.
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Section 1: |
The importance of call summarization and its challenges |
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Section 2: |
What is GenAI call summarization and what are the benefits? |
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Section 3: |
What’s the ROI increase from GenAI call summarization? |
Over 30% of customers state the most frustrating part of the service experience is having to repeat or reshare the same issue again and again. This is why post-call notes are of critical importance — they help document historical customer information so that any agent can pick up a conversation exactly where it was left off. Furthermore, while helping agents create seamless experiences, call notes or summaries also enable supervisors to assess an agent's performance and the quality of each customer engagement.
Yet, while being of critical importance, the process of post-call notes is often repetitive and time-consuming. It requires both note-taking throughout the call and a consolidation of these notes once over. SQM reports the average handle time (AHT) for a call is 10 minutes, while IFC reports summarizing the call takes, on average, 6 minutes. In other words, two-thirds of an agent's time is spent on call notes and summarizations, instead of helping the next customer. For a contact center striving for operational efficiency, that’s a massive amount of time and, by default, cost! To maximize productivity — and organizational profits — agents often feel rushed to proceed to the next call. As a consequence, call notes and summaries are impacted, leading to agents inserting incomplete notes or perhaps omitting them altogether. The impact? Important context and engagement details are lost, resulting in incomplete historical data for future reference. This introduces the opportunity for various forms of risk, including future broken customer experiences, additional call auditing needs, and less comprehensive agent training. Even if done well, note-taking is a bothersome task for agents, taking up valuable time that could be better spent elsewhere.
So what is Generative AI, and how can it help?
Generative AI is an advanced form of artificial intelligence with the ability to understand and create new data. Built with machine learning algorithms, it’s able to analyze speech patterns, identify keywords, and extract essential information to create new types of content — in this case, call summaries. Speech-to-text transcription is used to transform raw customer interactions into concise, actionable summaries with impressive speed and accuracy. As a result, workflows and processes can be optimized, making it easier to pick up a conversation exactly where it was left off. This completely eliminates the need for manual note-taking and post-call summaries, instead allowing agents to stay focused on delivering better customer experiences. In addition, supervisors are given reliable and consistent historical notes and insights to simplify their ability to identify where they can lend the greatest value (i.e.: agent coaching, customer outreaches, etc.) - together driving a higher-performing customer support operation.
Automated call summaries are pushed to the CRM or service management platform, in this example, the ServiceNow platform, making it easy for agents and supervisors to get a unified view of engagement data and performance.
Let’s take this a step further. While it makes work easier for agents and supervisors, how much money could GenAI Auto-Call Summarization save your organization? Is it worth the investment? In this section, we’ll take you through an example showcasing the cost reduction expected for Acme Contact Center (Acme CC) if deploying GenAI-powered auto-call summarization.
Acme Contact Center (Acme CC) is running a 24/7 contact center, serving customers 365 days a year. The organization has 100 agents handling a total of 2000 calls per day. As mentioned previously, IFC reports a call summary takes, on average, 6 minutes to complete. Acme CC’s agents are slightly faster, and complete their post-call summaries, on average, in 5 minutes. According to Indeed, the average salary for a contact center agent in the US is $17.42 per hour. In this use case, Acme CC is paying their agents $17 per hour.
20 calls x 5 minute summary = 100 minutes per agent per day
100 minutes / 60 minutes = 1.667 hours per agent per day
1.667 / 8 hour work day = 21% of the agent's day is spent on call summarization
1.667 hours x $17 hourly = $28.339 per agent per day
100 agents x 28.339 = $2834 per day
$2834 per day x 365 days = $1,034,410 per year
Acme CC’s agents are spending 21% of their working day on summarizing call notes, equating to over a million dollars of salary payouts — time and money that could easily be better served performing more complex tasks with the help of GenAI.
In summary, we can conclude that not only does automatic call summarization, powered by Generative AI, meaningfully reduce what has historically been a time-consuming and manual task, but it can also significantly increase organizational ROI. How much is agent note-taking and call summarization costing your organization?
If you found this topic fascinating and want to learn more, we’d suggest reading our more technical blog, “Why Do We Need AI? A Guide to Conversational AI”, or the blog called “Three Red Flags Your Contact Center is Falling Behind and How to Get Back on Track” for a more easy read.
Even more exciting — if you’re ready to put GenAI-powered auto-call summarization to the test for your organization, schedule a consultation with one of our experts. They’ll take you through a personalized demo, designed to show you how your organization can solve your specific problems.