The Contact Center of the Future: Driven by RPA, Transformed with AI

One of the most used buzzwords in 2020 was undoubtedly the “new normal.” The pandemic’s initial business impact was indeed unwelcome for most enterprises, but the consequent cost pressures and fluctuating demand have brought about a shift in their focus. Global businesses are now turning to digital channels at every touchpoint to deliver an unparalleled customer experience. At the heart of these customer touchpoints lies the customer service (CSM) function or contact center, as it is popularly known.

“All our customer representatives are busy; please hold the line.” Sounds familiar?

Across sectors, whether it’s transportation (Airlines, cab services), BFSI (Health and Life Insurance claims), or Retail (eCommerce goods and groceries), huge call volumes put tremendous pressure on the CSM workforce. This results in long average wait times (AWT’s), unacceptable customer experiences, and subsequently, high customer defection.

However, due to the pandemic, customers are also shifting to online channels. Recent months have seen an uptick in consumer spends via online platforms. In the United States, the e-commerce industry accounted for 33% of the total retail sales by July 2020, much higher than a forecasted 24% in 2024. In Europe, overall digital adoption increased from 81% to 95% during the pandemic . Online customer interactions have reached levels never seen before; the real question is, are most enterprises adapting fast enough?

Hyperautomation is the “new normal”

RPA has generated double-digit growth consistently in software revenue over the years. AI-augmented automation or Hyperautomation is not a new term; it has been used heavily in the context of process mining and discovery. While process discovery will continue to be a highly sought-after capability, the focus is shifting gradually towards augmenting traditional, deterministic automation with AI – especially in document processing, image recognition, voice and text analysis, and machine learning algorithms.

AI-augmented automation focuses on increasing the scope of automation by leveraging AI to make more sense out of unstructured data-heavy on text such as emails, customer feedback surveys, reviews, and social media posts. Since unstructured content forms the staple of most contact center backend systems, it is easy to infer why this solution is touted as a veritable goldmine of untapped insights.

AI use cases for the contact center: Low touch, more personalization

Banking on document processing

Processes that are traditionally paper-based require significant manual intervention to interpret each document and enter data into a backend system. Banking processes like e-KYC and mortgage processing are document-intensive and hence time-consuming and error-prone. Automated extraction of key information from documents allows service desk agents to close applications fast and revert in case of additional document requirements, thus speeding up the end-to-end process.

Handling insurance claims using image classification

Service desks at top insurance firms have two key challenges: to reduce handling time to process claims and to pinpoint fraudulent claims from valid ones. Damage assessment for auto and real estate claims is a manual process that results in significant inspection costs. Image classification algorithms can recognize dents, scratches from customer images and provide a damage assessment report in minutes, which can then be reviewed for exceptions by a human-in-the-loop agent. This brings the lead time to process a claim from days to a matter of minutes and reduces the cost-per-claim for insurance firms.

Virtual agents take centerstage

Voice and chat capabilities have been an integral part of the contact center ecosystem for many years now. But the capabilities of these components are primarily limited to handing over data from the customer to a contact center agent using text translation. Increasingly, these bots are being augmented using natural language processing (NLP) and natural language generation to interpret incoming text and respond, as a human would. This reduces each service agent’s burden and limits human involvement to the most critical and urgent issues.

Sense from sentiment

In industries with lower switching costs and multiple alternatives, such as airlines and eCommerce, creating a personalized customer experience becomes paramount to retain both mindshare and market share. Identifying the essence of a customer complaint or feedback using sentiment analysis models can help classify content into appropriate buckets and rout them to appropriate teams for faster redressal. This lets AI and automation take care of the essentials while the contact center agent can focus on a more personalized and empathetic customer conversation.

Customer service agents also use self-learning AI to get insights on cross-selling and upselling opportunities using machine learning recommendation algorithms. These algorithms analyze historical purchase patterns and spend patterns to suggest products that the customer is more likely to buy.

The race to the finish line

It is becoming increasingly clear that enterprises are looking at the contact center as a revenue generator. Solution providers are investing in big partner ecosystems to deliver on the AI promise via customer-centric use cases. The pandemic has ensured that siloed operations of business functions like customer service, sales and marketing, and operations are no longer feasible. With remote operating models and a digital-friendly customer, enterprises will need to rapidly test and deploy AI-augmented automation solutions to deliver pre-pandemic levels of customer satisfaction. If 2020 was about business resilience, 2021 will be about using all that self-learning to chart a road to recovery and create new opportunities. Isn’t that ironic?

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