Designing Multi-experience Contact Centers Powered By AI | HCL Blogs

Designing Multi-experience Contact Centers Powered By AI

Designing Multi-experience Contact Centers Powered By AI
January 15, 2021

Though most contact centers have jumped on the omnichannel bandwagon, many have struggled to deliver tangible business benefits as per envisioned customer journey. Gartner indicates that an omnichannel approach has left a shocking 85% of organizations with disjointed customer engagement channels, resulting in an inconsistent customer experience (CX).

In most cases, poor omnichannel CX strategy ties back to poor execution to boost customer engagement. Organizations have simply bolted on multichannel support in their contact center operations for new channels like social media, web chat, and SMS. But because these channels are operated in silos, agents do not have access to data on previous customer interactions, which means that what the customers get is far from being a seamless, omnichannel experience.

To deliver a truly omnichannel customer journey, all customer interactions, with all the relevant historical and personal information, must be integrated and made available to agents at every customer touchpoint. Most importantly, companies need to have an effective mechanism to measure and monitor conversations across channels in real time and track customer feedback across the journey for better customer engagement.

Consider the banking sector, where institutions need to understand each customer’s experience and engagement from a 360-degree standpoint. This means having complete visibility on changing buying patterns, web and mobile banking engagement scores, purchase behavior across the customer journey, the amount of loans and deposits, desired communication channels, and customer sentiment.

Quantifying customer experience (CX) by leveraging internal surveys and sentiment analysis measures such as net promoter score (NPS) is a good place to start, but they do not give the entire picture. Usually, customer satisfaction surveys only measure transaction results at specific touchpoints. Banks and other financial institutions need new-age tools that can help measure individual customer perspectives across the entire user journey in real time.

Financial institutions need 360-degree understanding of their customer. Omnichannel only creates silos.

Here, a holistic approach to omnichannel that accommodates both customer and employee experience will help organizations craft seamless and satisfactory user journeys, especially when touchpoints converge within and outside the contact center environment.

Enter multi-experience (MX).

Embracing Multi-Experience for a Consistent Customer Journey

Multi-Experience (MX) design involves deployment of high-end AI applications to deliver seamless multi-sensory user journeys across a range of video, voice, apps, text modalities, and digital touchpoints. Gartner has identified MX as a top technology trend for 2021. And, according to its Magic Quadrant report for MX development platforms, by 2023, over 25% of progressive web applications, mobile apps, and conversational apps in large enterprises will be built through multi-experience development platforms.

Clearly, MX holds endless possibilities for the customer experience journey with AI technologies. The new high-end AI applications provide customers with more opportunities in the way they approach, engage, and interact with a brand. It allows companies to consolidate all engagement channels across multiple, disparate devices, and deliver a unified, consistent customer experience across touchpoints. In short, MX collates actionable customer data, gives agents seamless access to this data, builds a deep understanding of customer sentiments, and is thereby able to anticipate their demands and cater to them, sometimes even autonomously, without real agent intervention.

AI as a Foundation of Multi-Experience Strategy

Advanced AI technologies are well-positioned to help companies deliver seamless multi-experiences. The ability of AI technologies to glean insights from disparate data sources, including voice calls, images, videos, and unstructured text makes it an obvious fit in an overarching MX strategy. On the one hand, it can be leveraged to deliver effective self-service. On the other, it can be used to bolster agent response to key customer issues. Either way, human effort remains a key element of the contact center ecosystem. And robust AI applications and agent interfaces need to be closely integrated with the contact center MX infrastructure.  Here’s how AI can help reinforce your contact center MX strategy:

  • Create Conversational and Intelligent User Interfaces

Conversational AI-based customer engagement platforms, also known as chatbots or smart virtual assistants (SVAs) serve as a bridge for customers to engage with a company at any time. With conversational AI, chatbots and SVAs can respond to complex customer queries and help brands achieve personalization at scale. However, companies need to ensure that these assistants are prepared for the limitations and capabilities of each channel. They need to have access to the same data to be able to deliver the same levels of intelligent personalization across all channels. The underlying algorithms need to fortify every engagement with insights, context, and suggestions similar to agent-customer interactions.

  • Achieve the Next Frontier in Voice Recognition

AI-based voice recognition technologies such as NLP can digitize spoken words and encode them in various machine ingestible formats based on pitch, cadence, and tone. These can be used to develop unique voice prints for every customer to deliver AI-based MX. An agent can use a customer’s voice print to authenticate and identify them. This can mitigate the risk of fraud and call center scams. The same voice print in combination with emotion analytics, can be used to prioritize engagements based on the mood of the customer.

For instance, based on emotion and mood, an angry customer can be routed to the customer grievance management team, while a content customer can be routed to the sales team where a new service or product is pitched to them. On a macro level, such solutions can help agents identify weak points in the customer journey in real time. Based on these holistic insights into customer data, agents can tweak the way they handle complaints or issues.

  • Amplify Image Recognition Capabilities

Next-generation, AI-based image processing technologies can identify malfunctioning product components and capture error messages to determine the condition of a product and what needs to be fixed. AI-based image recognition in contact center MX can provide decision support in real time during an agent-customer interaction, helping reps deliver visual instructions pulled from a common company knowledge base. This reduces the agent workload and resolution turnaround times. In a self-service context, this technology can allow customers to virtually interact with intelligent bots that use AR or VR to guide them through product setup, installation, and troubleshooting.

Achieving Contact Center Resilience– Agent Gamification, DevOps, and Much More

The pandemic is the ultimate stress test for business resilience and continuity. And contact center operations are not immune to its disruptive effects. Customer-agent interactions underpinned by a technology-based remote multi-experience will help companies create engagement levels that can match or even surpass physical meetings. Going forward, the ideal MX strategy will include:

  • Screen sharing for agents and customers to collaborate, design, and build together
  • Connected experiences between customers and in-store agents using Robotics and Tele-presence.
  • Virtual walkthroughs of products through mobile video feeds

As multi-experience becomes mainstream, focusing on agent productivity will become a critical objective for companies. Gamification can be an effective way to enhance agent performance, satisfaction, and ultimately drive ROI. And in combination with analytics, it can be used to measure and monitor KPIs, achieve the right human-technology synergy, and reward and inspire positive behavior within agents.

Another crucial aspect of a successful MX strategy is DevOps. DevOps can help companies accelerate the development cycles of multi-experience development platforms (MXDPs) through frequent delivery of continuous updates, fixes, and new business-friendly features.

In this blog series, we will continue to explore the latest trends and technologies that will shape contact center operations in the future. Stay tuned!