The Changing Contact Center: From Cost Center to Profit Center (Part 2)
The customer service center of the future is a relationship hub with the potential to inform all aspects of an organization’s business and build passionate employee and customer advocates. By implementing a system of continuous intelligence, the contact center can evolve from an incident-based reactionary measure to a personalized, proactive, insight-based solution. In our previous blog, we discussed the ways companies can empower their agents and re-envision the experiences they are delivering to make this shift. But where does technology, especially AI, fit in? Let’s dive in:
Deliver a Seamless Customer Experience across Channels & Touchpoints
Traditional channels (like email, web, TV, phone and in-store) are often siloed, which makes it difficult for companies to gather customer journey intelligence and results in an inconsistent customer experience. Developing a unified, human-centered user experience across multiple touchpoints drives engagement, building reputation and trust among customers. When consumers are assured that their needs will be met in a channel of their choice, it’s easier to move many of them to self-service. By conducting user research to gain a deeper understanding of your customers, their needs and preferences, you can develop a clear, cohesive customer experience vision. This vision combined with up-to-date software platforms and knowledge bases, ongoing training with clear feedback loops, and the right data enabled by an effective customer relationship management (CRM) system can help agents more deeply understand the customer and what stage in the lifecycle they are in. With that knowledge, they can personalize the interaction, leading to a more positive experience and an opportunity to build loyalty, enhance brand value and potentially upsell.
Optimize Interactive Voice Response (IVR) Call Flows & Omnichannel Routing
IVRs were initially created for self-service, but it really is another channel to reach agents to resolve issues. This drives up the cost per call in the contact center and leads to a bad customer experience. Consider the way the call flow could be optimized. IVR could perform the authentication, but when it reaches the agent, the agent does the authentication again. Bringing in an intelligent routing mechanism with an optimized call flow backed by machine learning (ML) and conversational design enables quicker resolution and a better customer experience. There are many products in market that offer variations to traditional routing (skills-based routing, idle time routing, etc.). These include predictive routing or behavioral pairing using AI, which identify patterns of interactions between customers and agents to provide a better outcome. Additionally, redirecting customers to their channel of choice instead of waiting on hold for an agent — like messaging via App Messaging, Apple Messages for Business, Google RCS, and Whatsapp when customers call via IVR — enables a positive experience and loyalty with the customer.
Drive Agent Effectiveness by Agent Assists & Insights Capabilities
Gaining insight from customer conversations provides many benefits to the organization, from more effective agent training to enhanced customer loyalty and trust to improved conversions which can lead to increased revenue. AI can be an effective tool both in chat and voice. Let’s break down the stages where it can be most beneficial:
Prior to Agent Interaction: When the IVR or conversational tool gathers information before transferring to the agent, AI tools can help identify the cause of the call – whether it is a new request or from a previous interaction. This equips agents with the information needed to resolve the customer's call more quickly, so they have more time to focus on revenue generation through any cross-sell/upsell opportunities like a new offer, upgrades, etc.
Concurrent to Interaction: During interactions, agents need two things:
- Easy access to the knowledge base/support material that will give precise answers. Agents can make better decisions if they have the right information in front of them, which leads to better customer service and revenue-generating opportunities. Amazon Kendra, Google's CCAI Agent Assist and many other tools provide cognitive search and agent assist features. With the advancement in LLMs, foundational models can be enhanced and fine-tuned with in-house enterprise data to implement a more effective semantic search. This allows agents to provide answers to questions or suggestions during the call that incorporate the context and semantics of that particular customer, which saves time and increases the accuracy of search results.
- Insights into behavioral interactions to elevate the customer experience. Providing real-time feedback and coaching on soft skills and suggestions to change an agent’s tone can help reduce training costs and win customers at the point of service. Agents often are more empowered to course correct with a real-time virtual AI coach rather than a supervisor or trainer who is reviewing their performance and pointing out their flaws.
After Interaction: Once the interaction is over, running the voice and chat transcripts through an AI-powered analytics engine provides:
- Information on how to improve sales conversions in conversations
- Effectiveness of campaigns
- Competition in the segment
- Other insights like measure of agent interaction, sentiment analysis, average handling time for different types of calls, voice dynamics of agent and customer, silence ratio, call talk ratio, etc.
These insights help in contextual marketing effectiveness, customer segmentation and targeting, customer churn analysis, the propensity of the churn along the customer journey, and customer experience analysis. Summarizing calls with the help of generative AI tools can help gain faster insights and enhance training and follow-ups.
Bring Agility to your Conversational Technologies
Contact center platforms typically leverage legacy technologies that were installed several years ago, and upgrades or new features often take a long time to plan and execute as they are complex to install and operationalize. Here are some ways to bring agility to your tech stack:
- Consider conversational interfaces as an extension of your brand. Design them not just for FAQs but create a personality that is reflective of the experience that you want your customers to have with your other channels. Create an intentional experience for the handoff between the conversational interface to a live agent.
- Cloud-based contact centers are easy to stand up and scale, while also bringing in rich features from the marketplace that can be integrated along the way. Fixes, upgrades and maintenance are done without impacting the agents. Many cloud-based contact centers like AWS Connect, Ujet, etc. have a no-code approach (at least for a preliminary set of features) and are backed by solid cloud-native AI technologies, making them cost-effective and easily scalable.
- Having insights into your conversations and interactions through analytics provides opportunities for immediate course correction. This could include virtual agent optimization or coaching of human agents to make your organization nimbler.
- Often, organizations spend significant time manually testing the natural language processing (NLP) model that powers the conversational interface. Automated regression testing for conversational AI can save time and money and reduce the impact on the overall performance of the bot. Consider implementing tools like QBox that provide this capability.
Leverage Knowledge Management
A good knowledge management strategy helps agents become smarter and drives customers to self-sufficiency. According to According to the Gartner® 2022 Market Guide for Customer Service Knowledge Management Systems report, “by 2025, 70% of virtual customer assistant and virtual agent assistant projects that lack integration to knowledge management systems will fail to meet customer satisfaction and operational cost-reduction goals”. Consider upgrading from a legacy content management system (CMS) that is non-responsive, less customizable and lacks content governance workflow to a more advanced platform, which can help you:
- Provide relevant information quickly to help customers self-service their needs and stick to your brand. The average time spent on a website is between 45-54 seconds. An engaging customer experience will bring in revenue through cross-selling opportunities on eCommerce sites. Revamping your website to provide customers with the resources needed while also enabling the personalization of user journeys helps increase brand image.
- Better support your agents by connecting your knowledge bases. Integrating your knowledge management solution to your CRM and call center to provide real-time guidance to agents on context and easily searchable articles helps reduce handling time and improve first contact resolution. Tagging articles like FAQs, videos and documents on specific topics and micronizing the content helps provide more reliable and relevant answers.
Technology can play a major role in bringing efficiencies to your contact center and achieving your operational goals. However, it’s crucial to keep the customer at the center of your technological decision-making. While advancements in AI can certainly bring productivity gains, it is even more important to invest in your agents and their upskilling. Leveraging AI as an enabler to transform your contact center from an incident-based reactionary measure to a customer relationship hub is key.
1 Gartner, Market Guide for Customer Service Knowledge Management Systems, Pri Rathnayake and Drew Kraus, 20 September 2022.
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