William Chen: Augmented Contact Centres Advance Banks”
September 22, 2019
By Isabella Mckay
On Saturday, September 22, 2019, William Chen, the Director of Business Insights and Digitization of Millenium One Solutions, a Canadian Business Process Outsourcing (BPO) solutions provider, discusses and promotes the importance of an augmented contact centre for banks at the Artificial Intelligence Squared (AI2) Forum. The Director outlines BPO issues and then he proposes three AI solutions: 1) business insights practice, 2) AI productization to agent success, and 3) human, AI and digital platform augmentation. Applying his statistical research and theories, Chen hypothesizes that augmented customer centres will elevate bank institutions in the expertise, security, and customer satisfaction areas of customer service.
Chen identifies the BPO dilemma of low agent knowledge and compares pre-AI and post-AI deployment agent performance to emphasize how AI benefits contact centres. On the y-axis of graph 1 (Fig. 1), that compares the regular agent and augmented agent progress curves, Chen has three, ascending agent knowledge levels: 1) a base product and system knowledge, 2) intermediate level, and 3) subject matter expert. The x-axis is the agent attrition overtime. Chen compares the progress curves of the two agents to discover that the augmented agent reaches the level of subject matter expert quicker than the regular agent. Chen also highlights how pre-AI agents have a retention score of 9.00%, whereas, post-AI they achieve a retention score of 30.00% in chart 1 (Fig. 2). This chart shows that post-AI has nearly four times more retention than a human agent. The charts of empathy score (Fig. 3) and sales improvement replicate the dominance of post-AI implementation in banks (Fig. 4) with an empathy score of 83.7 per cent versus 68.1 per cent for an increase of 19.6 per cent , and sales improvement combined percentage of 68.6 versus 25.0 per cent for an increase of 43.6 per cent. Applying the graph and charts, Chen demonstrates that AI bank deployment significantly increases agent expertise, retention, empathy, and sales support to advance their customer service centres. In order to implement the benefits of AI, Chen outlines three AI components: business insights practice, AI productization for agent success, and human, AI and digital platform augmentation.
Business insights practice involves a data driven engagement approach, which applies advanced analytics and AI tools of agent interaction and experiences to effectively use AI to improve banks. At the start, a business analyst, data scientist or data engineer monitors and acquires data on the performance of AI in production to assess and prepare the initial data for model training. A business analyst identifies and shares milestones, tasks, and evaluation and exit criteria of the AI model with the customer. A system and data solution architects conducts a design session to capture the system architecture, data ecosystem and AI products for business analysts to hold envisioning and discovering sessions with customers to capture AI solution visions. Collaborating with bank sectors, the AI community explains the significance of finance to a regular or finance customer to encourage AI implementation. There is another AI component that elevates financial institutions.
AI productization includes a process (Fig. 5) to implement the component of business insights to achieve agent success. The process starts with having bankers “onboard” with AI in financial institutions. The design data model channels interactions, contacts interactions, and assesses risk and compliance to create operating performance. In between circulating optimization and re-assessing/sustaining sections, the AI engineers commits and adapts for service delivery, standard operating procedures (SOPs), quality assurance, workflow enhancements and key performance indicator (KPI) improvements. Iterating between reassess/sustain and transform to the final step of the process, review and renew, the AI engineer provides additional capability and value-added solutions through AI tools that offer analysis, economies of scale, and more accurate, consistent and unbiased outcomes for a partnership to learn, evolve, and transform the client’s business. A third component completes the certification of AI into the financial community.
Human, AI and digital platform augmentation includes a process committee and model, which circulate among themselves, in order to fully encourage AI in financial sectors. The people led process improvement committee of human intelligence, in which performance distribution and outlier analysis identifies a high performer and a low performer to enhance a knowledge library, in which agent Small Medium Enterprise (SME) focus group share best practices and tricks. The data driven feedback model of machine intelligence includes multiple models: a sentiment model, which scores different levels of customer dissatisfactions, a classification model, which identifies the most effective de-escalation path based on customer Net Promoter Score (NPS)/Survey feedback, and conversation analytics, which targets the “stalled” interaction point.
Chen ends his presentation by highlighting the importance of an augmented contact centre to enhance banks at the AI2 Forum. He outlines how artificial intelligence, through business insights practice, AI productization to agent success, and human, AI, and digital platform augmentation collaboratively creates AI contact centres, which advances the expertise, security, and customer satisfaction of customer service at banks.
