Finding synergies between Business Process Outsourcing (BPO) and Machine Learning Operations (MLOps) offers a strategic advantage to enterprises. As practices that automate and simplify artificial intelligence (AI) workflows, machine learning operations (MLOps) add discipline to developing and deploying data-based programs. It not only allows organizations to document reliable processes and create safeguards to mitigate failures but also craft more accurate models with training data. When extended to the BPO industry, MLOps enhances the efficiency of data recording and analysis, making the BPO evolution with MLOps about improving quality, productivity, and profitability of conventionally manual operations.
Today’s BPO landscape is not merely about cost-saving through outsourcing functions such as customer support and lead generation. Companies serving customers through phone, chat services, and emails work to add strategic value to these solutions via tech-based process optimization. BPO and MLOps integration plays a significant role here. To increase the pace and coverage of services, businesses use intelligent routing of calls and chat support to specialists based on customer input.
Infusing ML into contact center operations is also an effective way to automate routine tasks. For example, self-help options provided by a bank’s toll-free number can enable callers to get quick responses to queries such as account balance, status of a cheque, and last few transactions, and also allow them to block a lost card quickly. Similarly, chatbots on a healthcare company’s website can speed up appointment booking, and on e-commerce sites, they can help customers track their order status. The ability to serve more customers without forcing them to wait for a human agent to come on-call/online is a competitive advantage with BPO-MLOps integration.
Other benefits of streamlining processes with BPO and MLOps technologies include:
Robust business intelligence
By regularly analyzing operations data, BPO firms can track customer service efficiency and flag potential issues. For instance, if a customer sounds annoyed on a call, ML can assess such sentiments and proactively alert a supervisor before the agent handling the interaction fails to provide a satisfactory resolution. Quick intervention by a more experienced person can prevent unnecessary escalations.
Better security
The strategic value of BPO and MLOps integration is also the enhanced security for the BPO firm’s operations and its clients’ processes. In an age where data that feeds CRM, ERP, and other enterprise applications is typically stored in the cloud, AI and ML help manage numerous endpoints. They can scrutinize suspicious account login activities, detect location-based anomalies, and conduct IP reputation analysis to identify risks in cloud platforms and thwart them before they cause any damage.
Improved customer experience
MLOps-as-a-service further strengthens this integration by providing a scalable and flexible model for implementing MLOps within the BPO sector. This service model ensures that businesses can easily access and deploy advanced ML capabilities without the need for extensive in-house expertise or infrastructure investment, leading to an enhanced ability to predict customer behavior, personalize services, and resolve issues swiftly.
Reducing call volume pressure
Innovative BPO-MLOps services enable callers to get the information they need in real time without always speaking to contact center agents. This also reduces the volume of calls that humans handle. The availability of self-service menus allows BPO employees to address more complex or unique problems that require empathy in resolutions and personalized services, going beyond documented rules. The overall outcome is higher customer satisfaction as well as optimum productivity levels with cost control.
Targeted leads and increased sales
ML technology used to run chatbots, automate mails, and personalize product recommendations, is an effective mechanism to approach new customers for a business. Organizations working toward lead generation can improve campaign efficiency through the BPO and MLOps integration. ML automates the storage of new leads in a centralized database and can analyze the quality of such leads. Based on such leads’ repeat visits to a website or calls/messages to an organization, ML algorithms classify them for the next targeted action, such as a callback or invitation for a meeting/event. With a focus on potential customers’ needs, the successive actions after lead generation and classification may boost sales.
Unlocking Efficiency and Innovation with CriticalRiver’s Integrated BPO Solutions
Besides being a trusted technology partner for companies across industries, CriticalRiver also serves as a reliable BPO solutions provider. With years of proven experience in the tech domain, we understand the nuances that enhance efficiency through BPO-MLOps integration. Our global service delivery infrastructure in India, Costa Rica, and the US leverages automation and human abilities to optimize customer experience. We invest in multiple technologies and skilled workforce to nurture your organization’s growth and business transformation.
To know more about our BPO solutions, write to us at contact@criticalriver.com.
