Five Ways Big Data Analytics is Empowering BPOs to Ensure Customer Delight
In a digitally-driven world, data is the new oil, and it’s not without reason. It is the source of actionable insights that help businesses differentiate themselves. Gartner defines big data analytics as “high-volume, high-velocity, and high-variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making.” And as a source of rich insights into customer behaviors and evolving market dynamics, data analytics is becoming increasingly significant for BPO companies. Although volume, variety, and velocity sound overwhelming, they offer numerous advantages to businesses that aim to improve routine operations for a competitive edge.
Big data analytics considers different channels such as voice, chat, and email used by a BPO for customer service and inputs data from all these channels into analytics platforms to support real-time issue resolution and deliver customer delight. While offering advanced models and algorithms, big data can be seamlessly integrated into existing workflows. It is the go-to tool for BPOs to measure and improve their customer experiences, boost employee productivity, reduce operational expenses and eventually enhance their profitability and brand value.
Here’s a deep dive into why big data analytics is emerging as a critical advantage for BPOs:
1. Employee performance:
While evaluating recorded calls and text messages, advanced analytics tools can help catch significant keywords, identify customer tone, and assess their overall experience of interacting with an agent. The results help in better decision-making to improve agent performance and can also be used to train others. With insights on which agent is suitable for which issue, a BPO can proactively route calls to the best resources. Such call distribution reduces average handling time and improves the first contact resolution (FCR) rate.
2. Customer experience:
Improvements in team performance matter only when they lead to a better customer experience. With insights from data analytics, an organization can drive real-time engagement, offer support through multiple channels and map the customer journey to ensure that their conversation with a BPO agent was fruitful. Suppose a customer rates a contact center’s service poorly after an interaction, they c. In that case, Past interactions with customers help gauge their sentiments better and create positive experiences.
3. Cost control:
The monetary investment in big data analytics deters many businesses from using the solution. Besides, they fear that using new-age analytics tools will render their legacy models redundant. What needs to be understood here is that big data supplement traditional data-based studies vis à vis reducing their value. Moreover, storing high volumes of data on a Hadoop cluster is more cost-effective than building a new data center. A BPO can retain its existing data storage setups and add innovative big data analytics tools to gain richer information that helps them improve its customer service processes. By taking actions based on data analytics insights, enterprises can avoid costly mistakes that result in customer turnover.
4. Better decision-making:
If a BPO relies only on random past experiences to strategize its future initiatives, it may make decisions that do not deliver desired results. Conversely, when it has a repository of data to back its plans, more valuable business decisions around customer services, marketing, sales, finance, and growth-related functions can be taken. Hadoop clusters can fetch data quickly, and in-memory analytics also support prompt and informed decision-making. In addition, big data helps focus more strongly on customer needs and eliminates bias from decision-making.
5. Predictive analytics:
A practical outcome of business intelligence, predictive analytics is a vital tool for a BPO to track call volumes, wait time, customer satisfaction, and service level. It assists team leaders in addressing current issues based on historical data. For instance, predictive analytics helps optimize staffing decisions, enabling managers to decide the number of agents needed on weekends or certain holidays per call volume patterns. It can also help monitor and document the impact of new product rollouts on call volume and customer response.
At Flatworld Philippines, we have an enterprise-wide vision of big data analytics with a clear link to our business strategy and roadmap for improving FCR and offering better self-service options to callers. We have built a culture of data-based decision-making to enhance customer