Driving customer service to the next level with data analytics and AI

Uncovering the opportunities in your data

The challenges that companies face when establishing an analytics framework are multidimensional and can hamper the organization’s ability to effectively take advantage of the wide range of possibilities that analytics, artificial intelligence (AI) and machine learning (ML) present. This includes everything from undefined analytics strategies to foundational infrastructure breakdowns.

But putting data to work is no easy task. Companies today face an array of challenges as they look to establish the data capabilities they need to enable world-class business decision-making. A PwC Chief Data Officer (CDO) study found that when companies talk about data in their annual reports, it is 30% more likely to be in a defensive context rather than mentioning data as a potential source of innovation and growth.

Bolstering service quality is an opportunity for companies to retain — and even expand — your customer base. Robust data insights can deliver important clues for which service improvement initiatives to pursue and how to pursue them. Examples include:

• Customer visibility. It’s important to understand where your customer is in the product or service life cycle and deliver the right message. It’s impossible to achieve this best practice without rich customer data that offers deep insights into how to meet customer needs and predict behavioral patterns and customer segmentation.

• Internal insights. The other side of the customer service analytics coin is the ability of a business to get visibility into its own data and business metrics. This drives best practice decision-making and the ability to prioritize initiatives that lead to operational efficiencies. 

• Optimizing costs. Data can also unlock clues about how to better optimize disparate systems and manual processes. The resulting efficiency can lead to dramatic performance improvements as well as cost savings.

• Demand forecasts. Predictive analytics and machine learning can deliver insights that help your business shift from reactive to proactive. Suddenly, it’s possible to adopt predictive marketing and services that more closely align to what a customer needs at any given moment.

• Transparency. Transparency can help your business understand a team’s performance and overall effectiveness. It may also help you understand how your customers use your company’s products. This guides crucial decision-making.

What does this model look like in the real world of business? 

It’s critical to first identify current challenges and what moments matter most to your customers — and then design a robust analytical foundational platform that aligns with product development and responds to each and every moment in the customer journey. Take a look at how this has worked across industries. 

• Utility company: Used advanced analytics to determine patterns and trends for meter theft detection, meter anomaly detection and repair diagnostics to improve equipment reliability and better monitoring. The company was able to generate high probability repair insights, thus saving hours through predictive maintenance and reduced operating expenses.

• Software company: Deployed self-service chatbots to fully contain 40% of common customer queries in the bot and helped reduce the customer churn through improved customer experience. 

• Telecommunications company: Used simulation modeling and optimization to improve the field service operations by optimizing the service tasks and service orders to drive overall revenue, order compliance on commitment dates and lower operating expenses by reducing overtime hours.

One of the keys to moving beyond static data and into dimensional data lies in building a framework that supports this advanced customer relationship model. This includes breaking down silos and building a data fabric that extends across the organization. In order to arrive at this approach, we recommend concentrating on several key factors. 

• Service data platform: It’s essential to establish a structure that connects and integrates broad sets of data (connected IoT devices, service tickets and point of sale data) to enhance context-based service during the moments that matter. A robust service data platform also provides insights into the product and subscription services development life cycle.

• Simulations: Improve efficiency through simulations and digital twins. This scenario analysis and testing can deliver insights and information that completely rewires the customer service equation. 

• Customer profiling: Best practice businesses use data and contextual understanding to develop a different view of customers. This data-driven approach provides personalized and contextualized behavioral insights that can be used for one-to-one communications and interactions. 

• Intelligent contact management: Conversational AI allows a business to use natural language processing and AI to take analytics to an entirely different level. You can address multiple goals in the same dialog — while generating insights into a conversation topic, whether it’s ordering, billing or monitoring. 

• Preventive/proactive maintenance: Performance matters. One way to back this up is through predictive and preventive maintenance. When performance is based on uptime and payments are designed accordingly, there’s no room for misleading information. Transparency rules, and that’s a winning approach with customers.

How can you transform concepts into real-world results? 

Here are a few actions you can take to embrace AI and analytics in the service space. 

• Identify the right opportunities based on the level of maturity of your company and/or your willingness to modernize. Assess whether the data and underlying infrastructure is sufficient and accessible for AI modeling — or how it can be changed.
• Establish a robust data platform based on a flexible cloud-based architecture that integrates data from different functional areas to enhance context-based service and drive 360-degree customer insights.
• Implement advanced analytics and AI/ML capabilities that help identify key insights during the customer journey, particularly at the moments that matter most to the customer.
• Utilize insights from advanced analytics to guide product development and innovation. This allows you to enhance and develop products that meet customer expectations and address the most pressing issues customers face.
• Operationalize for the long term by establishing an AI/ML center of excellence to support a continuous and iterative model deployment framework. This supports a more comprehensive approach that revolves around continuous monitoring and learning.

When you gain a 360-degree customer view and have the tools in place to address specific desires and needs at the right moment in the customer life cycle, customer service analytics ceases to be a guessing game or a one-size-fits-all model. It becomes a competitive weapon that drives best practice results. In an increasingly crowded and challenging business environment, it’s a ticket to excellence.