No Match Found
Having rich customer data offers insights into how to meet customer needs and predict behavioral patterns.
There’s never been greater competition for the hearts, minds and dollars of customers, particularly in the subscription-based services space. People have more choices than ever — and keeping them engaged has never been tougher.
In PwC’s 2022 Customer Loyalty survey, 55% of the respondents said they’d stop buying from a company that they otherwise liked after several bad experiences. This points directly to the need for faster, better and more differentiated experiences, including in post sales service. It’s one thing to sell a product, but it’s another to build brand loyalty.
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.
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.
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.
Here are a few actions you can take to embrace AI and analytics in the service space.
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.