Build a 360 degree view of your customer
My colleague Malla Poikela recently wrote about how important it is for communication service providers (CSPs) to gain and maintain a deep, 360-degree understanding of the customer experience to satisfy the empowered and demanding digitally savvy customers with the best experience. The obvious follow-up question is: “How do we generate those insights?”
To be able to provide this 360-degree view, CSPs need to build a customer experience hub to monitor and measure the customer experience and produce valuable insights. This hub needs to collect real-time customer data from all key touchpoints that a customer has with the service provider including: transactions (e.g., service plans, pricing and billing), the network (how customers experience its quality, reliability and reach), and interactions (how customers connect, access and experience service and technical support).
The way to build customer insights and ensure an excellent customer experience is to collect, analyze and apply data from all these touchpoints and associate the data points into meaningful and actionable insights. There are three key steps in this process:
1. Collect data
The journey toward customer understanding starts with collecting key customer experience data. This requires integrating all the various transaction, network data, and customer interaction and profiling systems. Traditionally, these systems have been implemented as isolated silos with the data points all stored separately. Bringing the data points together enables a CSP to leverage the full power of data, creating actionable insights.
For example, the following data points can be calibrated against Net Promoter ScoresTM or other customer surveys to reflect customer perception and develop an always-on proxy of customer perceived experience:
- Billing and charging transaction issues and customer reactions to campaigns offer important information on customer satisfaction.
- Customer Relationship Management (CRM) systems provide valuable data for customer profiling.
- Network data reflects the health of the connectivity infrastructure with detailed facts about customers’ network quality experiences in different locations.
- Data collected from customer care systems provides information about the issues customers are experiencing and the average handling times for different types of issues.
What’s important in taking advantage of these opportunities is that any solutions used to do it are agnostic and future-proof — so that the operator isn’t limited today or in the future as needs and technologies change.
2. Analyze the data in real time
Once the data is collected, the next step is to build a big-data platform that can integrate and correlate the data points. The kind of customer-specific information found in CRM systems needs to be associated with information about customer experiences like dropped calls and buying patterns to create a digital model of the customer.
This is the key: the capability to infuse the data with customer-specific details to create a model of the customer experience. That model can be used to create a customer experience index (CEI) that takes into account relevant metrics for each facet of the customer experience for each customer.
Machine learning-based clustering and profiling can leverage the CEI to dynamically identify new customer segments to help sales and marketing teams increase sales through more precisely targeted and personalized campaigns.
This is much more effective than traditional CRM segments, which were based on manual rules and a smaller number of variables, and are infrequently revised. With these traditional segments, marketing analysts had to specifically define and filter different queries to target their campaigns. Machine learning algorithms, on the other hand, can autonomously propose to focus on a very specific micro-segment of customers — for example, those whose data usage is higher than average and who had good network experience at their home locations during last three weeks.
3. Apply the data
The third step of executing customer experience transformation is to turn the data into use cases that provide business value for the CSP. This may seem simple, but it requires effort and prioritization to do it well.
CEI is a central concept in this solution, and the value of “good” customer experience will be different for each service provider. For Nokia, CEI is not just an absolute score that customers give to the service provider or a one-size-fits-all model. Instead, it depends on the ways a CSP interacts with their customers and the products and services offered. To be effective, CEI must be customized and tuned to match the needs of the organization it serves.
The insights that emerge from CEI may point to opportunities to improve and transform the customer experience at a fundamental level. Properly tuned, CEI can flexibly accommodate new aspects unique to each CSP to ensure it covers all important initiatives. This kind of index also augments human intelligence when drilling down to isolate specific areas where the customer experience breaks down — showing exactly where to target improvement efforts for maximum impact. Whether a CSP is looking to identify opportunities for valuable cross-selling, manage customer issues proactively, improve operations, automate business processes or boost network performance, the result will be happier customers who are more likely to stay with their CSPs.
Good customer experience is good business
At Nokia, we understand that a good customer experience is critical to success, and that good customer data is critical to a good customer experience. Nokia’s business and customer insights tools can help CSPs get a 360-degree view of the customer experience across all touchpoints. In addition, Nokia’s Customer Experience Management (CEM) Transformation Office service supports the introduction and deployment of a CEM solution, and comes with dedicated industry experts who can work with CSPs to leverage customer experience as a compass as they transition to customer-centric business decision-making.
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