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The connected home is becoming a very complex environment, requiring enhanced focus on knowledge gathering, data collection, and measurement in order to ensure customer satisfaction, competitive differentiation, brand loyalty, end-to-end support infrastructure efficiency, and continued subscriber ARPU growth.

Optimizing the customer experience in the connected home requires operators to embrace the use of analytics in order to:

  • Quickly identify anomalies, implement improvement programs, and understand customer behavior
  • Create and maintain a culture of customer experience (CX) excellence that is aligned with CX business goals and metrics around Net Promoter Score (NPS), customer satisfaction, churn reduction, brand loyalty, and subscriber ARPU
  • Manage the subscriber experience proactively and predictively, and
  • Continuously improve customer care processes and technologies

Areas in which analytics can be used by operators for managing the connected home will continue to evolve, but we have seen operators have a significant impact on improving the customer experience today by taking advantage of home device and home network analytics, online video analytics, Internet security analytics, and customer care analytics. 

Customer care analytics

Customer Care Analytics enables operators to continuously improve and optimize their customer care and business processes. Customer Care Analytics allows operators to leverage the wealth of intelligence embedded within customer care sessions and their associated workflow steps to improve the customer experience in the connected home.

Using Customer Care Analytics, operators can:

  • Identify newly emerging customer issues that drive up call volume
  • Identify workflows that have the most/least value to the business
  • Visualize workflow statistics to quickly identify optimization opportunities
  • Generate meaningful and timely reports to understand the most effective paths in workflows

In Customer Care Analytics, the data that is collected is related to the performance and execution of customer care sessions: workflow definition and execution data, CRM and case data, customer satisfaction data and net promoter scores (NPS), and call center agent data. This data is stored in Pentaho’s Hadoop Distributed File System (HDFS) and various workflow and path analysis KPIs are calculated and stored in a Relational Database Management System (RDBMS) within Pentaho. The data is then surfaced into Pentaho’s visualization dashboards, including optimization and reporting dashboards. 

Customer churn

A churn predictor predicts whether a customer is likely to churn. The churn predictor is built and trained from data collected from multiple customers. The data can include static configuration data and dynamic measured data. A churn predictor builder generates multiple customer instances and processes the instances based on the collected data, and based on separating the instances into one or more training subsets. Based on the processing, the builder generates and saves a churn predictor. The churn predictor can access data for a customer and generate a customer instance for evaluation against the training data. The churn predictor processes the customer instance and generates a churn likelihood score. Based on a churn type, the churn predictor system can generate preventive action for the customer.

Master data management is the processes, governance, policies, standards and tools that consistently define and manage the critical data of an organization to provide a single point of reference. One of the benefits of using MDM is that when that single point of reference is a customer profile, the master data can ensure that the treatment of customers is consistent and that preference information reaches all customer points of contact.

To ensure customer retention is front and center, broadband providers should be regularly scoring their database to understand the likelihood of a customer churning from their company (permanently disappeared and presumably to a better and more attractive offer from a rival).

Historical internal data is used to model the difference between a churned customer and one who is still engaged. There would be significant metrics in the data that identify the likelihood of churning. A parametric equation could be constructed that elicits the association and relationship between the target variable and the predictors.

This model would serve as an early warning system and also a strategic tool as to whether a customer was deemed worth retaining. The model would be run on a regular basis across the customer database to understand which customers have reached a critical value for their churn score. The theory is, these customers would then be targeted with an incentive/offer, in the process avoiding the likelihood of them churning. Alternatively, if the customer is deemed of little or no value, there would be no offer forthcoming to entice them to stay. 

Home network operation

Scalable data collection and real-time streaming analytics allows operators to collect and store any data, as often as they need. Sensors and streaming video QoE clients can be used to collect data from devices, and data is collected about network operations, services, and call centers interactions using, for example, CSV files, logs, CDRs, and SFTP.

Massive parallel processing and storage uses Hadoop for big data storage and batch processing, Cassandra for real-time data analytics (for example, for real-time customer support), and relational database for data storage for reports and dashboard tools. Data retrieval and processing that is built on top of Hadoop, and is used for data querying and analysis—using data processing frameworks and tools, such as Hive, MapReduce, and SQOOP.

Analytics engine and business intelligence consolidates, correlates, and analyzes data for automated actions or human interpretation. This includes filtering and normalization of raw data, and mapping of the data to particular KPIs and use case templates. Domain-specific analytics solutions allow operators to organize the resulting analytics events and alerts into particular business needs, such as home device analytics, online video analytics, or security analytics.

Home device and home network analytics allows operators to collect device and home network data, and use the resulting intelligence to proactively discover, diagnose, and resolve issues. Online video analytics combines data from video player plug-ins, CDNs and QoE agents to measure subscriber QoE, and assess viewing trends, content usage, and CDN performance. Security analytics helps to establish and maintain a safe home network environment by providing network-based analysis of Internet traffic for malware and protecting the network and subscribers. Care analytics leverages the wealth of intelligence embedded within customer care sessions and their associated workflow steps to create more efficient customer care processes.

Through the use of analytics, operators can: 

  • Quickly identify anomalies, implement improvement programs, and understand customer behavior.
  • Create and maintain a culture of customer experience (CX) excellence that is aligned with CX business goals and metrics around Net Promoter Score (NPS), customer satisfaction, churn reduction, brand loyalty, and subscriber ARPU.
  • Manage the subscriber experience proactively and predictively.
  • Continuously improve customer care processes and technologies. 

Online video analytics

Online video streaming is becoming an increasingly popular service in the home, delivered to a variety of different devices. As a result, it is important for operators to use online video analytics to measure subscriber video QoE and provide effective support for streaming video services. Online video analytics acquires data from a variety of different data sources, including streaming video player plug-ins, network QoE agents, and video CDN data.

A video player plug-in is software that is added to the subscriber’s video player(s), and it continuously and passively measures the subscriber viewing experience, reporting events that are used for QoE scoring and dashboard creation. The software can be added to the video player(s) in PCs, smartphones, tablets, and other Internet-connected devices

Network QoE agents are software clients that are installed on standard hardware, and deployed in the operator’s network. These QoE agents behave like virtual end-users, and they actively measure content availability and quality. Video CDN log files provide data about CDN performance, content usage, and trends that can be cross-correlated with other QoE data.  

Internet security analytics

Because of the variety of different devices that are being connected in the home, and the fact that many of these devices are wireless devices (often with different OSs) that connect to multiple Wi-Fi networks as well as mobile networks, Internet security analytics is becoming an important part of managing the connected home customer experience.

Operators can deploy Internet security analytics as a network-based service that is a supplement to client-based, anti-virus software. This service is built upon a Network-based Intrusion Detection System (NIDS) that is deployed in the operator network.

It analyzes the Internet traffic from subscribers’ home networks for the presence of malware, communicates events to an alerts reporting cluster (ARC), which provides data aggregation, storage, and analysis, and reports to a security analytics dashboard