NICE Customer Engagement Analytics - Architecture Whitepaper

Transcription

1 NICE Customer Engagement Analytics - Architecture Whitepaper

2 Table of Contents Introduction...3 Data Principles...4 Customer Identities and Event Timelines Data Discovery...5 Data Capture...5 Customer Data Store...5 Business Data Enrichment Cross-channel Categorization and Contact Reasoning Sequencing and the Customer Journey Predictive Profile...7 Model Scores and Behavioral Predictors Reading Data from NICE Customer Engagement Analytics...8 NICE Customer Engagement Analytics Dynamic BI Database...8 Summary...9

3 03 Introduction NICE Customer Engagement Analytics enables organizations to capture and analyze all customer interactions, transactions and lifecycle events to get a complete view of the customer journey. Powered by state-of-the-art Big Data technology, it identifies individual customers and sequences their interactions across time and touch points to understand the context of every contact, uncover patterns, predict needs and personalize interactions in real time. The NICE Customer Engagement Analytics solutions are built on parallel Big Data storage that enables efficient analysis of terabytes of diverse, granular, multi-structured, cross-channel customer data. Data capture is done in real time or through batch connectivity and drives a wide range of business solutions either proactively in real time or ad hoc business intelligence (BI) and analysis. NICE Customer Engagement Analytics stitches together unstructured and structured customer interactions and transactions from any source or channel, and then assembles it into concise, structured customer records suitable for ad hoc analysis, predictive modeling, contact reasoning, customer journey and advanced machine learning. Its data storage layer is customer, event and session-oriented and organized around the customer. Every single customer interaction is stored in a manner which provides low-latency access and drives real-time actions and decisions. In addition, NICE Customer Engagement Analytics provides special attention to customer service representatives who are at the heart of the contact center and the primary point of contact for customers.

4 04 Data Principles NICE Customer Engagement Analytics was designed with three data principles in mind: Scalability, Flexibility and Low Latency. Scalability across terabytes of unstructured customer interactions and transactions relies on parallel computing sharing data storage across horizontally-scaling servers and performing the analytic processes and data enrichment in parallel, close to the data. Flexibility is essential to cope with rapid and unpredictable changes in how customer data is generated and consumed. The platform does not impose a fixed database schema. Instead the schema model can be expanded to incorporate additional data attributes, the same attributes are made available for BI analysis automatically. In addition, the system allows analysis to be made dynamically on query time directly over the Big Data repository. Low latency data access is critical to allow business analysts and data scientists to perform interactive investigation of the data and drive real-time decisions based on data analysis such as personalized marketing. This means retrieval and assembly of customer profiles in 50ms or less, including the latest interactions across multiple channels. Customer Identities and Event Timelines A key element of NICE Customer Engagement Analytics Big Data engine is its customer identity association. By observing patterns of identifiers that occur together, NICE builds a graph connecting identifiers to an individual and ascribes each data fragment to the correct customer. This picture becomes richer over time as new pieces of related customer information are recorded. For example, if a customer logs into a web account from home and then a week later does the same from a PC at work, both cookies become linked and the two sets of web activity data are merged into a single event stream, providing a richer profile for that customer. Data from contact center calls, s, mobile applications, social media and brick-and-mortar channels are easily combined in the same way by matching identifiers such as credit and loyalty cards, account numbers, addresses and telephone numbers. The Identity Graph adjusts to new connection events, providing as complete a picture as possible of an individual customer at any point in time. NICE Customer Engagement Analytics organizes and stores interaction data by individual customer, forming a single eventbased customer timeline. Retaining the detailed Customer Journey drives a wide range of business analysis such as cause and effect in customer behavior and customer journey optimization. This essential time ordering is typically lost in other data systems, such as when data is pre-aggregated in data warehouse.

5 05 Data Discovery Data discovery is a process through which NICE studies the organization s unique environment including customer touch points, self-service solutions, the different channels used for interacting with customers, the different systems in which customer-related data is stored, unique business terminology and more. Each of NICE s customers has a unique environment including different touch points such as contact centers, IVR, retail stores, mobile applications, and multiple interaction channels such as voice, , chat, mail and self-service systems such as Web and IVR. The data collected in the data discovery process is used to design a solution that fits the customer s needs including a detailed data flow. In today s dynamic world, data discovery is an ongoing process which continues along the solution lifecycle. NICE Customer Engagement Analytics includes a set of tools which enable data analysts and data scientists to constantly analyze the data and explore ways to extract new and meaningful insights. Data Capture NICE Customer Engagement Analytics system can receive data in multiple formats such as flat files and database data sources, as well as real-time interfaces such as web service and simple HTTP data connectors. Since the platform is schemafree, it is easy to input any digital customer interaction and transaction. The platform consumes data through ODBC connections to the customer s databases or data warehouses, real-time feeds or streams, log files and CSV files. Data can also be loaded or streamed into NICE Customer Engagement Analytics from an existing Hadoop or HBase data store by running a map reduce job to generate input events. Examples of data set feeds to NICE Customer Engagement Analytics include contact center calls, IVR data, web tags and tag management systems, s, mobiles applications, social data streams, CRM and ERP data, machine logs, advertising click data and data management platforms (DMPs). Data upload time to NICE Customer Engagement Analytics can be tuned to fit customer needs from once a day to multiple times a day. The system can load and process millions of events in a very short period of time. Customer Data Store At the lowest architectural level, NICE Customer Engagement Analytics stores detailed customer interaction records from any touch point, such as phone calls, IVR menu selections, agent notes, web clicks, product purchases, s or tweets. Each data point is recorded as an event. It utilizes HBase to store a vast set of granular event data. HBase is a highly scalable data store which forms part of the open-source Hadoop product suite and provides a robust, inexpensive way to store individual customer interactions and transactions. Data is stored in an open schema model which enables storing different data per interaction and transaction and drives a dynamic model for every customer. For example, phone calls to the contact center may have hold time and talk time and after call work time; IVR menu selections may have an application name and completion status; a product purchase may have an SKU, brand, price, size and color; and a web click may have a URL, page category, browser type, language setting and time zone in a web engagement analytics solution. NICE Customer Engagement Analytics turns this messy, multi-structured event data into structured data for analysis, sometimes called rectangular data because each customer record has the same set of computed fields. Implementation of HBase supports the flexibility to add new customer interaction data types easily. It does not have a traditional fixed or relational data schema. Data from any source can be loaded or streamed into the platform. In order to enable fast access to individual customer records, data is stored redundantly in the platform across multiple servers. This protects against data loss and enables high volume data retrieval and analysis using parallel processing.

6 06 Business Data Enrichment NICE Customer Engagement Analytics includes a robust mechanism for data enrichment based on business needs. Customers can define a set of calculated metrics which translate business processes to specific key performance indicators (KPIs). These KPIs are calculated for each new piece of data that is processed by NICE Customer Engagement Analytics and can be used for ongoing business process monitoring as well as trend analysis. In addition, the flexible enrichment mechanism is used for data classification which drives faster BI analysis. By classifying customer characteristics, business processes and transaction results into clear sets of values, NICE Customer Engagement Analytics helps define dashboards and reports to monitor business process performance and optimization. Cross-channel Categorization and Contact Reasoning Organizations today strive to provide diverse and customer-specific services as customers are now increasingly using more channels to communicate with them. This complex environment makes it challenging to understand current customer needs. NICE Customer Engagement Analytics allows organizations to collect any interaction and transactional data. The information is then used for understanding customer activity across different organizational touch points, whether it was an inquiry over the web, self-service activity via IVR, or a chat with a rep. The system automatically classifies interactions into the correct categories as well assigns the relevant contact reasons. The process is flexible enough to support different interaction channels and transaction data sources as well as adapt to the business. Sequencing and the Customer Journey Once all of the customer data collected in NICE Customer Engagement Analytics is placed on the correct timeline, customers can analyze the sequence of events and extract business insights. The timeline is used for calculating a series of sequence-based KPIs which describe customer behavior. For example, the number of customers who left the organization after placing a call to the contact center that was granted a low score in a feedback report. NICE Customer Engagement Analytics enables complex types of sequence-based calculations to be calculated across all customer touch points, taking into consideration customer transactions as well as providing the ability of customer behavior statistical analysis.

7 07 Predictive Profile Event streams are valuable for path analysis but may be difficult to consume for statistical modeling. NICE Customer Engagement Analytics distills customer event streams and their descriptive attributes into a set of predictive variables computed over specific timescales. For example, total spending in the past month is computed by summing the prices of all customer purchase events in that period. The NICE Customer Engagement Analytics platform leverages its parallel computing power to calculate these variables on-demand as customer data is read. Calculation on-demand ensures that customer profiles are always up-to-date and takes into account the customer s most recent activity. New predictors or variables can be defined in seconds and made immediately available through customer profiles. Model Scores and Behavioral Predictors NICE Customer Engagement Analytics provides the ability to define regression models to determine the accuracy or predictive power of variables based on cause and effect. These linear and logistic regression models enable analysts and marketing scientists to quickly identify the most valuable variables for their customer analyses. Once an analyst or modeler builds a statistical, predictive model, it can be imported and deployed in seconds to the platform via an API for real-time, on-demand execution. Each time an individual customer profile is requested, any applicable models are evaluated and the model scores become part of the customer s Predictive Profile, just like any other variable. When querying many profiles, model execution is performed in parallel across the HBase cluster as profiles are assembled. Since a predictive model score is just like any other variable in a customer s predictive profile, it can be used in queries, for example to retrieve event streams, predictive profiles or even just a list of all customers with a high predicted probability of churn. Scores can also be used in real-time decision-making, for example to determine what content to show on a web page or to guide a call-center agent towards an optimal cross-sell offer for a customer.

8 08 Reading Data from NICE Customer Engagement Analytics Data is retrieved from NICE Customer Engagement Analytics on either the customer or event level. An event represents any customer activity or transaction stored in the system. On the customer level, a familiar SQL query language allows queries to be framed around customer behavior, enabling the business analyst or data scientist to ask structured questions of unstructured data. These queries are executed in parallel across all of the data stores, returning event streams, predictive profiles or modeled scores. The queries may include combinations of specific events, profile variables and predictive scores to select customer records. A simple example is an analyst in a retail bank performs a query and may select all customers who utilized online bill payments from a mobile device in the last week and who downloaded a promotional bank over the last 90 days. The output is a structured set of records for every customer who satisfies this query in a predictive record set for analysis. By allowing the analyst to ask new questions of a massive data set, NICE Customer Engagement Analytics saves a huge amount of time traditionally wasted in data-wrangling. Analysts and marketing scientists can choose to run a complete query for all customers who meet specific criteria or just retrieve a sample for initial analysis. The platform arranges the customer data to ensure that any sample is statistically unbiased and can be used for reliable analysis. The platform s SQL enables analysts to leverage data visualization tools. Statistical modelers can query and access data directly from their R packages and then easily import their R models into the system for real-time operational scoring. NICE Customer Engagement Analytics Dynamic BI Database NICE Customer Engagement Analytics includes a dynamic data repository optimized for Big Data analysis. By leveraging the latest technologies in the database, customers can run reports, perform ad hoc analysis, and create dashboards of any data stored in the BI repository. Customers can choose which data elements and enriched data will be populated to the BI repository. This unique capability gives customers a powerful tool to shape the BI repository for supporting any type of business process monitoring. The NICE Customer Engagement Analytics BI database is designed to support a wide range of BI activities such as aggregated reports running on a specific timeframe and measuring general contact center and agentspecific KPIs; long-term ad hoc reports used by business analysts and managers to monitor contact center performance over time; trend analysis and specific investigations. In today s world, dashboard and aggregated reports must include drill-up, drill-down and drill-through capabilities. Without those basic capabilities, users are forced to switch applications in order to receive all the data they need. The NICE Customer Engagement Analytics BI database and tools are designed to support the various needs of the different business users throughout the organization.

9 09 Call Volume Optimization Customer Journey Optimization IVR Journey Analytics Real Time Web Engage 3rd Party Application Shared Applications Using NICE Applications Framework Analyst Workspace Managerial Dashborads Data Exploration Scenario Analyzer Cross Channel Player Batch Export Real Time Interface SQL Based Interface Cross Channel Logic Customer Data Store CEA BI Database Analytics Engines Real Time Decisions Data Enrichment Real Time Connectivity Batch Connectivity Interacions & Transactions NICE Engage Agent Users Chat Feedback Voice IVR Web Mobile Social Retail Notes Billing Data Other Summary NICE Customer Engagement Analytics consumes interactions and transactions, as well as structured and unstructured customer data from all digital and traditional channels in real time or through batch connectivity. The platform connects and stores the data by customer event and assembles it into an optimal format for customer analysis, prediction and BI. A powerful query language allows for retrieval of customer records in a predictive record set structure for predictive analytics. NICE Customer Engagement Analytics scales up to millions of customer records and is a highly flexible application. It is easy to add new data sources and perform a variety of queries. Low latency access to individual predictive profiles enables real-time actions tailored to the individual customer. NICE Customer Engagement Analytics empowers a wide range of real-time and ad hoc business solutions which utilize this data for operational cost reduction in the contact center, customer engagement management, contact center agent guidance and web personalization.

10 CONTACTS Global International HQ, Israel, T F Americas, North America, T EMEA, Europe & Middle East, T F Asia Pacific, Singapore Office T F The full list of NICE marks are the trademarks or registered trademarks of Nice Systems Ltd. For the full list of NICE trademarks, visit All other marks used are the property of their respective proprietors. ABOUT NICE SYSTEMS INC. NICE Systems Ltd. (NASDAQ: NICE) is the worldwide leader of software solutions that deliver strategic insights by capturing and analyzing mass quantities of structured and unstructured data in real time from multiple sources, including, phone calls, mobile apps, s, chat, social media, and video. NICE solutions enable organizations to take the Next- Best-Action to improve customer experience and business results, ensure compliance, fight financial crime, and safeguard people and assets. NICE solutions are used by over 25,000 organizations in more than 150 countries, including over 80 of the Fortune 100 companies /12 Contents of this document are Copyright