Using Speech Analytics to Understand Insurance Customer Behavior. A White Paper from Saama

Transcription

1 Using Speech Analytics to Understand Insurance Customer Behavior A White Paper from Saama

2 Introduction There is an insatiable appetite for analytics today. Enterprises are increasingly investing in analytics to create sustainable competitive advantage for their products and services. Analytics is no longer nice to have, but a must-have capability in order for businesses to survive today s competitive landscape. Moreover, with the advent of Big Data, the amount of information that can be leveraged is enormous. Leading organizations like Amazon and Google have successfully pioneered their understanding of customer behavior using Big Data analytics to gain a competitive edge. Most enterprises are in the early stages of using a combination of internal systems of record data, external syndicated data and social media data to create cutting-edge analytics in order to better understand their customers. Speech Analytics: The New Kid on the Block One of the often ignored data sets in the analytics space is voice data; however, it is a data set that holds much promise, as so many daily business transactions are captured in this format. Customers today mostly interact with businesses using phone calls. For example, a customer service representative typically takes down notes on important aspects of a customer call. Employees typically use these notes later in order to provide appropriate services to the customer. Enterprises at the cutting-edge have begun to create analytics using these notes to understand customer sentiment using Natural Language Processing (NLP) technology. NLP technology has come a long way since its inception and today it s amazingly accurate in extracting sentiment metrics out of a given text. Companies are using sentiment analytics to connect with their customer in more effective ways. While speech analytics can provide a huge value to any enterprise s analytics efforts, the disadvantage is that there can be up to a 70% loss of data (sometimes more) between what the customer says verbally and what the agent captures as notes. This information delta, which is deemed less important to an agent, can be a goldmine for Big Data analytics practitioners, like data scientists, and predictive analytics in general. Voice data not only contains valuable words and sentences which can lead to better analytics intelligence, but also contains the tone, intensity and intent of the voice, which can add up to 80% more details, providing for deeper sentiment analytics. 2

3 Speech Analytics Benefits Some of the direct benefits of speech analytics include: Revenue Growth Cost Savings Reduced customer churn (better retention) using realtime contextual analytics Targeted marketing based on in-depth customer analytics Increase in upsell, cross-sell conversions Better sales conversion ratios Increased overall customer satisfaction Better insight into new product or feature development Better first call resolution reducing follow-up calls Optimizing processes based on why customers call, resulting in reduced call volumes Reduction in CSR headcount Improved fraud detection Better compliance adherence, saving potential fines and fees Reduced cost of quality assurance and monitoring Lower CSR attrition rates and recruitment costs through identification of specific training requirements Speech Analytics Market The market spend for speech analytics, though is in its primitive stage, is expected to grow with a Compound Annual Growth Rate (CAGR) of 21.4% to $614 million in North America by the year 2019, according to the research done by MicroMarket Monitor. Along similar lines, the European market is expected to grow with a CAGR of 23.5% to $347 million by the year The reasons behind this projected growth include: an increased number of call centers, technological advancements, rising demand for more precise risk management solutions, ability of voice analytics to derive key insights from the data, high competition amongst the companies, and a huge volume of voice data that already resides with the majority of large companies. 3

4 Available Technologies for Speech Analytics There are several commercial and open source technologies available in the speech analytics market. On the commercial side, there are vendors like NICE Systems Inc., Verint, and Avaya, who provide technologies that capture the voice data and provide mechanisms to convert it to text. On the open source side, CMU Sphinx is the prominent toolkit, offering several tools for various speech analytics capabilities and applications. Some of the important features of an ideal speech analytics solution include: audio-mining, emotion detection, and talk analysis combined with text analysis to analyze customer interactions from the phone, , chat, social media, and Web. By using a combination of these technologies, a solution could be created which is capable of providing contact center management with a unified view of speech and other communication channels, call flows, surveys, and agent desktop activity. It is also important that these technologies include robust compliance and security features for greater enterprise adoption. Core features customers are looking for include: ease of configuration, search flexibility, root cause analysis, and discovery. An ideal solution should offer both on-premise and cloudbased deployment options, as well as customer engagement optimization and the ability to capture, distill, and analyze complex and underused information sources, such as voice, video, and unstructured text. A Proposed Speech Analytics Solution for Insurance In this whitepaper, the solution architecture and business use case are described in the context of the Insurance industry. The solution outcomes and benefits identified above can be aligned to three key enterprise goals: customer acquisition, customer retention and cross sell/upsell. As shown in Figure 1, the Insurance business is very unique because it has few key customer touch points. It is not that often that a customer engages with an insurance carrier, unlike other industries. Most of the time, a customer calls her insurance carrier when she is in immediate need of assistance; it is important that the customer service delivery is seamless and the response is prompt. It is at this juncture, when a customer switch is highly likely if the customer does not receive a satisfactory call center experience. 4

5 Key customer engagement points include: new policy/quote, endorsements, claims, payments/ billing, renewal/termination, customer surveys, and ERS (emergency roadside assistance). It is imperative to monitor and analyze customer call data in order to take proactive measures to ensure a customer has a positive customer service experience and doesn t switch. Today s advanced technologies provide the ability to understand and predict various patterns and signals regarding customer satisfaction and sentiment, product, demographics, region & geography, and the extent of issues in order to identify the sentiment, trends, correlations, and segments. Speech Analytics - Customer Cycle GOAL 1: CUSTOMER ACQUISITION GOAL 2: CUSTOMER RETENTION GOAL 3: CROSS SELL/UPSELL Payments/Billing New Policy/Quote CSAT/ Sentiments Clustering Segmentation Endorsements Correlations Claims Product Customer Demographics Sentiments Renewal/Termination Signal Patterns Trends ERS Region & Geography Extent of Solution & Issues Customer Surveys Closed Loop Action Figure 1 This type of solution provides the ability to ingest audio data, perform speech recognition, and then translate the output into machine-readable data based on language and grammar models to perform analytics on top of it. Core Capabilities The core capabilities of a speech analytics solution should include the ability to: Identify the metadata of audio file/call. Conduct phonetic and linguistic analysis. Utilize machine learning capability to categorize calls and identify sentiment. Leverage real time search and visualization to highlight trends and alerts. 5

6 Key Features Emotion/Mood Detection: Measure the emotional state of customers by analyzing the pitch/ tone/tempo of their speech. Speaker Isolation: Distinguish between multiple people speaking on the same call. Speech Overlap Identification: Identify situations where there is an overlap of audio when speakers at both ends of the call speak simultaneously. Business Contextual Analyses: Determine root cause, trending, FAQs and voice of customer. Lexical Overview/Analysis Translate the audio data into words and phrases. Obtain overview of business context or utilize lexical analysis on one business area. Dictionary Look up Look up the translated data in business-specific dictionary to identify trends or themes of products and services being talked about in the call Pattern Matching Identify sentiments from patterns and phrases present in the translated data to understand voice of the customer and reviews. Word clouds are one example of pattern matching. Rule-based Categorization Sort key words and cues in the translated data to identify the category, line of business, and owners of the issue being discussed on the call and reviews. Demographics Leverage call metadata, along with privacy-sensitive details mentioned in the call (like age, location, date of call, occupation, etc.). This can be used for segmentation. Visualizations Intuitive user interface that enables a snapshot view for quick insights. 6

7 Reference Architectures for a Speech Analytics Solution The figures below depict the reference architectures of an ideal speech analytics solution. The architectures take into account the most commonly used technology components and business scenarios. The specific business case and technology platform choices may vary based on the specifics of each situation, but the essence should remain the same. Moreover, at a minimum, this architecture can be used as a starting point. Speech Analytics Solution - Logical Architecture Speech Recognition Visualization VOIP ACOUSTIC MODEL LANGUAGE DICTIONARY MODEL/ GRAMMAR AUDIO SIGNAL Voice Activity Detection SPEECH SIGNAL Recognition Engine Speech Channel Natural Language Processing Fluid Analytics Phone Response Text Generation KNOWLEDGE BASE Engine Speech Analytics Solution - Physical Architecture User Visualization Interface Authentication/Authorization Filter Graphical User Interface Browser-Based Admin Console Analytics Reports Interface External HTTP APIs REST API Security Filters Query Builder Result Parser Speech Channel DATA SEEDING Data Collection Managers Job Scheduler Whitelist Parsers Admin Processor Settings Parser Security Processor Data Whitener ADMIN ACTIONS Metadata Parser Stemmer Data Processor Categorizer Document Parser Machine Learning Engine Speech Recognition Engine DATA FEED Data Science Acoustic Model Dictionary Language Model NLP KNOWLEDGE BASE R NLTK ANALYTICS REPORTS ENGINE 7

8 Technology Choices There are numerous open source and commercial technologies available for use when implementing a speech analytics solution. Businesses can select the best option based on business need, capabilities, budget and existing technology platforms used in the enterprise. The table below compares an open source option (CMU Sphinx) to a licensed option (Apptek Omnifluent). OPEN SOURCE (CMU Sphinx) LICENSED (Apptek Omnifluent) Cost Free Fee [service (SaaS), local installation, and subscription delivery models] Data Security Secure, since data doesn t go to vendor Compromise on security File Types Can work only on written files Can convert real time audio Language Range Can be extended to new languages Limited set of languages for speech recognition and machine translation Language Models Need to build out language and grammar models Prebuilt and real world validated language models Development Time Higher development time and cost Lower development time and cost 8

9 Return on Investment for a Speech Analytics Solution The return on investment (ROI) for a speech analytics solution is multifold. On one side, it creates cost savings by reducing operational costs, improving quality, and lowering customer service representative attrition. On the other side, it increases revenue by reducing customer churn, and enabling cross-sell and upsell opportunities. Each of these factors will have a ripple effect on the other factors. At the same time, there will also be some overlap in return on investment. To simplify the ROI calculation, we should focus on operational cost savings in call centers and reduced customer churn as two key contributors of ROI. A reference ROI model for a speech analytics solution is shown below. The reference provides key contributing attributes for calculating the ROI for a specific situation. Average salary for a call center agent $40,000 Number of minutes a call center spends on calls per year 84,000,000 Number of call center agents required to handle the volume 600 Sample precentage of calls monitored per year 1 Agents required to monitor (Speech Analytics could save this) 6 Current repeat call percentage 15 Target improvement in repeat call percentage with Speech Analytics 20 Savings in number of agents by reducing repeat calls 18 Total operational savings $960,000 Approximate premiums collected $200,000,000 Customer retention improvement targeted to achieve with Speech Analytics 1% Approximate revenue retained due to improved retention rate $20,960,000 Total revenue improvement $20,960,000 Cost to implement Speech Analytics Cost of servers ($5k per server - fixed cost) $799,087 Cost of building the lexicon and implementation 10 Total cost to implement Speech Analytics - One time fixed cost $2,000,000 ROI 649% 9

10 Conclusion Enhancing the insurance customer experience is critical to business success. Firms that clearly understand the intent of customers and prospects and can leverage these insights across the enterprise will succeed. They will also benefit from reduced costs. Traditional text-based analytics solutions, however, are no longer enough to handle complex business problems. Next-generation speech analytics solutions are gaining in importance across contact centers. Speech analytics solutions can handle a complete range of customer interactions. They provide context and relevancy across multiple channels and help executives gain insights to make key business decisions. Speech analytics solutions provide deeper and faster analysis that helps devise a complete business solution for any contact center and customer center. Find out more about how Saama Speech Analytics utilizes advanced data science techniques to drive significant increases in return on investment. Endnotes 1. North America Speech Analytics Market, MicroMarketMonitor, May Learn More: Contact Us: or sales@saama.com A Technical Solution Paper from Saama 900 East Hamilton Avenue Campbell California Saama Technologies, Inc. All rights reserved. 10