Moving to Next-Generation Speech Analytics:

Transcription

1 50 Years of Growth, Innovation and Leadership Moving to Next-Generation Speech Analytics: Words are No Longer Enough Sponsored by A Frost & Sullivan White Paper www.

2 Frost & Sullivan Executive Summary... 3 Introduction... 3 The Complexity Problem... 4 The Speech Analytics Answer... 5 Limitations of Traditional Speech Analytics... 6 HP Autonomy s Meaning-Based Approach... 8 Conclusion CONTENTS

3 Moving to Next-Generation Speech Analytics EXECUTIVE SUMMARY Speech analytics is one of the most powerful tools on the contact center workbench. Its business value lies in understanding the precise intent of the customer and driving actionable intelligence. Traditional speech analytics solutions rely on spotting keywords, phonetics, and phrases, and miss out on the context, relevancy, and hence the precise intent or meaning of the interaction. Next-generation, conceptual speech analytics tools can unlock meaningful information and provide actionable insights into customer and agent behaviors and expectations. INTRODUCTION In the past, call centers were charged with a very limited task: respond to voice calls from customers as efficiently as possible, at the lowest cost possible. That simple mission has expanded to include new technology platforms, accommodating greater demands by customers and having a much greater degree of revenue responsibility. Ensuring an excellent customer experience has shifted, in the eyes of enterprises, from an expense that must be kept low into a vital competitive differentiator. It is not enough to simply react contact centers have to anticipate changing business conditions in the customer base. Empowered customers readily turn to social media to find buying information and express discontent. These customers are also highly mobile, creating uncertainty about their location, status and even their identity when they call. Enterprises therefore need to gather, understand and quickly act on customer intelligence while maximizing the profitability, longevity and satisfaction of each customer. One of the ways that modern centers have engaged in this more complex mission has been to add a layer of analysis to the data gathering. There is a special emphasis on the unstructured voice recordings. Speech analytics software has been helpful in turning voice conversations into vital customer and contact center performance intelligence. Yet, for many centers, the need for actionable insight is outstripping the capabilities of early-generation speech analytics. Traditional speech analytics tools rely on keywords and phonetics. This poses a problem: these solutions miss out on context and relevancy, both of which are essential to understand what individuals are saying. In short, they do not provide meaning. Words by themselves are no longer enough. The agent s desktop also contains valuable information about the customer being served, which points to the need for analysis tools that capture and digest agent s 3

4 Frost & Sullivan screen data. The same holds true for conversations captured on other channels: IVR/speech recognition, Web and mobile app self-service, chat, , SMS/text and social media, and freeform notes by retail and field staff. Speech analytics captures the valuable insights in customers remarks to agents. For example: I was on your website but I couldn t get into my account or I was in your store earlier today but the salesperson couldn t find what I wanted. Contact center professionals need to get at the most relevant information through speech and other analytics solutions in real time. The volume of data generated from speech and other channels is enormous and growing. Moreover, contact centers are not the only organizations that can benefit from information collected from customer-agent interactions. Accounting, billing, HR, IT, legal, marketing, product development and sales departments realize great insights by applying analytics. But speech tools involve complex deployments that often rely on expensive implementation, maintenance, and support. That has forced contact centers to look to these other departments when making deployment decisions on speech analytics, as they search for ways to extend the value of the tool enterprise-wide. THE COMPLEXITY PROBLEM There are three ways in which this increase in complexity causes operational problems. First, customers are coming to the interactions with more varied and demanding expectations. They have interactions that cross contact channels; they seek out information from different sources before making their calls. They are increasingly using mobile devices that add to their sense of urgency before and during interactions. All of these shifts lead to more data that can be collected by the center and then mined for insight. But often that data is scattered among silos that are not built to communicate. This leads to the second problem with the increased complexity: most contact centers have not yet built systems that give them an integrated view of the customer across all touchpoints or through time. Too often, each interaction is seen in isolation, apart from previous interactions with the same customer, let alone aggregated segments of similar customers. According to a Frost & Sullivan survey, three-quarters of contact centers have no single, integrated view of their customers, across all contact channels. 4

5 Moving to Next-Generation Speech Analytics The third element is a traditional contact center quandary, made worse by increasing complexity: cost control requires that you constantly improve the efficiency and productivity of your labor base. It requires investment to deliver high-quality service, and customers are unwilling to pay for better service in the form of higher prices. As a result, companies are under pressure to find new ways to boost the service experience without letting costs spiral out of control. One way has been to use speech analytics to leverage the enormous amount of data already collected in the form of call recordings. THE SPEECH ANALYTICS ANSWER The contact center generates, through the course of its normal operations, an enormous amount of raw data about its activities. Data is spun out of nearly every system, from the telephony platform to the quality monitoring engine. Speech analytics is a set of tools that parses that particular set of unstructured data contained in recorded audio calls. It aggregates voice calls from a contact center and finds patterns in them. These can be patterns in behavior of a single customer or group of customers. Analytics can also detect patterns in how agents respond to a particular type of problem or sales interaction. To illustrate, an ACD report can tell you how long one of your agents was on the phone with a caller. Speech analytics, on the other hand, can tell you what that call was about and issues that may have impacted the length of the call. As a result, 5

6 Frost & Sullivan speech analytics applications have become powerful tools for customer experience enhancement and cost control. They help centers obtain valuable insights into agent and customer behavior that bolster contact center performance and productivity. Speech tools use a variety of word-detection techniques to drill down into customer conversations, turning voice data into meaningful intelligence that can be acted on. They rely on business rules that launch scripts to agents based on what customers are saying in real time. It s the automated rendering of the conversation married with the system s understanding of the available enterprise resources (Web forms, FAQ, manuals, scripts, product recommendations, etc.) that can help resolve the issue or drive up-sell activity. This understanding will get the right resource to the agent. Two elements give speech analytics its power: the power of voice and its methodology. The spoken words contain emotion, context, relevance and insights into the individuals attitudes and states-of-mind. This is surpassed only by face-toface, in-person interactions and high-quality video. Speech analytics tools are designed to perform the difficult task of uncovering meaning from conversations. They provide an ordered, objective, efficient, systematic and readily retrievable means of obtaining this knowledge especially when compared to monitoring live calls or listening to recordings and taking notes. Speech analytics uncovers ways that agents can deliver higher-quality service and capitalize on cross-sell/up-sell opportunities. Applications listen for churn risks and provide information on moments of truth, leading to better agent performance and higher revenue. They also allow managers to find the friction points that led to the inbound call. Customers can also give clues about where the leads came from and which new products or services, pricing or promotion drove them to make contact with the vendor. LIMITATIONS OF TRADITIONAL SPEECH ANALYTICS For all of the advantages of speech analytics, several years of use have uncovered limitations of the early technologies. Conventional tools typically rely on keywords and phonetics. Keyword searches return files that contain the terms queried by the user, relying on vocabularies in the system. It is simple and intuitive. Phonetics goes deeper by matching search terms to words broken down into their phonemes, or smallest compositions of speech, and matches them to words, even though they may not be in the system dictionaries. While these methods are proven for general or common interactions, they are less effective with more complex exchanges. As centers try to push as many routine interactions into self-service to control costs, the calls that make it to agents are more complicated and unique. 6

7 Moving to Next-Generation Speech Analytics Keyword-based technologies risk misunderstandings by missing out on concept, context and relevancy. For example, they may miss that the sentence I have a question about last month s payment is a billing matter because the words bill, invoice or statement are nowhere in the phrase. Several speech analytics solutions use Boolean searches. This technique, which is related to keyword searching, permits deeper dives into conversations by using conjunctive words such as and, or and not to expand, define or narrow the fields. This requires users to know exactly what they are looking for and to manually conduct these searches. The keyword, phoneme and Boolean tools suffer from two limiting weaknesses. The first is that they do not try to determine the meaning of words. The second is they also cannot differentiate multiple expressions of the same concept having the same conceptual meaning within a certain context. For example, supervisor and manager are sometimes synonymous but sometimes are not. Methods have been developed to obtain meaning from conversations and to make querying more intuitive. They, too, have limitations. Grammar/lexicon parsing uses grammar and lexicon rules to understand what was said. It cannot, however, give the right weighting to multiple ideas that co-exist in the same sentence: I installed some software on my laptop and it isn t working. Parsing would have difficulty determining whether it refers to the software or the laptop. Natural language analysis approaches human conversations by letting users pose questions rather than keying in specific words to get answers. It only recognizes precise questions and stored matching answers. This is a major shortcoming because customers conversations cover many different and complex topics, such as billing and service issues, often even within the same sentence. Speech analytics tools also have difficulty coping with accents, dialects and words with multiple and different meanings. Language and usage is rapidly evolving with new words, expressions and meanings entering the common lexicon. Another speech analytics solution barrier is the lack of integration with multichannel and screen analytics solutions. As a result, they can fail to provide an accurate and unbiased view of agent performance and productivity or even customers experiences. Most analytics applications fall short because they are designed for precise queries, when the critical need is to make sense of what is being said, in real time, whether by voice or text. Analytics for speech and other media help contact centers obtain real-time information and insights from customers conversations. Contact centers are now seeking to put this into a broader context that includes other forms of important data and types of customer experience, including customer surveys and mobile applications. 7

8 Frost & Sullivan Speech analytics solutions are, however, frequently deployed as standalone tools, without direct linkages into enterprise systems. This can limit inter-departmental information sharing. The search tools in these applications may be too complicated for those managers who only have occasional need for them to learn and keep refreshed on them. Frost & Sullivan identified this lack of flow-through in an earlier study, Redefining Contact Center Analytics. In it, we noted that many contact center professionals describe what is missing in their operations as a control center or a conduit of useful information to the executive level. HP AUTONOMY S MEANING-BASED APPROACH A more effective and productive approach to speech analytics is known as meaningbased computing (MBC), pioneered by HP Autonomy. MBC stresses relevance, not just accuracy, in its methodology. It uses advanced statistical analysis to understand the intent of the unstructured conversations, and likely topics, not just a literal readout of what was said. HP Autonomy s meaning-based computing platform, which powers all of its solutions, is called Autonomy Intelligent Data Operating Layer (IDOL). IDOL goes beyond traditional speech analytics solutions in that it examines the entire universe of customer interactions to obtain the context required to form an understanding of this human-friendly information. It incorporates proven speech analytics tools: keyword, phoneme and Boolean searches, grammar/lexicon parsing and natural language, but more than that, it pulls from sources including customers previous conversations (as well as live ones), CRM records, IVR, and text-based and Web interactions. It uses automatic hyperlinking that connects to a range of relevant documents, services or products that are connected to the original text. IDOL then incorporates the features of standard speech tools while overcoming their limitations by tapping other sources to give relevance and meaning. IDOL shows users, like contact center managers, patterns or themes in conversations and interactions, and in data sets, by applying heuristic or computer learning and sorting methods, such as quantum clustering, to form them. It understands that there is an X probability that the content in question deals with a specific subject by studying the preponderance of one pattern over another. IDOL is built on algorithms rooted in Bayesian Inference and Shannon s Information Theory. With Bayesian Inference, the more information given, the more accurate the outcome; also, prior experience should be used to inform new data. MBC begins with a blank slate and allows incoming data to dictate the model; it mixes new information with a growing body of older content to refine and retrain the search engine. It learns what customers are saying, which improves relevancy, accuracy and user understanding when querying conversations. 8

9 Moving to Next-Generation Speech Analytics Shannon s Information Theory provides a framework for software to skim through information to grasp meaning from it, just as one skims through a news story, as much of what is in there is redundant. This theory enables IDOL to determine the most important, or informative, concepts within a document, like a recorded call. IDOL then replicates how humans think and act, in software form. It provides a better analysis by seeking out the gist of what the call or text is about. It can compare apples to oranges by understanding that they are both fruit. IDOL does not have to hear the word statement to see that a customer is calling back about a billing issue. It understands the context of the call, recognizes that the caller previously called about a similar issue, and compares it with other calls and interaction types (e.g., s, chats or social) to identify the root cause of the call. IDOL then matches its conceptual understanding of the call with potential solutions and suggests the best to the agent to drive successful resolution. IDOL takes general queries and automatically pulls up answers with very little drilldown on the user s part, which increases timeliness, effectiveness and productivity. It is easily extendable to most managers in an enterprise, compared with traditional speech tools that require training and experience to use the right search words or phrases to obtain insights. The technology is language-independent as it derives its understanding from context; it is not restricted by specific grammars or vocabularies. It is easily adaptable to multiple languages, accents and dialects, and to rapidly changing word usages and lexicons. IDOL powers HP Autonomy s speech and multichannel analytics solutions. It provides access to more than 1,000 data types, allowing users to assemble structured and unstructured data and connect to more than 400 content repositories. Its service-oriented architecture allows businesses to easily add HP Autonomy solutions as needs arise. Autonomy Explore is HP Autonomy s multichannel analytics product that puts IDOL s meaning-based capabilities into the hands of contact center managers. It slices the time and effort they need to obtain a more thorough, actionable understanding of customer interactions. It is designed to function across multiple channels. It can identify which customers should be moved to self-service versus agent-assisted service based on certain situations, which improves customer experience and operations performance. Autonomy Explore automates not just speech recognition but also audio-to-text and video, taking a snapshot of what was said and the emotions and sentiment behind them. It automatically spots trends, both over time and as they occur, by grouping together related concepts. It can automatically alert staff based on thresholds and other triggers, such as repeated interactions with a customer on the same issue. Moreover, Autonomy Explore is extendable to the rest of the enterprise. It is small-footprint, Web-based and intuitive. 9

10 Frost & Sullivan CONCLUSION Enhancing the 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 can also cut costs. Traditional word/phoneme/phrase-based speech analytics solutions, however, are no longer up to the complicated job. Next-generation, conceptual analytics tools are gaining importance across contact centers. HP Autonomy s meaning-based computing methodology that powers its speech analytics solution is attuned to the complete range of customer interactions. It provides context and relevancy across multiple channels and permits the gained insights to be shared across the enterprise. 10

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