Digital Self-Service. Tobias Goebel, Director Emerging Technologies April Aspect Software, Inc. All rights reserved rev: Mar 2013

Transcription

1 Digital Self-Service Tobias Goebel, Director Emerging Technologies April Aspect Software, Inc. All rights reserved rev: Mar 2013

2 AGENDA Experience Continuity Interactive Text Response, NLU DEMO Analytics 2

3 89% of consumers are annoyed when they have to repeat themselves about the same customer service issue.

4 The Solution: Experience Continuity CONTEXT CONTINUITY CONVENIENCE Wow your customers by knowing when and why they contacted you last, and whether there are any outstanding issues Provide a seamless Omni-Channel Customer Journey by preserving session state across customer touch points self-service and agent assisted Dynamically personalize and ease self-service interactions by managing customer preferences and segmentation data Show them you know them!

5 How? Context Cookies Think browser cookies : Small data bits that track the customer journey Examples Dropout points in IVR interactions Business tasks that weren t completed on last call Last transaction performed in mobile app Last time the customer talked to an agent Last issue discussed with agent Most recent page of your website the customer visited 5

6 Demo: Prime Telecom (Click to listen) 1. Max calls in and tries to update his credit card expiration date. His call gets dropped. 2. Max calls in again, expecting to start from scratch. To his surprise, the IVR remembers his transaction. OR Max decides to turn to Prime Telecom s mobile Web app instead Prime Telecom will always offer to complete the last transaction if unfinished in previous session 6

7 HOW?

8 Application Lifecycle Management CXP Analyzer Debugger, Trace Viewer, Phone-less testing, Load Testing Administration CXP Developer Storyboard Manager Project Documentation Grammar IDE

9 DESIGN ONCE Monument To make a reservation, please say the city and state first To make a reservation, please send the city and state first IVR TEXT DEPLOY ANYWHERE To make a reservation, please send the city and state first MOBILE APP SOCIAL

10 The Messaging Revolution

11 Know me Make it mobile Let me do it Make it social Fit into my life Save me time THE NOW CONSUMER EXPECTATIONS Make me smarter MAKE IT EASY

12 12

13 6+ of the Top 10 most used apps globally are Messaging Apps

14 900 Million ~11% of Earth s population use Messenger every month. Fastest Growing app in the U.S.

15 Messenger is the 2 nd most popular ios app of all time (behind Facebook itself)

16 A New Business Model Facebook thinks of Messenger as a platform on which entire businesses will be built. The idea is that Messenger will be an app for everything

17 Messaging: It s no longer the Millennials 33% 42% 77% 72% would rather CLEAN A TOILET than talk to customer service of daily active app users ARE LOST 3 days after install PREFER TEXT over picking up the phone and talking *

18 What if we could come up with the ultimate way to serve customers the way they communicate with each other WHILE SAVING COST ENTER INTERACTIVE TEXT RESPONSE

19 What are we building?

20 Hotel Virtual Concierge Retail Self-Service

21 Finance Loan Prequalification Banking Collections

22 The secret sauce: Natural Language Understanding

23 How Much do NLU and Keyword-Spotting Really Differ? 1/3 I need to tell you your pillows are awesome! Slept great last night. Keyword-Spotting: NLU: Type: [Request] Intent: [Housekeeping] Object: [Pillows]... Type: [Statement] Intent: [Kudos] Object: [Pillows]... 23

24 How Much do NLU and Keyword-Spotting Really Differ? 2/3 I need my room cleaned please Keyword-Spotting: NLU: Type: [Request] Intent: [Housekeeping] Object: [Room]... Type: [Request] Intent: [Housekeeping] Object: [Room]... 24

25 How Much do NLU and Keyword-Spotting Really Differ? 3/3 You need new cleaning staff Keyword-Spotting: NLU: Type: [Request] Intent: [Housekeeping] Object: [Room]... Type: [Statement] Intent: [Complaint] Object: [Housekeeping]... 25

26 Natural Language Understanding with Aspect NLU Aspect NLU Where is my package? Type: [Question] Intent: [Status Inquiry] Object: [Order] Sentiment: [Neutral]... 26

27 DEMO

28 Analytics

29 Strong Analytics Today

30 CXP Server Data Capture Customer Behavior Intelligence Dominant Path Analysis Interactive caller navigation pattern analysis Personalization Statistics Service performance by customer segment Business Tasks Transaction completion rates with detailed failure analysis Statistical data sampling Application & Grammar Tuning System Usage & Performance ASR Performance Speech recognition performance analysis Event counters Barge-in detection Server Statistics Call concurrency Call duration Technical metrics License Usage Utterance Statistics Grammar coverage, Word count stats, DTMF Fallback Session Details Call time & duration ASR Statistics Call termination Utterance Recordings Access to utterance recordings in application context Backend Stats Execution Count and Duration Errors and Timeouts

31 CXP Server Data Capture Customer Behavior Intelligence Dominant Path Analysis Interactive caller navigation pattern analysis Personalization Statistics Service performance by customer segment Business Tasks Transaction completion rates with detailed failure analysis Application & Grammar Tuning ASR Performance Speech recognition performance analysis Event counters Barge-in detection Utterance Statistics Grammar coverage, Word count stats, DTMF Fallback Utterance Recordings Access to utterance recordings in application context System Usage & Performance Server Statistics Call concurrency Call duration Technical metrics License Usage Session Details Call time & duration ASR Statistics Call termination Backend Stats Execution Count and Duration Errors and Timeouts

32 Reporting on Business Tasks Business tasks represent a business view on top of the call flow definition Task execution results are automatically logged to Infostore Business task related metrics Number of attempts Completion/success rate Failure rate (by category) Task completion duration Caller behavior analysis during task completion Aggregated metrics on recognition quality

33 Business Tasks: Completion Rates Cross-channel Reporting Business-level statistics on Task completion rates but 82% actively aborted the Order transaction Update Expiration Date was very successful and there were recognition problems and hang-ups while entering new credit card data

34 Individual Call Analysis Show only Calls where the task Enter New Credit Card failed due to a recognition failure only Calls with certain customer segments only Calls that terminated in a given Input State or Module Step 1: Define Filter Criteria

35 Individual Call Analysis Step 2: Select Sessions from List

36 Individual Call Analysis Step 3: Analyze Session Inspect dialog steps associated with the task Caller hung up after 2 failed speech recognitions attempts Listen to utterance recordings

37 Next Steps

38 Start with a Pilot: Selection Criteria Low Integration Complexity Predictability High Visibility High WOW Factor Limit required backend integrations Design ITR app for predictable user responses Choose a use case that will be noticed Pick a use case that will make an impact Standard APIs (Web Service) Limit domain Market it Design for I did not expect that!

39 Example Applications Banking Account management Collections Branch and ATM locators Alerts and reminders Payments Loan application updates Surveys Healthcare Appointment scheduling Prescription refills Clinical trial data collection Physician lookup Collections Patient surveys Utilities/SPs Account management Bill payment Appointment scheduling Upsell Same Use Cases as IVR!

40 Next Steps 1 Map out your Customer Journey What s there already? 2 Brainstorm pilot implementation of innovation How can we WOW our customers to stay relevant? 40

41 Let s Show our Customers the Art of the Possible

42 THANK 2013 Aspect Software, Inc. All rights reserved rev: Mar 2013