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The Future of
Conversational Interfaces
And how we get there
And how that fits with CPaaS and Conversational Customer Service Generally
20+ Years
Innovation
Software/
Network
Engineer
Strategical
Technical
Delivery
Product
Management
About Me
Walter Ong: Orality and Literacy
Why conversational?
History: input devices
History: interoperability
The Present: RESTful
interoperability
So how do we get context?
Need to get and keep context
The Mountain to Climb
Where we are: Voice &
Voice Recognition
Problem with interfaces
First step: Getting
conversational text right
Then get the design right
Then vocabulary and accent
Unguided Learning (self-correct)
Then we bring it all together!
Why Conversational
Customer Service?
Questions
& Suggestions
& Ideas

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CPaaS Conversational Platforms and Conversational Customer Service – The Experience Gap? Ben Waymark

Editor's Notes

  1. to make a truly conversational platform are: Restful APIs Good programming/development tools that facilitate rapid development Solid basis in conversational Text then move on to voice Get and Keep context Great UX driven conversational design Nomenclature, vocabulary and accents right Self-learning/Self-correcting training so it can grow
  2. Webio builds conversational interfaces for the credit and collections industry (at the moment) with emphasis on good customer experiences 20+ years experience in innovation including telephone trunk monitoring systems in call centres (20+ years ago), interactive tv, SMS bulk messaging for horse racing tips and automated chatbots Backgrounds as a software engineer, and have product management, technical delivery, strategic planning with strong leanings towards marketing and usability Pre webio was building chatbot for travel industry as part of their innovation lab Accent is Canadian, vocabulary is British and Irish as I live in England and work in Ireland
  3. a Jesuit who wrote “Orality and Literacy” 1982, in it he spoke about Oral societies where transformed by literacy Pre oral we memorise things and tell stories laden with pneumonic devices One of the big transformations is how we go from a memory based society to a written-law, written holy books, and physical boundaries, the fluidity of culture can change because we have ideas and rules set it stone He talk of oral societies, literate societies then secondary oral societies which is an oral society based on the written word (plays, radio, tv, and now internet) and this makes its orality more contextual, nuanced, and sophisticated The interesting progression here is how we don’t stop at literacy, the urge is to come back to orality Computer engineering similarly going “secondarily oral” conversationally
  4. Its how we think and its what everyone wanted Everyone bought Alexa because they wanted it to work But it doesn’t do what we want, it does single commands, not turn by turn conversations because it doesn’t manage context, its not secondary orality When Ong spoke of oral societies he spoke of the importance of memory and pneumonic devices and literacy erodes this: Conversation isn’t barking specific commands, its saying those commands in infinite variety of ways Alexa and the like only work if we remember all the commands, which is rubbish But first let me start with a quick history
  5. Punch cards to joysticks keyboards to mouse to fancy mice …. But we’ve always wanted to speak! Speech was held back until recently by voice recognition Conversational held back by the sequential logic of scripted programming languages with programming languages we went from logic gates to assembly to BASIC TO every sophisticated 3rd, 4th, 5th generation languages, OO etc all trying to make programming more conversation But as we get into pattern matching, AI and Data Science the sophistication grows but some of the core problem remain Until AI & DS context has always been supplied by logical flow. But context has also been limited by interoperability, because it can only get context from one application on one device, and without context we struggle, But first a history interoperability
  6. Connecting TRS80-100 modem to Apple SE, setting parity and NBIT, 9600 baud and away it went! Then Apple started reading DOS disks Then Usenet and the alt groups, Archie and Veronica Then Netscape and https Which leads us to ….
  7. Interoperability between all devices: IoT, phones, laptops, servers, containers etc all possible through restful services You can use any programming language or operating system You can setup your Alexa to connect to the RestFul API for the Dublin Bus service thanks to Resftul API When you want to get your CRM to connect to an SMS aggregator or to WhatsApp or to WeChat or Telegram you do that through Restful APIs But how do we get context
  8. To ask alexa what the bus time is, I need to know you are in dublin Cookies Data and data science tools like digital twinning (give examples), total life time value (recently, frequency, value and spend) CRMs Channel (Facebook? LinkedIn? SMS? WhatsApp?) Intent recognition in AI (give an example)
  9. But it is hard “How much is a flight to Paris?” “What is the weather like there?” “Do I need a VISA?” “Is it going to be sunny this weekend?” --play video-- They key will be to confirm what is being said, and not be afraid to get it wrong (Googles biggest innovation was accepting close enough rather than perfect)
  10. Big usability mountain to climb with rubbish design tools that build rubbish conversations Let me tell you about my Alexa, because it shows the problem with the interfaces. Alexa – playing radio is great, driving is great, turning lights off is great Bedroom not recognised as bedroom light, have to remember what to call everything. Still have to code; still need those tools we lost with literacy!
  11. Voice is always the goal WeChat is incredible and can translate Voice clips Voice searches (WeChat example) Pizza talking Alexa & Spelling Android Auto & Driving
  12. There hasn’t been a runway app WeChat style search filtering hasn’t arrived Accents still a problem – polish English fine, Hiberno-English less so It’s not a natural conversation … it’s commands by voice
  13. KLM did huge effort, had agents train, had all the data. Still isn’t great. UX and ‘calm technology’ Good UX starts with good interfaces to build; most of the awful interfaces that have been produced start with rubbish programming interfaces (Remember MS FrontPage?) IBM Watson or Amazon Alex and building a bot in there and how hard it is. If this then that interface Perfecting “Bricks” then stringing bricks together – ID&V is one example, Payment, Promise to Pay etc AI/ML and DS play a big role, but the solution is fundamentally UX not AI … buttons vs intent example
  14. When we get the bricks right, we need to get conversational right – thinking about conversation UX like any other interface This means not repeating the same thing over and over (variation) Erica Hall has a great book on conversational design, we need to do that: we need thought User Experience lead conversational design that looks at the lessons learned by great usability be that iPhones, Amazon “One Click” pay button, or googles search interface, Tinder’s profile importing or NEST smoke alarms This means having a great interface for the user, but also for the people developing, testing and wireframing conversations
  15. Understanding “Aye-up” is hello, “Youalright” is a statement answered with the same and fifty bucks is different when talking about currency or hunting licences Some of this is understanding WHO is speaking (which is difficult in a family) and WHY they are speaking (what do they want, intents in ML) Also means learning emerging words and vocabulary and learning the changes as they comes into usage … “wasssaaaappppppp”
  16. Self-correcting –unguided learning in AI nomenclature-- conversational interface will be key to both making and keeping the interface good. This will need to be a variety of mechanisms for understanding how words change, new words and saying arise and when the “bricks” change It will also mean understanding how the context of the same speaker may change when they age and when their interests, goals or circumstances change
  17. And this really is what the job of the conversational platform will be to do: take the different components: Interoperability Voice recognition Natural Language Generation with variety and tone The ability to build conversations through ‘bricks’ of functionality with good usability – photoshop of conversation! Store context and confirm context (including knowing who the speaker is) Design conversations with skill and attention to detail Get the accents, variations, and vocabulary correct (and keep it correct when language changes!)
  18. Conversational customer service is likely thoing to be a big driver because: Starting with an ID&V means we can definitively ID customer We can get contextual information through CRM The use cases and outcomes are specific There are clear commercial goals and aims: reduce call wait time, give customer care agents meaningful rather than repetitive tasks, upsell and cross sell So when we are building a Conversational Platform as a Service these are the elements/experience gaps are bridging to make a truly conversational platform are: Restful APIs Good programming/development tools that facilitate rapid development Solid basis in conversational Text then move on to voice Get and Keep context Great UX driven conversational design Nomenclature, vocabulary and accents right Self-learning/Self-correcting training so it can grow I expect that will start to see each of these element come together over the next years or so