iacsf: Case Study, Windstream Communications

Sarah Day, Windstream

Virtual assistants have been great. [Break]

Site optimization helped, aimed visitors interact with Wendy.

Reduced live chat interactions by 45%. There are topics Wendy can’t resolve–deep matters are where people work best. First chat resolutions are high performing.

Trailed internally fair 3 months a before launching–she recommends this as a best practice.

Customer reactions have been good, “thanks for your help,” also playful. Sometimes she has to remind people that she is a virtual agent.

Key learnings: as long as you have a knowledge base, a you will find partners to help create a workable, optimizables system. VAs will effectively reduce live support hours. Testing and optimization is fun. Customers love the VR.

Be aware of these: was a big process to port all knowledge into single platform, training for maintenance. What resources do you have? You will need more content than you know of and have at hand.

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iacsf: What’s Next? Shaping the Future

Panel:
Mark Yahiro, Intel
Timothy Tuttle, Expect Labs
Liesl Capper, IBM
Roberto Pieraccini, Jibo

Intel offers platform and foundation to use speech, motion, who you are, to use data in intelligent ways.

Jibo (a male character) is not humanoid, but has stereo camera, mic, speaker identification, motion or facial expression detection, display, touch points. Just got big crowned sourced funds. Japanese trend is to more humanoid, is creepy, uncanny valley. farthing can express feelings (teapot in beauty and beast).

Ongoing conversations? Watson uses experts systems, known data sets, to develop ranked diagnosis of medical conditions.

Movie Her and anticipatory agents? Tim suggests we’re not as far off as we think. We are going to starts seeing intelligence in new devices. Recent breakthroughs from IBM in deep learning remarkably reduce error rate within a couple of years.

What is the future? Cognitive glue working across other agents. Holy grail is all human interaction. There are already a lot of agents, interacting between and across them is learning a new language. Filter the right data specific usage, depending on the usage do we filter before or after? Where is context? What makes sense.

Speech recognition is about 60, 65 years old. Big problem is that once you understand how to did it, depends on the language and what you are talking about. Can’t create a closed loop.

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iacsf: Megan McCluskey, Schlage

Commercial brand spun off 10 months ago, including Schlage locks and Kryptonite bike locks. In business for 100 years, legacy of innovation. Strong brand awareness, customers trust them with security.

Two years ago they didn’t have a product management tool. They saw tipping point toward self-service, couldn’t keep doubling call center tech. Started with zendesk, moved to inBenta on contact us page and as widgets throughout the site for search, now also searching all site and social media. Goal is improved customer experience.

Implementing virtual assistants: leverage knowledge database collaboration space with partners and workers, use natural language for end users and agents. Preventative measures too–they looked at what people were looking for. Searches now specific, improved results by 88%.

Analytics tool shows what’s going on, top categories of topics, in real time.

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iacsf: Executive Summit

Panel:
Brett Beranek, Nuance
Fred Brown, NextIT
Brian Garr, LinguaSys (21 languages, today’s process release)
Phil Gray, interactions
Tom Lewis, Smart Action

Dan: people turn to others as they escalate customer care, going to people as last resort. Human interaction? Phil: path is invisible to customers. Highly conversational experience does take people, but systems learn too. Looking for same level of service. Fred says his work is getting very close, accurate. Brian works with semantics. Look at Google translate that works with swashes of words, vs LinguaSys which gets the right fidelity of the conversation. They build first 21 language maps, will add next 20 languages at no cost. Brett: great digital experience for gen y or millennials feel that if they have a to talk an agent, you’ve failed.

Dan: how much is automated and how soon? Fred: it’s not simple implementation. Must build it well first. Sargent Star is a good model, how much effort to build it up. Tom: $3.5 billion investments, but consumer has been programmed to think that IVR stinks. Call center manager perspective for 2050 will not be same as today, so where is tipping point? Investments in AI, also in voice recognition in HD vs calling a copper line device. Plumbing, standards, telcos.

How to resolve the zero out problem? Phil: from Hyatt, don’t remove people, maximize their expertise. Is about making things easier for customers. Tom: reflective interaction is key. Fred: start on smart device. Brett: consumer expectations are changing quite a bit, experience with e.g. Dom makes consumers aware that this experience may be elsewhere. Its up to enterprises to make that work. Brian: conversation needs to expand to consistent global experience.

Plumbing: when will this happen for smaller enterprises? Brian: we are doing this now, will change the price structure for NLU. Phil: call centers have different adoption curves, different technologies. Where are priorities? Today this is scalable. Brett: is accessible technology, kaspersky software implemented, got Roi within 3 months. Fred: it matters what consumers need, getting the natural language understanding, and how to hook that to back end. Brett: business models need to evolve, esp in contact center space. Dan: models, APIs, will happen, is in a parallel experience.

Brian: going for statistical to semantic application, coder I can build a system.

Brett: lot of enterprises waking up but not a good comprehension of where this might deliver good business value. Where are your cost drivers? What are your shopping cart use cases that cause problems? Build virtual assistants to address these problems. Fred: look at conversations, drop AI in after examining. Roi will be proof. Tom: need to present in a way that minimizes risk, adoption of technology has decreased over time. Business model includes understanding risk.

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