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.

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.

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.

Four years ago, brand started going through a reinvention (pizza tastes like cardboard), by listening, going digital to ordering online. Goals: increased customer satisfaction, increased revenue and profit, and better products and trials. About 45% of their orders come for digital, >$1B in business. Consumer experience drives results.

From order, you can track your pizza’s process from order to delivery, including who made the pizza. “FedEx of pizza.”

Pizza profiles enables simple order with 4 clicks. Now voice ordering in iPhone and android. 25 SKUs on their site, some items are customizable.ordering by voice is common, allowed them to break some constraints. This is a foundational technology, adds to a strong brand message. Their persona Dom is a character, fully integrated into ordering process. Nuance is partner. Huge learning curve. Was harder, took longer, was more expensive, but was worth it.

Demo done live (worked pretty well) for ordering pizzas. Knew from store what selection to offer, dealt with ambiguity, has a sense of humor and is conversational. “Easy order” would place order in one step. Consumers like it: appears faster, simpler (less steps), easier process, more intuitive, positive brand impact.

Problem: consumer needs to choose store first, then limited by what that store offers, not by what consumer wants.