## Information Report on: How AI Will Transform Customer Service
The Present And Future Of Artificial Intelligence In Contact Centers
For Application Development & Delivery Professionals
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* Artificial intelligence (AI) will help customer service agents complete repetitive, predictable tasks, and eventually take them over to be a complete autonomous system.
* AI is not expected to replacing human agents entirely, it will enhance their skills and allow them to move beyond routine tasks, like collecting and reporting information, to customer interactions requiring deeper insight and analysis, or more complex issues.
* Such interactions often take longer to resolve and are opportunities to nurture profitable customer relationships, which are increasingly rare in a digital-first world.
* AI will deliver differentiated customer experiences, delight customers by making conversations natural and effective, anticipating needs based on context, preferences, and prior queries.
* Deliver personalized experiences to customers.
* Deliver advice, resolutions, alerts, and offers.
* AI and customer support gets smarter over time.
#### AI is used To:
* Capture data and information related to customer, use machine learning algorithms and analytics to:
* Streamline inquiry capture and resolution, making operations smarter.
* Extract useful information from voice and digital conversations, images, and machine-to-machine communications to quickly surface trends in issues and customer sentiment that may affect customer retention and loyalty.
* Uncover new revenue streams and reinvent business models.
* AI finds patterns in large data sets that reveal new insights that companies can use to create and monetize completely new services for customers.
* Machine learning algorithms used for business and customer intelligence find answers to questions that humans didn't even know to ask.
#### Pure AI and Applied AI (Pragmatic AI)
###### Pure AI
* Pure AI aims to build machines whose overall intellectual ability is indistinguishable from or even surpasses that of humans.
* Despite tremendous excitement and five decades of investment, we've only made slow progress on Pure AI, which represents an immensely difficult problem.
###### Applied AI (Pragmatic AI)
* Aims to produce smart systems that are commercially viable.
* Many of today's consumer experiences are AI driven. Amazon and netflix recommend products based on our history; yahoo and Facebook tag photos; Waze and Google get us to destinations more effectively; and Lyft and Uber precisely communicate the arrival times of drivers.
* Consumers are starting to expect intelligent experiences — personalized, contextual, and highly relevant — from all of the companies they do business with.
* Is a superset of technology building blocks.
* pragmatic AI today isn't a single technology. rather, it is comprised of discrete technologies that — individually or in combination — are advanced enough to add intelligence to applications.
* They can learn, predict, adapt, and potentially operate autonomously.
* This intelligence produces quantifiable business outcomes that companies can exploit today.
* Pragmatic AI Delivers Quantifiable Value
* pragmatic AI provides measurable value to customer service operations all stages of the customer service journey:
* pre-purchase support
* guiding customers to the right product choices
* onboarding
* post-purchase support
###### The Building Blocks of Pragmatic AI
* **Speech recognition**, able to to take commands from humans, transcribe a conversation, or participate in a conversation.
* **Text analytics and natural language understanding**, able to classify words and parts of speech, understand emotions, sentiment, and, to some degree, intent.
* **Natural language generation**, able to express information stored and modeled in software in natural language that humans can understand.
* **Machine learning**, a set of analytical techniques that use algorithms to detect patterns in data, build segments, and create predictions.
* **Robotic process automation**, able to perform routine business processes and makes simple decisions by mimicking the way agents interact with applications though a user interface. Can automate entire end-to-end processes, with humans typically managing only exceptions.
* **Image analysis**, able to identify and assign text labels to digital images and videos for identification, classification, and — increasingly — sentiment analysis.
#### AI in Pre-sales Customer Service
* Support a customer prior to purchase, educate buyers, minimizing purchasing errors and buyer's remorse.
* Online shoppers may have doubts about purchase decision because of uncertainty, especially shoppers who buy products that they have never experienced in person. 53% of customers will abandon an online purchase if they can’t find a quick answer to their questions.
* **Proactive engagement**, can intervene in the customer journey via an invitation to chat or co-browse at points of struggle or abandonment, such as product pages with high abandonment rates, abandonment points in an application or checkout process, or session inactivity.
* **Personal recommendations**, agents can recommend cross-sells and upsells that are personalized to a customer's interaction and transaction history to increase revenue and build awareness of the product portfolio.
### AI in Onboarding
* Properly onboarded customers are less likely to churn and more likely to purchase additional products, boosting their average lifetime value.
* Customer activation. AI can help create tailored programs to educate and incentivize customers; collect communication data enriched with behavioral and demographic data; and use algorithms to predict when, how, and what to communicate.
* Tracking customer health. Use customer data and product usage data to create a "health score" that tracks the success of onboarding activities and long-term usage.
* Predict customer satisfaction. Identify potential customer dissatisfaction before it occurs and act preemptively.
#### AI in Post-Sales
* Search and knowledge discovery. Helps agents finding relevant information to customer in an automated natural language processing and text analytics knowledge management system, that extracts topics and classifies content to understand intent of queries.
* Automated conversations.emerging intelligent agents for customer service span a range of capabilities, from single-purpose chatbots powered by machine learning to help tune conversational efficacy.
* Case classification. Ability to classify support cases after a call and transcribing conversation into text allows to run analytics on cases. text analytics also provides early warning signs of trending issues.
* Contact routing. Match customers with the agents best able to handle their needs and communicate with customers based on their style, using predictive models based not only on agent skills, but also on real-time analysis of behavioral characteristics like performance, personal strengths, and communication style.
#### Uses of AI for Customer Service
* **AI Augmented Messaging (Chatbots)**
* (1) [LivePerson](https://www.liveperson.com/), for example, lets a ChatBot start a conversation with customer, gets input from customer
* (2) Bot hands conversation over to human agent, which helps with user complaint or request, understands what customer needs
* (3) Human agent hands it over to Bot for routine processe
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* **AI augmenting human customer service**, AI can work in tangent with human agents, machine learning can also help streamline processes to make human more efficient
* **AI for sorting and routing support inquiries**, having a human agent reading every incoming support email is inefficient, AI can scan and tag emails to direct them to the right office. It can also provide agents with macros and clips from the best past responses to quickly create a response.
* **AI Enhanced Customer Phone Calls**, Interactive Voice Response (IVR) systems are used currently for phone support. AI is harder to deploy for voice-based communications. For example, the startup [Cogito](http://www.cogitocorp.com/) has developed a real-time conversation-analysis tool based on behavioral science and deep learning.
* Their AI listens to conversations for both content and tone.
* hey claim it can detect mimicking, change in volume, change in pitch, etc. to gain real-time insight into how customers are feeling and how all company calls are going.
* **AI Assisted ordering/purchasing**, food companies like Subway, Dominos, and Starbucks have all recently started using AI to let people place orders in different ways without human involvement. Individuals can place pizza orders with chatbots in Facebook Messenger or verbally tell Amazon’s Alexa to order them a frappuccino.
### Examples of Customer Support/Service Offerings Utilizing AI
[Service Cloud](https://www.salesforce.com/products/service-cloud/overview/) (SalesForce)
* Surface trending issues and intelligently route and classify cases.
* Deliver instantaneous and personalized support.
* With AI, give agents a deeper understanding of customers, and managers a complete view of team performance with dashboards and recommendations from [Einstein Analytics](https://www.salesforce.com/products/einstein-analytics/products/).
[Dynamics 365 for Customer Service](https://www.microsoft.com/en-us/dynamics365/customer-service) (Microsoft)
* Identifies trending content and offers intelligent knowledge recommendations.
* Personalize interactions based on past interactions and behavior.
* Speed resolution using machine learning and advanced analytics capabilities.
* Predictive customer service using data analytics.
<!-- * [Service Cloud](https://www.oracle.com/sa/applications/customer-experience/service/solutions/service-cloud.html) (Oracle) -->
[Service Cloud](https://cloud.oracle.com/service-cloud) (Oracle)
* Personalize customer engagements with guided assistance based on customer intent.
* creates a real-time performance profile for each technician to optimize schedules.
* Knowledge Management analytics, guided knowledge
* [Service Cloud Platform](https://www.oracle.com/sa/applications/customer-experience/service/solutions/service-cloud-platform.html), virtual assistant, proactive chat, guided interactions, and analytics.
* Natural Language, ask and understand questions in conversational language, uses the context and intent of question to return the right answer.
[Service Cloud](https://www.sap.com/products/cloud-customer-engagement/service-cloud-software.html) (SAP)
* Maximize insight with access to SAP Business Warehouse analyses.
[Human+AI Customer Service](https://www.digitalgenius.com/) (DigitalGenius)
* Automatically fill Case Data & Suggest Answers, we train our AI to predict case meta-data & suggest or automate answers.
* Rapidly classifies tickets and customer support requests.