How Can A.I. Help Contact Centers?

Customers have more choice of product goods and more communication channels than ever before and the customers are without a doubt, in charge. At the same time, companies are fighting with new digital products, which channels to market, business models, looking to get used to the hopeful digital world. While these market pressures were first noted in the travel, retail and financial trading services industries, they are rapidly becoming the norm in other industries. It is becoming progressively more apparent that companies must engage their customers on their choice of channels, or risk losing them to competitors who do offer those channels.

Enter OmniChannel, one of the hottest approaches to customer engagement in years. OmniChannel strategies seek to deliver a reliable and tailored experience to customers across all communication channels and store the info in a single silo. OmniChannel now affects customer service and support, as well as to all aspects of engagement in sales. The history of the word “OmniChannel” go back a few decades to leading-edge advertising firms, who then sought to deliver consistent communication across all marketing media (TV, radio, newspapers, magazines, billboards and so on) hence the name OmniChannel.

Achieving success in OmniChannel is not easy. The seamless combination of consumer experiences in a customer’s pathway that cross mobile devices, PCs, phones, SMS, email, chat and social media is a massive ordeal. This is especially true where companies were just adding these separate silos of communication channels, as needed just to catch up with customer’s demands.

How does A.I. work with OmniChannel?

1. Classifies and Allocates - Incoming transactions are analyzed and allocated to available employees according to topic, skill, and urgency.
2. 100% Vision of Transaction History - Relevant background information from existing systems is displayed for customer support, tech support and sales.
3. Response Suggestions - The expert responses are learned from historical expert replies and are offered a small selection of text templates.
4. Sending and Archiving - Select the best reply channel (chat, SMS, Social Media messaging or e-mail) and automatic archiving – according to content in a single silo.

Artificial Intelligence (A.I.), which involves the design and development of applications that ‘learn’ based on flexible, evolving analytical models. One of the (many) areas where A.I. can make improvement is through customer engagement, and particularly for those seeking insights to optimize OmniChannel. These on-going evolutions, accomplished through algorithms that learn from repetitious data, are what really distinguishes A.I. OmniChannel is to identify hidden patterns and unseen trends without precise programming to look for these patterns and trends. As A.I. is exposed to new data, they adapt automatically, looking to produce reliable and repeatable answers.
Advances in computing technologies have fueled A.I. advancement, allowing many established A.I. algorithms to be repeatedly applied to enormous data sets with orders of magnitude boost the cost saving results. Today A.I. can be demonstrated in such notable areas as A Hong Kong VC fund has just appointed an algorithm to its board, Google’s self-driving car, online recommendation engines (such as Netflix using Amazon's A.I. cloud ), social media using A.I. and bank fraud detection by A.I.
Here is a list of: A.I. / machine learning / deep learning technologies companies:

• Add-Structure
• Angoss Software
•  Ascribe
• Attensity
• Attivio
• Basis Technology
• Bitext
• Brainspace
• Cambridge Semantics
• Clarabridge
• Content Analyst
• Revealed Context (Converseon)
• Dell, Digital Reasoning, EPAM
• Etuma
• Expert System
• FICO
• Fractal Analytics
• Haystac
• HPE
• IBM
• Infegy
• KNIME
• Knowliah
• KPMG
• Lexalytics
• Linguamatics
• Luminoso
• MaritzCX
• Meaning Cloud
• Megaputer Intelligence
• Northern Light
• OpenText
• Rant & Rave
• RapidMiner
• SAP
• SAS
• SpazioDati
• Squirro
• SRA International
• Taste Analytics

Let’s explore where A.I. promises to help OmniChannel Contact Centers.

1.    A.I. understands the customers’ pathways through the customer service or customer support process. Understanding customer experience from an ‘outside in’ standpoint, requires a regimented, controlled view of customers’ communications. Analysis of these pathways can highlight where procedure need to be improved by removing obstacles to customers accomplishing their goal. A.I. could take this one step further, predicting the customer’s goal through their actions, and providing faster track to improve their experience. Additionally, A.I. could improve customers’ experience while encouraging up-selling. A.I. could also be used to guide the pathways, to achieve a ‘win-win’ for both customer and supplier.

2.    A.I. can most obviously help product and service recommendations. We’ve already mentioned Amazon and Netflix recommendation engines as successful deployments, but these are early examples. Using A.I. along with analysis, history and current environment, A.I. could truly personalized product recommendations and tailored communication.

3.    Improving service and support. OmniChannel service and support are equally important to retain customers. A.I. can help in multiple areas:
       a.    Self-help can remove much of the more routine questions; A.I. gives opened questions and not boxed in choices that are answered with “Press 1”.
       b.    Based on current context and history, it could be used to predict why the customer is seeking support, speeding the response.
       c.     A.I. can provide recommendations for problem solving, both from the perspective of recommending a particular pathways, but also providing recommendations to the customer and support staff on how to address the problem, improving support effectiveness.
       d.    A.I. through continuous learning, it can refine these pathways and recommendations, optimizing the customer experience (CX).

In summary, A.I. promises significant breakthrough in improving customer engagement and experience, and OmniChannel is a clear target in need of application.

Please contact Call Center Pros, LLC for any call center technology need or telecom challenge.

Thank you,
James Wilson, CEO
Call Center Pros
+1-404 936 4000
james@call-center-pros.comr
http://www.linkedin.com/in/jameswwilson

What is Cloud vs. Cloud-Washing?

In July, Seth Robinson from CompTIA released his new report on the state of cloud computing with some puzzling results. The survey’s data ran contrary to statements from every consultant and tech research firm. CompTIA’s new Trends in Cloud Computing report shows that while well over 90% of companies still claim to use some form of cloud computing, but the pace of progress appears to have slowed. In some cases, it even appears to have taken a step backwards. What accounts for this phenomenon? Have attitudes towards cloud everything cooled, even though cloud continues to be a prime reason in IT growth? The survey included 500 CIOs and IT executives in the CompTIA survey and the use of SaaS applications showed a decline since the last time CompTIA completed the survey in 2014.

“When the data came back it didn’t look like we were marching along the adoption path we’d defined and we didn’t quite know what to make of it,” said Seth Robinson.

• Business Sectors        2014   vs.    2016
• Business productivity 63%    vs.    45%
• Email  59%    vs.    51%
• Analytics/BI   53%    vs.    35%
• Collaboration 52%    vs.    39%
• Virtual desktop         50%    vs.    30%
• CRM   44%    vs.    37%
• HR Management       42%    vs.    29%
• Help desk       37%    vs.    29%
• Expense management            35%      vs.    29%
• ERP     34%    vs.    26%
• Call Center     31%    vs.    23%

Why “cloud washing” artificially inflates the implementation numbers
The contrarian statistics found two trends:

1. Gartner expects the public cloud services market to grow by double digits in 2016, with $204 billion in worldwide revenue representing a 16.5% increase over 2015’s $175 billion. For 2017, Gartner believes the market will continue expanding, with year-over-year revenue growing by 17.3%.
2. The survey shows the drop of Cloud adaptation from 2014 to 2016 is due to understanding the definition of “Cloud”. The new term "Cloud washing," or on-premises software re-branded as cloud software, and CIOs' lack of understanding of what constitutes a cloud service. The most basic cloud washing practice includes a vendor that hosts an implementation of their existing packaged software and calls it cloud because they are maintaining it in a virtualized data center.

First let's have a common agreement or understanding what is the “Cloud” as defined by the National Institute of Standards and Technology (NIST). 

Crucial characteristics as defined by NIST:

• On-demand self-service. A consumer can unilaterally provision computing capabilities, such as server time and network storage, as needed automatically without requiring human interaction with each service provider.
• Broad network access. Capabilities are available over the network and accessed through standard mechanisms that promote use by heterogeneous thin or thick client platforms (e.g., mobile phones, tablets, laptops, and workstations).
• Resource pooling. The provider’s computing resources are pooled to serve multiple consumers using a multi-tenant model, with different physical and virtual resources dynamically assigned and reassigned according to consumer demand. There is a sense of location independence in that the customer generally has no control or knowledge over the exact location of the provided resources but may be able to specify location at a higher level of abstraction (e.g., country, state, or datacenter). Examples of resources include storage, processing, memory, and network bandwidth.
• Rapid elasticity. Capabilities can be elastically provisioned and released, in some cases automatically, to scale rapidly outward and inward commensurate with demand. To the consumer, the capabilities available for provisioning often appear to be unlimited and can be appropriated in any quantity at any time.
• Measured service. Cloud systems automatically control and optimize resource use by leveraging a metering capability1 at some level of abstraction appropriate to the type of service (e.g., storage, processing, bandwidth, and active user accounts). Resource usage can be monitored, controlled, and reported, providing transparency for both the provider and consumer of the utilized service.

Service Models as defined by NIST:

• Software as a Service (SaaS). The capability provided to the consumer is to use the provider’s applications running on a cloud infrastructure2. The applications are accessible from various client devices through either a thin client interface, such as a web browser (e.g., web-based email), or a program interface. The consumer does not manage or control the underlying cloud infrastructure including network, servers, operating systems, storage, or even individual application capabilities, with the possible exception of limited userspecific application configuration settings.
• Platform as a Service (PaaS). The capability provided to the consumer is to deploy onto the cloud infrastructure consumer-created or acquired applications created using programming.
• Deployment Models as defined by NIST:
• Private cloud. The cloud infrastructure is provisioned for exclusive use by a single organization comprising multiple consumers (e.g., business units). It may be owned, managed, and operated by the organization, a third party, or some combination of them, and it may exist on or off premises.
• Community cloud. The cloud infrastructure is provisioned for exclusive use by a specific community of consumers from organizations that have shared concerns (e.g., mission, security requirements, policy, and compliance considerations). It may be owned, managed, and operated by one or more of the organizations in the community, a third party, or some combination of them, and it may exist on or off premises.
• Public cloud. The cloud infrastructure is provisioned for open use by the general public. It may be owned, managed, and operated by a business, academic, or government organization, or some combination of them. It exists on the premises of the cloud provider.
• Hybrid cloud. The cloud infrastructure is a composition of two or more distinct cloud infrastructures (private, community, or public) that remain unique entities, but are bound together by standardized or proprietary technology that enables data and application portability (e.g., cloud bursting for load balancing between clouds).

CIOs are now savvier in their cloud choices
Many Contact Center as a Service (CCaaS) pitch it as cloud software just because it's hosted in their data center. They have not used something like CloudStack or OpenStack or built a proprietary cloud system to do the types of things to do the types of things that Amazon Web Services, Microsoft Azure or Google Cloud are doing. Cloud washing perpetuated as on-premises software providers and implementation partners told CIOs that they were adopting cloud. Some CIOs claimed they were using private clouds when they were actually depending on hosted data centers. So “cloud washing” was perpetuated.

The lower SaaS adoption numbers from his new study suggest CIOs have a better understanding of what comprises CCasS, SaaS, PaaS and IaaS and are becoming more savvy and making better educated technology choices. CIOs are still adopting cloud … they’re asking the right questions, before a making a decision.

Please contact us for any call center technology need or telecom challenge.

Thank you,
James Wilson, CEO
Call Center Pros
+1-404 936 4000
james@call-center-pros.com
http://www.linkedin.com/in/jameswwilson