t

Optimizing Customer Support with Machine Intelligence By applying learning algorithms, customer care centers can arm agents with the ability to quickly troubleshoot and proactively resolve customer challenges.

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Executive Summary

For most product/service companies, a major portion of their

maintenance expenses are typically directed at customer care, with

customer support agents playing a crucial role in continuously

improving customer experiences by supporting and troubleshooting

device and systems issues. At the same time, many customer care

organizations struggle to answer common issues, such as how to:

• Reduce the time spent by customer care agents to resolve problems.

• Improve the customer satisfaction index.

• Expand the troubleshooting guide for new issues and symptoms.

• Create a user-friendly system to ease problem resolution for

customers.

• Reduce support center expenses without compromising quality.

OPTIMIZING CUSTOMER SUPPORT WITH MACHINE THINKING 3

We believe machine learning will play a major role in addressing

these critical customer care support challenges. Specifically, machine

learning can automate the process of identifying problems and

recommending fixes, either with or without agent intervention, which

speeds problem resolution, improves customer satisfaction and

reduces costs. Machine learning-based solutions can create an expert

system for agents that continuously evolves, based on historical and

current troubleshooting data.

This white paper reveals how machine learning technologies can be

effectively applied to resolve many of the challenges that roil customer

care support centers. It also details how our machine learning solution,

code-named Cognizant ASIMOV, can help address customer care

troubleshooting challenges.

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Customer Care Center Flow Challenges In a typical customer care center, the workflow begins when the customer calls a support agent, who collects symptoms of the problem, makes a diagnosis, collects more clues, and refers to the knowledge base/troubleshooting guide for solutions. The knowledge base/guide should contain detailed steps that the agent can recommend to the customer. This is generally an iterative process, involving multiple steps until the problem is resolved (see Figure 1).

In the archive, troubleshooting data is recorded in the form of log data that contains information about the conversation. Generally, this content is neither structured nor straightforward, making it difficult to summarize without human assistance.

The ensuing data complexities include:

• Troubleshooting data is represented in a chatty question-and-answer pattern. The data is typically a dump that includes Q&A sessions between agents and customers, diagnostics information, responses from devices, actions per-formed to resolve the issue, and the outcomes of those actions.

• The troubleshooting flow differs for each product, based on the issue observed, symptoms and line of business.

• The overall volume of troubleshooting data that is collected for each interac-tion is typically very high.

The Virtuous Customer Care Cycle

Figure 1

Verify Problem

Diagnostics

Recommendation

Resolution

OPTIMIZING CUSTOMER SUPPORT WITH MACHINE THINKING 5

Applying Machine Learning to Customer SupportUsing pattern recognition and computational learning, machine learning involves the construction of algorithms to learn from and make predictions on data.1 Machine learning algorithms have existed for decades but were used only for limited scientific purposes because of their cost and dependence on high levels of computing power. In recent years, as per Moore’s Law, computing power has increased as provision-ing costs decreased, and with the advent of big data ecosystems, organizations can now process and analyze large volumes of data with machine learning algorithms. Today, practical machine learning solutions have transcended high concept to become near-term reality across multiple disciplines and industries.

With customer care, the primary focus of the machine learning solution is to equip the system with:

• Expert knowledge to understand the troubleshooting log.

• The ability and skill of support agents to resolve customer problems.

In summary, machine learning helps gather the knowledge of hundreds of agents and subject matter experts (SME), and feeds the knowledge to support agents to accelerate time to resolution and effectively solve customer challenges (see Figure 2).

Figure 2

Human vs. Machine Customer Care

SMESupportAgent

MachineLearningUnderstand the insights in unstructured

troubleshooting logs for a limited volume of tickets.

Understand the insights in unstructured troubleshooting logs for a large volume of tickets.

Understand the current problem of a customer and correlate it with the limited learnings of the agent.

Recommend an effective solution based on a large volume of history tickets.

Today, practical machine learning solutions have transcended high concept to become near-term reality across multiple disciplines and industries.

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Introducing ASIMOV ASIMOV is a machine-learning-based solution developed by Cognizant’s Global Technology Office High Performance Computing Labs (HPC Labs). The system contains software and processes to address common customer problems and overcome customer care troubleshooting challenges (see Figure 3).

ASIMOV’s machine learning software can help customer care organizations do the following:

• Extract insights from complex troubleshooting data.

• Correlate various symptoms, problems, actions, fixes and resolutions in the trou-bleshooting logs.

• Recommend fixes and solutions based on existing symptoms to quickly resolve customer problems.

ASIMOV works by creating a mathematically-based predictive machine learning model using the data gathered and analyzed by subject matter experts. Using a

Making Customer Care More Cerebral

Figure 4

ASIMOV’s Foundation

Figure 3

ASIMOV generates machine intelligence by gathering and applying agent and SME insights.

= +

TROUBLESHOOTINGHISTORY DATA

SUPPORT AGENT RECOMMENDATIONS

INSIGHTS

Customer Support Troubleshooting Assistance

CLASSIFIER DECISION TREE RECOMMENDER

OPTIMIZING CUSTOMER SUPPORT WITH MACHINE THINKING 7

large volume of troubleshooting historical logs, the system generates a more gen-eralized machine learning model for predicting problem resolutions and recom-mendations to customer issues. When a support agent feeds the system with a clue or looks up a current customer problem, ASIMOV quickly predicts and recommends the best solutions based on what it has learned from previous customer challenges and related fixes (see Figure 4, previous page).

This way, agents can access a solution that has proved effective for other agents, and can navigate the recommended path to quickly resolve the issue. The result: Agents can recommend solutions with more confidence and resolve issues more quickly.

ASIMOV is designed to work on top of a big data architecture, enabling it to effi-ciently handle large data volumes in a distributed manner (see Figure 5).

How ASIMOV Improves Customer Care TroubleshootingConsider the current experience of buying a product on a retail site. Many sites typically highlight and recommend options based on prior customer interactions and transactions to help customers choose the best product. In a similar manner, a customer care agent with only minimal knowledge of a specific problem can be assisted by ASIMOV to quickly choose the most accurate and specific resolution.

ASIMOV uses defined processes and algorithms, from data ingestion through data extraction, modeling and validation. It also uses a recommendation engine interface to guide agents for faster resolution.

Figure 6, next page, depicts a hypothetical use case for how ASIMOV uses recom-mendations to resolve a specific customer issue involving low-speed connectivity.

The system begins with broad recommendations using the initial symptoms provided, and when more symptoms are injected into the system, it offers increas-ingly personalized recommendations, based on the history of continuous learning it has with the ticket data.

ASIMOV MACHINE LEARNING PLATFORM

Customer Support Solution

Retail-Specific Customer

Support Solution

Healthcare-Specific Customer

Support Solution

PredictionEngine

InsightExtractor

RecommendationEngine

Figure 5

ASIMOV’s Technological Anatomy

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Looking ForwardASIMOV’s machine learning solutions support big data ecosystems and cloud-based environments to address a multiplicity of customer care challenges for call center optimization. ASIMOV also provides accelerators to bring machine learning-based solutions more quickly to market.

ASIMOV has been successfully piloted for a communication services provider, and has produced numerous insights for resolving customer support challenges. This has helped the company optimize customer complaint troubleshooting.

With its deep understanding of customer care support problems and customized machine learning support software, ASIMOV can efficiently address critical business goals that challenge customer support centers, including the following:

• Make more intelligent decisions to reduce time spent on customer support calls. ASIMOV can suggest solutions for resolution, based on the initial diagnosis of troubleshooting. As a result, it can speed problem resolution for agents and customers anywhere from five to 15 minutes, on average. Agent time is directly proportional to support center expenses, and reducing resolution time across millions of customer calls will save substantial spending in support.

• Provide recommendations on troubleshooting approaches and solutions. ASIMOV creates a real-time social navigation system for agents to use when re-solving issues. Agents can learn which solutions worked well for others and rec-ommend those fixes when resolving similar issues.

• Create smart agents backed by machine learning and increase agent effi-ciency. With the decision-making intelligence of ASIMOV, customer care centers can reduce the dependency on human intelligence and the impact of lost knowl-edge due to agent attrition.

Figure 6

Applying Clairvoyance to Solve Customer Challenges

ASMIOV provides personalized recommendation for addressing the problem

RECOMMENDATIONS BASED ON CONTINUOUS LEARNING

SYMPTOMS-BASED ISSUES

• Type 1 : 1,000 issues were due to cell tower signal booster failure

• Type 2 : 700 issues due to OS update problem in specific mobile platform

• Type 3 : 200 customers had data cap limits exceeded as per plan

ISSUE-BASED ACTIONS

• For Type 1 : 70% of the issues will be resolved by scheduling repair on booster devices

• For Type 2 : 90% of issues can be solved by sending carrier settings update

• For Type 3 : 100% of issue can be solved by upgrading plans

GUIDE RECOMMENDATION

For Type 1 : Refer to guide <link> - 85% success ratioFor Type 2 : Refer to guide <link> - 78% success ratioFor Type 3 : Refer to guide <link> - 94% success ratio

SYMPTOMS ANALYSIS

1,900 customers had the same symptom in the past 10 days within the same geographical area

Agent Customers

Send customer symptoms and diagnostics to ASIMOV

Complaint: High-speed wireless connectivity is not available

CASE ILLUSTRATION:Customer support for a wireless telecom provider

ASIMOV

OPTIMIZING CUSTOMER SUPPORT WITH MACHINE THINKING 9

About the AuthorsAravindakumar Venugopalan is a Senior Architect in Cognizant’s HPC Labs and is involved in a wide range of research and development activities in the areas of data science, HPC, big data and cloud computing. In his 15 years of experience in the IT industry, he has created assets that have been showcased in supercomput-ing seminars (SC12) and won Cognizant innovation awards. Aravindakumar holds a master’s of science in software engineering. He can be reached at Aravindakumar.Venugopalan@cognizant.com | https://www.linkedin.com/in/aravindakumar-venu-gopalan-95a23011.

Sivasubramaniam Renganathan is an Architect in Cognizant’s HPC Labs and has 11 years of experience in consulting and software development. He has worked on a wide range of projects involving Microsoft.Net, HPC, cloud and big data technolo-gies for various Fortune 500 clients. Sivasubramaniam holds a bachelor’s degree in computer engineering from Anna University, and his prime areas of interest include big data, machine learning and predictive analytics. He can be reached at Sivasub-ramaniam.Renganathan@cognizant.com | https://www.linkedin.com/in/sivarenga-nathan.

Rajarajan Gandhi is a Manager in Cognizant’s QE&A business unit and has over 12 years of experience in the telecommunications domain. He has worked on multiple networking, server management and OSS/BSS solutions, as well as Agile projects for various telecommunications clients. Rajarajan holds a master’s degree in software engineering. He can be reached at Rajarajan.g@cognizant.com | https://www.linkedin.com/in/rajarajan-gandhi-01848754.

AcknowledgmentsThe authors would like to thank the following associates for their contributions to this paper: Senthil Ramaswamy Sankarasubramanian, Senior Director – Technology, HPC Labs, Cognizant, and Srinath Narayanan, Senior Manager – Quality Engineer-ing & Assurance, Cognizant.

• Automatically resolve customer issues through highly confident decisions from machine learning solutions. ASIMOV can be used to automatically predict solutions and provide resolutions through interactive voice response (IVR) inter-actions before redirecting customers to a human agent.

Note: All company names, trade names, trademarks, trade dress, designs/logos, copyrights, images and products referenced in this white paper are the property of their respective owners. No company referenced in this white paper sponsored this white paper or the contents thereof.

Footnotes1 Wikipedia page on machine learning: https://en.wikipedia.org/wiki/Machine_

learning.

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OPTIMIZING CUSTOMER SUPPORT WITH MACHINE THINKING 11

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