1. Email Customer Service Request Improvement Ideas Proof of Concept ©Copyright 2014 ® All rights reserved Wayne Tarken – wayne@waynetarken.comwayne@waynetarken.com A technology, people and data approach

2. Proof of Concept Goals More efficient, effective handling of emails from customers on service issues by: – Finding resolutions to customer service issues – Providing high touch service even via emails – Reducing time it takes to move emails towards resolution – Leveraging technology to streamline process and reduce errors ©Copyright 2014 ® All rights reserved Wayne Tarken – wayne@waynetarken.comwayne@waynetarken.com

3. Proof of Concept Goals More efficient, effective handling of emails from customers on service issues by: – Scanning, mining and analyzing data from the process to identify bottlenecks as well as potential improvements – Reducing time spent on email by service staff – Improving customer satisfaction & engagement – Shifting reliance on email as a service delivery tool to other channels (Tweets, phone, etc) ©Copyright 2014 ® All rights reserved Wayne Tarken – wayne@waynetarken.comwayne@waynetarken.com

4. The Foundation The foundation of social customer service is to meet customers’ ever increasing expectations of rapid and seamless problem resolution This is accomplished by: – Developing highly responsive service channels via all social, mobile, phone, etc. customer touch points ©Copyright 2014 ® All rights reserved Wayne Tarken – wayne@waynetarken.comwayne@waynetarken.com

5. The Process Documenting every reason why the customer contacts the company Identifying the cost-to-service to process each of these reasons Determining the most efficient channel and process to handle each type of request to reduce cost while improving service Gradually incentivize and shift customers to the best channel(s) for them and the organization based upon the issue, cost, resources, performance and service. ©Copyright 2014 ® All rights reserved Wayne Tarken – wayne@waynetarken.comwayne@waynetarken.com

6. The Process Improve self-service by creating and tapping into a community-based, open knowledge base – Develop community to connect employees, customers – Use community building techniques to increase engagement and promote knowledge sharing – Capture all interactions in every channel, make them searchable with easy to use tools in knowledge base – Use Big Data to analyze data, determine correlations and surface to new ideas, opportunities, etc ©Copyright 2014 ® All rights reserved Wayne Tarken – wayne@waynetarken.comwayne@waynetarken.com

7. The Process Leverage the use of emerging technologies such as natural language processing, big data, analytics, etc. to: – Automate the process – Speed up response times – Reduce mistakes – Identify/resolve service issues proactively – Reduce personnel needed to resolve issues ©Copyright 2014 ® All rights reserved Wayne Tarken – wayne@waynetarken.comwayne@waynetarken.com

8. Know the Customer The key is to know each customer so the system can anticipate their issues and surface a solution at the time they make the request This is accomplished by: – Every customer having a unique profile – All interactions being tracked, stored and searchable – Analytics used to anticipate customers, preferences, needs, requirements – Predictive and prescriptive analytics are used to anticipate potential future issues and corresponding solutions ©Copyright 2014 ® All rights reserved Wayne Tarken – wayne@waynetarken.comwayne@waynetarken.com

9. Case Study Lisa is an executive with a Fortune 500 company She leads the customer service function Her organization has always had a reputation for a high touch, customized service function But now, customers have increasing expectations that they want their problems solved almost immediately

10. Case Study She is concerned that she will need to develop a formulaic, template-based approach to service That could lead to the reduction of their differentiator as a high service organization Her team has made great strides in service in many of their channels such as Facebook, Twitter, call centers, etc The one area of concern remains emails. They haven’t figured out the optimal way to handle service requests from this channel

11. Case Study Email service requests reach the company in two ways: – Completing a form on their web page – Emailing directly via their pop3 email clients such as Outlook or via web-based tool such as Gmail The issue for her is how to improve the process so that customer’s expectation are meet and her resources are optimized

12. Case Study Of course she realizes that email is not the most effective way to provide service They have done many things to try to shift customers to other channels that are more efficient and cost-effective for her system and resources But the fact remains that due to past behavior, many customers still prefer email as a vehicle for service

13. Case Study It is to use a customer profile as an integrated part of the process A profile that is updated with all interactions from every channel That can be used to guide the service responses based upon the customer's previous interaction, preferences, demographics, etc In either case – web-site generated email or email sent directly, if found, the profile will be used to shape the response. If not, they will have to make some potentially less accurate assumptions

14. Case Study Some of the newer technology that is needed to improve the service process includes: Analytics – used to customize information based upon individual needs Natural language processing – using technology to convert text in email into data for analysis Big-data – capturing and analyzing data from a variety of sources to search for correlations Community-based knowledge centers used to facilitate interactions, manage content, store data, enable searching

15. Case Study Email service request can be categorized in three ways: -Known – responses that can be found in the knowledge base -Assumptions - responses that can be reasonably developed through analytics -Unknown – responses that can not be generated automatically Known and assumption answers can be developed with technology

16. Case Study Unknown responses must be handled via call center personnel. This can be accomplished by: -Having the skills, experience of service center employees developed into a service profile -Using analytics to queue the request to the service center person who has the greatest likelihood of knowing the answer -Updating the knowledge-base so the responses can be retained and used again -Realizing that as knowledge base improves more responses can be handled automatically

17. Case Study For company website email requests, the goal is to: Provide a series of steps to present relevant information to solve the customer’s issue prior to submitting a form. This is accomplished by: ⁻Searching for the customer’s profile (if found) ⁻Using a series of structured questions to refine customer’s issue ⁻Scanning text fields and using natural language processing to determine key issues ⁻Searching knowledge base for answers ⁻Using predictive analytics to propose questions to clarify issues during the form updating process ⁻Proposing targeted FAQs to the customer based on profile, analytics, text analysis and knowledge base

18. Case Study For emails sent directly to company by user the goal is to : Provide a series of steps to analyze and push back answers directly to the customer in an email reply. This is accomplished by: ⁻Searching for the customer’s profile if found based upon email address ⁻Scanning the message and using natural language processing to determine the real issues ⁻Searching knowledge base for answers ⁻Proposing targeted response directly back to the customer based on profile, analytics, text analysis and knowledge base

19. Case Study In both email scenarios, sent directly to the company or via the company’s web-based form, the customer has the ability to: – Route the response back to the call center for additional help – Engage in a dialogue with others via the community to search for their own answers – End the process if they are satisfied

20. Case Study Her team knows that a critical piece is that: – All call center interactions must be captured in the knowledge base for use in future interactions – Keeping a knowledge base updated can be problematic unless employee’s expectation and internal processes are modified to encourage this behavior Especially in tier level support systems where Tier 1 personnel is often not aware of solutions proposed in Tier 2 or 3 interactions – There must also be a way to capture the knowledge acquired by the customer during the process

21. Case Study After the new process was implemented, the improvement in email throughput was dramatic – An increasing number of email requests were automatically handled by the system – The knowledge gained by response was used to improve other service channels – Customer service levels and engagement went up – Call center personnel was more engagement and effectively utilized – The volume of email requests went down – Call center resources could be shifted to other channels

22. Proposed flowcharts

23. Message is scanned using natural language processing Customer sends email from Outlook, Notes, Gmail, etc Analytics determines the problem Email received from SMTP server Customer profile is accessed Customer history and analytics are processed to determine best answer YES Sends response back to customer with suggested fix Knowledge base is searched for best solution Is email address recognized? NO Message assigned to customer service rep based upon rep’s experience with type of issue and size of their backlog Customer gets response back telling them of service rep name and anticipated response time Interactions, solution is stored in customer profile as well as in knowledge base Message is scanned using natural language processing Analytics determines the problem Customer profile is updated Customer profile is created YES Is problem new? NO Customer Sends Email from Outlook, Gmail, Notes, etc ©Copyright 2014 ® All rights reserved Wayne Tarken – wayne@waynetarken.comwayne@waynetarken.com Is customer happy with outcome? NO YES Finished Start here

24. Finished Is email address/IP recognized? YES Search customer profile Search knowledge base Use predictive analytics to propose questions to clarify issues Propose FAQs and other solutions to customer Customer profile is updated NO Search knowledge base Use predictive analytics to propose questions to clarify issues Propose questions to clarify issues Propose FAQs & other solutions to customer Customer profile is created and updated Is issue resolved YESNO Customer fills in online form to send email Customer submits form Customer Sends Email through Company Website – Initial Steps ©Copyright 2014 ® All rights reserved Wayne Tarken – wayne@waynetarken.comwayne@waynetarken.com Start here Customers wants to send email via website Is customer logged in? NO YES Customer is presented with FAQs Was issue resolved YES NO Finished

25. Sends response back to customer with suggested fix Knowledge base is searched for best solution Message assigned to customer service rep based upon rep’s experience with type of issue and size of their backlog Customer gets response back telling them of service rep name and anticipated response time Interactions, solution is stored in customer profile as well as in knowledge base Message is scanned using natural language processing Analytics determines the problem Customer profile is updated YES Is problem new? NO Customer submits form Customer Sends Email through Company Website – Processing Steps ©Copyright 2014 ® All rights reserved Wayne Tarken – wayne@waynetarken.comwayne@waynetarken.com Is customer happy with outcome? NO YES Finished

26. Sends response back to customer with suggested fix Knowledge base is searched for best solution Message assigned to customer service rep based upon rep’s experience with type of issue and size of their backlog Customer gets response back telling them of service rep name and anticipated response time Interactions, solution is stored in customer profile as well as in knowledge base Finished Is email address/IP recognized? Message is scanned using natural language processing Analytics determines the problem Customer profile is updated YES Search customer profile Search knowledge base Use predictive analytics to propose questions to clarify issues Propose FAQs and other solutions to customer Customer profile is updated NO Search knowledge base Use predictive analytics to propose questions to clarify issues Propose questions to clarify issues Propose FAQs & other solutions to customer Customer profile is created and updated Is issue resolved YESNO Is problem new? NO Customer fills in online form to send email Customer submits form Customer Sends Email through Company Website – Combined Process ©Copyright 2014 ® All rights reserved Wayne Tarken – wayne@waynetarken.comwayne@waynetarken.com Is customer happy with outcome? NO YES Finished Start here Customers wants to send email via website Is customer logged in? NO YES Customer is presented with FAQs Was issue resolved YES NO Finished

27. Message is scanned using natural language processing Customer sends email from Outlook, Notes, Gmail, etc Big data & analytics All interactions will be captured and remain searchable Analytics will be applied to data to identify recurring problems, unexpected correlations, etc These findings will be used to proactively develop, then push out solutions in all customer outreach channels Eventually eliminate most reasons to reach out to company via email Predictive and prescriptive analytics will be used to proactively anticipate potential future problems. Develop, expose customers to solutions before they are needed Analytics determines the problem Email received from SMTP server Customer profile is accessed Customer history and analytics are processed to determine best answer YES Sends response back to customer with suggested fix Knowledge base is searched for best solution Is email address recognized? NO Message assigned to customer service rep based upon rep’s experience with type of issue and size of their backlog Customer gets response back telling them of service rep name and anticipated response time Interactions, solution is stored in customer profile as well as in knowledge base Finished Is email address/IP recognized? Message is scanned using natural language processing Analytics determines the problem Customer profile is updated Customer profile is created YES Search customer profile Search knowledge base Use predictive analytics to propose questions to clarify issues Propose FAQs and other solutions to customer Customer profile is updated NO Search knowledge base Use predictive analytics to propose questions to clarify issues Propose questions to clarify issues Propose FAQs & other solutions to customer Customer profile is created and updated Is issue resolved YESNO Is problem new? NO Customer fills in online form to send email Customer submits form Customer Service Email Responsiveness Improvement Concept ©Copyright 2014 ® All rights reserved Wayne Tarken – wayne@waynetarken.comwayne@waynetarken.com Is customer happy with outcome? NO YES Finished Start here Customers wants to send email via website Is customer logged in? NO YES Customer is presented with FAQs Was issue resolved YES NO Finished