HOW CONVERSATION ANALYTICS CONTRIBUTES TO A SUCCESSFUL CUSTOMER

HOW CONVERSATION ANALYTICS CONTRIBUTES TO A SUCCESSFUL CUSTOMER JOURNEY Prepared by: Call Journey April 2020 CREDIT UNION

The Customer Journey CREDIT UNION

Eve and John is a newly married couple who are looking for a new house. Eve is the one in charge looking a good reputable credit union/mortgage company to help them get started financially. The following events shows Joe’s customer experience journey and missed opportunities without Conversation Analytics. In the absence of Conversation Analytics, these interactions go unanalyzed and do not contribute to customer experience journey data for Customer Insights. “EVE & JOHN” Newly married couple, in need of a new house 1 AWARENESS When considering about buying a new home, Eve and her husband realized that their savings were not enough to pay for the deposit. Eve’s best friend, Carla knew about her challenge and recommends a Credit Union company that she can inquire to. 2 4 RESEARCH POST-CALL SURVEY Eve searched about the Credit Union company Carla recommended but she is unsure about the right service or product she needed in their situation. 3 To make sure she gets the right choice, she phoned the Credit Union company to inquire: • She mentioned that the company was referred to her by a friend. • She was thrilled to share her new milestone of being married and getting a house but needs financial support to get started. PHONE INQUIRY After the call with the company’s agent, Eve was asked to complete a survey. • Reports positive experience. • Positive NPS journey measure provided in post call survey.

5 CONSIDERATION Eve is now sure that they need financial support to pay the down payment for their dream home. With the positive experience from the first phone inquiry, she further researched about this Credit Union company to be more familiarized with the products or services that will best fit to her needs. 6 7 FIRST EDM RECEIVED Still feeling positive, Eve patiently waits for a follow-up from the Credit Union company about her inquiries. But instead, Eve received an email from the company about a BUSINESS LOAN proposal. INTEREST Upon thorough research and consideration, Eve is now interested to proceed with this Credit Union company and called again the agent on what product or services they can offer to her. Unfortunately… • She received negative experience. The agent still can’t quite get that Eve needs to be well informed to properly addressed her needs and options. • Non-compliant conversation. • Triggers and Events – talks about getting a new house and not picked up again. POST-CALL EXPERIENCE 8 Eve was disappointed on how the call turned out as the first call she had with the agent was a positive experience. As “word-of-mouth” is influential, she went back to her best friend Carla and asked what she thought about the company. Carla suggested to look in social media and check the company’s reputation to see more information and customer experience about the company. 9 11 APPLICATION CANCELLED Very angry and frustrated, Eve called the Mortgage company again to just drop her application and would not like to push through due to the poor customer service and negative experience she’s getting. COMPLAINS Eve was again very disappointed about the email she received as the it was very irrelevant and not appropriate to what she needs. She called the Mortgage company again and complained about the poor customer service she received. 10 EDM FOLLOW-UP After the complains Eve told the Mortgage company, she again received an email still regarding the Business Loan proposal. 12 The Mortgage company just lost a potential lead, lost revenue and lapse Data Analytics using STRUCTURED data sources. LOST LEAD

The Executives are not happy with how the Customer Journey Experience turned out: Meanwhile inside the company’s Management Team: 1. 2. 3. Customer Retention Project Team MARCOMMS PRODUCT MANAGER LEAD DATA ANALYST CONTACT CENTER MANAGER ACTUARY REVIEWING LAPSED CUSTOMERS ONLY VIA STRUCTURED DATA – NO CONVERSATION INSIGHTS! Future customer / revenue Loss Bad reviews, decreased agency’s credibility Long AHT SALES DIRECTOR MARKETING DIRECTOR GENERAL COUNSEL

With VOICE DATA now being added to MICROSOFT ecosystem, this is now Eve’s new customer journey experience with Conversation Analytics environment. 1 AWARENESS When considering about buying a new home, Eve and her husband realized that their savings were not enough to pay for the deposit. Eve’s best friend, Carla knew about her challenge and recommends a Credit Union company that she can inquire to. 2 “EVE & JOHN” Newly married couple, in need of a new house 4 RESEARCH POST-CALL SURVEY Eve searched about the Credit Union company Carla recommended but she is unsure about the right service or product she needed in their situation. 3 To make sure she gets the right choice, she phoned the Credit Union company to inquire: • She mentioned that the company was referred to her by a friend. • She was thrilled to share her new milestone of being married and getting a house but needs financial support to get started. PHONE INQUIRY After the call with the company’s agent, Eve was asked to complete a survey. • Reports positive experience. • Positive NPS journey measure provided in post call survey.

5 ANALYTICS Adding VOICE DATA to Microsoft’s Customer Insights tool, the data analytics team pick up the fact that Eve discussed getting a house numerous times on the call and trigger a House Mortgage Loan proposal for her. 6 8 OUTBOUND PROACTIVE CALL FIRST EDM RECEIVED Getting excited that finally she can afford the new house with the help of the mortgage company, she received an email from the company with the House Mortgage proposal packages that she can look into. Eve was happy with how the conversation went with the agent. She was also very satisfied with the experience and was surprised that the Mortgage Company has a wide range of flexible mortgage loans to choose from. She proceeded with application. 10 7 POST-CALL EXPERIENCE With the positive and quick response from the Mortgage company, Eve turned to social media and shared her experience for other potential customers to know. With the new conversation analytics insights triggering a HOUSE MORTGAGE LOAN campaign and post the initial EDM, Eve receives an OUTBOUND call about house mortgage packages she can choose from depending on her capability and eligibility. 9 11 LEAD CONVERSION Finally, Eve became from “potential” to “converted” lead as a new customer. She discussed to the agent how satisfied she is with the conversation and process that she experienced, and she is excited to share this to her friends who might want to do the same. She assured the agent that she will be a returning customer! APPLICATION WELCOME EDM Yay! Eve received a welcome email about her approved house mortgage loan and details! 12 Eve is very happy with her newly approved house mortgage and assured her agent that she will remain a customer and will plan to get a car loan in the future! CUSTOMER RETENTION

ADDING VOICE DATA FROM POSITIVE CUSTOMER EXPERIENCES, THE RETENTION TEAM GET BETTER INSIGHTS Meanwhile inside the company’s Management Team: Customer Retention Project Team MARCOMMS PRODUCT MANAGER LEAD DATA ANALYST CONTACT CENTER MANAGER STRACTURED DATA – INCLUDING CONVERSATION INSIGHTS Happy Customer – I’m happy! More Customers – I’m happy! No Complaints – I’m happy! ACTUARY SALES DIRECTOR MARKETING DIRECTOR GENERAL COUNSEL

Let’s start a conversation today. EMAIL US: info@calljourney. com sales@calljourney. com VISIT: www. calljourney. com FOLLOW AND CONNECT: /company/call-journey /Call. Journey. Mktg /calljourney

Microsoft Assets For Customer Journey

Conversation transcription Data hits the Azure database and is added to Customer Insights. With augmented data, this now hits the Azure Machine Learning environment. Conversation insights around customer engagement are created in the Customer Insights tool and pushed into downstream Insights packages. Meta data and conversation Insights arrive in Dynamics 365 Marketing and a next best offer-based House Loan EDM is created. Predictive churn and NPS measures are added based on the interaction aligned to the customer mentioning being married recently and wanting to get a house a few times. Conversation Insights arrive in Dynamics 365 CRM adding to the single customer view, noting a House Loan campaign was created and that an EDM was sent. Predictive churn and NPS measures are added based on the interaction into the single customer CRM view. Conversation Insights arrive in Customer Service insights measuring employee performance soft skills and compliance and customer experience. Predictive churn/lapse and NPS measures are added based on the interaction as are key Conversation insights – for example where COVD 19 was mentioned and the context. Conversation Insights arrive in Sales Insights measuring employee performance soft skills and compliance and customer experience. Next best offer campaign is added, and a new revenue opportunity created for a new House Loan product. Key sales drivers and triggers are noted in Sales Insights. 1

Post call survey data and conversation transcription Data is added to Customer Insights and with augmented data hits the Azure Machine Learning environment. Insights around customer engagement are created and pushed into downstream Insights packages. In this case – data summarizing a positive NPS score was allocated. Meta data and conversation Insights arrive in Dynamics 365 Marketing and a House Loan EDM is created. Predictive churn and NPS measures are added based on the interaction. Conversation Insights arrive in Dynamics 365 CRM adding to the single customer view, noting a House Loan Campaign was created and that an EDM was sent. Predictive churn and NPS measures are added based on the interaction into the single customer CRM view. Conversation Insights arrive in Customer Service insights measuring employee performance soft skills and compliance and customer experience. Predictive churn/lapse and NPS measures are added based on the interaction as are key Conversation insights – for example where COVD 19 was mentioned and the context. 2

Social Media data is added to Customer Insights and with augmented data, hits the Azure Machine Learning environment Insights around customer engagement are created and pushed into downstream Insights packages. In this case – data summarising a positive NPS score was allocated and key social media comments added. Social media interaction data arrives in Dynamics 365 Marketing. Predictive churn and NPS measures are added based on the interaction Social Media data arrives in Dynamics 365 CRM adding to the single customer view, noting customer commentary. Predictive churn and NPS measures are added based on the interaction into the single customer CRM view. Social media data arrives in Customer Service insights. Predictive churn/lapse and NPS measures are added based on the social media interaction. 3

Conversation transcription Data hits the Azure database and is added to Customer Insights. With augmented data, this now hits the Azure Machine Learning environment. Conversation insights around customer engagement are created and pushed into downstream Insights packages. Meta data and conversation Insights arrive in Dynamics 365 Marketing and a positive response to the House Loan Proposal EDM is assessed. Predictive churn and NPS measures are added based on the interaction as are key points of positive reaction to product elements. Conversation Insights arrive in Dynamics 365 CRM adding to the single customer view, noting a House Loan offer was presented and received positively. Predictive churn and NPS measures added based on the interaction into the single customer CRM view. Conversation Insights arrive in Customer Service insights measuring employee performance soft skills and compliance and customer experience. Predictive churn/lapse and NPS measures are added based on the interaction as are key Conversation insights – for example where COVD 19 was mentioned and the context. 4

Conversation transcription Data hits the Azure database and is added to Customer Insights. With augmented data, this now hits the Azure Machine Learning environment. Conversation insights around customer engagement are created and pushed into downstream Insights packages. Meta data and conversation Insights arrive in Dynamics 365 Marketing showing a customer purchase outcome and drivers of purchase summary. Welcome EDM triggered as House Loan purchased information hits the loan administration system. Customer management campaign triggered across new and existing policies. Conversation Insights arrive in Dynamics 365 CRM adding to the single customer view, noting a House Loan. Purchase and key point conversation summary. Predictive churn and NPS measures added based on the interaction into the single customer CRM view. Conversation Insights arrive in Customer Service Insights measuring employee performance soft skills and compliance and customer experience. Predictive churn/lapse and NPS measures added based on the interaction 5