Prepared by: Call JourneyApril 2020

HOW CONTRIBUTES

TO A SUCCESSFUL CUSTOMER JOURNEY

The Customer Journey

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.

AWARENESS1

Eve searched about the Credit Union company Carla recommended but she is unsure about the right service or product she needed in their situation.

RESEARCH2

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 INQUIRY3

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.

POST-CALL SURVEY4

“EVE & JOHN”

Newly married couple, in need of a

new house

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 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.

COMPLAINS9

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.

POST-CALL EXPERIENCE7

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.

INTEREST6

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.

CONSIDERATION5

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.

APPLICATION CANCELLED11

The Mortgage company just lost a potential lead, lost revenue and lapse Data Analytics using STRUCTURED data sources.

LOST LEAD12

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.

FIRST EDM RECEIVED8

After the complains Eve told the Mortgage company, she again received an email still regarding the Business Loan proposal.

EDM FOLLOW-UP10

Customer RetentionProject Team

The Executives are not happy with how the Customer Journey Experience turned out:

MARCOMMS PRODUCT MANAGER

LEAD DATA ANALYST ACTUARY

CONTACT CENTER

MANAGER

REVIEWING LAPSED CUSTOMERS ONLYVIA STRUCTURED DATA –

NO CONVERSATION INSIGHTS!

Meanwhile inside the company’s Management Team:

1. Future customer / revenue Loss2. Bad reviews, decreased agency’s credibility3. Long AHT

SALESDIRECTOR

MARKETINGDIRECTOR

GENERALCOUNSEL

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.

AWARENESS1

Eve searched about the Credit Union company Carla recommended but she is unsure about the right service or product she needed in their situation.

RESEARCH2

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 INQUIRY3

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.

POST-CALL SURVEY4

“EVE & JOHN”

Newly married couple, in need of a

new house

With VOICE DATA now being added to MICROSOFT ecosystem, this is now Eve’s new customer journey experience with Conversation Analytics environment.

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.

POST-CALL EXPERIENCE7

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.

OUTBOUND PRO-ACTIVE CALL8

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.

APPLICATION9

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!

LEAD CONVERSION11

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

12

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.

ANALYTICS5

Yay! Eve received a welcome email about her approved house mortgage loan and details!

WELCOME EDM10

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.

FIRST EDM RECEIVED6

Customer RetentionProject Team

ADDING VOICE DATA FROM POSITIVE CUSTOMER EXPERIENCES,

THE RETENTION TEAM GET BETTER INSIGHTS

MARCOMMS PRODUCT MANAGER

LEAD DATA ANALYST ACTUARY

CONTACT CENTER

MANAGER

STRACTURED DATA – INCLUDINGCONVERSATION INSIGHTS

Meanwhile inside the company’s Management Team:

SALESDIRECTOR

MARKETINGDIRECTOR

GENERALCOUNSEL

More Customers

– I’m happy!

Happy Customer– I’m happy!

No Complaints– I’m happy!

Let’s start a conversation today.

FOLLOW AND CONNECT:/company/call-journey/CallJourney/CallJourneyMktg/calljourney

EMAIL US:info@calljourney.comsales@calljourney.com

VISIT:www.calljourney.com

Microsoft AssetsFor Customer Journey

1

Conversation transcription Data hits the Azure database and is added toCustomer Insights. With augmented data, this now hits the Azure MachineLearning environment.

Conversation insights around customer engagement are created in the CustomerInsights tool and pushed into downstream Insights packages.

Meta data and conversation Insights arrive in Dynamics 365 Marketing and a next bestoffer-based House Loan EDM is created. Predictive churn and NPS measures are addedbased on the interaction aligned to the customer mentioning being married recentlyand 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 churnand NPS measures are added based on the interaction into the single customer CRMview.

Conversation Insights arrive in Customer Service insights measuring employeeperformance soft skills and compliance and customer experience. Predictive churn/lapseand NPS measures are added based on the interaction as are key Conversation insights –for example where COVD19 was mentioned and the context.

Conversation Insights arrive in Sales Insights measuring employee performance softskills and compliance and customer experience. Next best offer campaign is added, anda new revenue opportunity created for a new House Loan product. Key sales drivers andtriggers are noted in Sales Insights.

2

Post call survey data and conversation transcription Data is added to CustomerInsights and with augmented data hits the Azure Machine Learningenvironment.

Insights around customer engagement are created and pushed into downstreamInsights packages. In this case – data summarizing a positive NPS score wasallocated.

Meta data and conversation Insights arrive in Dynamics 365 Marketing and aHouse Loan EDM is created. Predictive churn and NPS measures are added basedon the interaction.

Conversation Insights arrive in Dynamics 365 CRM adding to the single customerview, 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 thesingle customer CRM view.

Conversation Insights arrive in Customer Service insights measuring employeeperformance soft skills and compliance and customer experience. Predictivechurn/lapse and NPS measures are added based on the interaction as are keyConversation insights – for example where COVD19 was mentioned and thecontext.

3

Social Media data is added to Customer Insights and with augmented data, hitsthe Azure Machine Learning environment

Insights around customer engagement are created and pushed intodownstream Insights packages. In this case – data summarising a positive NPSscore was allocated and key social media comments added.

Social media interaction data arrives in Dynamics 365 Marketing. Predictivechurn and NPS measures are added based on the interaction

Social Media data arrives in Dynamics 365 CRM adding to the single customerview, noting customer commentary. Predictive churn and NPS measures areadded based on the interaction into the single customer CRM view.

Social media data arrives in Customer Service insights. Predictive churn/lapse andNPS measures are added based on the social media interaction.

4

Conversation transcription Data hits the Azure database and is added toCustomer Insights. With augmented data, this now hits the Azure MachineLearning environment.

Conversation insights around customer engagement are created and pushedinto downstream Insights packages.

Meta data and conversation Insights arrive in Dynamics 365 Marketing and apositive response to the House Loan Proposal EDM is assessed. Predictivechurn and NPS measures are added based on the interaction as are key points ofpositive reaction to product elements.

Conversation Insights arrive in Dynamics 365 CRM adding to the single customerview, noting a House Loan offer was presented and received positively. Predictivechurn and NPS measures added based on the interaction into the singlecustomer CRM view.

Conversation Insights arrive in Customer Service insights measuring employeeperformance soft skills and compliance and customer experience. Predictivechurn/lapse and NPS measures are added based on the interaction as are keyConversation insights – for example where COVD19 was mentioned and thecontext.

5

Conversation transcription Data hits the Azure database and is added toCustomer Insights. With augmented data, this now hits the Azure MachineLearning environment.

Conversation insights around customer engagement are created and pushedinto downstream Insights packages.

Meta data and conversation Insights arrive in Dynamics 365 Marketing showinga customer purchase outcome and drivers of purchase summary. Welcome EDMtriggered as House Loan purchased information hits the loan administrationsystem. Customer management campaign triggered across new and existingpolicies.

Conversation Insights arrive in Dynamics 365 CRM adding to the single customerview, noting a House Loan. Purchase and key point conversation summary.Predictive churn and NPS measures added based on the interaction into thesingle customer CRM view.

Conversation Insights arrive in Customer Service Insights measuring employeeperformance soft skills and compliance and customer experience. Predictivechurn/lapse and NPS measures added based on the interaction