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Two Medical Customer Support Conversational AI strategy

Two Medical wants to improve the customer service experience by allowing our customers to chat with a conversational AI-powered chatbot. The goal of this paper is to define the plan to take this initiative to market.

Goal: Improve customer support experience

What does this mean?

Better satisfaction

What satisfies our members?

Quicker time to resolution

Personalization (Knowing who they are, showing them relevant data, Etc.)

They don’t have to repeat themselves, even when passed from CSR to CSR

Learn, Build, Measure loop:

The end goal is to have a powerful conversational AI platform that leverages AI and ML to service our customers. Since we’ll have the opportunity to learn about how our customers interact with this type of technology along the way, we should use this to our advantage and take an iterative approach to our go-to-market.

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Customer:

Americans in rural who use us for their Health Care. Here are some things we know about them:

Likely have to drive a long way to get medical help

Agriculture employs less than 5% of rural Americans

In 2017, the rural poverty rate stood at 16.4% compared to urban at 12.9%

Most popular web browsers in our demographic: Chrome, Safari, Edge

Average $$ spent on smart phone: $200

Opportunity:

Higher customer satisfaction

Faster responses to our customer questions

24/7 customer service

More efficient customer service team - Less time spent answering easy questions, more time spent solving the complicated issue

Consistent answers for our customers

Learn more about our customers in a more scalable way

How do we measure this?

Improved customer satisfaction increases our bottom line, and we can measure that with the following proxy metrics:

NPS scores/CSAT scores

Churn Rate

Retention Rate

Lifetime Value

Customer service hours saved is also an easy way to measure cost savings

Who is involved?

This document is available to each department head, and I’ve schedule time with them or a representative on their team to understand any concerns they may have. More specifically the following groups will help with the following tasks as we plan this initiative:

User Research:

Build persona of our “model” customer who stays for our average retention period

Understand the customer service team’s daily jobs and figure out what all we can automate with conversational AI

Engineering:

Acquire live chat transcripts

They can use those transcripts to begin testing various NLP and ML tools we will need to make this overall vision a reality

Design:

They will be responsible for designing the conversation flows, so their input with the tools we use will be crucial too

Data/Analytics:

Current retention rates

Churn rates

Cost Per Acquisition

Average number of customer service hours to keep our “model” customer happy

Internal Training Department:

Build training documentation to help the teams across the company understand what this initiative is and what the goals are to weigh in with their domain knowledge.

Go-to-Market taskforce

Go-to-Market taskforce

Name

Department

Responsible

Accountable

Consulted

Informed

Joe

Product

Stephanie

Customer Service

Josh

Customer Service

Kathy

Ops

Matt

Data/Analytics

Jessie

Engineering

Frank

Engineering

Stan

Design

Oprah

Design

Seth

Marketing

Stacy

CEO

There are no rows in this table

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So everyone knows what sort of communication cadence to expect, here’s how I’ll be keeping you in the loop according to the Go to Market task force RACI chart above.

Responsible: Daily communication and working on the initiative

Accountable: Weekly updates, access to Slack channel where work is happening

Consulted: Consultation as needed and monthly updates

Informed: Monthly email updates on the progress

Rollout

I like to deliver value to the customer as soon as possible so that we can learn how they use the product and then continue to iterate to delight the customer. With that “learn, build, measure” approach in mind, here’s how we will roll this out:

Learn

Acquire a dataset (list of chat transcripts)

User Research will begin interviewing customers and customer service teams to start uncovering the most common use cases that we can automate.

Give engineering and design a period to explore the data set using various NLP and ML tools to see what will help us achieve our goal AND fit into our technology stack

Capabilities need to exist to roll out:

Capability to understand what our users are saying

Implement natural language processing/understanding tools

Capability to respond to our users

Begin building our machine learning model

Capability to predict customer behavior like churn based on their interaction with the chatbot.

Decide on the tools we’ll use

NLP/NLU

Analytics

Code Languages

Databases

Etc.

At this point, we can begin building.

Build

We’ll take an iterative approach here. We’ll call it “Crawl, Walk, Run.”

Crawl part 1: AI-assisted chat experience for customer service rep

Incorporate the NLP/ML in the existing chat tech.

As a customer service agent, when a customer sends me a message, a potential response is generated by our AI platform.

If the machine response is good, the CSR may send that response by clicking a button.

If not, the CSR can write their response. If the CSR types the response we’ll store the transaction in a DB so we can later work to solve these issues programatically.

Crawl part 2: CSRs help train the ML model

When a CSR types their own response, the ML model flags that transcript for the team to analyze and train.

Walk: Chatbot and voice experience.

Customers can chat with a chatbot/voice bot trained on customer interactions with Two Medical. This will fully automate many aspects of the customer service interaction, providing 24/7 customer service for a large number of issues.

Run: Predictive Analytics + integrated marketing = less churn

Predictive analytics from customer interactions to predict when a customer might not be happy paired with proactive “save” campaigns to reduce churn even more.