1) The document discusses lessons learned from a bank's initial chatbot implementation, including that top customer requests differed from traditional contact centers and it was difficult to classify messages among 300 intents. 2) To address these issues, the bank redefined the welcome menu/dialog, prioritized the most common questions, used machine learning to cluster messages and identify topics, and created "skilled bots" for each topic. 3) The improved approach had a router bot identify topics and hand off to skilled bots that each focused on a single topic, with an iterative process to refine the bots.

1. Lessons learned
with Chatbot Analytics
Chatbase Summit
Sérgio Passos
Co-founder & CTO

2. Chatbot Agency Chatbot Platform

3. Platform
Development
Provider
Cool
Vendor
2017
Business
Partner
Early
Access
Program

4. Extensible by
Developers
No Coding
Required
Carrier-grade
SLA & Support
Built for
Teams

6. Codeless
Integration
Fully
Supported
6,000,000
API Calls
per Month
+100
Active Bots

7. F I N A N C E
U T I L I T I E S
R E T A I L
I N S U R A N C E
A U T O M O T I V E
H E A L T H
E N T E R T A I N M E N T
E D U C A T I O N R E A L E S T A T E
G O V
I N T E R N E T

8. Global Bank 133 million
Customers
(20 million in Brazil)
Use Case

9. Customer
Experience
Customer
Service Costs

10. Frequently
Asked Questions
Human
Hand-over

11. Experimental
Initial
Advanced
Optimized
Chatbot
Maturity
Model
Dimensions
ü Strategy
ü Experience
ü Technology

12. 1st approach
M AY, 2 0 1 7

13. Knowledgebase
& Contact Center
Welcome Menu & Dialog
NLP Model
(300 intents)
Not handled
Human Agents
Performance Team

15. Lessons Learned
#1: Top requests differs from traditional contact centers
#2: It’s hard to classify messages among 300 intents
#3: Team effort was equally distributed to all intents

16. How do we solve that?

17. #1: Top requests differs from
traditional contact centers
Redefine welcome menu and improve dialog
ü A/B testing with CTR metrics
ü Session flow and exit rates
ü Human hand-over pain points

18. #2: It’s hard to classify messages
among 300 intents
Prioritize most asked questions on Messenger
ü Machine Learning to create clusters
ü Word clouds and word trees to identify the topics
ü Sampling of messages to create intents for topics
Messages Clusters
Clouds &
Trees
Topic Sampling Intents

19. #3: Team effort was equally
distributed to all intents
Team works on skilled bots
ü Router bot identifies between topics
ü One skilled bot for each topic
ü Hand-over protocol between router
and skilled bots
ü Iterate on every skilled bot
Router
CreditCard
Account
App
Security
Credit
Branches
Universities

20. Embedding
on Mobile
App
Iterate,
Iterate,
Iterate…
Sentiment
analysis
for human
hand-over
Account
Linking &
Transactions
Next Steps

21. Chatbot Agency
contact@take.net
www.take.net
Chatbot Platform
contact@blip.ai
www.blip.ai