Chatler.ai prezentáció, az Országos Ügyfélszolgálati Konferenciára.
- Chat trendek a világban?
- Miért fontos a hatékony chat az ügyfélszolgálatoknak?
- Hogyan lehet belevágni a chat automatizálásba?
- Hogyan tud ebben a chatler.ai segíteni?
Még több infó:
https://chatler.ai/
https://blog.chatler.ai/
https://twitter.com/ChatlerAI
https://www.facebook.com/chatlerai/
15. Fully manual
Ctrl-f, Ctrl-c, Ctrl-v
Recommendation
engine Semi automation Automated
functions
Full automation
Chat agents answer each message
one-by-one, manually.
They follow preset playbook
High cost and time for onboarding
16. Fully manual
Ctrl-f, Ctrl-c, Ctrl-v
Recommendation
engine Semi automation Automated
functions
Full automation
Chat agents copy-paste most repetitive responses
They have knowledge documents, FAQ
More centralized way of communication,
agents should be familiar with document structure
17. Fully manual
Ctrl-f, Ctrl-c, Ctrl-v
Recommendation
engine Semi automation Automated
functions
Full automation
Chat agents select from recommendations
AI shows them possible ways
to continue discussion
Quicker onboarding, more productive chatting
18. Fully manual
Ctrl-f, Ctrl-c, Ctrl-v
Recommendation
engine Semi automation Automated
functions
Full automation
AI takes over some questions, topics
if confident enough
Human focus on more complex conversations
Clear differentiation of bot vs. human answers is needed.
19. Fully manual
Ctrl-f, Ctrl-c, Ctrl-v
Recommendation
engine Semi automation Automated
functions
Full automation
AI takes over repetitive functions (booking, balance
checking). Functions are triggered by customer
Humans take over if problem occurs
Special development, and
cross database connectivity is needed
20. Fully manual
Ctrl-f, Ctrl-c, Ctrl-v
Recommendation
engine Semi automation Automated
functions
Full automation
AI manages all the conversation
Human only teaches AI
with new answers for new use cases
21. AI suggests for
repetitive questions
Human chat agents
select from recommendations
and send message to customer
22. 1. Smart response
library 2. Response recommendations 3. Track, analyze, learn
- Categorized collection of
frequently used responses
- Based on personal chat history
- Constantly and automatically
updating
23. 1. Smart response library
2. Response
recommendations 3. Track, analyze, learn
- Response recommendations
to incoming messages
- Based on smart response
library
- Easy to discover alternatives
- Chat agent makes final call
24. 1. Smart response library 2. Response recommendations
3. Track,
analyze, learn
- Learns from every interaction
- Adapts to user behaviour
26. “Any community manager would kill
for a product like Chatler”
Imola Gáspár
Social Media Manager
Interactions and time needed to answer a
message with and without Chatler