A/B testing a chatbot script is to run 2 different versions of the script for 2 groups of customers, compare the metrics to see which version is better — then apply the winning version. Chatbots are not "installed and done": the script must be tested, measured and iterated. Zenify recorded a 21% increase in conversion rate with optimized chatbot (approved).
TL;DR
- A/B test comparing 2 script versions with each other.
- Only run when there are enough customers — few customers are not meaningful enough.
- Only test one change at a time.
- Measured by clear metrics: conversion, containment, CSAT.
- Iterate continuously — a good chatbot is a tried-and-true chatbot.
Why do we need A/B testing?
You "think" the script is good, but the guests decide.
Small changes (greetings, menu order, asking questions) have a big impact.
Avoid emotional editing — data shows which version is better.
Zenify recorded a 21% increase in conversions — a number that comes from measured optimization.
A/B test what?
Yếu tố | Ví dụ
Greetings | "I'm a shop assistant" vs "What do you need my help with?"
Menu order | Put "Quotation" first or "Meet staff" first
How to ask for information | Ask each question gradually vs send the form together
CTA | "Book now" vs "Let me send you the price list"
Fallback | Menu recall vs early transfer (see Fallback chatbot)
How to perform A/B testing
Choose just one factor: don't test 2 things at the same time.
Divided into 2 groups: randomly, equal.
Run enough time: until there are enough meaningful conversations.
So index: conversion, containment, CSAT.
Select the winning version: apply, then try the next change.
Measured by which index?
Goal | Index
Sales | Order closing rate, order value
CSKH | Containment rate, CSAT (xem Containment rate)
Experience | Customer abandonment rate, completion time
Transfer people | On-time agent switching rate
Principle: choose the main index in advance — don't "look later and choose".
Lưu ý khi A/B test chatbot
Enough data: a few dozen conversations are not enough to conclude — need a few hundred or more.
Don't test things that aren't worth testing: the greeting changing "yes" to "yes" is not worth testing.
See context: conversion decreases but CSAT increases — need to understand why.
Record results: save to not repeat old tests.
Continuous optimization process
Measure current baseline.
Choose a suspect point.
A/B test changes.
Apply the winning version.
Go back to step 1 — a good chatbot is one that is continuously improved.