# How to A/B Test AI Voice Call Scripts

> A simple way to A/B test AI voice call scripts: split the list, change one line, compare outcome rates on enough calls, then roll out the winner.

Source: https://edesy.in/blog/ab-test-ai-call-scripts-2026

**The fastest way to improve an AI calling campaign is to test one line at a time.** Make two versions of
your agent that differ in exactly one thing (usually the opening line), split the list randomly, run both
at the same times, and compare the outcome rate on answered calls. Roll out the winner, then test the next
line. Teams that do this steadily beat teams that rewrite the whole script every week.

> **Set up two script versions:** [Try Edesy free](https://voice-agent.edesy.in/?utm_source=edesy.in&utm_medium=blog&utm_campaign=ab-test-ai-call-scripts-2026) — Rs 50 free credit, no demo needed.

## The method

1. **Pick one outcome** (payment promised, booked, completed, qualified).
2. **Change one thing** between version A and B.
3. **Split the list randomly** into two equal halves.
4. **Run both at the same times**, same calling window, same retries.
5. **Compare on answered calls**, with a few hundred answered calls per version as a minimum.
6. **Roll out the winner**; test the next line.

## What to test, in order

| Test | Example |
|---|---|
| Opening line | "EMI reminder" first vs name + company first |
| The ask | "Will you pay by Friday?" vs "When can you pay?" |
| Offer wording | "Free specimen book" vs "book nominated by your students" |
| Language mode | Hinglish vs Hindi ([comparison](https://edesy.in/blog/hinglish-vs-pure-hindi-ai-calls-2026)) |
| Call length | 3 questions vs 1 question + WhatsApp ([30-second calls](https://edesy.in/blog/designing-short-ai-calls-30-seconds-2026)) |
| Calling time | Morning vs evening |

## A worked example

A reminder campaign tests two openings on 2,000 contacts (1,000 each):

- **A:** "Namaste, main XYZ Finance ki taraf se bol rahi hoon. Aapki EMI ke baare mein..."
- **B:** "Namaste {{name}} ji, aapki {{amount}} rupaye ki EMI {{due_date}} ko due hai..."

Compare **payment promised ÷ answered calls** for each. If B is clearly higher across enough answered
calls, B becomes the default and the next test changes the ask.

## Read the failures too

Beyond the headline outcome, compare **drop reasons and where calls ended** for each version. See
[why AI calls fail: drop reasons by script stage](https://edesy.in/blog/why-ai-calls-fail-drop-reason-analytics-2026).
A version that wins on outcomes but gets more complaints isn't a winner.

## Common mistakes

- Changing several lines at once.
- Running A in the morning and B in the evening.
- Declaring a winner after 50 calls.
- Comparing on dialled calls instead of answered calls.

## Cost

A/B tests cost nothing extra: the same calls you'd make anyway, at Rs 4-6 per started minute on Edesy
depending on your prepaid top-up, with unanswered calls free.

## Try it

[Create a free account](https://voice-agent.edesy.in/?utm_source=edesy.in&utm_medium=blog&utm_campaign=ab-test-ai-call-scripts-2026),
duplicate your agent, change the opening line, and split your next list in two.

## Frequently Asked Questions

### How do I A/B test an AI call script?

Create two versions of the agent that differ in one thing (the opening line, the offer, the language mode), split a list randomly into two equal halves, run both campaigns at the same times, and compare the outcome rate on answered calls.

### How many calls do I need?

Enough that the difference isn't just noise. As a rough guide, a few hundred answered calls per version is a reasonable minimum, and more if the difference is small.

### What should I test first?

The opening line, because it decides whether people stay on the call. Then the main question or offer, then the language mode (e.g. Hinglish vs Hindi).

### What should I measure?

One outcome that matters: payment promised, booking made, survey completed, lead qualified, measured as a share of answered calls. Also watch early hang-ups.

### Can I test more than one change at a time?

Not in a simple A/B test. If you change two things, you won't know which one caused the difference. Change one line, find a winner, then test the next.