Top A/B Testing Tips from Acumen’s Lean Data Experiments
Posted November 21, 2016 by +Acumen in Data & Impact
One of the ways Acumen’s Impact Team is working to make Lean Data stronger is through implementing more A/B testing. They are increasingly experimenting with different combinations of tools, questions and techniques to increase response rates, improve data quality, and make the experience more delightful for customers. Below are a few of their recent findings as well as their top A/B testing tips for Lean Data mobile surveys.
The BEST IVR RESPONSE RATE TO DATE
Acumen’s Impact Team ran 12 separate quick experiments leading to these results:
- Tool: IVR (Interactive Voice Response)
- Provider: EngageSpark
- Response rate: 59%
- Country: South Africa
- Sample size: 959 responses
What made this IVR survey so powerful?
- Fun, engaging quiz format—including true or false questions
- Upbeat “radio voice” recording by one of the company’s local brand ambassadors
- Lottery incentive—”chance to win a Samsung smartphone”
- In these 12 experiments, they found that:
- An SMS priming message = 14% higher response rate
- Local vernacular = 9% higher response rate
- Time of day = 8% higher response rate
By running multiple, quick A/B tests, we increased the response rates by 32%!
MOBILE SURVEYS: A/B TESTING TOP TIPS
1. Isolate 2-3 variables that may affect response rates and customer experience. Examples include:
- Time of day
- SMS priming
- IVR example: “Hello [first name]. You will receive a call at 5 pm today with a fun, 1-minute quiz. Pick up & have a chance to win a Samsung smartphone! The call is free!”
- Phone interview example: “[Company] is collecting feedback about the [product/program]. Please expect a call from us next week. Thank you! SMS STOP to opt out”
- Incentive: airtime vs. lottery vs. no incentive at all
- Voice: female, male, different accents
- And of course the questions themselves! Take note if a particular question causes drop-off.
2. Split your sample into the necessary amount of segments to ensure you are only changing one variable at a time.
3. Analyze the results to isolate the best combination of variables.
4. Continue testing!
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