At the very beginning, it is important to
Posted: Tue Jan 21, 2025 5:19 am
For example, the difference in conversion to order between the options was insignificant: in the first group, with a promo code - 5.4%, in the second group, with a lead magnet - 6%. The null hypothesis is confirmed: the offer has almost no effect on conversion to order. This means that if you send clients a lead magnet, you can save your budget on promo codes without losing customer loyalty.
4. Analyze the results. If the difference between the groups is not statistically significant, then we can conclude that the changes in the offer did not affect the conversion.
5. Additional testing: To verify your findings, conduct additional analysis, such as comparing how many people added items to their cart after opening the emails in both groups.
6. Make a decision. If the hypothesis was effective, implement the list of afghanistan cell phone number changes. If not, put it aside and move on to the next one. Later, you can return to the postponed hypotheses and try to improve them; perhaps, after revision, they will be useful.
This approach allows you to make informed decisions based on data, even if the amount of data is limited.
Why Hypotheses Don't Work and What to Do When Testing Doesn't Go According to Plan: The Dashly Team's Experience
You spent months formulating, prioritizing, and taking small steps to test hypotheses. Everything suggested that you would get results. And the final conclusion says that there is no result. In this section, we describe the course of action in case you find yourself in such a situation.
The team of the foreign service Dashly helped us to compile tips for it. By the way, even more tips on launching projects and testing can be found in the telegram channel of Dashly CEO Dima Sergeev .
find out the reason. Not enough traffic? Bugs? Or everything works, but the script itself is weak? Depending on the reason, we either make sure that we have enough traffic to get statistically significant data, or we fix the bugs, or we dig into why the script does not work.
If the reason is bugs, we fix them and record them. We have created a doc where we record each such case. This allows us not to waste time searching for already known solutions if the situation repeats.
If it's due to external circumstances, such as insufficient traffic, we determine what efforts are needed to achieve the result. If this is impossible, we adjust expectations so that they reflect reality.
If a weak scenario is to blame, it is important not to drown in self-criticism, but to squeeze the maximum benefit out of the situation. Why didn’t it work? What did we miss in user behavior? What changes should be made to the next hypotheses? We answer these questions before putting the hypothesis on the shelf and moving on. They are a guide to stronger experiments.
You should always keep records. Create a knowledge base where all the developments, mistakes, and conclusions will be entered. This will help the team avoid repeating mistakes and work more harmoniously. And it is also important to accept that something always goes wrong in experiments. This is normal.
Want to collect more targeted leads on the same traffic?
Contact the Carrot quest growth team — they will test up to 25 trigger messages with A/B tests in 1.5 months and help increase revenue by 25%.
We will offer the first mechanics for the site during a free consultation.Surely you have heard about this situation in companies: they want to increase revenue, marketing starts to attract leads more actively, and later it turns out that only 10% of them are targeted and buy. The marketing budget is spent ineffectively, and sales have weak closing rates. As a result, sales blame marketers for poor lead generation, and marketers blame sales for poor deals.
4. Analyze the results. If the difference between the groups is not statistically significant, then we can conclude that the changes in the offer did not affect the conversion.
5. Additional testing: To verify your findings, conduct additional analysis, such as comparing how many people added items to their cart after opening the emails in both groups.
6. Make a decision. If the hypothesis was effective, implement the list of afghanistan cell phone number changes. If not, put it aside and move on to the next one. Later, you can return to the postponed hypotheses and try to improve them; perhaps, after revision, they will be useful.
This approach allows you to make informed decisions based on data, even if the amount of data is limited.
Why Hypotheses Don't Work and What to Do When Testing Doesn't Go According to Plan: The Dashly Team's Experience
You spent months formulating, prioritizing, and taking small steps to test hypotheses. Everything suggested that you would get results. And the final conclusion says that there is no result. In this section, we describe the course of action in case you find yourself in such a situation.
The team of the foreign service Dashly helped us to compile tips for it. By the way, even more tips on launching projects and testing can be found in the telegram channel of Dashly CEO Dima Sergeev .
find out the reason. Not enough traffic? Bugs? Or everything works, but the script itself is weak? Depending on the reason, we either make sure that we have enough traffic to get statistically significant data, or we fix the bugs, or we dig into why the script does not work.
If the reason is bugs, we fix them and record them. We have created a doc where we record each such case. This allows us not to waste time searching for already known solutions if the situation repeats.
If it's due to external circumstances, such as insufficient traffic, we determine what efforts are needed to achieve the result. If this is impossible, we adjust expectations so that they reflect reality.
If a weak scenario is to blame, it is important not to drown in self-criticism, but to squeeze the maximum benefit out of the situation. Why didn’t it work? What did we miss in user behavior? What changes should be made to the next hypotheses? We answer these questions before putting the hypothesis on the shelf and moving on. They are a guide to stronger experiments.
You should always keep records. Create a knowledge base where all the developments, mistakes, and conclusions will be entered. This will help the team avoid repeating mistakes and work more harmoniously. And it is also important to accept that something always goes wrong in experiments. This is normal.
Want to collect more targeted leads on the same traffic?
Contact the Carrot quest growth team — they will test up to 25 trigger messages with A/B tests in 1.5 months and help increase revenue by 25%.
We will offer the first mechanics for the site during a free consultation.Surely you have heard about this situation in companies: they want to increase revenue, marketing starts to attract leads more actively, and later it turns out that only 10% of them are targeted and buy. The marketing budget is spent ineffectively, and sales have weak closing rates. As a result, sales blame marketers for poor lead generation, and marketers blame sales for poor deals.