How to show the great influence of email marketing?

It's not easy to understand http://www.aliyun.com/zixun/aggregation/8805.html "> How email marketing affects customers. Why customers are involved in your marketing activities at a specific time, there are many factors affecting it. Let's take a meeting to register as an example.

Customer y is considering a registration meeting. The factors that affect his decision may be---a colleague, a brochure sent to his office, an e-mail he received, or all three factors.

To some extent, he has decided to attend the meeting, but has not taken the time to register. This time, he received your mail "the last registration opportunity". This email provides a registration code that can be hit 90 percent. He then submitted a registration invitation. Would you attribute his registration to your last registered email? Very likely.

But what you don't know is that customer Y has been invited by a guest speaker and must be registered. Coincidentally, before he wanted to sign up, he received a 90 percent registration code, the registration code did not change his plan, but did help him to save money for the company.

This story illustrates the potential pitfalls you can make in attribution analysis of each of your marketing strategies. 11545.html "> > What's our solution?

One technique that e-mail marketers can use is to measure the impact of messages in audiences by differentiating between experiments and control groups; You may be familiar with the control group, which is often used by the medical community for drug clinical Trials---the experimental group to take real drugs, and other groups (control group) to take placebos.

In the e-mail marketing experiment, the control group did not have any special treatment. The number, frequency, and content of emails sent to the control group were the same as the conference marketing Campaign. In contrast, the activities of the experimental group will have new variables---different mail content, number of mails and promotional activities. Ideally, you can change one variable at a time in each experimental group, and if the customer population is large enough, you can do multiple experiments at the same time in multiple groups.

When you compare the reaction between the experimental group and the control group, there is a lot of inspiration. What are the impact of mail marketing activities on each group? What are the differences in the performance of the experimental and control groups? Suppose the email sent to the experimental group contained 85 percent of the registration code, while the control group was 90 percent of the control code; In those two groups, was the actual number of people enrolled in the experimental group more than the control group?

Keep the other variables unchanged, and the experimental and control groups will give you more confidence and accuracy in measuring the impact of e-mail marketing campaigns.