You will almost certainly be using open, click-thru, bounce and unsubscribe statistics to analyse your email campaigns but here is some additional statistics and reports to gain further insight into your email campaigns.

1) Respondents (Number of people clicking on at least one link)
If recipients click on 3 different links in your email this adds 3 to the click-thru’s total. However Maxemail also provides respondents, which gives you a much better insight into what percentage of your list is bothering to click on any link in the email. A statistic of 100 respondents indicates that 100 people clicked on at least one link in the email. However the click-thru statistic might show 150 people as a number have clicked on more than one link.

2) Opens vs. Clicked
An important statistic as a poor click-thru rate might actually be because very few people opened your email! If this statistic shows that a good proportion of those opening the email are clicking on your links then you know the content is relevant and accurate but you need to look at achieving a better open rate with a different subject line and ‘From’ name.

3) Text vs. HTML Links
Understanding the percentage of your list that is text only will give you an overview of how important your text email is. This can be achieved by comparing the click-thru numbers on text tracking links and those receiving the HTML version. By looking at the results of these overtime you can also identify newsletters where the text or HTML performed above or below par.

4) Compare segment performance
By reporting across your different segments enables you to identify which segments are performing well. For example your reports could be broken down by industry sector, demographics, product preferences or purchase history. If there is a significant variation in performance then this is likely to be because the content is not targeted enough for particular segments.

5) Response Curve Analysis
This is opens and click-thru’s displayed over time, usually 1 week. This enables you to identify the best times to send your campaigns and gives you an indication of how long recipients keep your emails for.

6) A/B Split Performance
Testing your creative to a sample of your data enables you to ensure you send the best performing email possible. A/B Split variable testing usually works as follows:

1. Decide the variable(s) you want to test (E.G. Subject line, Timing, Main offer)
2. Set-up as many emails as required and define the sample size for each (E.G. List of 100,000, set a sample size of 10,000)
3. Send the emails and look at the results
4. To the remainder of your list use the best performing variables

7) Compare marketing teams
If you have different teams working on different campaigns it can be a useful training exercise for these teams to compare their results. For example if on average your campaigns get a 50% open rate but one of your teams are only achieving 30% you can look at why this is the case by analysing the ‘From’ address and subject line used by this particular team.

8) Report over categories of campaigns
Not all email campaigns you send will be of the same type. The statistics you should get on an event promotion will vary considerably to that of a newsletter. Therefore when you are reporting across campaigns over time make sure you are only reporting over similar types of emails to enable you to identify reliable trends.

9) Click-thru distribution
Analyse which links are the most popular in your email campaigns to identify where the most appropriate points for your call-to-actions are.

10) Categories of Links
Categorise different links in your email messages to work out over time which types of offers recipients are clicking on. For example a computer company can categorise links by type of product such as PC, Laptop, Printer etc.