Things You'll Need:
- Email marketing program such as Constant Contact, Lyris or others
- Most recent report
- Website traffic reports
- Two hours
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Step 1
Download your email marketing metrics report from your email marketing system. Be sure to include the key metrics that you want to analyze. If possible, import it into Excel to save as a spreadsheet. It's easier to manipulate and analyze data in Excel.
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Step 2
Determine your priority metrics. What customer response data is most important to your business? Retailers, for example, may want to look at click-through rates, conversion rates, and shopping patterns once customers enter their website. Everyone should look at the SPAM report rate. High spam report rates indicate something wrong on the FRONT end of your email campaign. Look especially at the customer permission box when people give you their permission to receive information. Are you sending what you promised to send? High spam report rates indicate that customers aren't seeing the value of what you are sending. It's time to re-examine your email messages and autoresponder if you are getting a high spam report.
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Step 3
Look for patterns in the data. Are your overall metrics consistent or showing big leaps and drops? Begin to note the highs - highest open rate, for example, may point to a winning email subject line, while a low open rate points towards one that isn't attractive to your customers. Note patterns in the response rate such as the use of certain offers, timing of your emails, or possibly words used in the subject line.
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Step 4
Compare your new understanding of your email metrics to your web site statistics. Which pages of your site are visited most frequently? Do these pages correspond to information or subject lines in your email? A retailer, for example, may find that pages of jeans get the most traffic, and emails offering free shipping the highest open and click through rate. Combining this into a new email marketing message, such as "Free shipping with the purchase of any two pairs of jeans" may yield an even higher response rate. Set up a split test or another test to examine this hypothesis.













Comments
Kallicat said
on 1/29/2009 Well written.
kskaggs said
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onlyhoney4u said
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