When it comes to data, there are three C’s, three key factors that can impact your data. I discussed these at length in Marketing Blue Belt, but I wanted to reiterate one here that I screwed up royally.
The 3 C’s of good data are chosen well (selection), compatible (format), and clean.
Recently, I was doing attribution analysis to find out what has worked for me in 2015 as part of my personal 2016 marketing planning. I looked at my benchmarks and saw that email was underperforming:
I thought I was a pretty good email marketer. Maybe not? I ran a custom report to see what was happening under the hood, and then the answer became clear as day:
I don’t post to social media on the weekends, yet Twitter has been one of my top referring sources on Sundays.
If you don’t get my weekly email newsletter, for the past couple of years, it’s looked like this:
Do you see the problem?
By re-using my tweets as they were, I have been re-using the social media campaign tags built into the shortened links. Social media – Twitter, specifically – is getting credit for email’s impact.
Above you can see the tags Buffer automatically adds to my tweets. When I reuse those bit.ly URLs in email, they keep their social media attribution.
This is the essence of insight from the MAISTER framework in Marketing Blue Belt. We have the data. We know what happened. We had to establish why. Now we know what to do to fix it.
Be careful as you do your marketing analysis of 2015 and planning for 2016! Understand your data. Don’t blindly assume that it’s correct and infallible. Mine certainly wasn’t – and I have only myself to blame.
You might also enjoy:
- What Is The Difference Between Analysis and Insight?
- How to Measure the Marketing Impact of Public Speaking
- Retiring Old Email Marketing Strategies
- Marketing Data Science: Introduction to Data Blending
- B2B Email Marketers: Stop Blocking Personal Emails
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Although it may be tempting, the general rule of thumb when it comes to real estate agent email list of addresses is: Don’t. Aside from the obvious reasons, such as the fact that nine times out of 10 the audience is not relevant, there are a ton of major problems that come with buying bad lists.