This week we spent some time doing a complete redesign of our statistics in Newsberry. I’ve been wanting to do this for a while. As we started reviewing ideas and sketching, it was obvious that with email marketing statistics can be actionable, not just informative. We also fit in the use of Sparklines, inspired by Google’s recent redesign of Google Analytics. Here are some the ideas that we came up with.
We reviewed a lot of email marketing systems to gather ideas, each had similar tools for tracking campaigns. The usual tracking methods include opens, link clicks, unsubscribes, the various bounce types, and so on. All of this data is great for reporting, but what can you really do with it?
Showing what matters
We had a lot of ideas before creating our mockups. For email analytics we could show lists of who opened emails or clicked on links. We also thought of displaying detailed graphs of open rates, clicks, bounces and so on over time. The tools are not difficult to design or implement, but they add to the complexity of the interface.
After evaluating the real motivations of the user, these extra features seemed pointless. It is simple to show a graph of opens over time, but the goal is to figure out when the most people opened your emails. In the same regard, we could have easily shown who exactly opened your emails or clicked on links. Again, what is the goal? The goal is to react to this behavior for follow up or targeted communication. When dealing with a large subscriber base, the details should only be scrutinized if you can react on them.
This is an example of how we simplified open times. Instead of showing hourly graphs, we just show the high and low.
Our solution can be defined in two main areas:
- Understanding subscriber trends across campaigns
- Reacting to behavioral data
Understanding Subscriber Trends
A lot can be learned about delivery and the success (or failure) of campaigns by viewing common trends across all campaigns. This explains more about subscribers, not just the campaign or newsletter. To accomplish this goal, we concentrated on the stats that we track and how they fluctuate across a series of campaigns. For instance, over a certain period of time, it is valuable to know if opens, clicks, subscriptions, and delivery rates are improving or declining.
We borrowed the excellent date slider from MeasureMap to handle the navigation of time periods across campaigns.
Here is an example of Campaign Trends:
As you can see, the report will display the average opens and clicks across all campaigns from the selected dates. As the date slider is positioned, the increase or decline in these numbers change for that time period.
In the same regard, we also track list activity:
This will help customers understand their list activity, along with deliverability issues, over the selected timeframe.
Reacting to behavioral data
Analytics on email campaigns give us a unique opportunity to learn and improve from each campaign that is sent. Noticing improvements or decline in open rates, link clicks, unsubscribes, and bounces help us learn from our mistakes and triumphs on each occasion. The problem is, most (most, not all) systems do not allow customers to react on this behavioral data.
Let me offer a few examples:
- Create a sub-list of all subscribers who opened an email or clicked on a link in the last 5 campaigns.
- Create a sub-list of all subscribers who clicked on link A, B, and C from the last campaign.
- Add all subscribers who did not receive your emails due to network issues to a sub-list.
Each of these examples allow for targeted follow up emails to a list of subscribers based on behavioral data. This creates an opportunity to send subscribers the most relevent information possible. In each of our reports, we added small action items to “Add to sub-list” the subscribers from the behaviorial data.
The screen shots above include small example graphs. When we had a look at Google’s recent redesign of Google Analytics and noticed these little graphs (called Sparklines) we had to find a use for them. Sparklines are small graphs that represent real data. The purpose is to display understandable trends in a small “word-sized” image. Each of the graphs in our reports will be generated on the fly from real campaign and list data.
The concepts we created may or may not work, we will only know once people start using them. Based on what we know from email marketing, we focused on what matters most to improve results, instead of just numbers that can be printed out for board members. Being able to react to subscriber behavior and understanding a campaign’s success or failure is what really matters.
I’ll make sure to post a follow up once we implement and release the new reports.