Forecasting with Analytics to Optimize Your Marketing

We all know that the content marketing space is getting more and more competitive each day. Around the world, two blogs are created every second.

It’s more challenging than ever before for content marketers to stand out from the crowd. The focus today must be on creating value for an audience. But how can you do that when it seems like things are changing at the speed of a tweet or pin?

One answer is predictive analytics.

I was recently given the opportunity to participate in a beta program for the new Watson Analytics application. As a content marketer, I have a pretty good grasp on the basic analytics and data tools that I already use, but Watson Analytics opened up my eyes to an entirely new world. Brian Fanzo, another participant the program noted, “Continually analyzing data should be a priority for all marketers in today’s hyper-driven, content world.” The application is free to use here at this sign up, Watson Analytics Registration .

Fighting Off List Fatigue

In the past 18 months, I’ve sent hundreds of emails to my subscriber list. This correspondence is designed to promote content I’ve created (blog posts, podcasts, interviews, etc.) and interact directly with my subscribers.

Before my Watson experiment, I knew I was doing a great job of growing my subscriber list. What I wasn’t sure about was my policies on list fatigue and list cleansing. My assumption was that I needed to check-in with my subscribers and send more personalized emails to keep my list engaged. I hoped that Watson would help me understand how often I should be sending these messages.

Setting Up Watson’s Analytics

Watson was fairly simple to use: after spending about an hour with the platform and watching some of the tutorials, I felt like I had a good grasp on the basics.

It was a snap to upload my data to the Watson Analytics platform. One thing I loved about the system is that it provides you with a data quality score, meaning Watson helped me understand the chances of my project returning meaningful results before I even started.

I can’t stress the importance of making sure that you give your analytics system a good data set at the outset. Even the most powerful computer in the world can’t give you the analytics you need if you give it poor-quality data!

My Results

Using Watson Analytics predictive function I took a look at almost 40 different charts and data visualizations. These were presented with both positive and negative correlations. Check out image #1 to see an example of the negative correlation between total clicks and send date.


This chart helped confirm my earlier suspicions about my email list: I was adding to my subscriber base, but my clicks were decreasing as more of my content went out. List fatigue was setting in among subscribers.

The next thing I wanted to do was determine when I should take steps to offset this problem. Looking at a different chart that helped me understand unique opens, I noticed that there was a small decline in unique opens roughly every six months.



My Key Takeaways from Watson Analytics

I now had the conclusion I was looking for: I was right that subscriber fatigue and infrequent list cleansing was hurting my email list from a high-level perspective. And thanks to Watson Analytics charts, I found out that I should audit my list and send a check-in email roughly every six months.

Since it was only conducted on my email list, this experiment was a relatively small one. But I hope that its example can help marketers understand the power of emerging analytics programs like Watson Analytics.

The days of tools like Watson Analytics only being available for big analysis departments in enterprises with huge budgets are over. Entrepreneurial and SMB expert Brian Moran shared with me on Watson Analytics; “Used properly, predictive analytics can be a huge boon to the marketing efforts of companies of all sizes, in every industry”.