Building Quosity for Oracle Eloqua

When I joined the product team at Relationship One, the very first thing I did, before building my first visualization, before connecting to my datasources, was sit down with our marketing team and ask “Where are we at today?  How do we look at our data?  How do we learn?”.

The answers came with caveats (time involved, legwork, insights, etc.) that fell short of answering the real questions at hand regarding our marketing performance. Our marketing team members would log into Eloqua, spend a lot of time trying to find a specific report that contained only one of the several metrics they needed to conduct their analysis, then crossed their fingers as they ran the report, hoping it wouldn’t kick back an error. Once they had the numbers, they were often in difficult-to-interpret tables, which then had to be exported so they could be combined with data from other reports and sources to paint a complete picture. The interface was unappealing, the user experience was cumbersome and not intuitive, and sharing reports was actively avoided.  For our marketers, using data to drive strategy and learning was like having to eat your broccoli as a kid; you know it’s good for you, but you hate the work required to reap the benefits, and as a result, you probably avoided it.

We knew there had to be a better way to enable data-driven marketers.  What if you could go to ONE place to answer all of your questions related to, for example, the performance of your emails?  What about an easy way to gauge campaign performance and benchmark it against others in your portfolio?  What if the data was already put into context for you, visualized for fast and intuitive insights and blended with all the relevant data to support it?  We got excited about the possibilities, so we went to work on building a new way to access and analyze marketing data and ended up with Quosity for Oracle Eloqua.

First, we focused on the common questions our marketers had about their marketing efforts.  How is my campaign doing?  What are my contacts responding to? Who is clicking through my emails?  What is the profile of the average contact that is (or isn’t) responding to my marketing assets?  What activity took place yesterday?  Once we determined what we wanted to know, we asked ourselves what we could track and measure to provide answers to those questions.

In a nutshell, we framed the results-based questions into activity-driven metrics.

Once we had the specific metrics we wanted to include in our dashboard, we moved on to context.  Sure, I have 500 clicks on the last email asset I sent, but how do I know if that’s good or bad?  We wanted to bring in any data that would help provide perspective on if we were actually succeeding or not.  To help accomplish that, we had to ask ourselves if success was goal-based or relative.  For example, do we aim for a 5% click-through rate on every asset or is our goal to increase clicks by 3% from the previous year?  Once established, we could finalize our visualizations by adding the right things to compare to.

Finally, we wanted to improve the user experience and make it more intuitive.  What if our users didn’t have to sort through endless reports in a clunky, hard-to-use interface, and then guess what the metrics actually meant and how they were determined?  We started by looking at analytic flow (What our marketers asked first and the questions that followed) and structured the dashboards accordingly.  Rather than jumping around from report to report and trying to juggle different numbers and questions in their head, our team can now experience their metrics as a story. They can interactively explore their data, highlight points of interest, drill down on interesting trends, and when they’re not sure what a specific number means or how it was generated, all they have to do is hover over the metric to get a detailed explanation.  Needless to say, our marketing team loved it. I am confident you will too.

Now, with Quosity, using data to make decisions for marketing feels less like forcing down broccoli (no offense, broccoli lovers) and more like indulging in a chocolate lava cake. Through LOTS of listening, data visualization best practices, and principles of user experience design, we’ve removed a lot of the pain points our marketers used to experience with their old reports and replaced them with analytics that give them the context and confidence to make informed decisions.