What Sets 3Q Decision Sciences Apart: 6 Lessons Learned from Joining the Team
Published: August 3, 2017
Author: Asa Jordan
Adam Hanford and I both joined the 3Q Decision Sciences team earlier this year. We’ve gained a lot of great exposure, and we’d like to share six lessons we learned in our first few months as Analysts:
1. Consider the Whole Story
While analyzing data is an incredibly important part of Decision Sciences, being able to communicate what we find in a concise and effective way is equally important. We have to understand why an interesting piece of data would be useful to our client and figure out how to explain that to them. Thinking about data in the context of the client and in the context of the client’s customers allows us to understand the narrative and share that narrative. Knowing that a piece of data is important but not knowing how to explain it isn’t that useful; we need to think about the whole story.
2. Learn about Convertro
Joining the Decision Sciences team meant learning several new tools for analysis. Among the most powerful of the programs is Convertro, which is a multi-touch attribution tool used for traffic monitoring. Convertro’s primary function is to allow the team to measure how much of a role each traffic touchpoint plays in a transaction. Using a machine learning or “algorithmic” model, the program can estimate how much of a factor each part of the transaction process was, including taking into account external factors, such as, word of mouth. Learning how much value each channel and interaction provides is absolutely critical to smart marketing budgets, especially since clicks are only getting more expensive to generate.
3. Ask Lots of Questions
There’s no such thing as a bad question. As cliché as it sounds, it is true. In analytics we can never have too much information. It’s important for us to ask questions that our clients may want answered as well as to ask questions of our peers. The more questions we ask of data we are analyzing, the more likely it is that we’ll come up with actionable insights. Asking questions of our coworkers, even if they may seem silly (the questions, not the coworkers), aligns with the issues other team members are having or makes issues that hadn’t yet been noticed come to light.
4. Learn about Lotame
The Decision Sciences team also uses the Data Management Platform (DMP) Lotame to provide insights on the characteristics of audiences. This is a potent tool for learning targeting information about the audiences of clients. It can be determined which income group is mostly likely to complete a transaction. Or it can be established if audiences are more likely than the general internet population to hold an attribute, like if customers have a higher propensity for homeownership. Learning more (and more) about your audience allows for advanced, high-powered segmentation of messaging, offers, and more.
Working on the Decision Sciences team requires being innovative. It is strongly encouraged that members seek to add value for clients by introducing new procedures. This is accomplished through searching for new dimensions for analysis. One prominent example of innovation has been the incorporation of the R statistical computing language into the team’s processes to provide greater scope and depth to our analysis.
6. There Is Always More to Learn
An important part of working in Decision Sciences is constantly learning. Analytics is constantly changing, and staying up to date is an integral part of our job. In just the past few weeks, Google announced new services they’ll be rolling out, and we’ll need to know how we can use these to stay ahead. We not only have to learn how to use new products and services, we have to think of new and creative ways to implement the products and services we already use. It’s also important that we are constantly learning from our coworkers and understanding different perspectives on the work we are doing.