Forecasting digital advertising returns

  • Agency
  • Rise Interactive
  • Role
  • Senior Interaction Designer
  • Focus
  • Product design
  • User interface design
  • User experience design
  • Design systems
  • Front-end development

Designing for data visualization

Marketing agencies often use data to drive and measure the success of their campaigns. But manually copying reports from different sources into one spreadsheet is error-prone and time consuming; time that is better spent actually using that information in a more meaningful way. As part of the product team at Rise Interactive, we designed a platform that pulled data from multiple sources, forecasted future returns and created new, transparent methods of reporting.

Designing a data-heavy application required a deep understanding of the data that went into it. Google AdWords certifications and collaborations with data scientists led to a better grasp of digital advertising intricacies and how to identify the signal from the noise.

We designed with code — using the same JavaScript libraries our developers used to visualize data: C3, D3 and Datatables. Our design-focused front-end environment eventually grew to become our design system. Everything from buttons to templates were documented and nested using Pattern Lab.

Our audience

Our audience contained two major groups: analysts within the organization and clients outside of the company. We regularly met with both to understand how each would use the same product-and there were some distinct differences. Because of the unique needs of each group, user flows were customized to better deliver the most appropriate content to the right person.

We designed new ways to compare data, like moving budgets across tactics and platforms. If a sponsored ad on Amazon wasn't performing as well as the equivalent paid search ad on Google, analysts could shift spending to the latter and generate a report for the client.

Data quickly became more substantive and reliable because of this hyper-focused reporting. Analysts began writing algorithms to find patterns that had generated higher returns. Our team then integrated those algorithms back into the application. From a single interface, analysts could adjust a series of variables to programmatically forecast returns for any campaign.

Results

Comparing and forecasting data at every level provided a view of information that wasn't available on any other platform. Features like automated dashboards, cross-department reporting, cross-category reporting, location segmentation and custom date ranges further propelled our digital product as one of the company's main differentiators.