What Can Presidential Electoral Campaign Teams Teach Organizations About Analytics?

[LisaA]

No matter your political views, most will agree that data and analytics have forever changed how presidential electoral campaign teams approach the election process. And for organizations, there’s a lot to be learned from this process because it serves to demonstrate how adopting a unified, analytics-driven focus can change the competitive landscape.

In presidential elections, campaign teams focus on the three levers for votes: registration, persuasion, and turnout; these concepts aren’t too dissimilar from key organizational levers: awareness, consideration, and purchase. And in both cases, when analytics is fully harnessed and embedded in all key operational functions, it can result in huge impacts effecting target markets to take action.

Since the 2012 presidential elections, campaign teams on both sides of the party line have elevated analytics to center stage, and in many ways are driving the analytic revolution faster and more efficiently than many organizations do today.

As with most organizations, presidential electoral campaign teams used to base campaign activities on experience and gut instinct alone. But once analytics was adopted, they moved through three distinct stages of analytic evolution: first, moving from gut intuition to measuring distinct moments in the campaigning process. In this first stage, insights were fragmented and only applied to areas such as electorate and campaign operations.  The second evolution in analytics shifted toward data discovery and analytic decisions, integrating analytics into operational components such as media strategies, and social sentiment analysis. The third evolution took place in the 2012 presidential election; with the Obama campaign team taking analytics to a new level of analytic evolution, shifting to a single unified analytics environment where not only all areas of a campaign process were analyzed, but where all of those insights were rolled up and consolidated to provide a comprehensive view of every aspect of the campaign.

We have recently developed a 2016 presidential electoral app to help predict the winning presidential candidate at the granular zip code level. This model was built by blending multiple datasets such as demographic data, location data and voter data to then build spatial and statistical models in order to understand and predict the hyperlocal electoral preference. Using similar analytic techniques, organizations can be well positioned to understand their competitive position within a given area.

The success of presidential campaign teams lies in the evolutionary adoption of analytics. Similarly, enterprises large and small are well suited to follow the analytic lessons of these campaign teams: develop friction-free, analytics processes that deliver a compressive view of all aspects of key operations, or face getting outmaneuvered by the competition who can.  The predictive output by local voter level in the 2016 presidential electoral app demonstrates the analytic capabilities that organizations can and should be leveraging in their own efforts to win mindshare.

The 2016 presidential electoral app is just a small example of the various models that can be produced to create competitive advantage. You can explore how analysts at leading organizations such as JPMorgan Chase, Western Union, Ford and Hyatt are building groundbreaking, unified, friction-free analytics within their organizations without requiring teams of coding specialists.