Leveraging data science to illuminate the modern consumer decision journey

Today’s consumer decision journey is rapidly expanding into something bigger and more intricate than ever imagined when the internet first arrived on the scene. Searcher behavior continues to evolve at breakneck pace, especially as new forms of mode and modality push marketers further into the realm of AI and machine learning. In order to fully understand the complexities of the new consumer decision journey, today’s advertising teams must accommodate a new role: data scientist.
The advertiser analytics group at Microsoft Advertising (my employer) is taking deeper dives into internal search query data to help marketers get the visibility they need. What exactly does today’s CDJ look like? Well, like this:What we’re looking at here is an actual representation of recent search queries on Bing related to enterprise cloud software. It’s a network comprised of search queries and the relationships between them, with relationship defined as searches conducted by the same person in a close window of time. Let’s dive in and explore. Messy, right? Well, understanding searcher behavior is a complicated problem. First off, let’s look at all the different communities within this network, which are visualized by color. It should become quickly apparent that these queries are clustered thematically; queries around VPN are given their own color, and big players in the space such as Azure and AWS have large communities. It is important to note that queries are not placed in communities based on the content of the query itself, but rather based on the regularity with which they are searched by the same user. This is an important distinction, and it gives us something that is hard to come by: a raw, unbiased look at where brands are positioned in their space