What it is: Big data analytics tackles the toughest questions marketers face, such as measuring reach and providing customer insights via online and offline sources. The approach looks at trends across vast stretches of data, online and offline, allowing marketers to uncover patterns in consumer behaviors that might be missed when insights are derived exclusively from online data or based on click-throughs.
How it Works: Big data analytics uses a combination of first-party and third-party data from retail outlets, social media and ad networks to paint a portrait of a brand’s digital presence and offline sales health. Companies with an extensive retail presence, for example, can view an uptick in sales at retail locations and view connections between the launches of various online or offline campaigns, if any. It is also possible, through the use of big data, to look at overall societal trends and compare the spending habits in retail outlets in one geographic area versus another.
Why it Matters: Launching an online sale and promotional campaign in one area and seeing brand lift in that area is one thing, but the true measurement of the impact of that campaign can’t be accessed unless that data is put in context. That context might include the regional economy, demographic shifts, a resurgence of brand awareness in social media, or a number of other variables. Big data analytics creates a bridge between data-driven marketing and business intelligence, using the most complete portrait of brand health, online and offline, to create actionable strategy insights. Big data analytics are now beginning to power real-time bidding and audience-buying, two core elements of the evolution of digital advertising. Some digital marketing analysts believe that as the integration between
Who is Using it: This integration of multi-sourced data has most frequently been within the domain of the data-management platform, but some demand-side platforms plug in to DMPs and offer multiple data sources and analytics as well. Some well-known DMPs are WPP’s Xaxis, BlueKai, MediaMath and DataLinx.
Analysis: There’s no direct pipeline into consumer motivation that can make every ad buy perfectly targeted, but better data means less wasted ad spend. Merging online and offline data into a cohesive portrait is the first step of many to bringing digital advertising’s potential in step with most modern methods of business analysis.
It’s a strategy that should secure its future as an information data repository for the AI era, and widen its customer base to include more enterprise clients by meeting their AI needs,
