Thursday April 6, 2017
Consumers may remember this search and advertising legend as large books dropped on doorsteps. They’ve since transformed into a digital business that delivers consumer value online and drives revenue through 200K+ advertisers and 400M+ digital visits per year.
To prove ROI and drive ad renewal, marketing analysts needed to deliver on-demand insight on ad performance, but were challenged with queries and reports running slowly directly on their Hadoop system. IT added a BI ready data mart to aid self-service, but introduced a delay in analysts’ access to the data. The process began to look something like this:
- Place search ads data on 424 million annual visits into Hadoop
- Extract, structure and load data through MapReduce/Pig into a dimensional BI-ready data mart
- Run Tableau reports
- Rinse and repeat for updated analysis on the latest data
Tableau-wielding analysts began circumventing IT to create their own extracts, but introduced governance issues in the process. IT became frustrated with limited adoption of their big data investment which held huge value potential. Marketing and other business groups became frustrated, unable to run key ad activity reports, and advertisers became frustrated with limited and delayed ad results insight. Online activity, and data, grew. Executives, IT, analysts and clients agreed, something had to change.
Capitalizing on existing investment in Hadoop and Tableau, with the addition of AtScale the ad legend delivered analytic dashboard access to advertisers for on-demand insight of respective ad activity data as it landed in Hadoop. Simultaneously, internal analysts run Tableau reports live on the same ad big data, empowering them to catch low performing ads and make ad change recommendations to help advertisers achieve maximum value.
Through secure, consistent and immediate ad insights, this ad veteran cum digital advertiser has increased customer service (to advertisers), improved consumer experience (to their advertisers customers), and driven corporate value through ROI of existing big data and BI. Ultimately, improving the bottom line (expense saving and revenue generating) for all involved.