Verizon media optimization and data strategy

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Verizon's wireline services include a large national footprint for consumer fiber optic (FiOS) Internet, television, and telephone services. Their marketing arm spends tens of millions of dollars annually on online display and search. They also have a valuable proprietary data warehouse that includes service addresses and customers who have interacted with their Website.

Verizon engaged with Cadreon (now MediaBrands Audience Platform) to take their current proprietary data and impression investment and improve their reach and efficiency. I was part of a senior three-person team, where I served as business and data strategist alongside an advertising tech platform advisor and a senior client and program executive.

Together we were tasked with determining how Verizon's marketing database and Website data could be married with other sources of data, ad platforms and networks--then show Verizon new, more efficient ways to reach new audience segments with offers. We needed ways to refine existing segments and uncover new ones from large data sets. This also had to be conducted with respect to consumer data and emerging privacy legislation. Additionally, Verizon wished to have segment insights drive content optimization for all marketing programs online and offline, including mailing, door-to-door, call center, and all interactive efforts.

We first established the current state of affairs by interviewing various company stakeholders, including marketing strategy leadership, data warehouse owners, and Verizon legal counsel. Then we painted a clear picture regarding where Verizon was targeting accurately and compellingly on the Web...and then pointing out where poorly targeted segments were resulting in significant impression (and investment) waste. Like most large companies, there were silos to bust and "personalized" consumer impressions that were simply wrong.

 Personalization is only personal if the data modelng is correct.

Personalization is only personal if the data modelng is correct.

 The segmentation data use cases worked well independently but multiplied in effectiveness when used in parallel.

The segmentation data use cases worked well independently but multiplied in effectiveness when used in parallel.

We proposed a unified intelligence and segment modeling platform for all marketing where tactical managers pulled from the same targeting system. These segments were informed by ten new interdependent "use cases" for all available data, mashed together with dimensions of offer availability, cookie match, location, competitive offer timing, and other segments from publishers, DMPs, DSPs, aggregators, and ad exchanges. A lot of our work occurred on-site in New York in collaboration with R/GA, Verizon's digital agency.

The result was a new big data segmentation model, complete with discreet ROI projections that significantly improved reach and efficiencies--with no additional media impression investment.  

The strategic work and investment analysis was praised by MediaBrands Cadreon executives as the best deliverable the company had ever produced. It was a huge effort that also helped me learn about the platforms, data, and legal implications behind online advertising. I also met quite a few smart people with whom I would gladly bring in to improve the effectiveness of your online media investment.