2019 Attribution Accelerator Conference: Insights on Moving Attribution Out of a Silo

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2019 Attribution Accelerator Conference: Insights on Moving Attribution Out of a Silo


Thought leaders and innovators in the marketing analytics field across industries and verticals came together at the 2019 Attribution Accelerator conference in New York City on October 17th. They each gave a deep dive into their unique set of challenges and approaches in advanced attribution modeling. The day included lectures on balancing short-term results with long-term brand equity; overcoming silos, imperfect ecosystems and data inconsistencies; and the ongoing challenges presented by increased concerns over privacy.

Performics’ Analytics lead, Dr. Ram Singh, co-presented with Remberto Del Real, Chief Marketing Officer of DeVry, on the current environment of the education sector. They focused on the increasing importance of integrating and sharing data, KPIs, and business goals across the agency and client side to maximize marketing investment. The lecture particularly stood out as it evangelized how external variables, not just limited to media data, can have a profound effect on outcomes. In DeVry’s case, some of these factors included data from the Department of Labor, like employability statistics, job market trends per DMA, as well as data pulled via proprietary tools, such as the number of people actively seeking degrees in each market, as identified by Benchtools, Performics proprietary competitive tracking platform. Plus, Performics took into account inputs DeVry is privy to and shared with Performics, their trusted agency and partner, including the kinds of disciplines covered by degrees provided by DeVry. Taking into account all these factors was a daunting prospect as it made market estimations extremely complex, but the outcome was an incredibly robust final analysis. It’s imperative that all possible variables and headwinds are considered when trying to identify varying scenarios and ultimately decide on what an optimal mix should look like.


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