Verizon partners with Intent HQ to improve sector-leading churn prediction accuracy by up to 3.5%.
Verizon sees a 51% marketing ROI uplift with Intent HQ’s Audience AI
Verizon Protect – one of Verizon’s phone insurance offerings – is the company’s highest revenue-generating service add-on. During a recent “open enrolment” campaign for Verizon Protect, the marketing team wanted to drive even more incremental lift with improved audience targeting that better matched this limited-time offer to the most relevant customers.
However, advanced modeling and audience-building are complex and time-consuming jobs, often requiring significant technical resources. The marketing team knew that they had behavioral and event-based data which could reveal insights into customer needs and intent, but they did not yet have the means to unlock this value in a scalable and privacy-secure
Applying Audience AI, Verizon successfully broadened its enrolment propensity model using consumer interests and behaviors. Compared to the existing audience selection model, the campaign achieved:
- 51% incremental take rate
- $378k incremental campaign revenue
“We wanted to see if we could take our audience targeting to the next level by leveraging behavioral insights developed with Intent HQ’s platform. Our goal is to create marketing that is so relevant to our customers that they view our messages as helpful suggestions as if we were a friend.
Audience AI is helping us do that by giving our marketers fingertip access to human-level insights and making them truly actionable. The results have been exceeding expectations, sometimes by a very wide margin.”
Andy HerzDirector, Value-Based Marketing, Verizon
Case Study Abstract
Verizon wanted to understand the different customer groupings based on the current Verizon Protect customer and past purchase behaviors. With those insights, they could then create scalable target audiences paired with personalized campaign creative, and messaging.
Applying Audience AI, Verizon successfully broadened its enrollment propensity model using consumer interests and behaviors identified within customer profile data, past campaign engagement data and insights driven by permissioned weblog analytics. By harnessing these different data sources in one place, Verizon was able to build out an optimized persona that would prove to be significantly more successful than its traditional target.