In 2020, Dun & Bradstreet set out to drink our own champagne and became an avid user of Rev.Up.
What problems did our operations team set out to solve?
Segmentation & Modeling Challenges
- Updates to models/segmentation were manual, which meant they were being updated on a quarterly/semi-annual basis
- Keeping models refreshed and up-to-date was a full-time job diverting resources from new development
- Lack of agility as requirements evolved
- Activating by individual channels in one-off manner
- We could not scale out to engage with a prospect based on where they sat in the buyer journey
- Demand generation team was manually activating in Marketo, Outreach and programmatic
- Orchestration was painful and time consuming
- GTM was being driven around processes and not analytics
- Were not leveraging all data sources in our processes; needed a source of truth
- To many hand-offs between siloes was inefficient and delayed GTM activities
We started with a pilot
- Objective 1: Support launch of intent data in D&B Hoovers to generate new business revenue
- Target Audience: 4,400 large NCA accounts with High Fit for Sales Acceleration Propensity Model
What were the results?
- We were able to reduce the amount of time spent on generating an audience list. Where it used to take 6 analytics engineers to manage all the work, it now takes two.
- We were able to create models that are always live and scoring accounts even though information about the account might change. We are easily able to incorporate new data points into models without having to start over.
- We were able to build always on campaigns that are continuously adding new targets to marketing and sales programs.
- We were able to see all of the target accounts at the point at which they were in the buyers journey and target them differently based on their awareness.