UserVoice Increases MQLs by 37% Using Predictive Marketing

Many of our customers come to us with a common problem: they have no good way to differentiate best-fit prospects from the tire-kickers, and are often left to rely on “gut instinct” when it comes to prioritizing who they should target. This was a particular pain point for UserVoice, who needed a way to more efficiently prioritize lead flow so their reps could focus their effort on those prospects with the most revenue potential. Additionally, both the sales and marketing teams wanted more transparency into what attributes defined an ideal customer profile so they could personalize and prioritize high-value outreach to these buyers.

UserVoice deployed a fit-based Infer Predictive Scoring model, and is now able to identify and prioritize leads based on how likely they are to purchase the company’s product management software. Armed with new predictive insights, the company saw a 2x increase in conversion rates and a 37% increase in marketing-qualified leads.

Connor Fee, COO at UserVoice, recently joined us to share his company’s predictive intelligence story, and how Infer has become a core technology in their sales and marketing stack:

Additionally, you can download the full snapshot to learn more about how UserVoice is using Infer Predictive Scoring and Profile Management to:

  • Increase sales efficiencies and alignment by identifying UserVoice’s best leads and accounts.
  • Gain a comprehensive, at-a-glance view of the company’s prospects and conduct more targeted and high-value outreach at scale.
  • Visualize real-time performance measurement capabilities that allows marketing to rapidly test and invest in the campaigns and events that deliver the highest quality leads.

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