They found that leads who have engaged with their own Twitter handles are at least 2.5 times more likely to convert into Marketing Qualified Leads (MQL) compared to their baseline conversion rate. In addition, they found that leads who have engaged with their competitors are about twice as likely to convert to MQLs compared to their baseline. Last but not least, they found that when a lead engages with one of their tracked keywords two or more times, the lead is at least twice as likely to convert into an MQL compared to someone who has not taken any social actions. By lead scoring social actions that have higher conversion rates than their baseline, the company’s marketing team can pass additional MQLs to their sales team and increase their contribution to pipeline.
Another company—a provider of M&A, P&E and VC transactions data for investors—found that leads who engaged with any of their tracked keywords are 29% more likely to convert compared to their baseline. Also, leads who talk about certain topics (i.e., blockchain, crowdfunding) and follow certain competitors (@cbinsights) are 50% to 200% more likely to convert compared to their baseline. In this case, a conversion is someone getting a demo Italy Phone Number List of their platform. These are just a couple of results I’ve seen. I am not trying to convince you that social intent data will be a strong predictor of purchase behavior for your particular audience.
The important thing is that with any intent data, you want to run some tests and see if the contacts or accounts the provider flag as “showing intent” are in fact “better leads” than your typical lead. You’ll be able to answer the question for yourself by looking at performance metrics like leads’ response rates and conversion rates. To understand whether an intent data provider can support your specific use case, I recommend asking the following questions: What data sources do you provide? What websites/online communities/social networks are you getting this data from? Are you providing account/company level data or individual contact level data?