Utilize first and third-party demographic, technographic, and firmographic data to inform whether a lead should be enrolled in an automated self-serve path or speak directly with sales. With MadKudu, you can build unique scoring models for your product-led and enterprise go-to-market motions. MadKudu's data science studio enables rapid model iteration and alignment with ever-changing inbound and rep feedback.
To understand engagement and identify true PQLs, you need to identify the statistical correlation between events and actual conversions at the lead and account levels. MadKudu processes your product data from sources like Segment or Amplitude and your data warehouse (Snowflake, Redshift, etc.) to find critical milestones in the journey that warrant action.
Visibility into product usage, firmographic, and demographic data is critical with any product-led motion, so sales knows who to reach out to, when, and with what message. With MadKudu, you can customize and surface signals on lead, contact, and account records to highlight critical milestones like inviting the first user, taking 10 admin actions, or whatever is most relevant to your business.
Provide feedback to your demand gen team on who to target for acquisition campaigns, reducing your cost per qualified lead, and cutting through the noise. MadKudu provides you with data-driven insights like which personas to target within specific accounts, enabling your demand gen team to optimize programs towards the best outcomes.
In this guide, we'll debunk common myths around product-led growth, share real-world examples of lessons learned, and dissect the strategies that have helped companies scale at lightning speed.
We worked with ProductLed and marketing teams at PLG companies to develop a framework to help you increase conversions by providing a highly personalized experience.
Learn how Chartio increased product trial conversions to paying customers by 40% by implementing a predictive lead scoring model with MadKudu.