What We Covered
In our latest session with Dutta Satadip, the former Chief Customer Officer at Active Campaign, we explored the challenges and opportunities of using product data for business growth. Dutta shared his experience at two very different companies: Pinterest and Active Campaign.
Pinterest & Active Campaign – Different Business Models, Same Principles for Growth
At Pinterest, Dutta focused on using data to drive advertising outcomes. He combined many different data sources to identify opportunities for sales teams. By analyzing ad performance and providing insights on audience targeting, Pinterest salespeople created informed conversations with advertisers.
For Active Campaign, a B2B CRM solution, product data was a focal point to enhance customer success. Dutta emphasized the need to understand customer behavior and identify moments that matter, such as seasonal events or personal milestones. By leveraging product signals and user data, Active Campaign equipped its sales team with tailored pitches and opportunities to engage customers. The focus was on providing value and actionable insights, enabling the sales team to deliver personalized and targeted customer recommendations.
For any SaaS company, utilizing product data to inform sales strategies, optimize campaigns, and drive better customer outcomes is crucial. Product data contains the strongest insight into predictive outcomes. By connecting different data sources and providing insights relevant to each customer, businesses can enable sales and customer success teams to have meaningful conversations, make informed decisions, and drive revenue growth.
Making Data Consumable: Lessons in Prioritizing and Leveraging Product Data
Finding data insights is not easy. And neither is making those insights consumable for the internal business uer. Dutta found this was a critical element: making data relevant and consumable for sales teams, specifically when engaging with advertisers. Leadership teams need to learn how to prioritize data and avoid overwhelming salespeople with excessive information.
At Pinterest, Dutta highlighted the challenge of connecting different data sources, such as product usage, sales, and support data, to identify actionable insights for salespeople. There was plenty of data available, but the team had to learn how to provide tailored recommendations to advertisers, optimize campaigns, and drive better outcomes.
At Active Campaign, Dutta built internal processes so that customer success could scale its efforts. Proper segmentation and a proactive engagement model led the way. They used product adoption data to understand customer behavior, prioritize engagement efforts, and provide high-touch service. They also aligned engineering teams with business teams. They created adoption scores based on core features and value-driven utilization, empowering its customer success teams to have more meaningful conversations with customers and drive success.
Business-Driven Hypotheses Are Key
Data insights don’t jump out of reports for you. They must have clear business-driven hypotheses. Otherwise, data overwhelms team members and there’s no clear direction of how the information aligns with their goals.
Companies, especially early-stage ones, should build a solid foundation for data integration. This involves creating a common set of data standards and connecting different systems to ensure seamless data flow. Poor data infrastructure means that analysis and insights are slow to come by in the future. By thinking ahead and addressing data integration challenges early on, companies can avoid the pitfalls of disjointed data sources and enable efficient data-driven decision-making.
Let us find your hidden revenue
At Immersa, we specialize in helping companies make the most of their product data. Our team of experts can guide you through the process of integrating and analyzing your data, identifying valuable insights, and implementing strategies to capitalize on them. Whether you’re a startup or an established enterprise, we will uncover the full revenue potential of your product data.