Every product manager on our team uses Indicative to monitor their KPIs. They’re measuring how their releases are going, identifying potential problems, and pinpointing areas of opportunity.
Benjy Boxer, Director of Product Strategy at NewsCred
Software as a service (SaaS) startup NewsCred is at a critical stage in its growth story. In the last year, the company has more than doubled its team, raised $25M in Series C funding, and brought on industry leading clients like Monster, HP, SAP, The Hartford, and Pepsi.
Within this context, NewsCred’s Director of Product Strategy Benjy Boxer has built an internal startup, consisting of an entirely new product line: a dual platform, called the NewsRoom and Content Marketing Cloud, that helps brands and agencies create high-volume content programs.
“Every product manager on our team uses Indicative to monitor their KPIs,” says Boxer. “They’re measuring how their releases are going, identifying potential problems, and pinpointing areas of opportunity."
In this case study, see how Benjy and his team:
› Make data-driven decisions as part of their everyday routines
› Seamlessly analyze and visualize multiple sources of data together
› Monitor critical KPIs in real-time
› Build new products based on actual customer engagement
› Identify potential roadblocks and issues before they occur
It was hard to make data the center of our work, decision-making, and customer relationships. It’s this pain point that led us to Indicative
Emma Ferguson, Central Analytics Manager at Gild
SaaS startup Gild uses machine learning to make recruiting more efficient. Hundreds of companies like Facebook, Progressive, HBO, and TripAdvisor rely on the platform to source talent, match prospects, and cultivate relationships with passive candidates.
With data being central to Gild’s product vision and value proposition, the company pays close attention to its customer performance metrics and usage trends. Until recently, however, the organization lacked a comprehensive view of customer activity and relied on instinct to guide support and product decisions.
Data was hard to come by, and information was very fragmented. If we wanted to look at customer product usage, for instance, we’d need to go into the admin area of our platform and pull a list of every action taken in our product. We had no choice but to pull thousands of events that were unrelated to our research questions. We’d then have to repeat this manual process in Salesforce or a customer-specific view.
Processes were manual and clunky, and teams struggled with customer data silos. Ferguson found herself buried under information avalanches.
In this case study, see how Emma Ferguson and her team leverage Indicative:
› System for identifying and managing highest value and highest risk customers
› Immediate access to real-time account data across sales, account management, and support
› Transparency into customer-level product usage through integrations with third-party platforms like Salesforce