Backstory completed a full retiering of its 141-account customer base in just three to four days, a process that previously took five teams an entire quarter, according to a presentation at SaaStr AI Day. Haya Kamola, who leads customer success at Backstory, detailed how the company used AI-driven signals and streamlined data connectors to accelerate this key go-to-market exercise.
Kamola explained that the project began by defining the "golden customer" profile through input from account teams and senior leadership, focusing on qualitative judgment rather than tenure or size. The team then developed new repeatable signals, including a five-level AI maturity score for each account with an explanation. Data collection was simplified by replacing cross-functional pulls with four connectors and a single CSV export from Salesforce. After four iterations, they refined eight initial signals into four scoring buckets, correcting one signal that was initially scoring in reverse.
This rapid retiering effort highlights how AI and automation can transform traditional sales and customer success workflows. The approach reduced a process that used to require multiple teams working over three months to just days, enabling faster strategic decision-making. The use of AI maturity scoring as a signal is notable, reflecting growing interest in measuring customers’ AI adoption levels to prioritize engagement. Backstory’s method may serve as a model for other SaaS companies seeking to optimize account prioritization.
The retiering project was completed within a tight timeframe set by the board, demonstrating the operational impact of integrating AI signals and streamlined data workflows. Kamola’s presentation at SaaStr AI Day provided a detailed case study of this transformation, underscoring the potential for AI to accelerate complex sales processes.