Backstory Retiered Its Entire Customer Base in 3 Days
AI and data analytics accelerated the customer segmentation process.
1Customer Segmentation Project
Backstory launched a segmentation project to reclassify its customer base. The project aimed to prioritize customers and allocate resources efficiently. It covered 141 accounts requested by the board for quick completion. Previously, a similar effort took five teams a full quarter.
2Defining the Golden Customer
The first step was defining the 'golden customer.' These customers placed Backstory at the core of their tech stack, built systems around the platform, and influenced the product roadmap. This definition ensured customers were classified based on strategic importance rather than random CRM fields.
3Unmeasured Signals
The project identified many previously unmeasured signals, including go-to-market process maturity, tech stack mix, and AI maturity. These signals were scattered across various tools and were filled in before the analysis began.
4AI Maturity Score
Backstory developed a five-level framework to measure AI maturity, covering culture, investment level, tech stack, talent, and approach to hard problems. This signal was measured automatically for each account using a systematic prompt.
5Data Collection Process
In the past, data collection took a full quarter. This project sped up the process using four connectors. Tools like Amplitude, Atlassian, Jira, and Slack automatically collected customer data, streamlining the analysis.
6Role of Slack
Slack played a critical role in early detection of customer strategy and risks. Backstory created internal channels for each customer, providing the earliest and most candid insights. This information rarely made it into structured systems.
7Analysis Process
The analysis was conducted in a defined sequence using Claude. This sequence included data normalization, usage data analysis, and growth potential evaluation. A full analysis took about 20 minutes.
8Iterations and Improvements
The project went through four iterations, reevaluating the weight of each data point. Initially, eight signals were used, but it was narrowed down to four scoring buckets. These iterations highlighted the importance of human intervention in the process.
9Outcome and Impact
Backstory completed customer segmentation in just three to four days. The process was significantly accelerated using AI and data analytics. The project marked a crucial step in customer success and efficient resource allocation.
Original article: SaaStr — Backstory, Müşteri Segmentasyonunu 3 Günde Tamamladı