Data Science Teams Powering GTM

Stripe shifted to data science models to determine which prospects to pursue and direct their go-to-market strategy. They made their processes more efficient and relevant by considering key factors, including industry, business model, number of employees, and other pertinent details.

1. When pursuing prospects, what is the first change in approach?
The first change is to be more data-driven in thinking about the market and deciding which prospects to pursue. This is achieved through a smarter, data science-driven model rather than typical heuristics.

2. Why is the quantity of data important in a data science model?
Having a lot of data is important for assessing and understanding which companies are the right ones to pursue.

3. What type of data is used to identify prospects?
Prospects are identified using publicly available data, including information about a company's industry, business model, number of employees, and other relevant attributes.

4. What is the role of data in this new approach?
Data is at the forefront of this approach. It helps identify the right companies to pursue. When combined with signal data, you can determine the optimal time to engage, making you efficient and relevant.