In this episode of Selling Signals, we’re joined by Sheraz Bhatti, who led Customer Success at Similarweb and now does the same at Quartr.

We discuss what Customer Success means in a data business and why it differs from the standard SaaS playbook. Sheraz explains what good adoption looks like, why usage metrics can be misleading, and how data vendors can tell whether their product is making it into real investment decisions.

We also cover churn risk, internal champions and what “land and expand” looks like when the product being sold is data.

In this episode of Selling Signals, we’re joined by Julia Meigh, who has spent years helping investors decide what data to buy, with a particular focus on macro and ESG. Few people have had a closer view of how macro investors think about new datasets or how providers break into the market generally.

We discuss what macro funds actually need from data and where vendors still have room to win. Julia also shares her view on geopolitical uncertainty and on prediction markets, both of which are playing a bigger role in macro investing.

This episode is essential listening for anyone building data products for macro investors or trying to understand where that market is heading.

In this episode of Selling Signals, we’re joined by Florence Broderick, CRO at General Index, where she is helping scale a commodities data business. Florence has worked across both software and data companies, which gives her a rare perspective on how different those worlds are when it comes to selling, hiring and supporting customers.

We discuss why SaaS is still ahead of DaaS in go-to-market maturity and how AI is changing sales teams in practice. Florence shares what is actually working in prospecting today, how data vendors should think about AI use in customer contracts, and why in-person events may become even more important as more of the sales process becomes automated.

This episode is essential listening for anyone building a data business, modernising a GTM team, or trying to understand what data companies still need to learn from SaaS.