Interviewee

Ben Petersen - Product Lead, Data Feeds at YipitData

As markets become more reactive to macro shocks, AI disruption, and rapid shifts in consumer and enterprise behavior, investors are demanding datasets that reflect what’s happening in the real economy — not just what’s being said about it.

We spoke with the team at YipitData about how their transaction-driven data products help investors test models, generate differentiated signals, and bridge the gap between discretionary and systematic strategies.

Testing Models Against Sudden Market Shifts

When markets move abruptly, investors need clarity on whether thematic narratives are translating into measurable changes in real-world behavior. For YipitData, the answer lies in transaction-level visibility.

“By providing visibility into real-world transactions and purchases, our data products give systematic investors empirical tools to measure how thematic or macro factors are translating to shifts in actual customer and business spending and behavior.”

They pointed to AI-driven disruption in software as a practical example of this framework:

“Our B2B Software Spend Panel allows investors to monitor the real-world impact of AI-driven disruption on software companies, including being able to identify which tickers are being cannibalized vs. those that are benefiting from AI-related shifts.”

In fast-moving environments, this type of real-world validation helps investors adjust exposures based on evidence rather than assumption.

Growing Quant Interest — and Crossover Appeal 

YipitData has expanded its suite of data feeds in recent years, drawing increasing attention from systematic investors seeking differentiated alpha signals.

“Over the past three years, Yipit has rapidly expanded its data feed portfolio, which has allowed us to increase our footprint with systematic investors.”

Their strategy has centered on launching exclusive, differentiated datasets designed to unlock new signal pathways. “By focusing on launching exclusive, highly differentiated offerings, we have driven strong interest from quant researchers looking for new alpha signals.”

A key example is their B2B Enterprise Spend Feed (Summit):

“Our new B2B Enterprise Spend Feed (Summit) offers high capture, high breadth B2B sell-in data for a panel of 200 of the largest companies in the world (primarily Fortune 500s), which offers investors an unparalleled view into CPG and Consumer sell-in trends, which are a leading indicator of sell-out volumes, as well as unlocking under-covered sectors such as Industrials, Enterprise IT, and others.”

This breadth not only enhances systematic model coverage but also supports discretionary managers looking to augment fundamental research with transaction-backed insight.

What Matters Most to Quant Teams

When asked what differentiates their approach for systematic investors, YipitData emphasized three guiding principles.

On accuracy:

“Our data must capture & reflect real-world trends, and perform better than consensus on KPI predictions.”

On timeliness and fill rates, particularly in historically lagging sectors:

“Our data must meaningfully improve fill rates over competing datasets – especially within sectors that have historically suffered from data and lag issues (eg. MedTech, CPG & Consumer).”

And on differentiated sector coverage:

“Our data must help investors cover sectors or tickers that are not already well covered, or where data products are not meeting investor needs (eg. Industrials, Software, etc).”

For systematic teams, these three pillars — predictive accuracy, speed, and differentiated coverage — determine whether a dataset becomes a durable alpha source.

From Observation to Edge

In an environment where narratives move quickly and capital reallocates even faster, empirical transaction data offers a powerful anchor.

By translating real-world spending into structured, investment-ready datasets, YipitData enables investors to ground strategy in observable behavior — helping bridge research and execution across both quant and discretionary approaches.

In today’s markets, visibility into how money is actually moving may be one of the clearest signals of all.