Deal Sourcing Platform

How are you currently using the PredictLeads News Events dataset within your product or workflow today?

We use the dataset to enrich company profiles we track with recent news activity. It helps make profiles more dynamic and provides quick context on what has been happening with a company.

When you were evaluating news data providers, what were the most important criteria for your team?

It can vary depending on the type of data we are acquiring, but in general we want to get a good sense of quality and coverage first, and then price.

One way to do this is to look at sample data, which allows for an apples-to-apples comparison between providers in terms of quality and coverage.

It also comes down to implementation: how easily we can integrate the data into our product. For news data, this mainly depends on how well the data is structured.

We also appreciate detailed answers to questions we raise. It was great working with the team, who answered many of our points in depth.

What challenges were you trying to solve when looking for a news events data provider?

It would have taken a lot of time if we had wanted to build something similar in-house. Setting up pipelines to collect articles (some of which are behind paywalls) and then analyzing those would require significant effort.

Especially for the analysis part, we prefer not to take focus away from our core work. So for us it mainly came down to whether the quality is good enough and how well the news data is structured, allowing for faster integration.

Which types of structured company signals from our 29 event categories are the most valuable for your use case (e.g., funding, partnerships, product launches, expansions)?

For our use case being able to show factually correct news from reliable sources is the key - compared to any specific category.

How important is it for you that events are categorized and deduplicated, rather than relying on raw news scraping?

Deduplication is important, as the same “event” can be reported by multiple different sources. We don't want to show multiple rows in our UI that essentially describe the same thing.

In your experience, how helpful is it that every event is linked directly to a company domain, making it easy to integrate with your existing company records?

This is extremely important, as it allows faster entity mapping and overall easier integration.

How are you currently integrating the dataset into your system (API, internal database, analytics pipelines, etc.)?

PredictLeads sends fresh data periodically (S3), which we ingest into our pipelines and process into our internal systems, where it is then used in production.

Which event attributes do you rely on most when analyzing events (event category, timestamps, article sentence, location data, source URLs)?

We are using almost all components. The article sentence is especially useful for quickly understanding the context.

Are there specific industries, company sizes, or regions where the dataset has been particularly useful for your team?

The majority of our end users are located in the US and Europe, so having good news coverage in these regions is critical.

How important is high signal-to-noise ratio in news data compared to working with raw or unstructured news feeds?

In our case, we'd rather accept lower coverage if the quality is high and the content is accurate.

Do you primarily use the dataset for monitoring companies, identifying opportunities, building models, or generating alerts?

The most visible use case is showing the news feed in company profiles to make them feel more complete and to provide quick insights. However, news can also be used as a search attribute to filter companies as well as to set up alerts.

Has the historical coverage of events since 2016 been useful in your analysis or product development?

It has been useful. Not all companies have frequent news activity, so having a longer timeframe increases the chances that something interesting has happened for a company.

How do the structured event signals help you surface insights compared to traditional news monitoring approaches?

This is not the primary focus for our use case. The main value comes from having structured data that is easy to integrate and use within our product.

Are there additional event signals or categories that would further improve the dataset for your use case?

I think the key is just increasing coverage and quality with the existing ones. Our end users are interested in small and medium-sized companies, so having stronger coverage for those compared to large companies is especially valuable.

If you were to describe the value of structured news signals like those from PredictLeads in one sentence, what would it be?

Gets the job done in a reliable manner: the coverage and quality are good.