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Podcast 🎧: How to prove the value of your tech product with data
Steffen Hedebrandt Steffen Hedebrandt

Podcast 🎧: How to prove the value of your tech product with data

What do you do when you’re in a situation where: Your product handles millions of visits a month, you generate thousands of B2B emails, but sales get all the credit for your companies revenue?

Well, for Ole Dallerup, Dreamdata CTO and cofounder, the answer was simple: Let’s prove our argument with data. In this episode of the Developer on fire podcast Ole Dallerup goes deep into the topic of value in relation to technology.

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Podcast 🎧: Dreamdata on the B2B revenue leadership show
Steffen Hedebrandt Steffen Hedebrandt

Podcast 🎧: Dreamdata on the B2B revenue leadership show

In the podcast, Lars touches upon: why B2B attribution is messy and demands good data, what’s a good B2B attribution model, why judging your efforts based on Google Analytics is dangerous, why all leads are definitely not equal worth, how to align sales and marketing based on revenue, and much more.

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The (technical) recipe for measuring the lifetime value of B2B ads
Steffen Hedebrandt Steffen Hedebrandt

The (technical) recipe for measuring the lifetime value of B2B ads

Most companies get the point by now. Digital ads are smart and measurable. The more buyers, the higher price. What looked nice a year ago probably doesn’t anymore. Actually, if you don’t understand the lifetime value of your B2B ads, your data-driven growth is looking at an insecure future.

But don’t fret just yet; we have made a solid recipe for understanding the lifetime value of your B2B ads.

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How to do attribution with Segment
Steffen Hedebrandt Steffen Hedebrandt

How to do attribution with Segment

At Dreamdata, one of the partners, we regularly recommend companies who are serious about growth, to use Segment.com as their customer data infrastructure. With Segment you plugin all your customer data and clean, collect, and control it.

This means you can integrate all of your favorite tools and move their data to where it’s needed ie. send it to Google Big Query (a data warehouse) as one part of building your own analysis.

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