Author: Igor Pshenychny Machine Learning and mathematical rigor are tools at data scientists’ disposal. But we all know that having a better calculator won’t exactly result in better test scores, and having a more powerful computer won’t necessarily make you a better programmer. To develop a strategy that doesn’t underperform, a data scientist will need […]
Incorporating Decision Science into Data Science Read More »
While marketing in general is capable of bringing additional sales, effectiveness of any particular campaign, channel, touch point etc. may vary substantially. To evaluate performance, historically there have been two approaches – Marketing Mix Modeling (MMM) and Attribution modeling. Here we examine differences, advantages and disadvantages of both approaches. Marketing Mix Modeling: Cost and Sales
Marketing Analytics Techniques Compared: Marketing Mix Modeling vs. Attribution Read More »
As data science becomes a key factor in the decision making process of society, along with that comes the democratization of data analysis allowing anyone, not just statisticians, to draw conclusions from data. There is an undeniable ease to summary statistics that makes them appealing, but here at Fulcrum, we often encounter clients throwing away
Don’t Throw Away The Distribution: The Importance of Probability Distributions Read More »
Everyone has heard of “market research” as a way to learn more about the customers for a product or service, but did you know that through data-science-enhanced market research, organizations can potentially save marketing and deployment dollars in the long run? Through methods such as discrete choice modeling and segmentation, companies can ensure their longevity, sell
Segmentation and Discrete Choice Models from Market Research Data Read More »