Data science is key to helping retail marketers get the edge through personalised pricing, data-driven attribution, intelligent up-sells and customer lifetime value modelling.
Digital marketing is now so fiercely competitive that it’s no longer enough to rely on the techniques that everyone has mastered – you need to get clever by using data science. In this section, we explain the various types of analytics and show you how to apply data science to your digital marketing.
Data-driven attribution accounts for the importance of every touchpoint a prospect goes through before converting, and can significantly increase your marketing ROI. In this article, we explain how it works and how you can develop your own.
In this short blog, we explain what data science is in the simplest of terms, plus list some practical examples of how it can help marketers supercharge their marketing efforts.
It’s no longer enough to rely on standard SEO techniques. If you want to get the edge, you need to use advanced data science techniques to unearth hidden opportunities. In this article, we explain a four-step framework to help you do this.
A quick and simple explanation of what machine learning is with working examples of how marketers can use this technology to improve their marketing.
Many marketers might think AI is beyond their reach but this isn’t the case, as we showcase in these four examples of how companies are using it right now.
Data science is playing a growing role in every aspect of our lives. So it’s only natural that interest is increasing. In this FAQ, we answer the most common questions asked about data science.
What are predictive and prescriptive analytics in marketing? In this third chapter, we explain what you can achieve with a mature big data analytics setup.
This article moves us on to the more thrilling part – learning how to ask the right questions (using data science) and receive the best answers (using data analytics).
You know that data science is your strongest bet for remaining competitive. But where do you begin when that data of yours is well… big? Our team is here with some answers.
Identifying important social media cues, trends and signals now requires extensive analytics capabilities. So how do you stop the incoming data flood and transform it into a steady stream of distilled insights? By applying science to the most pressing problems at hand.
To reach the ‘sceptic’ consumers, brands should be raising the quality of content they are producing and sharing. Data-backed content is now in high demand. And data science should become your tool to elevate your brand’s authority and credibility.
Data science – it’s the talk of the town for certain. Do you need to invest in data science Here are five signs indicating that it may be time to do so.
Data has always been crucial to marketing, but our ability to make the most of it has always been limited to the technology we have available. These limitations have almost disappeared thanks to machine learning and automation, which make big data accessible to businesses of all sizes.
This is a case study about how our data scientists are writing automation scripts to free our PPC specialists from hours of manual work, enabling them to focus much more on strategic and creative tasks.
To understand the performance of a website and identify areas ripe for improvement, we created a model based on intent, called the Buyer Intent Model.
Data science allows you to extract practical knowledge from data at your disposal. Knowing where your customers want to go next, what kind of experiences they prefer and what prices they are ready to pay are just a few things achievable through strategic data analysis.
Big data is a marketer’s goldmine, able to pinpoint and understand target audiences with an accuracy and level of detail unimaginable less than a decade ago.
Data science is the practice of revealing hidden insight from existing data in a manner that enables businesses to make better decisions. Intelligent decisions are based on accurate predictions.
There are two types of people in this world – the creative, and the logical. The latter love data, but us creative types, data can feel like a scary word.