Data & analytics

The new Google Analytics 4 uses machine learning to deliver more powerful insights to help you make smarter marketing decisions.

We debunk the myth that data science is hard by showing you how to run your very own forecasting model in Excel that takes minutes to do.

Introduction to Google Data Studio, including how to import your data sources and create a report that you can easily share with your colleagues.

A brief overview of conversion rate optimisation, what data you need to have in place before you start testing, and advice on running your first A/B test.

First announced in July 2018, Google Signals is the name given to the Google product that enables cross-device reporting and remarketing. Enabling Google Signals allows you to take advantage of new and improved advertising and reporting features across different devices. 

There are several reasons why you might be seeing (not set) in Google Analytics – here are some examples and what you should do about them.

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.

Understanding your customers’ journey is key to optimising conversions and sales. In part one of this series, we look at the pros and cons of the 6 main attribution models to help you decide which is best for your business.

Event tracking is one of the more advanced Google Analytics features that allows you to track specific user actions on your website – down to the very elements they’re clicking on. With these data insights, you can measure how effectively key parts of your page are performing and diagnose issues with more precision.

Predictive analytics turns your existing data into a roadmap of future user actions. By modelling user behaviour against a range of influences that impact buying decisions, you can use predictive analytics to understand when people are most likely to buy.

Data journalism, or data storytelling, means telling your story with charts, graphs or diagrams, enabling your reader to view the data that forms the story.

If you invest some time in research and planning to gather data insights, you can then add design flair to your content to tell stories in an engaging, visually appealing way.

It is perhaps possible to predict the winner of the World Cup based on data alone. We decided to give it a go, but only on a small scale. Come and play with the data to do your own analysis.

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.

Data visualisation helps us make sense of difficult concepts. Marketers can benefit from transferring data from spreadsheets to visual canvases, as we illustrate in this post.

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.

Conventional data visualisation methods such as pie charts or bar charts are great to visualise a quick stat or two but when you are dealing with larger amounts of scattered data, you need a bolder solution.

Vertical Leap analysed the impressions and clicks of millions of search queries across almost 200 websites to compile stats for the top 50 positions, which you can see here.

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.