We all know how important attribution is for data-driven marketing, yet why is it so hard to take action on those attribution insights? After hearing about attribution from every corner of the marketing world, many of you try to catch the attribution monster, but much like the Loch Ness Monster, as soon as you get close, it disappears. Find out about why attribution is so important and how it fits into your overall measurement stategy in our first post.

You finally decide to take the plunge to catch the Attribution Monster. Before you dive in though, you need to do some research to understand the best strategy to catch it. In the second of our three part series, find out how you can measure and then take action with attribution.

How do you integrate attribution thinking across your organisation? Check out the final article in our three part series to find out more about the People, Process and Platforms challenges that you might face when shifting your organisation to a data-driven attribution model, along with simple ways you can prepare to ensure that attribution becomes the default way of thinking about media in your organisation.

With the huge amount of change around us, investing serious time into your Analytics platform might seem like the last of your priorities..but we reckon that a few simple hacks can help you to unlock serious revenue opportunities this year. Combining our tech and industry experience, we’ve hand-picked our current Top 3.

2020 has been a year of big changes, and I'm not just talking about COVID. Earlier in the year, Apple implemented the latest iteration of its tracking protection to further thwart any attempt by ad tech platforms to track and identify users. In this post, I discuss the ways that the Cookie Apocalypse will impact both marketing and analytics functions, touching upon ways that we can best prepare for a cookieless future.

Which of your customers drive the greatest value over the long term? And more importantly, what steps can you take to transform more of your customers into high value customers? Click through to our first article where we share the challenges with optimising to the average customer, and talk through a simple model that you can build to start segmenting your customers today!

What actions should I take with my highest value customers, and those customers that are at risk of churning? In the second post in our series we discuss the different ways we can use Recency-Frequency-Monetary Value (RFM) scores to create different segments and drive targeted action to keep the highest value customers, develop the mid-range customers, and protect the customers that are likely to leave.

How much will our customers spend in the future? How likely are they to churn? In this article, we go a step further, and demonstrate how we can use models from the field of Biology to predict the likelihood of a customer churning. We also show how lifetime value models can be used to predict future customer spend, by giving us an estimate of expected purchases and expected order value.