Data science is something that everyone in fund marketing must understand and use, as it represents the future of the sector.
Pretty much the whole investor journey, from initial awareness right through to sales and customer retention, is now digital. And – whether you realise it or not – data science is already at work in tracking and automating this journey.
At every stage of the journey there are digital touchpoints – opening, clicking, liking etc – where you can see what a prospect or existing investor is doing. And data science can track and use this information to guide your interactions and increase the likelihood of raising and retaining AuM.
But in all too many cases the potential of data science is not realised, as it is only asked to produce generic statistics, such as open rates.
Data science can go much further than that to reveal deeper, more important information that gives marketing teams the ability to align more closely with sales and demonstrate ROI.
And while a lot of jargon and perceived complexity surrounds data science, the basic principles underlying it are not actually that complicated.
I’m going to use this two part series to dive into this subject.
In this first post, I’ll start by examining the key question that many people are asking themselves:
What exactly is data science?
Perhaps the easiest way to answer this is to tell you what it isn’t.
It is not data engineering, which is all about the back end of a system and actually building a database.
It is not data visualisation, which is about presenting digital information in a way that makes it easily understandable.
And strictly speaking it is not even data analysis, as that is the art of looking at past and present data to answer questions.
Instead, data science is all about what happens in the future. In fund marketing terms, it involves looking at what data tells you about a person to predict what they are likely to do further down the line.
The potential of that, from a marketing perspective, is obvious – as when you can predict what a person is most likely to do, you can then adapt your behaviour to make sure things work out for you. For example, knowing a prospect is engaging well with your marketing material means you should carry on with what you are sending them. On the flip side, if data science reveals that a client has suddenly stopped looking at your material – thus could be considering a redemption – then you can take steps to head that off.
But you obviously need to know more about data science then simply what it is. You need to know exactly how to use it to boost your digital marketing efforts.
So, in my second post I’m going to look at the five major facets of data science and how they can help you raise and retain AuM.
If you want to find out how ProFundCom can help you use digital marketing to raise assets schedule a demo here