How Data Science Can Help You Raise AuM

by | 27 Aug 2020 | Asset Management, Data

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.

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.

What about it’s actual practical application – how do you sue it to boost your sales figures?

Of course, it’s a huge field. But I’ve boiled the answer to this question down into the five tenets of data science that are most relevant to working out how well your marketing campaigns are performing:

Remove the noise

When you have a lot of data to analyse, the easiest option is to go for the average – or the ‘mean’ – to find out what piece of your content is getting the most attention. But this is a simplistic method that can create a problem, as it can be skewed by having too much or not enough data, or by outliers – e.g. a webpage that is getting huge amounts of traffic but without any positive result. All this is data ‘noise’.

Instead, you must look at either the ‘median’ (the middle value of your data) or the mode (the most common value). These avoid the problems caused by mean averages and focus on the most useful statistics.

Confused? Don’t be. Data science has it all in hand, as it cuts through the problems caused by mean averages – it removes the noise – to reveal the mode and median. These are reliable indicators of what is having the most valuable effect and allows you to concentrate your efforts accordingly.

Standard deviation

This theme can strike fear into the maths-averse. So, let’s keep it simple – standard deviation is the opposite of data noise. In marketing terms, it tells you when someone deviates from the norm and looks at something unusual. For example, an investor who always looks at information on the same topic suddenly looks at something completely different. Immediately you have a cross-selling opportunity on your hands. Data science can identify these deviations for you.

Behavioural analytics

In simple terms this means understanding the client journey and how prospects and investors react to your content. The beauty of data science is that it tracks and analyses this for you, to reveal behavioural nuances that help you divide your database into segments according to like and dislikes. And when you have segments you have the power to send much more tightly focused communications that are much more relevant to each individual, thus far more likely to be read and acted upon.

Clustering

In marketing terms, this means the concept of the word cloud. You can use data science to look at all the themes running through your campaigns and pull out certain words and phrases from your campaigns that are resonating most with your audience. This can be used to produce a word cloud that instantly shows what is working best – both the big attention grabbers, but also the outliers that are creeping into the picture and may be worth some attention.

Negative correlation 

Another horribly mathematical phrase. But this can be boiled down to one very useful function – data science uses negative correlation to calculate the prospects within your database who are most actively engaging with your content, but who haven’t yet had contact with sales. This enables you to pull out the hot prospects and present them to your sales team, who then have an excellent chance of converting potential into actual AuM.

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