How to use AI’s predictive analytics to better target your investors
No one can predict the future, but AI can use analysis to help understand what may happen soon better than most. If you’re wondering how to get an overview of every investor or prospect’s future behaviour, look no further.
After processing hefty amounts of unstructured data – even alternative data that’s tough to quantify or analyse – AI’s main aim is to make it easier to identify common trends or patterns. With that unrivalled insight, its self-learning nature can (over time) help to unearth timely popular themes, funds or additional services on a website, or which digital channels work better for the different forms of content that you produce.
Better data, better targeting
Tracking data over time is the perfect way to see what resonates with potential investors at every opportunity, or showcases whether current clients are tuning into some materials, and out of others.
Multiple users perusing a range of digital channels could indicate a range of buying journeys ready to be capitalised on, but it’s always best to know who wants to hear more about your fund and who does not.
Let’s face it, working in collaboration with sales and fund distribution teams is essential. And making them happy is even better. They demand to know the best route to take for individual contacts to achieve ROI. Would some be open to a cross-sell? Are some customers re-engaging with certain content after a long while? All of these questions can be answered by looking into the predictive analytics gained using AI.
Whatsmore, compartmentalising data in whatever format preferred by individual teams is possible. It could be an Excel document, or a word cloud, for instance.
Segmenting engagement
Much like AI’s ability to tag automatically, it can also categorise your users based on scoring systems that indicate their levels of engagement. Being able to view these levelled segments in a single view is even better, showcasing which prospects should be considered the top contacts to reach out to. Multiple platforms offer this visualisation, but our platform segments users as follows:
Low Hanging Fruit – those interacting with pretty much all of your content, and highly interested in it, so they could be significant leads.
Cross-Selling – existing investors clicking through funds or services they haven’t interested in yet and are worth talking to.
Redemption Risk – basically ’inactive’ investors that have cooled their interactions for a long while, and should be checked with the investor relations team.
Marketing Alpha – those that have been dormant but have started actively engaging recently with your current content, who may also be valuable prospects.
Ultimately, to take marketed content to the next level in a go-to-market strategy, it’s imperative to know these predictable behaviours of those that want to do business with you.
AI automation makes this evermore possible to understand and provides unrivalled customer insight for planning future digital marketing campaigns.
