How Fund Marketing’s ‘Data Scientist’ Disguise Helps Target Sophisticated Investors
Recently you may have heard a lot of colleagues delving into the weeds of machine learning fundamentals. It’s not without reason. Even at hedge funds and private equity firms where person-to-person investor experiences have been the marker of excellence, the digitalisation of the financial world means there’s nowhere to hide from data science’s growing importance in sustaining engagement. And as we predicted at the start of the decade, fund managers that utilise it should see their marketing efforts differentiating themselves from the pack.
Given how many various marketing technologies (MarTech) are out there that promise so much, from tools to speed up onboarding to automating email surveys, some fund managers may fall into the trap of amassing a whole lot of niche marketing capabilities that do not gel together. With that, investor relations teams then risk targeting the wrong prospects, repeatedly sending the same communications, and generally sticking to their own asset-raising agendas like lone wolves, separate from the marketing function, to potentially diminishing capital.
Instead, an integrated sales and marketing architecture provides a workflow that runs off of the same investor data, where some quantitative analysis helps define which content should be distributed, which leads should be tracked, and which marketing metrics match the boardroom’s ROI expectations. If executives are paying out of pocket for better data-led strategies, they need to see them contribute to raising AuM!
Fund marketers now act as custodians of powerful automated technology, and it’s how they train their data science skills over time that can do wonders for identifying key allocations faster, as follows.
Are Funds Living In the Past?
Data science is so necessary to a marketing strategy that neglectful firms run the risk of becoming Jurassic, not that utilising it is a simple one-time learning experience. Gathering fund sales intelligence and tying together data from websites, portals, and social platforms is ultra-efficient, but requires some system engineering know-how. Or, at the very least, a clear understanding of AI’s actionable use cases and limitations.
Already, some data science investments in the space have been completed without due diligence. AI’s ‘must have’ status has ballooned enough to overshadow its intended task: to improve a firm’s pre-existing marketing and sales outcomes, such as generating quality leads and conversions. AI can become a cost centre through expensive vendor contracts, especially when failed AI experiments are eventually abandoned for not proving their worth in raising assets.
Left with what’s effectively a MarTech platform graveyard, valuable investor data gets siloed. This causes problems for everyone; notably, institutional investors themselves are leaning toward consolidating accounting and fund document software to understand their clients better. When LPs are actively making better use of data for their own communications, private equity firms targeting them should be demonstrating the same commitment.
When it comes to data-driven marketing, many asset managers have been tentative to dive in headfirst, choosing to see how the rest of the financial world takes to the water. It’s left managers relying on fluffy metrics (see: open rates), manual email distribution to generic lists, and little to no insight into individual preferences. These are integral to tailoring content to high-net worth investors (HNWI) that largely commit substantial capital over long periods. Ultimately, proper data science usage can provide live-action visibility into engaged investors, and evidence for which channels contribute to inflows.
How Data Science Separates the Fund Managers of the Future
However, hedge funds have more notably pursued data-driven techniques to make investment decisions and improve distribution models for their end users. Automatically gaining quantitative details is just as imperative for their sales and marketing function, as the team must deliver a ‘white glove’ service to ideal HNWI from behind a screen on fund websites or email platforms, and understand their financial backgrounds and needs. Likewise, leading private equity firms can leverage data science for deal sourcing, to collate detailed profiles for businesses they’re marketing to.
Fund marketing platforms must be able to house vast volumes of historical investor data to understand digital behaviours. And firms that utilise APIs are able to pull together multi-channel data by unifying internal systems. A core, flexible infrastructure helps marketers, IRs and distribution leads work effectively together via single-source databases and interfaces. Integrating MarTech systems is integral to ensure data analysis becomes regular, effective practice.
There’s an interplay between man and machine going on here. AI automations can work independently to pull data together, and cut out the hours and errors lost to manually searching the most active contacts in a CRM. But humans are there to determine how these engagement insights should be heeded to improve marketing strategy. Data science surfaces information faster, and therefore enhances strategic decision-making – a whole new level of functionality for time-and-cash-strapped fund managers.
Ways to (Practically) Start Using Data Science
Creating a workflow that best utilises data science to improve outreach should be done incrementally. A free-for-all approach to AI only leads to inefficiency. Modular build ups help funds streamline their MarTech stacks without a complete overhaul, and house useful data for reiterating digital marketing campaigns.
Implement the right ‘build’: based on a fund’s size, deploying a fully data-first infrastructure can range from fully customised internal builds, installing out-of-the-box platforms (with less financial industry specialisations), agency outsourcing, or creating a hybrid of vendor technology and existing in-house platforms.
Create dashboards: CRMs and business analytics platforms can use AI to feed data into user-set visualisations, which identify patterns of regional investor activity, benchmark ongoing campaigns, and grant senior management an insight into complex online buyer journeys.
Lead scoring: platforms allow marketers and IRs to assign pre-defined scores according to certain digital interactions, building a rich picture of which allocators and LPs are more readily available for a potential sale
Email automations: dynamic content can be auto-filled into email templates, according to individual investors’ preferences, so that the right content and messaging makes it to its intended audience per campaign.
Hire a data scientist: fund marketing teams can benefit from an expert able to deploy integrations, streamline operational systems, test the quality of AI models, and upskill sales and marketing staff.
In its simplest terms, data science is all about swiftly extracting value from the information that you collect. It does not take away from the planning and execution of a content marketing strategy, but is a vital future step for marketers at hedge funds, private equity firms or asset managers that need to personalise outreach to valuable investors and produce quality leads.
A MarTech ecosystem built on shareable insights will be a fund’s major sidekick going ahead, spotting investor behaviours, maintaining sales enablement, and ultimately providing clues as to how marketing can provide greater capital in the long-run. Now that’s a valuable investment even the most scrupulous budget allocators can get behind!
ProFundCom helps marketers drive engagement with valuable investors through integrated tools tailored to hedge funds, private equity and asset management including:
Website Analytics: Use analytical tools to provide insights into visitor behaviour, traffic sources, and conversion metrics.
Unified Data Management: Consolidate customer data, engagement metrics, and campaign performance from various sources to gain a comprehensive view of audiences and marketing activities in one platform.
Data Analytics and Reporting: Measure conversion rates and analyse the effectiveness of marketing strategies to monitor key metrics and optimise lead generation efforts for reliable results.
Automated Lead Nurturing: Set up workflows to engage with leads at different stages of the customer journey, delivering targeted content, follow-up emails, and personalised messages to drive conversions.
Lead Scoring and Qualification: Prioritise leads based on engagement levels and interactions through scoring and focus efforts on high-potential prospects.
