Published on March 16, 2020 by Sandani Fernando
Where it began
Over 60 years ago, a wise man pondered whether machines could learn and act. This idea is now changing the lives of millions. Artificial intelligence (AI) was brought to life by Dartmouth College Professor John McCarthy in 1956. He wanted to see whether machines could perform tasks so far performed only by humans.
AI has grown exponentially since then, particularly with the emergence of its siblings such as machine learning, natural language processing and deep learning. But why the rapid growth?
When we feed machines with large quantities of data relating to functional examples and scenarios, they build up patterns using algorithms, store them and intellectually improve them to provide solutions in a split second. Machines provide prompt solutions, unlike humans, whose performance is subject to numerous external factors. Machines are active 24/7, with memory beyond compare and zero distractions.
Making an impact
AI is redefining contemporary marketing activity. Customers no longer need to actively express what they want. Through AI, businesses can now predict customer expectations by regularly analyzing behavior. Forbes reports that 90% of the world’s data has been created in the past two years and smart marketers use AI to manage the tedious tasks of consolidating and filtering out large amounts of data to create meaningful patterns. They also conduct predictive analysis, freeing up their time to build creative strategies.
Limited presence in the asset management industry
AI delivers added value and impressively reshapes business models. However, it has not been receiving due recognition among marketers, particularly in the asset management industry. As peculiar as it may sound, the industry that hailed the digital revolution has been rather content with its existing marketing practices mostly aimed at maintaining business relationships.
Conventional marketers in the asset management industry seek to establish reliable distribution channels and achieve set targets, and often resist the need to add further value thereafter. Moreover, the limited immediate ROI, coupled with the inability to demonstrate value to management in order to obtain backing and funding, restricts implementation of AI practices.
A 2018 survey of asset managers revealed that 62% of the respondents’ marketing priorities are personalization and targeting. Interestingly, 56% of them had not used any technology to personalize/target audiences with more relevant content until then. Moreover, 43% of them believed that AI/machine learning will have the most impact in financial marketing in the coming years. Indeed, and here’s how.
Revolutionizing financial marketing efforts
Big data insights
Each of an investor’s moves may prove vital in building a feasible strategy. With AI and big data, the significant time investment required to make sense of data gathered can be saved. AI is non-functional without data, and big data serves the purpose. It comprises large sets of data, ever increasing in terms of volume, velocity and variety. Pattern recognition is a vital AI-powered machine learning technology that can be used with big data.
To move forward from just knowing who your prospects are, smart big data insights provide investor overviews on an individual basis. This helps to build recurring patterns and highly personalized content. The insights range from knowing where your most profitable investors are to what services and benefits they seek and how they prefer to be contacted.
Such insights not only enhance the efficacy of investor engagements; they drive success rates of investor retention and loyalty as well.
Predictive lead scoring
A core goal of machine learning is propensity modelling. This enables efficient predictive analysis, to identify behaviors of prospective investors.
Conventional practices of building lead scoring systems involve numerous one-on-one calls with potential investors and setting up databases manually. However, vital insights on investors would remain unknown unless obtained from primary sources. Propensity modelling transfigures how leads may be scored.
By applying trained propensity models and predictive analysis, marketers will receive automatic alerts of the propensity of investors to purchase their offerings, i.e., a low/medium/high propensity to buy. Having such vital information would assist financial marketers in contacting the most relevant leads, while reducing the time consumed in engaging with unprofitable leads.
Figure 1: Predictive lead scoring through propensity modelling
Contemporary marketers in the asset management industry believe the traditional two-page fund factsheet is outdated and needs disruption. Natural language processing (NLP), a branch of AI, is making this a reality. In brief, NLP attempts to make sense of constructed content in human languages in a valuable manner. Accordingly, traditional commentary write-ups and marketing material can now be generated digitally using NLP methods. Content marketing and voice search technology are additional areas where NLP can assist.
AI provides support in numerous financial marketing analytical functions. From analyzing web traffic to identifying investor patterns and intelligent client outreach using a wide span of alternative data sources (social media, investor platforms), AI is multi-functional. The intention behind conducting all these activities is to stay relevant and to keep connecting with existing and potential clients. If you stay relevant, you grow.
Is AI adoption visible?
Despite its slow growth, AI is being used to revolutionize marketing efforts.
Chatbots are becoming increasingly popular among financial marketers. Chatbots support in addressing on-demand reporting/queries of investors.
By feeding thousands of predefined conditions, intelligent responses to and highly personalized solutions for investor queries can be generated on a real-time basis. Chatbots are likely to eventually replace existing email blasts. However, it is critical that marketers ensure the legality of their chatbots given the increase in GDPR in this regard.
In 2018, BlackRock, the world’s largest asset manager, announced the setting up of its dedicated AI research center, BlackRock Lab for Artificial Intelligence, to understand how AI can transfigure aspects of this industry. JPMorgan is also at the forefront of AI adoption.
We believe growth potential for financial marketers in terms of AI is significant. At present, only 26% of marketers make use of autonomous AI, according to Albert and Forrester, while 74% follow a manual approach with assistance from AI. The use of conventional financial marketing approaches is waning, while highly interactive and persuasive communication methods are trending.
By implementing AI, marketers could become more personal with investors, using relatable and meaningful investor interaction methods within a virtual environment. Although AI is unlikely to replace biological intelligence, we believe it will absorb and enhance its capabilities.
Above all else, we think AI is not another fad – it is here to stay.
At Acuity Knowledge Partners, we stay up-to-date with the latest technological trends and financial domain knowledge to provide our clients with a competitive edge.
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About the Author
Sandani Fernando is currently part of the Fund Marketing Services (FMS) team at Acuity Knowledge Partners. She supports and contributes to digital marketing strategies for clients, handles marketing collateral projects for finance-related content and periodically updates and maintains digital marketing assets. She has over two years of experience in content management, marketing production, finance and capital markets. Sandani holds a degree in business management and finance. She is a CIMA passed finalist and is currently preparing for her master’s degree in Business Administration.
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