Using AI to Support Productivity and Drive Revenue for the Buy and Sell Side
TABB Forum: Opinions
A Gartner webcast in January of this year found that 63% of CEOs were already planning to change their business models to address the growing importance of data and analytics in driving value across the enterprise. The pandemic environment has only accelerated the need to use data and analytics in daily business practices for leaders in financial services institutions.
The skill that sets one sales professional apart from another is their ability to react and respond to changing market environments, synthesize this information rapidly, and capitalise on opportunities inside and outside of their existing client base and sales pipeline. It is unsurprising therefore, that sales has historically been largely a ‘face-to-face’ discipline, where information is exchanged rapidly and frequently.
The impact of Coronavirus and the shift to working remotely and digitally has therefore impacted the sales and client-facing disciplines significantly. The high-frequency, high-value interactions which are now generated by a sales team on a daily basis are crucial in building a full picture of an enterprise’s revenue generating activities. Capturing and understanding this data was already becoming pivotal for businesses prior to COVID-19, but has now become business critical in the absence of face-to-face contact.
Digital Technologies for the Front-Office
Front-office revenue generators in financial services institutions are taking full advantage of the digital suite of services on offer to them. A typical sales professional will communicate with colleagues, clients, prospects and partners, using email, chat, digital conferencing and social media. A FeedStock case study found that a sell-side enterprise with 2,500 capital market professionals could generate 2.5 million action records per day, with 33.6 attributes, leading to 21 billion data points annually.
The need for smart technology to help cut through the noise and get the value out of this unstructured data is essential. It is increasingly necessary to drive improved client communication with instantaneous client-insights generated by AI and Natural Language Processing (NLP). With access to this data, financial services leaders will be equipped with the data and analytics to fully understand and evaluate the human-to-human relationships taking place within their organisations to make smarter enterprise decisions, drive ROI and protect valuable revenue.
Getting Insight for Business Focus
C-Suite executives in financial services institutions are looking to use this data to identify where the cost-control opportunities and operational risks lie. They want answers to questions such as: which clients are we over or under servicing? Who in the organisation holds the key relationships with our top accounts? Can we leverage those relationships for other clients? What are our clients’ topic preferences over time, and are there gaps or duplications which could be streamlined? And ultimately, how can we use our data to make our clients’ lives easier and more productive.
Deployment of NLP algorithms on unstructured data, already sitting within the organisation can bring datapoints and intelligence to the dataset and deliver a whole new layer of transparency for the business. The untapped cost-management and revenue-generating insights which lie hidden in this data are key to moving from surviving to thriving in whatever the new normal might be.
For example, deploying deep learning NLP Sequence Analysis models enables the automatic and instantaneous classification of client interactions into over 30 different categories. This has enabled a particular investment bank to identify three times as many chargeable client interactions compared to the manual CRM approach it was previously using. As we enter a more difficult economic environment, an institution’s ability to identify and correctly charge clients for their services will be pivotal.
In other use cases, with Named Entity Recognition algorithms, institutions can understand trending topics, stocks, commodities and sectors and visualise who is talking about what, and to whom. For the sell-side, this enables them to refine and improve their coverage and for the buy side, provides the insight to see duplications or gaps in coverage and manage research spending accordingly.
Compliance and IT managers can also gain access to a centrally managed, cloud-hosted data lake from which they can pull custom reports and gain a full 360-degree understanding of their organisation’s activities. As firms continue to adjust to rapidly changing working and regulatory environments, the ability to access and process this data-driven business intelligence is critical.
Manual data logging cannot address the quantity of enterprise data that is being generated every day; in general, 30% of front-office time is wasted on manually input, error prone data. Data entry can also never be in real-time and causes unnecessary drag to workflows across the business. For example, by removing the requirement for manually logging client interactions, FeedStock’s automated data-capture platform saved front-office employees on the sell-side an average of 12 hours per week in the past quarter – freeing up this time for revenue-generating activities is essential in the current environment to empower front-office sales professionals with the data and analytics they need, in real-time, to outperform.
Looking at the benefits of implementing data-driven practices across the organisation, it becomes clear that transitioning away from traditional, manual data management systems towards an automated AI-driven data analytics platform needs to be an urgent priority in today’s environment.
Even before the COVID-19 pandemic kick-started a focus on leanness and resiliency across every organisation, a Gartner webcast from January 2020 found that 63% of CEOs were already planning to change their business models to address the growing importance of data and analytics in driving value across the enterprise. The pandemic environment has only accelerated the need to use data and analytics in daily business practices for leaders in financial services institutions.