Asset 1
Customer communications is the new competitive battleground for capital markets

The business dogma “what you can measure, you can manage” isn’t without some controversy. There is nothing more valuable than an accurate and actionable overview of business performance and clients’ needs, but the increasing ease of gathering and analyzing at least certain types of data has made it possible to over-measure certain factors. With our attention diverted, we risk losing focus on what really matters.

Many a successful business leader will credit their accomplishments to a certain amount of intuition. Such conclusions are usually inferred from data that exist, but are hard to measure. Insights revealed in communication – myriad conversations and messages – may be anecdotal but a wise leader has an instinct for what’s important and what to disregard.

Financial services business are arguably more measurable than most, but I know from experience that gathering facts in capital markets can be more difficult than one might assume. My business relied on the insights gleaned in monthly meetings involving client facing desks: sales, trading, settlement, and relationship management. However, time limitations meant that such detailed assessments were limited to the most important accounts. Even then, I was aware that there were many things that could not be accurately measured. Instead, proxies or correlated datasets were the next best option.

The limitations of most customer data

Consider customer account data about any capital markets client. The information is mostly collected by client facing personnel and fed into a CRM system. Some of it is numerical or purely factual, making it easily captured and organized. Even more of it is highly variable or nuanced – clients’ questions, instructions, opinions, complaints, even moods. Sales and relationship managers are usually required to input a weekly overview about their clients, summarizing recent information about their trade inquiries and market views. Of course, salespeople are not highly motivated by this task. It’s non-productive and gives bosses the means to gauge and micromanage each salesperson’s performance.

It’s hardly surprising that clients reports are partial, lacking detail or maybe giving a biased perspective on a client’s satisfaction. Either way, the client information available to management is not reliable. Moreover, to use the reports for review meetings, bank management must dig through all the comments, summarize and analyse them, in order to better understand each particular client. This adds another layer of interpretation to the analysis.

There are obviously a few challenges here that produce headaches for a bank. The main issue is getting an accurate picture of each client’s needs and market views when the principal data source is sporadic and often based on subjective reports from internal sales desks. This is compounded by the time it takes to capture all client information, delaying the bank’s ability to respond. Adding to the uncertainty are the routine difficulties in getting accurate data on the bank’s own performance for each client, namely its ability to deliver the right services at the right price, including settlement issues and missed client trades due to subpar pricing.

For business leaders that prefer to limit their reliance on intuition, the good news is that artificial intelligence is coming to the rescue by providing deeper and holistic analytical insights that elegantly address all three issues.

A window to the future

Imagine an interactive dashboard providing comprehensive charts in a single view that answers the key questions management will have about a client: what are his current market views, about which financial instruments has he most inquired, which and how many trades did the bank lose due to bad pricing, how may settlement issues arose and where, which department of the bank has how much contact with the client, etc. These insights are contained within interactions between a client and bank – conversations as well as transactions. However, conversational data, also known as unstructured data, has always been too complex, difficult to access, and captured in such enormous volumes for it be a practical source of management information.

Some organizations within banks, such as compliance departments or retail operations, have been driven to analyze unstructured data due to regulation or competition. In capital markets businesses there are few such precedents. Today, however, AI is enabling all divisions within banks to accurately and speedily turn communications data – emails, chat messages, even phone conversations – into understandable and actionable insights. AI helps banks capture what’s important, accurately and objectively, and automatically updates CRM systems and management dashboards.

In capital markets that means trade inquiries and ideas, market talk and views, complaints, portfolio positioning information, etc. – all of it captured directly from customer interactions rather than mediated through salespeople. AI machines can be trained to automatically recognise the content of greatest interest, making sense of context and the natural variances in human expression to filter and organize what’s relevant. Resolved to entities – such as client accounts, key contacts, products, or topics – the insights provide an objective and continually updated source of information.

The new competitive battleground

Richer, more accurate, and timely data is an enabler for both customer-facing and management staff. It can inform a heatmap of last week’s trade inquiries, with volumes for an individual, a client account, or customer groups. It makes it possible to visualize a client’s overall positioning, allowing the bank to adapt offering and pricing easily. An analysis of most discussed topics can help predict which stocks or bonds will be in high demand. Other insights include the hit/loss ratio per trading desk with one particular client, the hours each sales team actually engages in communication with customers, a tendency graph of settlement issues with one particular client or maybe an aggregation of all settlement issues per desk for the whole bank.

What this means is that the barriers to identifying and understanding what’s most important in customer communications data have been dramatically lowered.

This opens up all manner of possibilities for using communications insights to guide customer interactions and inform business decisions. Inevitably, the capital markets business of the future will compete in new ways as all relevant client data  is analyzed, accessed, and quickly reacted upon. Even information across multiple desks, which was not possible to aggregate due to the amount of people and tasks involved, now lies at the fingertips of the bank’s management.

The future of capital markets

For the first time, capital markets businesses can operate with an accurate and up-to-date picture of their relationship with any client without any need for costly and time consuming meetings. Change will not happen overnight, but banks that have invested in AI-enabled communications analytics are exploring innovative uses for advanced analytics.

If there is a competitive edge to be found by better serving a client’s needs, and if that can be done while increasing efficiency, it is inevitable that capital markets will adopt new, data-driven practices. Those which don’t, risk underperforming and will lose business to customer-oriented competitors. In the financial markets this can happen practically overnight.

More by Christhi Theiss
Buy-side digital disruption is just beginning, but its real innovators are already using AI to double retention and win share
Why Capital Markets should use AI to Manage Customer Satisfaction and Triple Business
How AI Boosts Front Office Productivity and Profitability

Written By
Christhi Theiss

A financial innovation enthusiast, Christhi Theiss advises financial institutions and fintechs within the capital markets and lending sphere. As former Head of Europe in Capital Markets, including roles at RBS, Santander, and Société Générale, he combines over a decade in investment banking with a global entrepreneurial fintech trajectory. An Expert Advisor to the European Commission on Innovation, he is passionate about new business models which are reshaping our daily lives.