UNDERSTAND YOUR CLIENTS BETTER WITH DATA How Data-Driven Decision Making Improves the Way Advisors Do Business

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1 UNDERSTAND YOUR CLIENTS BETTER WITH DATA How Data-Driven Decision Making Improves the Way Advisors Do Business Executive Summary Financial advisors have long been charged with knowing the investors they support. Indeed, the know your customer rule might be the industry s ultimate responsibility. In practice, that means any advisor who recommends the purchase or sale of any security to a customer must believe that the recommendation is suitable for his or her customer given the customer s financial situation. But given the multiple methods by which people can invest, how much can an advisor really come to know about them? The need to gain accurate customer insight is becoming increasingly important for the investment industry. Yet, in a world where retail giants like Target Corporation can accurately predict their customers next purchases and digital marketers can tailor online ads to consumers behavioural patterns, advisors should be able to answer these questions: What are you doing to get an up-to-date detailed consolidated picture of your client to identify financial planning opportunities? What are you doing to better understand your client and refine the opportunity to improve service while better meeting your regulatory requirements? It s time for advisors to accelerate the use of data and advanced analytics and use a scientific approach instead of professional intuition to achieve advisory excellence. The good news is that by leveraging data from customer books, the industry, and a multitude of external sources, advisors are in position to unlock the answers. All it takes is a data science partner with the right technology and knowledge of how to mine for insights. Ninety percent of the world s data has been created in the last two years the ultimate question is really what insight and value can we draw from that data. - Goldman Sachs

2 Asset managers who can quickly invest to build and deploy such capabilities will be armed with a powerful competitive advantage that could redefine their positioning in the marketplace. Blending Science with Art to Capture Growth in U.S. Retail Asset Management McKinsey & Company, July 2014.

3 A REAL RESPONSIBILITY Asset managers and their firms have tough challenges in today s market they re expected to deliver good performance, maintain and improve fund profitability, and grow market share. At the same time, they re expected to know more about who is buying their funds, more about the suitability of the customer s product range, and more about the governance that sits around it all. Meanwhile, all of the additional effort, cost of advice, risk monitoring, distributor relations, and reporting might be charged back to the customer through the fund, which inevitably leads to squeezed margins that could hurt performance. All of this, of course, is taking place in an environment of more concentrated fund flows, increased regulations, and increased service demands of investors and distributors. The responsibility to understand and know customers has become an altogether complicated cycle. On the flip side, not knowing your customer and potentially misselling an inappropriate product leaves an advisor and the firm open to large fines and negative publicity. It s no surprise then that asset managers and financial advisors have an increasing appetite to develop a data strategy that delivers insights on a more effective selling practice. In the process, they are turning to data to help validate their product offering as well as understanding who is buying and selling and why the investor s decision journey. Distributors are having more impact in product requirements and voting on performance Distributors Investors Government requires Asset Managers to be more responsible Government Investor Register DATA Product /Fund Regulator Asset Manager Asset Managers need to better understand their underlying Investor and Distributor

4 Asset managers have a responsibility to know their customers, whether these are the end customers or the institutions purchasing on their behalf. Blending Science with Art to Capture Growth in U.S. Retail Asset Management McKinsey & Company, July 2014.

5 In Search of Insight Fortunately, there is data. A lot of it. There s the manager s own data. There s data from the industry. And there s a multitude of external data sources that can help an advisor unlock the answers. And, very importantly, there are new technologies and mechanisms in data science that allow for the fast load, manipulation, and layering of multiple and often unstructured data sources. These data science technologies allow better access and processing of the data that enable advisors and their firms to derive meaningful insights. The Data Journey In practice, data can be declared or inferred. People may expressly say what their investment objectives or their attitudes to risk are when questioned, but what they say may differ from their observed behavior in terms of their interests (e.g. responding to a specific topic , dwelling on a certain page, attending an event, etc.) and their investment purchase history. Actually discerning what customers have done previously can be an excellent guide to predict what motivates them, what their preferences and risk profiles are, and, most importantly, what they are likely to do more of in the future. The discovery process begins with an audit of available data along with useful external sources. Issues such as multiple data entries, profiles and leads, aged data that is unchecked and unreliable will need to be reconciled to give a consolidated product holdings picture. 2) Getting data ready what can really be used? Using data fit for purpose and in good governance is an absolute necessity. The process will require access to data science and domain experts who can actively review security and appropriate handling of the data sources, present a solution for the treatment of data anomalies, and future proof the use of data for product development, marketing, and customer suitability analysis. This will involve matching and mapping key relationships to present a single investor view and the delivery of consolidated records. From a business perspective, data scientists have to mine and explore seemingly unrelated data sources, essentially taking in information and looking for consistent themes. That process typically means pulling in the right data, cleaning it, prototyping it, and disseminating it, all with an eye toward creating scalable, repeatable product solutions. Thus the need for cross-functional team collaboration it s the only way data scientists can understand how to use and manage data to solve complex problems. Data Sources are Many and Varied Sales Data Market Trends Personal Relationships Investor Demographics 1) Strategy what do you really have and what should you really use? Traditionally to be able to make the most of data, it has had to be structured, organized into hierarchies, and comply with rigid requirements, but the real world of Big Data isn t like that. There are a myriad of sources that can be unstructured, disparate, and non-hierarchical and you need a toolset that can deal with this, drawing from these sources of both internal and external data. A data audit will capture and review multiple internal and external data feeds brought together to provide a clear foundation of data, mapping out essential connections, and highlighting any data issues. Analysis and identification of critical data would be completed, the data validated and cleaned. This taxonomy is key in the development of common data attributes. By itself, this process can often identify ways to improve operational service and other efficiencies as well as provide the foundation for a Single Customer View. Marketing Responsiveness Industry News Customer Feedback Advisor, Office and Firm Profiles Social Media Expense and Commission Data Advisor Interactions (e.g. Web Visits) Fulfillment Activity

6 3) Insights Data enrichment and mining: What can you find? Mining the data for insight will detect the signals from the noise by providing a clear view of customer demographics, their attitudes to risk, and their investment objectives in order to match suitability against investment products. Most importantly, perhaps, the data mining process will help forecast buying behavior based on predictive analytics and understand the probability of inflows versus outflows. Data science, by definition, is about extracting meaning from data. In practice, that means the use of various techniques and theories drawn from mathematics, statistics, and information technology, including probability models, machine learning, pattern recognition and learning, data warehousing, and high-performance computing. Practiced well, data science will explore and commercialize disparate information to create competitive business advantages. Data mined for insights, then, is the fuel that allows a financial professional to: Pinpoint the right people to influence for the right fund Understand them better, their preferences and interests (whether declared or inferred) Know what transactions were made and when, the value of them, and how that might influence them in the future Deliver triggers for communications Benchmark against the competition, industry, and product Harnessing Big Data to Really Know Your Clients To get started, advisors should ask two questions: What am I doing to get an up-to-date detailed consolidated picture of my clients in order to identify suitable opportunities? What am I doing with data to help better understand my client and refine the opportunity to improve, service, sales, compliance, and profitability? There is a myriad of data out there and finding and isolating the right signals may seem like looking for a needle in a haystack. But the robust, scalable tools and methodologies that can collect and prepare the data and providing quick analysis in near realtime can make finding those insights an everyday reality. It s reasonable to think that advisors who avail themselves of advanced analytics capabilities will be armed with a powerful competitive advantage that could redefine their positioning in the marketplace. Finding a partner well-versed in the asset management sector combined with a depth of expertise in data manipulation and analysis would be a great first step to a better informed approach. Taken together, this kind of data can provide a foundation that allows for the discovery of insights and the understanding of a customer s behavior.

7 About DST Applied Analytics DST s Applied Analytics Group exists to assist clients in creating value from data across their organization through the creation of actionable insights. We leverage deep financial services industry experience across technology, data, analytics, marketing, and sales to design solutions that inform strategy through action. Our proven methodologies help you quickly and accurately realize results that deliver superior ROI. With 130 associates in the U.S. and EMEA, DST Applied Analytics brings together a wide range of talent and experience from within DST and leading analytical organizations. Professional Services from our Applied Analytics Group include data and insight strategy development, data auditing, insight creation and customer engagement. Our Data Science and Development arm enriches existing DST solutions through the incorporation of analytical capabilities, bringing scale and accessibility to industry solutions. Execution Guidance Review Strategy Insight Understanding Your Customer Identity Getting Data Ready