Academy of BFSI,

Advanced analytics: The key to success for wealth managers

The financial crisis of 2009 has ushered in sea changes in the wealth management industry. The uncertainty and increased regulations in the aftermath of the crisis is pushing industry players to become more agile than ever before. Surrounded by challenges such as market shifts, stringent regulatory requirements and cost pressures, wealth management firms are realizing that big data and analytics can be the game changer they are looking for. However, success can prove to be elusive in the advanced analytics space – especially when implemented in a siloed fashion.  Leading wealth management firms understand this and are changing the rules of the game by integrating analytics into the very fabric of their business.

Here we explore how wealth managers at some of these leading firms are leveraging insights extracted from big data to enhance every element of the client lifecycle in order to bolster their competitive advantage.

Strengthening new client acquisition

A recent Forbes research discovered that 85% of companies succeeding with analytics are organizations that have an enterprise-wide analytics strategy in place and consider themselves as trendsetters in the market. These organizations are also seeing revenue growths of more than 7% p.a. Take the success story of GFNorte, a large Mexican financial group that established a central analytics business unit (ABU) within its organization. The ABU was given the mandate of increasing revenue through new client acquisition. Dedicated work by the ABU in creating comprehensive prospect profiles, mapping relationships, and identifying new markets and geos for expansion led to high quality leads for wealth managers, yielding profits 200 times its cost – just within three years of implementation.

Enhancing existing client sales

Advanced analytics strategies not only have the potential to uncover new revenue streams but also improve the quality of products and services. This, in turn, allows organizations to cultivate a closer relationship with existing clientele and increase the share of their wallet. A large consumer bank in Asia, for instance, leveraged data analytics to gather client insights through customer demographics and key characteristics like products, credit-card statements, transaction and point-of-sale data, etc. Using these, they built a model to predict the next probable buy of their client. Using this proactive approach helped them improve their existing client sales by three times and gained a strong competitive advantage in a highly saturated retail banking market.

Delivering tailored client advice

As more wealth management firms turn to data analytics as an integral part of their growth strategy, many leaders are still sceptical about its ROI. Using client data to tailor custom products based on their preferences and market conditions still seems like a distant dream to many due to lack of adequate stakeholder buy-in.  A New York-based asset management company Silvercrest Group, was successful in turning around stakeholders’ views. They successfully put in place an analytics engine that allowed in-depth access to data and generated modern, flexible, user-friendly reports, statements, and dashboards. With the ability to tailor reports as per clients’ needs, wealth managers were able to significantly improve client loyalty and gain better control over client investments.

Sustained client retention

McKinsey’s research shows that organizations that use analytics to gain customer insights have a higher probability of getting ahead of their competition. By establishing analytics as a true business discipline, wealth management firms can tap into its enormous potential to retain clients. Let us take the case of Australia’s Westpac Corp, who grew their customer engagement by 25% with an analytics-driven customer initiative KnowMe. The initiative captured and centralized existing customer activity such as ATM usage, call center interactions, financial transactions, and usage of its other products and services – from its 12 million customers. The banking group then leveraged the information to make behavioral analysis and offered its clients customized products and solutions to mitigate client churn.

Gartner predicts that “Through 2022, 80 % of AI projects will remain alchemy, run by wizards and only 20% of analytic insights will deliver business outcomes”. This means that the perfect database or the latest statistical model may not bring the desired results. This is because most analytics initiatives are viewed solely through a technical lens. This creates a situation where organizations get lost within the detailed statistical models and database structures. While technology matters, the key to analytics success lies in harnessing technology to empower wealth managers on the front-lines of the business by building an all-pervasive analytics culture.

 

https://www.forbes.com/sites/insights-cisco/2018/08/15/6-reasons-why-investment-in-analytics-is-essential/#262987785eff

https://hbr.org/2018/01/how-one-company-made-its-analytics-investment-pay-off

https://www.mckinsey.com/business-functions/marketing-and-sales/our-insights/why-customer-analytics-matter

https://blogs.gartner.com/andrew_white/2019/01/03/our-top-data-and-analytics-predicts-for-2019/

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