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Wealth Managers, are you ready for millennial customers?

Wealth Managers, are you ready for millennial customers?

Segmenting clients across various lines using a pre-defined set of parameters has been a common phenomena across industries. As such, wealth management is no different from its other industry counterparts in relying on this practice. However, as the figure below suggests, in spite of the prevalence to segment clients across the wealth industry, a substantial 25% of firms choose not to segment their clients at all. Moreover, of the remaining ones who do so, the segmentation methods used are static and do not change automatically based on a change in the input parameters. In fact, any “re-bucketing” of clients across segments is usually done on a quarterly or half-yearly basis. Also, the modelling process remains technologically sparse and does not incorporate a wide variety of variables (which firms have access to at times).

by Mrinal Mishra.

 

With the advent of big data technologies, it is possible for wealth firms to record a large amount of client data and monitor it real-time. At the same time, customers have increasingly higher expectations (of customer experience) in terms of digital software and tools. A BCG report states that digital excellence remains a key component in choosing and switching wealth managers for millennial customers. This trend is expected to accentuate as millennials become a larger percentage of serviced clients over time. Also, this customer group demands improved experiences over time – some examples of these experiences include online and video chatting capabilities, robe-advisory and the presence of mobile or iPad apps.

AI enabled dynamic predictive segmentation – forward looking in time based on the information available till the current period – can solve the challenges faced by the current static segmentation processes. Dynamic segmentation accurately segments customers based on their propensity to take a specific action. The segments change in real-time based on new data captured from customer interactions. A close comparison can be made to Ant Financial which provides small business loans to vendors in China (with low credit rating who would find sourcing credit from traditional banks difficult). The credit score computed by Ant Financial’s algorithm changes dynamically (on a daily basis) based on the sales, transactions and business conducted by these vendors. The algorithm then uses this updated information to recompute the credit score which subsequently impacts the decision to grant credit.

In spite of the use of better techniques, it should be noted that the main conduit for wealth managers is the financial advisor. Expecting advisors to enter every bit of information related to clients into a database is impractical and overly time consuming. With the advent of Natural Language Processing (NLP) technologies, this job can be made easier for the advisor as one may use voice recognition technology to convert speech to text. Thereafter, the critical points of the conversation can be extracted using NLP algorithms, which can be used to feed databases, updated sales management pipelines and support decision making. However, despite the fact that new technologies can substantially enhance sales management processes, it is the implementation of various tools and training of employees to properly apply AI solutions that will remain the key component in bringing sales management to the next level.

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