Dynamic Yield: The Power of Recommendations in Finserv

In this deep-dive, we speak to Harry Hanson-Smith, Sales VP at Dynamic Yield by Mastercard, on the positive impact of tailored recommendations in finserv

In the retail industry, offering tailored recommendations to customers using the latest technology has fast become the norm. 

But so far, particularly for legacy financial services providers, offering similarly bespoke recommendations to clients has been hard to achieve. 

This is why Harry Hanson-Smith, VP of Sales at Dynamic Yield by Mastercard, says recommendation strategies remain a much-untapped area for many financial institutions (FIs). 

Why FIs must leverage recommendations effectively

As the financial industry grows evermore vast, and customers shift increasingly to digital banking offerings, decision-making processes for financial products have grown more complex.

As such, the scope for offering tailored recommendations is wide and constantly evolving. For Hanson-Smith, the scope for recommendations goes beyond products alone. 

“FIs can leverage recommendations to provide valuable financial advice, educational resources, and personalised content,” he says, “to empower customers to make informed decisions about their financial well-being.

“However, many FIs have failed to leverage personalisation to facilitate learning and discovery effectively. 

“Where eCommerce has the ‘guided selling’ concept, which aids users in discovering products, FIs can adopt a similar approach to offer personalised, relevant and seamless customer engagement through product and resource recommendations based on user behaviour.”

Indeed, the financial services landscape is ever-evolving, and new technological advancements mean customer preferences are changing – so recommendation tools must be able to adapt to frequent market shifts. 

Hanson-Smith adds: “Emerging fintech players are reshaping the way FIs engage with their customers, which means recommendations can serve as a powerful tool to differentiate FIs from their competitors.

“By harnessing data analytics and machine learning algorithms, FIs can unlock deeper insights into behaviours and financial goals, enabling them to deliver highly personalised recommendations that really resonate.”

How to choose the right recommendation strategy

Below, Hanson-Smith outlines how FIs can chart a course to choosing the right recommendations strategy, and the factors to consider:

Goals: Define key performance indicators (KPIs) for personalisation efforts, such as open account clicks, application starts, or mobile app downloads. These goals guide the choice of recommendation strategies and their placement on digital platforms.

Audiences: Segment the audience into distinct groups, focusing on 3-4 primary segments based on factors like engagement level, customer lifecycle phase, or product attainment. Each segment's unique needs dictate the type of recommendations they receive.

Data feed: A well-structured data feed is essential to power recommendations effectively. Proper metadata tagging, based on product categories, attributes, customer status, and engagement levels, ensures that recommendations are meaningful and relevant.

Strategy: Select recommendation strategies that match various user segments and preferences. These strategies could involve suggesting items based on user interests, recently viewed products, similar choices, or items frequently viewed together. Tailor these recommendations to suit specific user profiles and enhance their overall experience

On-page location: Recommendations' relevance varies depending on the page location within an FI's website. Tailoring recommendations to the expectations and intent of visitors on the homepage, blog pages, and account overview pages improves their effectiveness.

Examples of recommendations in financial services include:
  • Chatbot recommendations
  • Article recommendations
  • Improved customer acquisition and lifetime value recommendations

Keeping data governance in mind

Indeed, to unlock the full potential of recommendations, FIs must prioritise data governance and data quality

Hanson-Smith says: “A robust data governance framework ensures that customer data is accurate, secure, and compliant with regulatory requirements. It also enables FIs to effectively manage data from various sources, including transactional data, customer profiles, and behavioural data.

“Additionally, data governance plays a crucial role in maintaining customer trust. Customers are increasingly concerned about the privacy and security of their financial data. 

“FIs that demonstrate a commitment to protecting customer data through strong data governance practices can build trust and credibility in the eyes of their customers.”

The benefits of enhanced personalisation

It’s clear, recommendations hold immense potential in the financial services industry when used effectively. 

Hanson-Smith concludes: “The above framework can enable FIs to follow a path to greater personalisation, improved customer engagement, and enhanced business metrics.

“By embracing recommendation strategies tailored to their unique needs, FIs can unlock the power of personalisation in the digital age, adapt to changing industry dynamics, and position themselves as trusted advisors in their customers' financial journeys.”

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