How Data Science Can Increase Banks' Revenue

Data science gives your sales and marketing teams the tools they need to be effective in customer acquisition, retention, cross-selling, and up-selling. If your financial institution isn’t yet taking advantage of all these benefits, now is the best time to get started. As a financial institution, you already have the data. It’s time to put it to use. 

This article explores three ways data science can optimize efforts at your organization to increase revenue.


Customer acquisition and churn reduction

Have you done a thorough analysis on your customers? Do you understand why customers select you, what products they purchase, and why? These questions are often answered subjectively. With data science, you can start defining customer profiles based on your bank’s historical trends and then predictively target certain profiles with specific products based on data.

Additionally, a data science solution can effectively identify customers at risk of leaving, then develop a personalized strategy for taking care of at-risk accounts. These initiatives can help your organization swiftly identify potential issues and reach out to begin addressing and resolving issues faster, as well as providing automated outreach to the customer or to the advisor, notifying them to take action.


Optimize Pricing

Through data science processes and procedures, you can analyze data on sale history, product descriptions, customer reviews, competitive landscape, and geographical information to provide strategic pricing recommendations. Additionally, advanced data science activities such as machine learning models are skilled at predicting trends, instead of having to react to them.

One key benefit comes from a machine learning model’s ability to consider a surplus of data sets. For example, adjusting pricing on one product will also impact sales of all others in an incredibly complex way. While humans are aware of this impact, we largely struggle to accurately predict the outcome


Product Upselling

Utilizing all the data you already collect from a customer, machine learning can provide strategic recommendations for upselling and cross-selling. Not only does doing so help your institution generate more revenue by personalizing recommendations, but your customers also experience a more tailored, frictionless experience.

One example you’ve likely encountered comes from Amazon. When purchasing a product, Amazon recommends additional products to purchase that complement the original. This allows Amazon to upsell, ensuring the customer doesn’t have to go through the effort of searching for a product they might not even know they need.

Data science has a multitude of benefits when introduced at banks. Notably, machine learning models can have immense effects on revenue generation by optimizing pricing, identifying key opportunities for upselling, and reducing customer churn. Additionally, there isn't a huge barrier to get into data science. There aren't a lot of prerequisites to start doing advanced analytics—your institution can get started today.


Interested in exploring more ways data-driven decisions can make improvements at your organization? Download our guide on the benefits of data science for financial institutions.

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