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Financial Services

Analyzing all aspects of the customer relationship in personal finance can help companies cross-sell, up-sell and create lifetime value.

The Finance Industry Has Always Leveraged Data

Before the term “data science” was a buzzword, Finance was using it. Similar to banks having risk analytics, financial industries use data for similar purposes.

Data Analytics Strategy

Financial industries need to automate risk analytics in order to carry out strategic decisions for the company.

Machine Learning

Identifying, monitoring, and prioritizing the risks and monitoring customer data with ML yields insights into efficiency and longevity in business practices.

Predictive Analytics

Financial institutions leverage machine learning for predictive analytics, allowing companies to predict customer lifetime value and inform stock market decisions.

Case Study:

Faster Data Operationalization

We worked with the marketing team at the world’s largest sporting goods brand in their sport. Their team initially came to us looking for a Customer Data Platform (CDP). Following our analysis of their business needs and technology solutions, we discovered a deeper problem: They were accessing their data through a third party service provider.
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I view Nick and DAS42 as the single most important strategic partner external to Snowflake that I have in the business. If somebody came to me and said, "You get to wave a magic wand and you can place any partner on this account, who are you going to choose? It’s DAS42 first every single time.

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Evan Blake
Sales Director, Strategic Accounts, Snowflake

With proper analytics and data management practices, finance companies are able to take strategic decisions and turn them into increased trustworthiness and security among their customer base.

Financial Analytics for Different Customer Types

With financial analytics, our tools and strategies help guide institutions’ decision-making processes surrounding the lifetime value of different customer segments, relative to the cost of acquisition (i.e. how much a financial institution should pay for a given customer). For example; how many young customers do they have vs. retirees and how much did they pay to acquire individuals in each of those different market segments?

Analytics for Different Demographics

High worth consumer analytics will display data vastly different than financial analytics for the younger population. For instance, a young person might need student loan financing rather than a mortgage. Being able to analyze this large spectrum of behaviors can be very helpful in determining efficient business practices.

Analytics for Marketing Financial Products

Another aspect of financial data analytics is for product marketing purposes. Financial services companies have the benefit of a somewhat automatic high lifetime value. Once a person decides on a bank, they usually stick with them for a long time. If the analytics are done right, the potential for recurring up-selling and cross-selling additional financial tools, apps, and the like is automatic. 

Our FullStack Philosophy

Find out more about our FullStack philosophy and how we help you evolve from data chaos to targeted predictive analytics.

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