How data analytics is being used to simplify personal finance for consumers


by Analytics Insight
November 19 2021

The need for data analytics to deliver personal finance to consumers

Personal finance is complicated. There are many moving parts, and it is almost impossible to capture everything one needs to know without consulting a financial expert. But in the digital age, data analytics allows companies to simplify personal finance for consumers in significant ways.

How data analytics is used in personal finance

The concept of data analytics is simple: companies take large sets of data, analyze them, and turn them into meaningful, easily digestible patterns. These patterns are then used to inform new product design, anticipate customer preferences, and increase profitability.

In personal finance, powerful data-driven decision-making tools are introduced at every stage of the product decision-making cycle, from initial product comparisons to delivering personalized offerings and maintaining long-term customer engagement. Here are three ways we see data analytics being used in the personal finance market to simplify the process for consumers.

Data-driven portfolios simplify investing

These days, the opportunity to invest is literally in the palm of your hand. But many consumers are still not sure how to create an investment portfolio that takes into account their risk aversion. Companies are looking to change that by leveraging data analytics to create data-driven portfolios based on investors’ risk tolerance.

An app like Acorns allows clients to press a button to indicate their risk tolerance, from conservative to aggressive. Then, the app takes that input and automatically decides on a set of investments and invests in that money. This creates a seamless investment option for consumers who do not have the time, energy or knowledge to build their own portfolio.

AI-based recommendation tools lead to smarter decisions

Years ago, a person looking at debt consolidation loans may have sat down with their financial advisor or bank to discuss options. Today, tools like Credello’s AI-based debt consolidation recommendation engine are removing middlemen. Instead, they make use of an advanced algorithm that takes key information and user goals and produces a customized list of solutions.

The beauty of a simple platform experience like this is that it offers users debt consolidation recommendations that are easy to understand and act on. This means that customers are more likely to sign up for a loan that will help them improve their financial situation.

Personal offers increase conversion rates

Personal finance companies can now take a customer-centric approach to their marketing, onboarding, and retention strategies. And at a time when customers expect first-class service, it is more important than ever for businesses to offer personalized offers.

Data analytics offers opportunities to target individuals based on search patterns, spending patterns, and even their geographic location. For example, an individual who does several searches per day on buying new homes considers the opportunity for a targeted email about low-priced mortgages. A person who has spent twice their regular monthly home improvement budget may have the opportunity to push notifications about personal loans for home improvement projects.

These personalized offerings can help attract more customers and retain existing customers by offering things they might already consider.

bottom line

Personal finance is still personal. But data analytics enables companies to offer customized solutions to customers at scale. Data-driven portfolios, AI-based recommendation tools, and personalized offerings are just a few of the ways companies leverage data insights to create value for customers. Expect to see data analytics simplify personal finance even more in the coming years as companies find new and innovative ways to use it.

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Insight Analytics


Analytics Insight is an influencer platform dedicated to insights, trends, and insights from the world of data-driven technologies. It monitors the developments, recognition, and achievements of AI companies, big data, and analytics around the world.

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