The need for data analytics to offer personalized finance to consumers

Personal finance is complicated. There are many moving parts, and it's virtually impossible to capture everything someone needs to know without consulting a financial professional. But in the digital age, data analytics makes it possible for companies to simplify personal finance for consumers in big ways.

How Data Analytics is Used in Personal Finance

The concept of data analytics is simple: companies take large datasets, 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 the personal finance space, powerful data-driven decision-making tools are being introduced at every stage of the product decision cycle, from initial product comparisons to delivering personalized offers and keeping customers engaged long-term. Here are three ways we're seeing data analytics 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 still aren't sure how to create an investment portfolio that considers 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 customers to push a button to indicate their risk tolerance, from conservative to aggressive. Then, the app takes that input and automatically decides on a suite of investments and invests in those funds. This creates a seamless investing option for consumers who don't have the time, energy, or know-how to build their own portfolio.

AI-Based Recommendation Tools Lead to Smarter Decision-Making

Years ago, someone looking into 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 remove the middlemen. Instead, they leverage an advanced algorithm that takes in basic information and user goals and produces a tailored list of solutions.

The beauty of a simple platform experience like this is that it presents users with debt consolidation recommendations that are both easy to understand and easy to act on. And that means a greater likelihood of customers signing up for a loan that will help them improve their financial situation.

Personalized Offers Increase Conversion Rates

Personal finance companies can now take a customer-centric approach to their marketing, onboarding, and retention strategies. And in a time where customers expect top-notch service, it's more critical than ever for companies to deliver personalized offers.

Data analytics presents opportunities to target individuals based on their search patterns, spending patterns, and even their geo-location. For example, an individual who has several searches a day about buying new homes is an opportunity for a targeted email about low-rate mortgages. Someone who has spent double their regular monthly home improvement budget may be an opportunity to push notifications about personal loans for home improvement projects.

These personalized offers can help bring on more customers and retain existing customers by offering them things they might already be considering.

The Bottom Line

Personal finance is still personal. But data analytics enables companies to offer customers personalized solutions on a mass scale. Data-driven portfolios, AI-based recommendation tools, and personalized offers are just a few of the ways companies are leveraging data insights to create customer value. Expect to see data analytics simplify personal finance even further in the coming years as companies find new and creative ways to use it.