Data-driven Credit Card Comparison Platform, GigaPoints, Launches Hyper-personalized Recommendations To Maximize Rewards; Announces $1.5m In Seed Funding
Startup gives consumers a fast, easy and secure way to earn the most rewards or cash back from their credit cards based on their individual spending habits
SAN FRANCISCO (October 6, 2020)— GigaPoints, a new platform that provides personalized credit card recommendations based on a user’s spending, announced its launch today, along with a $1.5 million seed round of funding. Participating in the round are Next Coast Ventures, Sam Kingsland (former Granite Ventures founding member), and additional strategic angel investors comprised of venture capitalists, private equity leaders, technology executives and entrepreneurs. GigaPoints plans to use the funding for marketing, additional hiring, and continued platform development.
GigaPoints uses data, not guesses, to deliver personalized card recommendations—unlike other credit card recommendation sites that offer generic suggestions or push sponsored cards on users.
“Too many consumers are leaving money on the table by using a credit card that doesn’t maximize the rewards they can earn—or meet their goals,” said Erik Budde, CEO and Founder of GigaPoints. “With the resources that currently exist, it can be hard and confusing to make smart financial decisions. We wanted to give people an alternative to one-size-fits-all recommendations or hours of online research. GigaPoints takes just seconds to provide objective matches tailored to your personal spending habits.”
Objective, accurate credit card guidance is more crucial than ever, especially with the impact of COVID-19 on consumers’ habits and wallets. GigaPoints gives users fast, easy, and safe advice—without bias or sales tactics. The user-friendly interface and eye-catching data visualizations make it easy to understand, compare, and filter options, whether users want to prioritize cash back, no-fee cards, dining perks, maximum points, or anything in-between. Its patent-pending algorithm features end-to-end encryption and allows users to delete linked cards and data from GigaPoints at any time.
GigaPoints is different from every other credit card site because it not only uses big data analysis to deliver personalized recommendations, but it also shows exactly how much a user can expect to earn each year with a card. Here’s how it works:
- Users securely link current credit card accounts to their GigaPoints account. All data is encrypted and kept private, using the same security technology (Plaid) that is behind the top peer-to-peer payment and banking websites.
- GigaPoints analyzes a user’s existing spending, looking at types of spending (like gas, groceries, clothes, or plane tickets), merchants and more than a dozen other variables. It calculates how much users are earning from their current cards and how much they could be getting from competing cards.
- The algorithm recommends the credit cards that will provide the maximum points, miles or cash back—and estimates the annual value of those rewards.
“We’re excited to stand behind GigaPoints as they reimagine the marketplace for credit cards,” said Thomas Ball, Co-Founder and Managing Director at Next Coast Ventures. “GigaPoints has the potential to profoundly change how consumers make important financial decisions. They are providing consumers with transparency and personalization in a crowded market, and relieving them of endless research that is not specific to their needs.”
For more information, please visit www.gigapoints.com.
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GigaPoints is a data-driven credit card comparison platform that gives consumers a fast, easy and secure way to get highly personalized credit card recommendations so they can earn the most rewards possible for their spending. Founded in 2020, the company was built from Founder/CEO Erik Budde’s passion for helping consumers maximize their credit-card benefits. GigaPoint’s patent-pending technology analyzes a user’s spending, mapping it to hundreds of credit cards and their unique rewards systems.