In this episode of The Future of Consumer Marketing, host Roman Kirsch interviews Mat Megens, Founder of HyperJar. HyperJar is transforming how consumers manage their spending through behavioral psychology and gamified saving mechanics. Starting from a supply chain financing concept, the company evolved into a consumer spending app that helps people avoid debt and spend more intentionally. With nearly 700,000 customers, HyperJar has achieved remarkable organic growth primarily through word-of-mouth and credible influencer endorsements, while building sticky retention through shared money features that create network effects.

Topics Discussed:

• Evolving from B2B supply chain financing to consumer spending management

• Building the first 100,000 users through retail partnerships and organic PR

• Applying behavioral psychology to make saving feel rewarding

• Creating viral growth through credible influencer endorsements versus paid advertising

• Developing shared money features that create network effects and retention

• Monetizing through merchant partnerships and B2B licensing

• Leveraging economic downturns as tailwinds for financial wellness products

Lessons For Consumer Marketers:

Prioritize Credible Influencer Endorsements Over Paid Advertising

Mat discovered that customers acquired through Meta and Google ads were less engaged than those who came through credible influencers like Martin Lewis (Money Saving Expert). These unpaid endorsements from trusted sources converted better because the influencers could authentically explain the product’s value proposition in a way that resonated with their audiences, leading to higher-quality, stickier customers.

Use Behavioral Psychology to Counter Consumer Impulse Patterns

HyperJar gamified saving by creating visual feedback loops like odometers that showed savings growing in real-time, even by pennies. This countered the instant gratification messaging that dominates consumer marketing by making delayed gratification feel rewarding. The key was making the saving process feel active and engaging rather than passive and restrictive.

Build Network Effects Through Shared Value Features

The app’s “shared jars” feature creates powerful retention because it solves problems for groups of people simultaneously. Whether it’s families managing children’s spending, couples budgeting for trips, or flatmates splitting expenses, once multiple people are connected through shared financial goals, switching costs become prohibitively high due to social and functional dependencies.

Leverage Macro-Economic Trends as Marketing Tailwinds

Mat recognized that economic uncertainty (COVID, inflation, cost of living crisis) naturally drove consumers toward financial wellness products. Rather than fighting against consumer psychology, HyperJar positioned itself to benefit from these macro trends, allowing the economic environment to do much of the customer education and motivation work.

Focus on Use Case Clarity Over Feature Complexity

Early mistakes included launching features that weren’t properly tested for user comprehension. Mat learned that with innovative products, the biggest challenge isn’t building features but communicating how customers will integrate them into their daily lives. Clear use case messaging proved more valuable than extensive feature sets.

Treat Consumer Products as R&D Labs for B2B Opportunities

HyperJar’s consumer app serves as a live testing ground for financial wellness features that institutional clients (banks, pension companies) want to license. This dual-purpose approach allows the consumer product to subsidize innovation while creating scalable B2B revenue streams through proven, market-tested features.

Test Product-Market Fit Through Prototype Validation, Not Live Launches

Mat’s biggest regret was launching features that required expensive real-time tweaking instead of conducting cheaper upfront testing through prototypes and focus groups. For complex consumer products, the cost of iteration after launch far exceeds the investment in pre-launch validation through low-fidelity testing methods.