Cracking the Code: How Fintechs are Overcoming Commoditization in 2024

As of 2024, there are at least 400 fintech and neobank companies globally. This number reflects the growing popularity and adoption of digital-first financial solutions, driven by the demand for more accessible and efficient banking experiences​​. A recent survey conducted by MX revealed that 45% of U.S. consumers are performing finance-related tasks on a mobile app at least once per day, and that 48% of consumers have three or finance-related apps installed on their phones.

The race to become the wallet and bankroll of consumers and businesses means fintech is starting to become commoditized. This trend can be seen in many areas of fintech, including neo-banks, commission-free brokers, and buy now pay later services, which often lack differentiation. While commoditization can lead to increased competition and innovation, it also pushes some players to find new niches: it's simply no longer enough to offer a basic feature set.

To remain competitive, the strongest players are not just rolling out new features - they’re prioritizing a seamless and intuitive customer experience. A McKinsey report noted that companies focusing on customer experience can increase revenues by 10-15% while lowering customer acquisition costs by 15-20%. But even that may not be enough in today’s competitive landscape.

Elsewhere in the industry, companies are responding to commoditization by balancing their focus on feature differentiation with innovations in customer acquisition strategies. But the advertising landscape is becoming increasingly saturated and expensive, too. As more fintech companies vie for consumer attention, the cost of acquiring new customers through platforms like Facebook and Instagram continues to rise. This is compounded by Meta's ongoing efforts to impose stricter limits on advertisers. Last month, Meta announced that it would be expanding special ads categories to include financial products and services starting in 2025.

To navigate this, companies must refine their audience targeting strategies. Precision targeting allows for more efficient use of advertising budgets by reaching the most relevant and engaged potential customers. Leveraging data analytics and machine learning can help fintech companies identify and segment their ideal audiences, ensuring that marketing efforts are both cost-effective and impactful.

The headwinds for fintech marketers have never been stronger, but some companies are adapting by using first-party data and audience modeling strategies to reach their customers. With Meta’s powerful lookalike audience modeling out of the picture and other platforms likely to follow suit, fintech advertisers can build lookalike audiences elsewhere: Customer Data Platforms (CDP) like Blueconic and data collaboration platforms like LiveRamp offer their clients some version of lookalike modeling.

We at Fantix launched Fusion, a deep, unsupervised learning model capable of uncovering complex, high-dimensional patterns and relationships within data sets that are often missed by conventional methods. Trained on $3.5 trillion of recent consumer transactions and expansive mobility data sets, Fusion can create extremely accurate targeting audiences to be used on Meta and other ad marketplaces. Some early adopters in the fintech space have seen CAC improvements (compared to lookalike audiences created with Meta) of nearly 56%. This is consistent with a report from Forrester, which found that companies using AI-driven audience strategies can reduce CAC by up to 40% - we actually beat that estimate.

As the fintech landscape continues to evolve, staying ahead requires more than just innovative features. Effective customer acquisition and precise audience targeting have become critical strategies in a market where differentiation is increasingly challenging. By leveraging advanced data analytics and machine learning, fintech companies can navigate the competitive advertising landscape and achieve significant cost efficiencies. Tools like Fusion and other first-party data models are not only helping companies improve their customer acquisition costs but also setting new standards for operational excellence.