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Fintech Analytics: The Top 7 Metrics Every Fintech Company Should Monitor

In an incredibly competitive sector, fintech companies collecting big data streams should ask themselves:

  • How do I make sure I’m poised to take advantage of market opportunities the moment they emerge?

  • How do I diagnose and fix incidents before they have time to impact my business?

  • How do I monitor the information I need to stay ahead? Am I monitoring the right data?

Agility and innovation in fintech depend not only on collecting the data, but how you’re able to leverage it. Knowing which metrics to measure, and especially the interplay between them, will help illuminate the complete story about your customers, your business and the market.

After all, these metrics can only tell you so much in isolation. Proper fintech analysis means mapping the complex relationships between how your app or your product is functioning, how your marketing efforts are performing and the end business results to figure out how it all fits together.

Let’s take a look at which metrics to focus on, how you measure them and how you bring it all together for a clear picture of business performance.

Which Metrics Should You Measure?

You may have heard of AARRR, the famous startup metrics model developed by Dave McClure. These five metrics give you a great idea of customer behavior, which is vital for understanding your performance - but they miss out two key areas: marketing and technical.

Acquiring a holistic view of your fintech business typically depends on seven metrics:

Acquisition Metrics
This indicates how many new customers you’re managing to onboard. That means measuring KPIs such as app downloads and new user creation rate.

Be sure to keep an eye on your cost per acquisition, too. As Palo Alto Software’s Sabrina Parsons writes in Forbes, this should be as low as possible: “If your CPA ratio is too high, consider making a few changes to your customer acquisition methods, perhaps adjusting your advertising strategies or pushing for more in-person events.”

Activation Metrics
This metric tells you how you’re doing at getting people to use your product. Useful KPIs include Monthly Active Users (MAUs) and app/site traffic.

Retention Metrics
Here, you’re measuring your success at keeping people engaged with your product - particularly vital if your business is subscription-based. So, KPIs such as active accounts, inactive accounts and returning customers.

Unsure of how to calculate your overall retention or churn rate? Writing in Inc, Craig Bloem explains that: “The formula can be a little complicated, but one way is to subtract the number of new customers from your total customers at the end of a given period, then divide that number by the number of customers you started the period with.”

Willingness to recommend your product to others is the best possible feedback your customers can give you, and strong performance in this area will do wonders for business growth. Focus on KPIs such as social shares.

In the words of Don Markland of MoneySolver: “I strongly encourage every sales team in every industry to track, measure and push referrals. Those are free leads and have extremely high conversion rates yet are never a core KPI on the salesperson's scorecard. This is a must-have.”

This is pretty self-explanatory, but ultimately it’s the most important factor. Look at KPIs such as daily revenue and number of transactions to get a nuanced picture of performance.

Marketing Metrics
This is a huge area to distill in one point, but figuring out which of your efforts to attract customers is working is crucial, especially as your business grows and your costs magnify. Important KPIs here include conversion rates, web traffic sources, cost per lead and customer lifetime value.

As Convince and Convert put it: “These data points are the vehicles that make your goals real and concrete, and make your attempts at reaching them observable and quantifiable”.

Technical Metrics
Often overlooked, technical metrics are crucial for any SaaS or app-based company, especially in fintech where users need to trust you with their (and their own client’s) money. Keep an eye on page load times, rates of timeouts, number of simultaneous connections, and percentage of database memory used to make sure your delivery stays smooth and frictionless.

How Do You Measure These Metrics?

There are a wealth of tools and platforms out there to help you track metrics, all of which integrate with data collection points or your CRM in different ways. These range from dashboarding tools that help you visualize individual KPIs through to more comprehensive BI platforms that allow for detailed data analysis and querying.

While these technologies approach the problem in lots of different ways, depending on the level of technical experience they’re designed for, they do tend to share the same limitation.

Typically, tools that help you track metrics give you a siloed view of your performance: they will help you spot patterns in your data, but only in isolation. It’s very difficult for you to cross-reference KPIs to gain a complete picture of how each metric influences another - which, as we’ve seen, is vital.

For the same reason, these tools tend to be pretty weak when it comes to identifying anomalies. That’s because these outlying or surprise results may well be dictated by factors from different KPIs or areas of the business that aren’t grouped under the same metric, such as a technical glitch causing a sudden drop in sales.

Without an easy way to merge your insights, you can be left scrambling to look for an answer that would be glaringly obvious if seen side by side.

What’s more, without the right technology to help you do this with ease, you could be wasting the talent of an expensive resource: your data science team. According to Gartner, 46 percent of marketing teams say that their data scientists dedicate significant time to preparing data for analysis, while 48 percent say they take the lead on data visualization.

Bringing it All Together

How do you get over this hurdle to gain a comprehensive picture of your fintech business? How do you bring together the insights gained from your metrics in useful, actionable ways?

Ultimately, you need to go beyond superficial data or BI visualization tools, using an analytics platform built on AI in order to sweep through masses of data, crossing over siloed data sets. The new wave of analytics solutions, interchangeably referred to as augmented analytics or AI analytics depending on the source, identifies patterns, correlations and anomalies, and helping you uncover blind spots that can lead to unhelpful decision-making or automated responses that aren’t based on the complete facts.

Final Thoughts

The metrics and KPIs you choose to track, and how you use the information you collect, will depend on the precise nature of your business, who uses your product and how. Whatever your focus, though, to really understand your metrics you must have a clear way to investigate how they intersect and interplay.

Incorporating AI into your BI and data analysis allows you to make these connections quickly and reliably. If you opt for a platform that’s based on machine learning algorithms, it will even keep improving and honing its approach as it goes.

This means you can monitor big data without wasting previous man-hours and detect anomalies in real time. You can obtain a holistic picture of the financial and technological sides of your company in one place. You get a strategic, birds-eye view of your business without ever sacrificing on granular details.

Most importantly, with AI analytics you can see exactly where obscure connections and correlations emerge across all areas of your business, enabling you to fix problems, spot patterns and seize opportunities faster than ever before.

Author Bio

Amit Levi is VP of product and marketing at Anodot. He is passionate about turning data into insights. Over the past 15 years, he's been proud to accompany the development of the analytics market. Having held managerial positions in several leading startups, Amit brings vast experience in planning, developing, and shipping large scale data and analytics products to top mobile and web companies. An expert in product and data, his mantra is "Good judgment comes from experience and experience comes from bad judgment."

Image sourced from Pixabay

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