FinTech is about connecting with customers. They expect something different from classically understood banking. The more you know about your audience, the more you can offer them. It’s similar to prices – price optimization through machine learning is a great tool to grow your revenue. What can you learn from real-market examples?
Figuring out the best pricing model can be tricky. Especially with a newly developed product, when you have to convince people to set up accounts and trust you with both: data and money. That’s where machine learning algorithms come into place. By processing and analyzing big amounts of data, they can help you establish optimized pricing plans. How exactly?
Hire machine learning to make optimal pricing decisions
Solutions mentioned below will boost your product in real-time. They can help both: established companies and startups. Think of them as a multiple-step guide to designing your app with specific features and customer-centric solutions in mind.
This is how you can improve a pricing model:
- Use machine learning to process data and discover services that need a boost. There are highly specialized FinTech applications that offer only one product; loans for example. There are, however, applications that are very popular and sell multiple solutions to the same audience. What product generates more money? Which solution is better? Do an A/B testing and find out. By going through data, you can figure out what works and what doesn’t. This solution can free up resources (money, employees’ time) to pursue more profitable features.
- Use automated pricing models to drive up revenue. The Boston Consulting Group created a study and it seems that revenue can be boosted up by 5% with this. The BCG believes that machine learning offers optimal pricing rules in revenue management systems. It also enforces contractual pricing.
- Generate insight on changing user’s behaviors through automated pricing solutions. It gives a highly valuable context on transactional data, providing the necessary perspective. One of the companies that offer interesting solutions is Vendavo. Their model and industry integrations work great with custom software development, powering your app. This combo of data and development solutions will help you make pricing decisions. Especially based on cross-border parameters.
- Use machine learning to figure out which customers are willing to pay for a product or a specific feature. You can pull information by linking spending or monthly fees in a software-as-a-service (SaaS) model with discounts, promo codes, etc. It’s especially valuable in the case of VIP pricing plans.
- Predict pricing impact with AI-powered user personas. Try to predict whether a first-time user or a paying customer will perform a certain and desirable action. Thanks to artificial intelligence propensity models, you can increase the customer retention and reduce churn.
- Use rule-based artificial intelligence (AI) models to establish the risk-to-revenue. Software development, specially dedicated to the B2C market, isn’t always fully predictable. Customers’ needs and the market itself change rapidly. Friction in user experience can be managed but what about mobile app development? You can use the customer even before you know about the issue. The price is not acceptable. The solution is brilliant, but underdeveloped. Microcopy inside the app doesn’t transmit the offers very well. User experience and user interface design are not attractive enough. Machine learning pricing algorithms won’t give you all the answers, but they can show you the right direction.
How to achieve your goals?
According to McKinsey, the estimated AI-based pricing solutions can have a global worth of $259.1B to $500B, globally. According to Mordor Intelligence, the AI market in the sector will grow from $7.27B in 2019 to over $35.4B by 2025. Those are, however, numbers you can’t use. As previously mentioned, 5% is something real. How to get to that? Use these factors to drive your decisions:
- customers’ personas
- operating costs and preferred margins
- seasons and holidays
- other, especially unforeseen, economic variables
Also, focus on something called a dynamic price. It’s adjusting prices, usually for a number of products, to react to the competition’s strategy. This model assumes frequent changes. It’s risky, unstable, and leads to churn. We have broken down the differences between price optimization, dynamic pricing, and price automation with machine learning.
Massive amounts of data and machine learning can generate pricing recommendations but you still have to base decisions on experience. Machine learning can and will give you a lot to think about, it can also free you from many mindless business operations. It can also be faulty.
As well as software development, which requires real specialists. Financial software development company can save you a lot of trouble and create a performing digital product worthy of your customers’ attention. Care to join the future?
The post How Can You Use Machine Learning to Optimize Pricing in FinTech? appeared first on SmartData Collective.