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How to improve marketing and sales in the banking sector with the use of AI? For instance, you can use AI-ML models to analyze customer data and provide them with tailored product offers and content. Or, you can introduce intelligent onboarding automation to help you turn more leads into customers. You may also use artificial intelligence for planning and deploying your marketing campaigns or to attract customers with personalized offers. Do you wish to learn more? Then read on.
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How to Increase Sales in Banking Using AI
What is AI sales in the banking sector? The present. While for a long time it seemed like a future, unreachable scenario, currently artificial intelligence has quite an impact on banking, so you need to learn how to benefit from using it. Let’s see some examples of how AI can improve sales in your financial institution.
Lead Generation and Management
Artificial intelligence is mostly known in business for how it can handle large quantities of data effortlessly and efficiently. This feature can also be utilized to improve sales in the banking sector.
Take lead generation and management. With machine learning and artificial intelligence, it becomes possible to automate these processes and, more importantly, turn them toward the data-driven approach. You can use AI-ML applications to segment your leads and define which you should prioritize based on data such as financial behavior, online interactions, demographic information, and so on. This way, you can optimize your lead generation and management processes to acquire as many customers as possible.
At the same time, using AI for lead management reduces the time your sales team members spend on evaluating leads manually. This means that they can focus more on value-adding tasks, such as nurturing the leads (though it is also possible with AI) and converting them to customers.
Cross-Selling and Up-Selling
Another advantage of using AI in banking is that you can leverage predictive analytics. With insights generated by artificial intelligence based on real-time data (and trained through historical data), it becomes possible to optimize cross-selling and up-selling to a level your organization has never seen before. How does it work?
- Time windows – AI can find the best time windows to offer customers additional products or upgrades, increasing the chances for conversion.
- Product recommendations – With artificial intelligence, you can analyze the data on every one of your customers and leverage this information to provide them with intelligent product recommendations and offer banking products that match the client’s needs.
Optimizing Transactions
Two of the main factors that may prevent your customers from purchasing new products are… time and effort. Therefore, if you wonder how to increase sales in banking, the answer is simple: make it convenient.
AI will help you achieve convenience. It’s quick data processing will help you accelerate the processes to complete them during the customer’s engagement span. Additional features, like intelligent identification (with the help of biometrics), will let your customers get those products without the need to visit a physical branch. As a result, obtaining new banking products will become simple for them, so they will be less likely to abandon the processes on the way.
Customer Engagement
You can also use AI to build customer engagement, hence improving your sales passively – a retained customer will, sooner or later, get additional products; all you need to do is keep them happy and engaged. Let’s look at two examples of how AI can help you with this in practice.
- Content recommendations – Imagine your customer just got a savings account. AI may utilize this fact and recommend your content related to investing and saving.
- Finance management – Even simple things like alerts about recurring transactions or trusted receivers may keep the customer engaged and loyal, thus more willing to make additional purchases. For instance, while working with one of our clients, we are able to improve engagement simply by improving transaction classification. We achieved this by structuring and categorizing transactional data and implementing an ML system, which proved to be 92% accurate in labeling the transactions (in comparison to 40% accuracy before), hence enabling the client to deliver tailored, personalized alerts and recommendations that truly met the needs of their customers. (Read more in our PFM case study)
Customers nowadays have high expectations regarding the level of personalization, customer service speed, and convenience. AI enables all of this by structuring data, serving content based on it, and delivering insights in an easily digestible format. In the context of customer service, it can be useful to use AI Prompter for Banking, which provides rapid answers to questions.
How to Improve Marketing in Banking Using AI
Knowing how to do sales in banking with the use of AI, we may focus on a closely related area – marketing. What is the impact of AI in banking when product promotion is concerned?
Creating a UVP
Before we get to the practical uses of AI in banking marketing, we need to discuss the general principles of using artificial intelligence and what benefits come with them.
AI enables you to implement new features, ones that other banks might not have implemented yet. In practice, this means that introducing solutions to improve sales in banking or to deliver personalized customer experiences might also affect your marketing – you can make it your UVP.
For example, we can take the previously mentioned finance management systems. If no other banks in your country offer intelligent alerts based on the customers’ history, you can start advertising your bank by showcasing this feature, combining enhanced customer engagement with a higher number of leads. Or, you might make a simple and quick onboarding process your selling point. A process that you optimized thanks to AI.
Building Trust
One of the decisive factors for consumers when choosing a bank is the trust they have in it. Take a look at the Americans – according to a study in 2017 conducted by Statista, 66% of them listed that the security of their personal data is important when selecting a bank for checking/savings accounts[1]. This means that trust is paramount.
How to build it? With artificial intelligence. A common use of AI in banking is exactly for security, so implementing this technology will lead to improved marketing results. Yet, in this case, you need to remember that this will be a lengthy process – you will not build trust overnight just by advertising the fact that your financial institution uses AI-driven cybersecurity measures.
Optimizing Your Campaigns
You may also run your marketing efforts through a campaign manager platform, like the one we offer at Ailleron. This will help you optimize your strategies and achieve better results, ones with higher ROI. How does it work?
- Time-saving – You may use AI to automate processes, hence releasing your employees’ time to work on the more creative side of the campaigns.
- Content creation – Writing content with the help of AI is also an option – check out our generative AI development services if you want a tailor-made model.
- Full overview – AI platforms can integrate your campaign software into one system, giving you a holistic view of all the marketing efforts and results.
- Data-driven approach – Intelligent data integration will also enable your AI-driven system to create predictive insights and determine which elements of your campaigns need improvement, enabling constant evaluation.
Conclusions
How to increase sales in banking with the use of AI? How to utilize it for marketing? As you can see, it’s all about managing your campaigns, creating personalized experiences, and optimizing your processes. In the end, this is exactly what AI does – it helps you automate simple tasks and extract valuable insights from data in a timely manner. If you need a ready-to-implement AI solution or desire to have a custom model, feel free to contact us – with our expertise, we will build the AI-driven system that you strive for.
You may also read: Machine learning in banking and finance – use cases
[1] https://www.statista.com/statistics/803198/factors-americans-consider-important-when-choosing-a-bank/
