The fact that your organization should have a customer focus instead of a product focus, is not new. There are several ways to improve the customer experience, and one of them is with AI. It allows your marketers to discover new opportunities within the customer base by identifying new audiences and setting up offers and experiences for these audiences. This way, your organization will significantly increase your brand loyalty and engagement.
Servion predicts that AI will power 95% of all customer interactions by 2025. In fact, 57% of enterprise executives believe the most significant growth benefit of AI and machine learning will be improving customer experiences and support.
It’s important to first fully understand the exact value of advanced analytics and AI/ML for optimized and dynamically curated customer experiences in retail banking. An overview.
Find out more about CDPs with AI
The need for AI in Customer Data Platforms (CDP)
It’s impossible to understand your audience without analyzing them. When you talk directly to them and gather feedback, that feedback is essential data on how they are responding—or not responding—to your product. This is step one. You gather all data from your customers to identify their needs, and a CDP allows you to do so. The actual challenge lies in how to use that intelligence in order to meet those needs. This is all perfectly feasible when you focus on a very limited number of customers, but of course, when you have millions of customers to manage a relationship with, this is just impossible. This is where AI is a real asset as it allows you to gather this intelligence and put it into action more efficiently.
In the case of a Customer Data Platform (CDP), it gives your business users real-time actionable insights into individual customers so you can maximize your targeting precision by communicating the right personalized and relevant message to the right people. In other words, CDPs provide a holistic view of the customer to help execute and optimize personalized journeys. With such a platform you can analyze, contextualize, and identify new opportunities on-the-spot. The system immediately responds with the best recommendation in real-time to inbound requests, coming from any channel. And this for each individual customer or audience.
With more advanced CDPS, you can analyze, contextualize, and identify new opportunities on-the-spot. Next to that, the system also sends out personalized experiences in real-time.
Next to that, the system sends out personalized experiences in real-time. Those experiences are triggered by alerts when a customer becomes part of an audience or shows a change in behavior. This way, you will deliver better, faster, and more precisely predicted customer experiences.
But it does not end there. With machine learning, you’re no longer reviewing spreadsheets and crunching numbers after the facts. The machine learns as it goes, interacting with individual consumers and adapting to their individual needs in real-time based on their responses. This gives your employees a lot more time to focus on other—more complex—cases.
Clustering capabilities and look-alike modeling
From an organizational perspective, AI projects are currently delivering value. We see data science teams as the driving force in those pilot projects in commercial departments like marketing & sales. However, to truly get value out AI, it needs to be scaled across the department, allowing business users to integrate it into their day-to-day work.
At NGDATA, we saw that common use cases could leverage from AI as long as it is easy for a marketer to apply, in order to discover new opportunities. This is why our Intelligent Engagement Platform (IEP) provides AI capabilities built for the marketer: clustering and look-alike modeling.
To truly get value out AI, it needs to be scaled across the department, allowing business users to integrate it into their day-to-day work.
There are probably quite a few contacts in your database that you haven’t reached out to yet. You want to have a relevant conversation with those people, but that’s difficult on an individual level. The alternative is to create an audience based on a set of characteristics you think might be relevant. This often turns out to be a very generic audience or one that only contains a small number of people.
What you need is the capability of your CDP to create a clustering algorithm. This will leverage AI to automatically divide a group of contacts into smaller clusters with people who have similar profiles based on all attributes. Marketers can then create an offer specifically tailored to those individual clusters.
When customers recently acquired one of your products or services, this is obviously good news for your business. You can then assume that individuals with comparable attributes have a higher probability to show similar behavior. Look-alike modeling enables the marketer to select a look-alike group of customers who resemble the most to the ‘exemplar’ customers based on all available data inside the IEP, including declarative next to behavioral and transactional data.
Through AI, you can select an audience that shares similar characteristics (attributes of the customer profiles) with the group of customers that has already shown the desired behavior. And to get them to take that same action, the look-alike audience probably just needs a little nudge.
Transformative marketing and business approach
Closing this gap between analytics and execution means letting marketers truly manage the conversation with the customer at scale, both in- and outbound, on an individual level. By leveraging your data to deliver the most relevant, timely, and context-aware actions that match the needs of each and every individual customer, you’ll become transformative in the way you approach your marketing and your business.
NGDATA’s Intelligent Engagement Platform (IEP) unifies customer intelligence and allows you to engage with the customer in real-time.