Artificial Intelligence (AI) is rapidly evolving as an important part of the underlying technology that enables companies to target and track customers. The use of AI to anticipate what a customer needs and wants in order to deliver better, faster service is a new frontier for the financial services sector. The volume and scale of today’s digital interactions means that financial services companies need automated technology that can understand and interpret human emotions. AI has the capability to facilitate consistent, cross-platform experiences that would be impossible to deliver manually.
Meeting customer expectations.
Delivering custom content is key to meeting customer expectations. Implementing AI to provide automated, virtual customer service solutions can help financial services companies understand what a user wants at any touchpoint within an online ecosystem. “With AI you can deliver more personalized communication at scale, meaning that you can tailor messages to specific audiences, which increases engagement and revenue,” says Jaekob Chenina, product manager at Adobe.
One of the biggest hurdles many financial services companies face when considering AI solutions is that their customer data is scattered in different legacy databases.
“Siloed datasets is the number one challenge preventing companies from developing personalized communications that leverage augmented intelligence and automation,” says Jaekob. “If you don’t have your data integrated together into one spot, then the ability to have success with artificial intelligence and automation is limited.”
Data is the fuel that makes AI work. Great algorithms and sophisticated software will only translate into optimized customer experiences when they are combined with real-time access data about every online user.
“Data is the new oil, it’s so valuable,” says Jaekob. “The more data that you can integrate from different parts of your business into your AI solutions, the more it will empower your algorithms for analyzing and targeting customers.”
Integrating and optimizing your data ultimately fuels an automated process that can learn in real time what the customer needs and wants, and can serve up the appropriate content automatically.
Data integration also unlocks the potential for AI and automation to help companies leverage not only internal customer data, but also third-party information. For example, you can automatically pull in data from an external source to enhance your profile of a customer. Now, instead of being limited to your internal data, your system can automatically cull data from other sources in order to provide more intelligence about your customers. That, says Jaekob, becomes a powerful tool for enabling companies to deliver highly customized experiences to every user at every touchpoint.
TD Bank, for example, has implemented AI-driven technology to improve analytics and customer experiences. The company is using AI to merge its internal treasure trove of analytics information with external data sources.
“We’re starting to map non-banking data that shows how customers are interacting on Amazon and how they are using their phones, or how they are communicating on social media,” says Parin Kothari, senior vice president for digital channels and strategy at TD Bank, North America, in a recent interview with Money Summit.
Enterprise-level analytics.
Massive amounts of historical and real time data also require an enterprise-level solution capable of analyzing the information and implementing targeted content solutions that are relevant to each individual customer.
To ensure data integrity and to monitor other systems, your solution needs the ability to monitor itself. One cutting-edge tool that is already in use today is the equivalent of a “virtual analyst” that continuously combs through massive amounts of data looking for statistical anomalies.
A virtual analyst can help ensure that a company’s targeted content aligns with every individual customer’s specific needs. But the benefits of AI extend way beyond marketing and optimizing content delivery. According to Jaekob, a powerful example of this is ensuring overall website functionality. For instance, one company using automated monitoring technology recently discovered that a bug had been introduced into its shopping cart that resulted in a 73 percent decrease in sales conversions from user shopping carts. Items were being removed from the carts, preventing customers from completing their purchases. Automated monitoring detected the problem and facilitated a quick fix, preventing the potential loss of millions of dollars in sales.
Optimizing user experiences.
Optimizing customer experiences requires the ability to deliver relevant, engaging content across all digital interactions using any data source. But equally important is understanding how your content is being consumed. That requires the ability to assess engagement patterns and test delivery options in order to ensure that you are maximizing your conversion rates.
According to the 2017 Digital Marketing Study, 93 percent of all companies surveyed cited the importance of a 360-degree perspective of their customers. That perspective comes from integrating insights from analytics, customer relationship management (CRM), and other data sources.
More companies — across all sectors — are using automation to create personalized content. Mobile automation, in particular, grew 115 percent over last year. At the same time, one-third of the companies surveyed said they plan to focus more on predictive analytics.
The financial services sector reflects the broader industry trends. According to an eConsultancy survey, 33 percent of financial services leaders regard utilizing artificial intelligence and bots to drive campaigns and experiences over the next three years as an “exciting prospect.” While AI is still not a top priority for the financial services sector, 21 percent of those surveyed said they are already using some form of artificial intelligence in customer-facing applications. Looking ahead, potential areas for implementing AI include sales and marketing (67 percent), customer service (57 percent), and product expansion (42 percent).
One example of AI in action is being implemented by Wealthfront. The California-based automated investment service is offering asset management through a service called Direct Indexing. The robo-advisor manages more than $4 billion in assets, and recently added AI capabilities to track account activity on its platform in order to provide a more personalized experience for its customers.
In the insurance sector, Brolly, a UK based start-up, has developed a free personal insurance “concierge,” powered by AI. The company uses an automated approach to providing personalized service for comparing insurance policies. Other insurance applications include the use of of AI to automate the underwriting process and for improving access to “big data” to help inform insurance company decisions.
AI is evolving.
The next wave of AI and machine-learning applications will help financial services companies create smarter products and deliver a higher level of personalization at scale. This has the potential to impact everything from personalization and day-to-day transactions to larger and more complex transactions such as advisory services.
For financial services companies, the key to benefiting from automated systems for reporting and analysis is data integration. Once data is readily available, it will fuel the use of AI to help deliver more personalized customer experiences.
For more insights on how financial institutions are adopting new technologies for more personal customer experiences, read more from our digital marketing FSI Series (need URL link).
Find out more about how Adobe’s Experience Cloud platform and integrated technologies designed to support your experience business. http://www.adobe.com/experience-cloud.html
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