Article | December 10, 2020
Customers in the financial services industry want personalized experiences. They, in fact, expect and demand them from their service providers. They prefer to stay loyal to a company as long as they receive this special treatment. As a result, personalization has become the number one priority for marketers in the industry today. They are waking up to the realization that delivering personalized experiences highly depend on understanding customer data.marke
Very few companies have the means to understand this data and use it to enrich the customer experience. The technology that has been recently making waves in every industry is known as the Customer Data Platform (CDP). A CDP is a packaged SaaS (software-as-a-service) product that is designed to build a unified customer database for an organization. Implementing a CDP can help achieve consistent customer engagement, increased loyalty, and higher sales.
David Raab, CDP evangelist and Founder of the CDP Institute, was invited as a chief guest at the Customer Data Summit 2018 event organized by Lemnisk.
David is a widely recognized thought leader in marketing technology and analytics. He was one of the first people to recognize that digital marketing systems were not just proliferating but also the data that these systems were throwing up were getting grouped into silos, making it really hard for marketers to understand customers holistically.
David also realized that there was a tremendous opportunity if he could bring these disparate systems together. Around this insight, David coined the term CDP and founded his institute in 2016. The CDP Institute’s work has been seminal in helping marketers understand the need for a CDP and the ways that they can derive value from it.
David’s thought-leadership session imparted the following key insights:
The most challenging barrier to Marketing Automation success is data integration between the various marketing systems of an organization. Financial marketers in Asia face the same challenges as their peers elsewhere, which include unifying customer data, providing superior customer experience, working within compliance constraints, and finding the budget to pay for solutions.
The CDP industry has seen a good growth rate of around 73% over the last 12 months. Two-thirds of the growth is attributed to new vendors and the remaining to existing vendors. The adoption rate has been high for B2C marketers as their businesses depend highly on user engagement and digital conversions. Companies that opt for a CDP prefer to have a complete packaged solution that includes the core CDP functionality along with analytics and engagement.
A CDP works well when all marketing systems are interconnected. One interesting observation is that one-third of CDP users lack an integrated technology stack. Companies that claim to have a CDP do not have this system integration and, therefore, do not fall under the CDP-classified vendors.
Things such as churn prediction and predictive modeling are a set of classic algorithms that thrive on good data. Artificial Intelligence (AI) is totally data-driven and works well with data that is highly detailed. A CDP can play a major role in developing custom algorithms and advanced intelligent systems such as AI. One of the things that it can do is create a standardized variable or model score and make that shareable to all systems that it connects to.
Of its various capabilities, a CDP also enables cross-device personalization by associating each device with the customer’s master ID when they log in. The master ID is used to build a unified customer profile with all device data. The right message for each master ID is selected and shared with all devices. The unified and complete customer profiles help financial marketers in selecting the right message and deliver a consistent experience across all devices.
It is still early days for a CDP in Asia. Many organizations are still at the stage of learning for themselves why options such as DMP (Data Management Platforms), Enterprise Data Warehouses, and marketing clouds won’t solve the problem that a CDP addresses. The core technologies used in Asian financial institutions can support any level of marketing sophistication that their users are ready to deploy. The early CDP adopters are touted to have an advantage over others in the industry.
Article | March 20, 2020
Fintech has drastically improved the products and the services of the traditional financial services in the past few years. However, even after many financial institutions have readily adopted fintech services, there are still some hidden risks in the aforementioned industry. For instance, the integration of the fintech services in the existing banking solutions raised a severe concern for data security. Also, the rapid growth of digital platforms made the fintech industry and its customers uniquely vulnerable to various breaches in IT security networks.
Article | April 20, 2020
Here are ten steps to defining a data strategy based on a data capability maturity assessment, for a Financial Institution, Identify and Simplify Maturity models, and customize the yardstick as well as benchmarks based on local study and future organization strategy. Conduct workshop with leaders and grassroots to sensitize the assessment and questionnaires.
Article | April 13, 2021
Over the past decades, banks have been improving their ways of interacting with customers. They have tailored modern technology to the specific character of their work. For example, in the 1960s, the first ATMs appeared, and ten years later, there were already cards for payment. At the beginning of our century, users learned about round-the-clock online banking, and in 2010, they heard about mobile banking. But the development of the financial system didn’t stop there, as the digital age is opening up new opportunities — the use of Artificial Intelligence. By 2023, banks are projected to save $447 billion by applying AI apps. We will tell you how financial institutions are making use of this technology in their operations today.
Chatbots are AI-enabled conversational interfaces. This is one of the most popular cases of applying AI in banking. Bots communicate with thousands of customers on behalf of the bank without requiring large expenses. Researchers have estimated that financial institutions save four minutes for each communication that the chatbot handles.
Since customers use mobile apps to carry out monetary transactions, banks embed chatbot services in them. This makes it possible to attract users’ attention and create a brand that is recognizable in the market.
For example, Bank of America launched a chatbot that sends users notifications, informs them about their balances, makes recommendations for saving money, provides updates to credit reports, and so on. This is the way the bank helps its clients to make informed decisions.
Another example is the launch of the Ceba chatbot, which brought great success to the Australian Commonwealth Bank. With its help, about half a million customers were able to solve more than two hundred banking issues: activate their cards, check account balances, withdraw cash, etc.
AI functionality in mobile apps is becoming more proactive, personalized, and advanced. For example, Royal Bank of Canada has included Siri in its iOS app. Now, to send money to another card, it’s enough to say something like: "Hey, Siri, send $30 to Lisa!" - and confirm the transaction using Touch ID.
Thanks to AI, banks generate 66% more revenue from mobile banking users than when customers visit branches. Banking organizations are paying close attention to this technology to improve their quality of services and remain competitive in the market.
Data collection and analysis
Banking institutions record millions of business transactions every day. The volume of information generated by banks is enormous, so its collection and registration turn into an overwhelming task for employees. Structuring and recording this data is impossible until there is a plan for its use. Therefore, determining the relationship between the collected data is challenging, especially when a bank has thousands of clients.
There used to be the following approach: a client came to a meeting with a bank employee who knew their name and financial history and understood what options were better to offer. But that's history now. With the wealth of data coming from countless transactions, banks are trying to implement innovative business ideas and risk management solutions.
AI-based apps collect and analyze data. This improves the user experience. The information can be used for granting loans or detecting fraud. Companies that estimated their profit from Big Data analysis have reported an average increase in revenue by 8% and a reduction in costs by 10%.
Extension of credit is quite a challenging task for bankers. If a bank gives money to insolvent customers, it can get into difficulties. If a borrower loses a stable income, this leads to default. According to statistics, in 2020, credit card delinquencies in the U.S. rose by 1.4% within six months.
AI-powered systems can appraise customer credit histories more accurately to avoid this level of default. Mobile banking apps track financial transactions and analyze user data. This helps banks anticipate the risks associated with issuing loans, such as customer insolvency or the threat of fraud.
According to the Federal Trade Commission report for 2020, credit card fraud is the most common type of personal data theft.
AI-based systems are effective against malefactors. The programs analyze customer behavior, location, and financial habits and trigger a security mechanism if they detect any unusual activity. ABI Research estimates that spending on AI and cybersecurity analytics will amount to $96 billion by the end of 2021.
Amazon has already acquired harvest.AI - an AI cyber security startup - and launched Macie - a service that applies Machine Learning to detect, sort, and structure data in S3 cloud storage.