The groundbreaking success of Netflix, Spotify and Co. boils down to the same fundamental approach, namely intelligent data analysis and enrichment using relevant additional information. Smart data is the key to unleashing the potential of personalization, enabling providers to create real surprises and happy moments for users on a regular basis. This way, they serve as a model for entire industries. After all, who doesn’t dream of having enthusiastic customers?
So, how do you create such customer experiences in banking? How can data-driven personalization work at the highest level in the financial sector? How does this quality of data analysis and enrichment work in banking? In this blog post, we discuss what banks need to achieve personalization at the level of Netflix and Co., where they currently stand and what they still lack.
Personalizing the banking experience: Where are the greatest opportunities for banks? Learn more in our expert interview with Irina Lahtadire, our specialist for data-driven personalization strategies.
The Status Quo of Data-Driven Banking: Solid But With Room for Improvement
Banks have impressive data sources that are suitable for many use cases. However, data practices in day-to-day banking tend to be narrowly defined and geared towards regulation. Data collection and security have long been a priority for banks, the majority of which have formal systems for data security, protection and compliance.
The situation often looks rather different when it comes to transaction data that is clean and can be used for genuine personalization in banking. Transaction information in particular provides a wealth of customer insights, which, when properly prepared, create new use cases for banks and personalized banking experiences for bank customers. A recent study conducted by the Business Engineering Institute St. Gallen examined how Swiss banks view this topic.
Given that banks are generally data-conscious, they do have a head start in data analytics and enrichment. Many internal data analysts already work with modern tools, although the usability and sophistication of these tools varies. Most banks still heavily rely on simple descriptive and prescriptive models and historical transaction data. More sophisticated technologies, such as real-time predictive analytics enabled by machine learning, are still in the planning stages in many places.
How Much Data-Driven Personalization Is Needed in Banking to Inspire Customers?
First of all, what banks are striving for today with this data-driven approach has long been expected by customers. Bank customers see no reason why banking offers should not be tailored to their personal challenges or needs. They want financial solutions that fit seamlessly and naturally into their daily lives.
Banks must respond urgently. Data processes should be streamlined to ensure that there is a sufficient amount of available transaction data necessary for personalization. In addition, this data must be correct, clean and usable if it is to be utilized in a targeted manner. In short, only a very good database can create excellent customer experiences.
1. Correct transaction data is the beginning of everything
Accurate transaction data is the logical prerequisite for the sensible use of data. What is immediately obvious is actually not that easy to implement. In order to be successful in this respect, banks have to rethink tools, platforms and methods and are increasingly turning to strategic partners. By the way, the Open Banking movement shows to what extent cooperation with FinTech specialists is often the fastest and most elegant solution.
2. Clean transaction data makes big data usable
The more the better, right? Big data is a great starting point for data analysis and data processing in order to provide real personalization based on transaction data. However, without excellent selection and analysis of transaction data, big data is a dangerous shortcut to chaos. When it comes to data, relevance is paramount! To achieve personalization, banks must have accurate and exclusive information that they can use in a meaningful way. Anything else leads to confusing and therefore slow and expensive databases that can no longer be used efficiently.
3. Enriched transaction data offers highly individualized information
What does clean and relevant data mean for banks? In a nutshell, it means that they have access to transaction data records that are enriched, meaningfully categorized and thus directly usable. Once this preliminary work is complete, banks can extract a large amount of information for personalization purposes, including customers’ interests and personal characteristics. However, the insights for banks do not stop there: spending and saving behavior, products from third-party banks, geoinformation, payment methods, product-specific information as well as the detection of transaction patterns and predicted behavior can also be recognized.
4. Only targeted data enables banks to help customers
To be able to work with their customers in view, banks need a target for the use of data. Even finely categorized data is worthless if no objective is pursued. By contrast, high-quality bank data, such as exact geodata, can be utilized to identify categories such as lifestyle or local interests and provide bank customers with appropriate tailor-made offers. The situation is similar with information on chargebacks or liquidity planning for business customers. Based on this data, banks can identify risk at an early stage, intervene in a supportive manner and protect customers from financial traps.
Data Analytics & Enrichment: The Road to the Data that Makes Personal Banking Excel
Step One: Various sources
User data is in many ways perhaps the greatest treasure of banks. On the one hand, it must be extracted completely and correctly. On the other hand, the wealth of material does not exclude the possibility of deriving valuable insights from information originating from various places outside the organization. By aggregating transaction information from core banking, issuer APIs, PSD2 aggregators and other parties that exchange data, banks are able to create a largely complete picture of their customers, serving as the basis for understanding them in great detail.
Step Two: Automatic enrichment
Banks that are already at the forefront of data analysis have strategies and tools for interpreting data to provide relevant real-time insights for the company. Using new technologies, automatic enrichment feeds this data strategy by enriching each transaction with individual categories and metadata. This ultimately brings them one step closer to customers.
Step Three: Intelligent shared learning
Shared learning refines and improves this categorization of transaction data. The principle also works across banks and involves millions of transactions. Speaking of shared learning, banks should democratize their data internally as much as possible. This means that open but secure access is guaranteed for all those who are entitled to access the data and who will play a role in optimizing it.
Step Four: User-generated enrichment
The glossy finish is provided by user-generated enrichment, otherwise known as the feedback loop. Customer feedback can be optionally used to gather authentic, individual insights and take personalization in banking to the next level. Integration and customization based on individual customer feedback really gives “personal banking” a whole new meaning!