In these days of the big data revolution, multiple ways exist in which financial institutions can use analytics to support strategy and drive its execution. Statistics show approximately 90% of senior bank executives believe in the power of big data, and its potential impact on growth and profitability over the next five years. To achieve this, however, institutions need to develop a clear understanding of how to use advanced analytics tools to align with client interactions, anticipate needs, and make decision processes simpler.
Getting the Most Out of Your Analytics
Retail financial institutions typically have narrow margins, which forces them to run lean, efficient operations. Analytics can help them to understand issues such as:
- Most profitable customer segments, with identifying characteristics
- Their geographic locations
- Percentage of market share occupied by them
- Most frequent needs and wants
- Communication channels most often accessed
- Most effective marketing actions
- Average lifetime value
The availability of rich, real-time data in the form of numbers, text, voice and images now exists for practically every customer interaction, every product and service financial institutions offer, and the processes they use to deliver them, according to McKinsey & Company.
Making Your Analytics Work for You
Set your financial institution up for success by using advanced analytics to identify the real challenges facing the company. Perhaps you have a large market share, but lag behind competitors in products per customer? Find out who your clients are, what products they hold, examine their credit card statements, transactions and point-of-sale data. Review their online and mobile transfers and payments, and map this against their credit scores. By doing so, it’s possible to determine key characteristics that define microsegments in your customer base.
Ensure the data you collect is high-quality information, and the input is done correctly. Use a specialized data model and reporting solution like JOHO OneSource™ to analyze your intel and “connect the dots.” This will enable you to identify insights from different data sets and build a complete picture.
Use the data to inform your product development. For example, knowing what the next product a microsegment is likely to buy enables you to produce a targeted product with a higher chance of success.
Putting Theory into Practice
Some ways financial institutions are making analytics work for them could give you inspiration for using them in your own operations. For example:
A bank in Europe was experiencing significant customer churn, and implemented various retention techniques aimed at making inactive customers active again. By using machine learning algorithms based on big data to predict which active clients were more likely to reduce their interactions with the bank, it became possible to target them with a campaign at an earlier stage before they became inactive. The level of churn was reduced by 15% as a result.
A U.S.-based institution reviewed the discounts private bankers were giving clients and found patterns of unnecessary concessions. By implementing a number of policy changes the bank’s revenue increased by 8% in a few months.
These are completely opposite examples, with one being a cost saving and the other a revenue-generating action, but both would be impossible without insights delivered by analytics data.