A radical transformation is taking place in the banking industry that involves investing and adopting modern, complex technologies. One of the key objectives is to deliver a much better customer experience.
In Banking, data applications are a crucial resource, and by leveraging new technologies, such as machine learning and artificial intelligence, data can be collated and interpreted in real time and at a level of complexity only imagined a few years ago. This provides a highly valuable analysis of customer behavior, communications and interfaces. It also identifies which products and services are in demand, and which are likely to be in demand in the future.
The 360 Objective
The objective is to create a single, integrated view of all customer activity, tracking it in an omni-channel approach and making critical data available to key stakeholders. By collecting, tabulating and analyzing data from a myriad of sources, banks can create in-depth and panoramic views of the customer, and by applying predictive analytics, can predict future customer behavior, preferences, choices, likes and dislikes.
With such a highly competitive market, this data helps to build loyal customer relationships for a higher rate of customer retention and increased revenue growth.
In the banking sector, analytics can greatly improve the customer experience if used efficiently to create detailed customer relationship and satisfaction heat maps. This greatly enhances the future accuracy of buyer profiles aligned with customer needs.
Banks can now go from being reactive to proactive by discovering issues, concerns, and opportunities in advance, which helps them to better strategize and to avoid customer churn. With predictive analytics, insights on key business parameters like market trends, revenue, costs, customer relationships, and risk factors become streamlined for a better customer experience.
Benefits of the 360 view
The more complete and more accurate the view of the customer, the more effectively products and services can be developed to maximize customer satisfaction. By having the ability to predict behaviors and buying propensities, banks can leverage this data for the following:
Sales and Marketing:
Detailed knowledge of the customer helps banks streamline their existing products to maximize engagement and customer loyalty while also keeping down costs. In order to create new products, services, and customer experiences, banks can track and analyze customer expectation and external factors while testing out marketing strategies and channels for effectiveness. Banks can use predictive analytics to adjust their marketing and sales strategies to find the best potential cross-selling products. In short, behavioral analytics can supercharge a bank’s marketing efforts.
Customer Satisfaction and Loyalty:
Customers expect fast and more personalized services and products, and banks in particular need more cost-effective ways to empower and enable customer-facing employees with data that provides actionable insights. With a panoramic, accurate and real-time 360 view of the customer, banks can anticipate customer wants and needs, and identify and create the next portfolio of services and products. This enhances customer satisfaction, reduces churn and helps to grow revenue.
Customer Lifetime Value:
Customer lifetime value indicates the total revenue a business can reasonably expect from a single customer account throughout the business relationship. The metric considers a customer’s revenue value and compares that number to the company’s predicted customer lifespan. With happy customers, churn rate reduces and lifetime revenue at an individual customer level increases. Banks provide customers with what they need and customers reciprocate by staying loyal, a win-win relationship.
Selection and Collection:
Using predictive behavioral analysis, banks can identify customers who are eligible for specific services like credit cards and loans. This is called customer selection, and it works by identifying those who are of less risk as well as those who are high risk. This allows for streamlining the collection process and gives banks a method to easily identify high-risk customers so that specific policies can be applied to mitigate risk.
Since banks are always at risk of fraud, it’s important to detect and prevent fraud rather than deal with it after it happens. Behavioral analytics provides fraud detection tools to leverage the power of machine learning and AI to detect usage patterns that might be consistent with fraud. This significantly helps detect real cases of fraud while cutting down on false positives. Banks are able to stay updated about new methods of fraud, create fraud prevention models that are process agnostic, while creating early warning systems that can cut down exposure and risk. This protects banks and customers from becoming victims of fraudulent actors.
Know Your Customers:
Acquire and retain more profitable customers with better targeting, prediction of future buying behavior, and understand which customers are most likely to buy. Build on overall profitability by customer, segment, and geography with profile heatmaps.
Opportunity to Optimize Marketing Spend:
When a 360 view of the customer is achieved, marketing and sales strategies can be finely tuned to target various demographics based on their individual needs and requirements. This reduces a lot of stop and start, and resetting and recalibrating of campaigns.
Like all transformation journeys there may be roadblocks. To achieve a successful 360-degree customer view, banks must understand the foundational role that data plays in driving a more customer-centric approach, and promote and encourage a data-driven culture.
There are potential impediments such as:
- Large volumes of data spread across multiple sources and buried in silos.
- Data duplication: The same records reside across multiple applications and databases.
- How data is and can be shared, how it’s stored, data formatting, how available it is, and how it’s visualized.
- Legacy on-premise based data architectures can further complicate getting to the single version of the truth.
- Static dashboards for marketing, sales and product teams, and limited buyer persona profiles that fail to understand customer buying patterns as well as factors driving influence.
- Outdated governance structure across multiple functions creates more work across teams and lacks centralized cohesion.
The Future Becomes Predictable
Predictive analytics continues to emerge as a perfect solution, to provide banks with a complete picture of customer engagement with their products and services, which can influence and drive outcomes, conversion, retention and revenue. Banks must consolidate the key data components to eliminate duplicate efforts and obtain a single view across products, locations, assets and most importantly, customers.