Faced with increased competition and lower profits margins on traditional services, banks have to look for other ways to generate profits. Here are four proven ideas to draw in more customers and more revenue.
Profit margins are being squeezed by the financial tech revolution, start-ups, more ways to access banking services online or mobile, fewer customer visits to branch locations and low-interest rates. Banks are under immense pressure to generate income by selling more products and services to existing customers. This provides a host of opportunities. Customers are telling banks HOW to beat the squeeze. Whether actively or passively, they are generating data clues that show how to bring them back. The right computer algorithms will let banks decode these clues to create a nimble and proactive sales system.
HOW TO DO IT IN 4 WAYS
Don’t tell a customer, “Here is a something to think about” or “Can we set up a free account for you?”
Approach this as a customer. Visit a branch office. Listen to what the teller says and how they act. Likely you'll get a standard sales pitch along the lines of:
"Why don't you open a savings account?” or “You are eligible for X service/account.”
This is how tellers and customer service reps are trained. It's based on a time-tested algorithm to reach branch level sales targets. It's also outdated. With today's smartphones and mobile tech, customers already know everything the customer service rep is talking about.
The problem is the tradition. The branch sales methods and targets are so deeply rooted that often they outweigh what customers really want. Yes, they may open a few accounts, but do customers use these products? Do the sales efforts help generate more deposits or loans or they are just redistributing funds? Think about how these sales tactics affect a customer's trust in the bank.
More focus must go on cross-selling to improve value and improve margins and not just hitting the sales quota.
Learn the explicit and implicit needs of customers to empower front-line staff to sell more effectively.
Gone are the days when banks constantly asked for direct feedback from customers. Traditional surveys have useful information to help banks face the challenges of today.
Instead, use every transaction and interaction as a way to learn what customers think, what they want and how to predict what they will want today and tomorrow. This is where next-generation cognitive science-based models help detect the clues, integrate them into the overall customer knowledge base, and recommend just the right product and services. Integrating these tools at the branch level can empower the account managers and tellers to put customer needs first while producing double-digit growth in new accounts.
Don't let the loyalty metrics fool you!
A bank created a data lake, big data (hadoop) clusters and ran Spark-based models to find drivers of NPS. They believed they cracked the code for loyalty drivers. They identified customers (advocates) most likely to respond and started cross-selling. However, after just a few months, the bank realized the switching rate and the number of inactive accounts were steadily increased among these advocates.
While we solved the puzzle through the forensic-based algorithm, the lesson learned is, just because they say they are likely to recommend does not translate into loyalty or recommendations.
Even a simple offer of iPad mini from the competition can lure these so called advocates away.
Don't get fixated on using big data as real-time actionable
A bank recently invested millions of dollars in building huge data lake, the library of Spark-based models and prepared itself to ride on the bandwagon of "real-time." However, after 11 months of going live, the bank did not see the expected substantial conversion of leads to sales, opening more active accounts, and increasing customer engagement.
The primary reason for such lackluster results is applying traditional approaches to understanding complex and irrational consumer behaviors.
Being real-time has value but right-time produces far better results compared to real-time. For example, we implemented an AI-based learning module on top of existing algorithms at the branch level. The AI learned from every interaction.
For example, when a customer talks about his family planning a vacation end of the year the teller was able to quickly plug few details in the algorithm and update the CLV. The service rep was able to immediately offer the customer a product to complement the vacation planning. The entire eco-system using artificial intelligence produced measurable results that justified returns on analytics. Such eco-system enabled bank to realize these bottom-line impacts:
Among pilot banks, just in two months, the bank generated 29% lift in new core deposits
Sold 18,455 new products and services to existing customers
Received 4% lift in customer referrals
Banks have a tremendous opportunity to cross-sell but need an adaptable system to cross-sell, not for the sake of hitting the numbers but to creating value. That will eventually improve margins.
To find out how you can compete in a digital world, please contact :
Director, Client Success Dextro Analytics Inc.
+1 647 273 2309 (Canada)
+1 206 460 1800 (USA)