Pricing is one of the most important and challenging decisions that retail banks face. Pricing affects not only the profitability and growth of the bank, but also the satisfaction and loyalty of the customers. However, many banks still rely on traditional and simplistic approaches to pricing, such as cost-plus pricing, competitor-based pricing, or one-size-fits-all pricing. These approaches often fail to capture the true value that customers perceive and receive from the bank’s products and services, and may result in leaving money on the table or losing customers to competitors.
In this blog post, we will explore how analytics can help retail banks optimize their card and loan pricing strategies, by using data and advanced techniques to segment their customers, assess their price sensitivity, design personalized offers, and monitor the impact of pricing changes on profitability and customer behaviour. We will also provide some examples and best practices from leading banks that have successfully implemented analytics-driven pricing strategies.
Customer Segmentation
The first step in analytics-driven pricing is to segment the customers based on their characteristics, preferences, needs, and behaviors. Customer segmentation allows banks to identify different customer groups that have different value propositions, expectations, and willingness to pay for the bank’s products and services. For example, a bank may segment its credit card customers based on their spending patterns, credit scores, rewards preferences, channel usage, etc.
Customer segmentation can be done using various analytical methods, such as cluster analysis, decision trees, or machine learning algorithms. These methods can help banks discover hidden patterns and relationships in the customer data, and group customers into homogeneous segments that are distinct from each other. For example, a bank may use cluster analysis to segment its mortgage customers based on their income, age, loan amount, loan-to-value ratio, interest rate type, etc.
Customer segmentation can help banks tailor their pricing strategies to each customer segment, by offering different product features, prices, fees, discounts, incentives, etc. that match the segment’s value perception and willingness to pay. For example, a bank may offer a lower interest rate or a higher credit limit to a credit card segment that has a high credit score and a low default risk.
Price Sensitivity Analysis.
The second step in analytics-driven pricing is to analyze the price sensitivity of each customer segment. Price sensitivity refers to how customers react to changes in prices or fees for the bank’s products and services. Price sensitivity can be measured by various indicators, such as price elasticity of demand (PED), reservation price (RP), or net promoter score (NPS).
Price elasticity of demand measures the percentage change in the quantity demanded of a product or service due to a percentage change in its price. For example, if the PED for a credit card segment is -0.5, it means that a 10% increase in the annual fee will result in a 5% decrease in the number of customers who keep using the card. A high PED indicates that customers are very sensitive to price changes and may switch to competitors or alternative products if prices increase. A low PED indicates that customers are less sensitive to price changes and may remain loyal or indifferent if prices increase.
Reservation price measures the maximum price that a customer is willing to pay for a product or service. For example, if the RP for a mortgage segment is 4%, it means that customers in this segment will not accept any interest rate higher than 4% for their mortgage loans. A high RP indicates that customers have a high willingness to pay and may be willing to pay more for additional features or benefits. A low RP indicates that customers have a low willingness to pay and may be looking for the lowest possible price.
Net promoter score measures the likelihood that a customer will recommend a product or service to others. For example, if the NPS for a checking account segment is 50%, it means that 50% more customers in this segment are promoters (who give a rating of 9 or 10 out of 10) than detractors (who give a rating of 0 to 6 out of 10). A high NPS indicates that customers are very satisfied with the product or service and may be willing to pay more for it. A low NPS indicates that customers are dissatisfied with the product or service and may be looking for alternatives.
Price sensitivity analysis can be done using various analytical methods, such as conjoint analysis, discrete choice experiments, or regression analysis. These methods can help banks estimate how customers value different product attributes (such as interest rate, annual fee, rewards program, etc.) and how they trade off among them when making purchase decisions. For example, a bank may use conjoint analysis to estimate how much customers are willing to pay for an extra 1% cash back on their credit card purchases.
Price sensitivity analysis can help banks optimize their prices and fees for each customer segment, by finding the optimal balance between revenue and volume. For example, a bank may increase the annual fee for a credit card segment that has a low PED and a high RP, while offering a higher cash back rate to attract and retain customers. Alternatively, a bank may lower the interest rate for a mortgage segment that has a high PED and a low RP, while charging a higher origination fee to cover the costs
Personalized Offer Design
The third step in analytics-driven pricing is to design personalized offers for each customer or customer segment, based on their price sensitivity and value perception. Personalized offers are customized proposals that offer different product features, prices, fees, discounts, incentives, etc. that match the customer’s preferences and needs. For example, a bank may offer a personalized credit card offer to a customer based on their spending behavior, credit score, rewards preference, etc.
Personalized offer design can be done using various analytical methods, such as optimization models, machine learning algorithms, or recommender systems. These methods can help banks find the best combination of product attributes that maximize the customer’s utility or satisfaction, subject to constraints such as budget, capacity, regulations, etc. For example, a bank may use an optimization model to find the best interest rate and loan amount for a mortgage customer that maximizes their expected profit, subject to their credit risk and capital requirements. Personalized offer design can help banks increase their conversion rates and customer loyalty, by offering relevant and attractive products and services that meet or exceed the customer’s expectations. For example, a bank may increase the likelihood of a customer accepting a checking account offer by offering a personalized overdraft protection feature based on their transaction history and cash flow.
If you talk about it, it’s a Dream, if you Envision it, it’s a Possible, but if you are Schedule it, it’s Real.
Tony Robbins
Pricing Impact Monitoring
The fourth step in analytics-driven pricing is to monitor the impact of pricing changes on profitability and customer behavior. Pricing impact monitoring is the process of measuring and evaluating the outcomes and effects of pricing decisions, such as changes in revenue, margin, volume, market share, retention, acquisition, churn, satisfaction, etc. For example, a bank may monitor the impact of a price increase on its credit card portfolio by tracking the changes in its revenue per account, attrition rate, usage rate, etc.
Pricing impact monitoring can be done using various analytical methods, such as experimentation, causal inference, or dashboarding. These methods can help banks test and validate their pricing hypotheses, identify and isolate the causal effects of pricing changes from other factors, and visualize and communicate the key performance indicators and trends. For example, a bank may use experimentation to test the effect of different pricing strategies on different customer segments by randomly assigning them to different treatment groups and comparing their outcomes. Pricing impact monitoring can help banks learn from their pricing actions and improve their pricing performance over time. By monitoring the impact of pricing changes on profitability and customer behavior, banks can assess whether they have achieved their pricing objectives, identify any unintended consequences or opportunities, and adjust their pricing strategies accordingly. For example, a bank may learn from its pricing impact monitoring that a price decrease for its checking account product has increased its market share but also increased its cost of servicing and reduced its cross-sell potential
Conclusion
Analytics can help retail banks optimize their card and loan pricing strategies, by using data and advanced techniques to segment their customers, assess their price sensitivity, design personalized offers, and monitor the impact of pricing changes on profitability and customer behavior. By adopting analytics-driven pricing strategies, banks can capture more value from their customers, enhance their competitive advantage, and improve their customer satisfaction and loyalty.
We hope that this blog post has given you some insights and inspiration on how to use analytics to optimize your card and loan pricing strategies. If you have any questions or comments, please feel free to share them below. Thank you for reading!
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