Last updated: 15th May, 2024
Have you ever wondered how your bank decides what to charge you for its services? Or, perhaps how do banks arrive at the pricing (fees, rates, and charges) associated with various banking products? If you’re a product manager, data analyst, or data scientist in the banking industry, you might be aware that these pricing decisions are far from arbitrary. Rather, these pricing decisions are made based on one or more frameworks while leveraging data analytics. They result from intricate pricing strategies, driven by an extensive array of data and sophisticated analytics.
In this blog, we will learn about some popular pricing strategies banks execute to set their prices. From the simplicity of cost-plus pricing to the dynamic world of real-time pricing models, we’ll explore it all, highlighting specific examples of banking products.
Popular Pricing Strategies in the Banking Industry
Here’s a deep dive into pricing strategies for banking products along with associated business drivers and key decisions associated with each of the pricing strategies.
- Cost-Plus Pricing: This is the simplest form of pricing where banks set the price of their product by adding a certain percentage of profit to the cost of creating or sourcing the product. While this ensures coverage of costs and a certain margin, it doesn’t consider market conditions, customer willingness to pay, or competition. Basic checking or savings accounts could be priced using this model, taking into account the cost of maintaining these accounts and adding a margin.
- Business Drivers: Predominantly cost structures, inflation, and overall operational expenses.
- Decisions: The key decision is determining the margin or percentage to be added to the cost to ensure profitability while also maintaining competitiveness.
- Value-Based Pricing: This is a customer-centric pricing strategy where prices are set based on the perceived value of the product or service to the customer. It requires a deep understanding of the customer’s needs, wants, and their perception of value. Banks need to effectively communicate the value their products offer to justify the price point. Premium credit cards that offer high-value rewards programs, travel benefits, insurance, etc., are priced based on the perceived value of these benefits to customers. Advanced analytics techniques including AI/machine learning can be used to determine the most appropriate pricing. Regression models can be very helpful in recommending pricing points based on value.
- Business Drivers: Perceived customer value, differentiation of product/service, customer loyalty, and overall customer satisfaction.
- Decisions: Understanding what customers value and are willing to pay extra for, and determining how to best deliver and communicate this value.
- Market-Oriented Pricing: Here, prices are determined primarily by market conditions, including supply and demand, and the prices offered by competitors. In a highly competitive market, this can often lead to price wars, as banks try to undercut each other to gain market share. Mortgages are typically priced based on market rates. Banks adjust their mortgage rates in response to changes in benchmark interest rates. A bunch of machine learning models can be used for market-oriented pricing.
- Business Drivers: Competition, market demand, market saturation, and economic trends.
- Decisions: Constant monitoring of the market to adjust prices accordingly, and decisions regarding when and how to respond to competitors’ pricing changes.
- Dynamic Pricing: This involves adjusting prices in real-time in response to market and customer behavior changes. With the help of advanced analytics and AI, banks can dynamically adjust their pricing based on various factors such as customer demand, competition, time of day, and even individual customer behavior or credit score. Overdraft fees could be dynamically priced based on a customer’s account activity, relationship with the bank, and the time of the month (e.g., just before payday vs. just after).
- Business Drivers: Real-time market conditions, individual customer behavior, customer demand, and technological capabilities.
- Decisions: Investing in advanced analytics/AI capabilities, defining the rules and thresholds for price adjustments, and ensuring transparency to avoid customer backlash.
- Segmented Pricing: In this approach, banks set different prices for different segments of customers. The segments can be based on various factors like income level, age, geographical location, or risk profile. This allows banks to tailor their pricing to specific customer groups, potentially increasing their reach and profitability. A bank may offer different loan interest rates to different customer segments, based on factors like income level, credit score, and risk profile. Unsupervised learning techniques such as clustering can be used to identify potential customer segments based on different attributes.
- Business Drivers: Customer demographics, behavior, risk profile, and income level.
- Decisions: Identifying meaningful customer segments, setting different price points for each, and managing potential customer dissatisfaction due to price discrepancies.
- Penetration Pricing: This strategy involves setting lower prices to gain market share when a product is first introduced, with the idea of raising prices later once customer loyalty has been secured. When launching a new digital banking service, a bank may initially offer it for free or at a very low price to attract customers, with the intention of increasing the price later. How much price should be lowered can be determined using machine learning models.
- Business Drivers: New product launch, market share objectives, and customer acquisition targets.
- Decisions: Determining the initial low price and the subsequent higher price, and timing of the price increase. Managing customer expectations and potential dissatisfaction when prices rise.
- Freemium Pricing: Some banking services, like certain digital banking or money management tools, are offered for free, with the idea that once customers become engaged with the free service, they’ll be willing to pay for premium features. Many banks offer basic digital banking services for free, while charging for premium features like personalized financial advice, advanced security features, etc. Again, which banking services can be offered free for what time period can be determined using machine learning models.
- Business Drivers: Customer acquisition, product adoption, and upselling/cross-selling opportunities.
- Decisions: Determining which features to offer for free and which to include in the premium version, and developing strategies to convert free users to paying customers.
- Risk-Based Pricing: This strategy is often used in lending, where the price (i.e., interest rate) is based on the borrower’s credit risk. The higher the perceived risk, the higher the price. Personal loans and credit cards are typically priced based on the customer’s credit risk. Higher-risk customers are charged higher interest rates to compensate for the increased risk.
- Business Drivers: Customer credit risk, regulatory requirements, and risk management objectives.
- Decisions: Assessing customer credit risk, setting prices (interest rates) accordingly, and managing potential customer dissatisfaction due to high prices for high-risk customers.
Pricing Analytics driving Key Decisions in Banking
In the prevision section, we delved into some of the common pricing strategies used for different banking products along with examples, business drivers and key decisions. The following represents data and analytics which can be used to drive those key decisions around executing different pricing strategies for banking products. These analytical techniques provide banks with the insights they need to make informed pricing decisions. They help banks understand their customers and the market better, predict future trends, and assess the potential impacts of different pricing strategies.
| Pricing Strategy | Data Driving Decisions | Analytics |
|---|---|---|
| Cost-Plus Pricing | Production costs, overhead, labor, and materials | Cost trend analysis, inflation rate prediction |
| Value-Based Pricing | Customer surveys and feedback, usage data, demographics | Price sensitivity analysis, perceived value modeling |
| Market-Oriented Pricing | Market research data, competitor pricing, supply and demand trends | Competitive analysis, demand forecasting |
| Dynamic Pricing | Real-time customer behavior, purchase history, market demand, competitor pricing | Real-time price optimization using machine learning |
| Segmented Pricing | Customer demographic and behavioral data, customer lifetime value | Customer segmentation using cluster analysis, predictive modeling for willingness to pay |
| Penetration Pricing | Market research data, competitor pricing, customer price sensitivity | Market share forecasting, customer acquisition prediction |
| Freemium Pricing | Usage data for free and premium features, customer conversion data | Conversion rate prediction, churn analysis |
| Risk-Based Pricing | Credit history, loan repayment data, economic trends | Credit risk modeling, predictive analytics for default risk |
Conclusion
Based on the discussion so far, it’s clear that pricing strategies play a pivotal role in shaping a bank’s competitiveness and profitability. From cost-plus to dynamic pricing, each strategy is driven by specific business objectives and requires distinct decisions. Whether it’s determining the right profit margin over costs, adjusting prices based on real-time market conditions, or tailoring prices to specific customer segments, these decisions are crucial for a bank’s success.
Underpinning these pricing strategies and decisions is a wealth of data and advanced analytics. Whether it’s predictive modeling to ascertain perceived value in value-based pricing, machine learning for real-time price optimization in dynamic pricing, or cluster analysis for customer segmentation, the importance of analytics cannot be overstated. These analytics not only drive the pricing decisions but also validate their effectiveness, guiding banks towards more informed and profitable decisions. As we continue to witness the ongoing evolution in the banking industry, it’s evident that those banks that harness the power of pricing analytics effectively will be the ones that thrive in the competitive landscape. If you would like to discuss or learn more, please feel free to reach out.
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I found it very helpful. However the differences are not too understandable for me