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May 13, 2022


  • Pricing innovation and dynamic-pricing strategies can play an instrumental role in changing the pricing equation.
  • Data-driven pricing approach enables banks to react to changes in the environment of a product or service while pursuing certain goals like profit or revenue maximization.
  • A robust data-driven pricing strategy will enable banks to achieve business objectives while strengthening customer relationships.




With spiraling costs and competition, pricing power, or the lack thereof, determines the profitability future of a company or bank.

Pricing innovation and dynamic-pricing strategies based on factors such as demand forecasting, competitor pricing, and decision theory can play an instrumental role in changing the pricing equation.

When it comes to banking products and services, pricing is set primarily through the cost-plus method, which comprises direct and indirect costs (including the cost of funding), an assessment of risk premium, and markup observed in third-party transactions. A few banks have also adopted value-based pricing, especially in segments such as the credit card business. For retail financial products, pricing often follows a market-based approach. This includes analyses regarding customers’ willingness to pay, price sensitivity, and the behavior of the competitors.

The complexities, however, stem from the disruption and innovation in the financial industry, such as open banking, blockchain adoption, data partnerships, and more, across business models, processes, and technologies. Set against this backdrop, strategic data-driven pricing is still not adequately leveraged as a tool for competitive differentiation and achieving business objectives like maximizing topline and improving customer loyalty.



Pricing presents banks with a fair share of both opportunities and challenges.

Here are some of the challenges often encountered:

Regulatory-driven transparency: Increase in regulations and oversight by regulators, which constrains pricing flexibility

Banking services as a commodity: Commoditization of services leads to increased price transparency and prices under strain

Lack of customer contextual awareness: Insufficient understanding and differentiation of client segments

Lack of relevant data: Limited availability of relevant, high-quality internal (historical customer and transaction data) and external datasets (comprising competitor pricing decisions and accurate demand data for products and services)

Inadequate technology platforms: Lack of pricing inputs for relationship managers and other business stakeholders who need pricing insights.




Data-driven pricing is the rapid adjustment of prices to customer context and demand conditions based on real-time internal and external data.

This approach enables banks to react to changes in the environment of a product or service while pursuing certain goals like profit or revenue maximization.

Asynchronous approaches, such as cost-plus and value-based approaches, can lead to inaccurate pricing due to dynamic internal and external environments. On the other hand, a data-driven approach facilitates synchronized dynamic pricing in banking to estimate demand functions and adjust the pricing strategy.

Typically, pricing algorithms encompass a specific approach that begins with collecting and processing historical and current data. Next, the demand function is derived, followed by generating optimal pricing to a predefined goal, such as revenue maximization, within the constraints imposed by the pricing policy. After the optimal pricing is applied for a certain time period, the realized demand is observed, and the cycle is repeated.

Another approach that is more relevant for banking segments such as commercial banking is leveraging a banking dynamic pricing index that considers real-time data points. The dynamic index is then applied to pricing determined through traditional pricing models to arrive at an updated data-driven price. The dynamic pricing index is generated using algorithms that consider internal factors, such as business transaction volumes, credit line utilization and relationship size, and external factors like corporate customer financial news, credit ratings, and quarterly results. 

The price recommendations of the pricing algorithms can be integrated into banks’ operational processes according to different levels of automation and specific business segments.


It is in the banks’ strategic interest to adopt data-driven pricing. Here’s why.

A data-driven pricing strategy offers several benefits, such as:

  • Customer loyalty and stickiness: Increased customer insights can help with better contextual awareness of customer needs, leading to improved customer loyalty and potential re-bundle and cross-sell opportunities.
  • Immediacy: Real-time data (internal and external) allows for dynamic updates and adjustments to pricing. Asynchronous approaches to pricing lead to deviations from demand functions and pricing strategy due to current dynamic environments.
  • Pricing granularity: Pricing strategy can be aligned with the overall business strategy (for example, relationship wins versus revenue maximization) at a micro-segment or individual customer level.
  • Pricing automation: Data-driven pricing can completely or partially automate price adjustments – depending on the business needs. Pricing algorithms evaluate several internal and external factors to generate prices that align with the bank’s pricing strategy


Banks need to rethink their pricing strategy and imbibe learnings from other industries to build and expand businesses at a rapid pace.

For example, the rideshare industry utilizes techniques such as special price differentiation and surge pricing to their benefit for additional revenue generation. 

For banks, the pricing strategy needs to align with the business strategy and commercial objectives. They must take a critical look, assess their current pricing strategy, and then approach in view of the commercial objectives. That said, a pricing method should be adopted along with a specific dynamic pricing technique. Moreover, evaluation of data assets ensures that they are adequate for supporting the specific pricing method selected. If that is not the case, corrective actions should be taken, either by generating the required data assets or adopting the pricing method for available data assets.

To summarize, data-driven pricing presents banks with opportunities for achieving their business objectives while also strengthening customer relationships.

For more information on TCS' Banking, Financial Services, and Insurance (BFSI) unit, visit,, and