Product Offer and Pricing Personalization in Retail Banking

Introduction

Over the past decade, retail banks have leveraged advanced analytics to power pricing and campaign optimization strategies in deposits. These models collected and analyzed historic data, competitors' pricing, economic conditions, demand elasticities, and other factors to optimize the pricing and marketing of deposit products to consumers. However, these efforts were applied to single-product lines and conducted in silos without consideration for the customer's broader relationship with the bank. Deposit marketing campaigns often had myopic objectives that sometimes ran counter to larger institutional goals, and a broad stroke approach meant marketing-yield efficiency were usually very low.

The rise of parametric (e.g., classic regression and optimization) and non-parametric modeling (e.g. machine learning, multi-armed bandits, etc.) capabilities has opened up new possibilities for banks to shift from this fragmented, product-centric approach towards orchestrated one-to-one marketing where the bank is able to automate and tailor communications, pricing and products offers to speak directly to their customers in context-appropriate, relevant ways. Developed using internal and external data, these models can consider a customer's wider relationship with the bank, to personalize next-best offers, adapt messages based on reactions and feedback, and nudge customers towards desired actions with high-levels of accuracy and relevance.

Several types of parametric and non-parametric models are needed to support one-to-one marketing orchestration capabilities. These include flow of funds, response models, and customer lifetime measurement and segmentation clustering models. Data on product usage and transaction patterns can support customer segmentation models that anticipate and predict future customer needs to trigger up-selling and other tailored communications, while behavioral science can refine machine-learning models to deliver nudges and improve decision making.

Additionally, intangible objectives like financial well-being, customer lifetime value and customer satisfaction must be made measurable and tangible. A dynamic score, similar to a FICO metric, to measure a customer's financial well-being and reflect how well the customer is managing his/her finances based on banking behaviors and transactions, should be considered.

The transition from a marketing and pricing organization that executed blanket marketing campaigns to one capable of delivering the right message to the right customer at the right time for the right product through the right channel, is a multistep and complex undertaking. For banks, there are challenges at the analytical and organizational levels.

Ideally price setting should be integrated with marketing. However, many banks separate these functions where pricing decisions for savings, certificate-of-deposits, mortgages and other retail banking products are made by the bank's treasurer responsible for managing the bank's daily cash flow and liquidity-of-funds to meet regulatory, operational, financial, and risk requirements. For larger banks, a product promotion campaign might also require the involvement of multiple departments. The result are teams operating with fragmented views at the product level, as well as at the marketing execution and pricing level. The barriers preventing a bank from greater personalization in marketing and customer communications, are often related to its organizational structure and not because the bank lacks analytical capabilities.

One-to-one marketing orchestration can be challenging for organizations with segregated teams, inadequate systems and untargeted marketing. Any project must begin with an assessment to identify gaps and maturity along organizational, conceptual and implementation dimensions to ensure the requisite capabilities including internal collaboration, data and analytics capabilities and transformation processes are present.

A well-executed one-to-one marketing strategy can deliver many benefits to a retail banking including higher customer retention, bigger share of wallet, sales profitability per customer and customer satisfaction. Personalization also directly impacts sales growth, marketing efficiency and profitability.