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Fair Pricing with Price Optimisation

Blog: Enterprise Decision Management Blog

Analytics graphic with words FAIR PRICE

Pricing fairness is coming under a higher degree of regulatory scrutiny in the UK than ever before. The UK government announced on 4th November 2018 that it is launching an investigation into personalized pricing practices in online retail, following growing concerns that vulnerable consumers are at risk of price discrimination through the use of ecommerce technologies. This follows the publication of a discussion paper by the Financial Conduct Authority on 31st October 2018, on the fairness of certain pricing practices in financial services.

The FCA are focusing their debate on price discrimination and pricing inertia. They are looking into practices where firms charge different prices to different consumers based solely on differences in consumers’ price sensitivity (‘price discrimination’), and on practices where firms charge existing customers higher prices than new customers (‘inertia pricing’ or ‘loyalty pricing’).

Key areas of concern for the FCA are:

The FCA has set 6 “fair treatment” outcomes that financial services providers should strive to achieve. Two are particularly relevant to pricing:

The approach to pricing therefore needs to be easily explainable. It should take into account, as much as possible, who the customer is and what their needs are. It should be defensible as approached fairly and without discrimination across the customer base.

Why Price Optimisation Is Needed

Some people believe that the application of price optimisation models takes advantage of price-insensitive customers and raises their pricing in order to meet profit goals, leaving institutions exposed to conduct risk.

In fact, the application of a powerful pricing engine is more necessary than ever before. In fact, it would be difficult to make fair pricing decisions without this technology. Where the challenges arise on conduct risk in pricing, the models and tools used for optimisation can be your best chance to find the right balance and the right solution for your customers, the regulators, and your board.

Pricing optimisation can address the need for auditable pricing controls and additional segmentation for vulnerable customer groups. It can balance the growing number of pricing and operational constraints needed to meet fairness rules, and factor in the increasing number of attributes needed to make pricing decisions, such as customer tenure, depth and breadth of customer relationship, a growing competitive market, and the continued need to meet strategic objectives and balance sheet targets.

Optimisation chart

Achieving financial growth is not as simple as a price-to-profit calculation. With the sharper focus from the regulators, and customers focused on personal service, banks that provide the best customer experience and sustain their customer relationships will be the winners in the end. That means developing a product pricing strategy with the customer at the heart of every decision.

In addition, to address customer inertia will require more frequent and targeted re-pricing of the existing portfolio (the back-book) as well as new customers (the front-book), in a holistic manner, which will require more time and effort from pricing teams. Time and effort which can be largely reduced with use of the right tools.

It’s Not All About Profit

Price optimisation solves the complex problem of balancing lots of customer behaviours and sensitivities against business rules and constraints to find the best answer to meet any mathematically computable objective function. Not only that, but the best optimisation solvers are both powerful and fast, and can present many alternative potential pricing scenarios with their forecast impact at a granular customer segment level, in seconds.

Its value derives in part from its complexity, which balances financial or business targets against other dimensions:

Price optimisation does not necessarily mean higher interest rates for customers of credit lending products. Volume-neutral scenarios typically involve increasing rates for some segments while decreasing rates for others; indeed, volume-aggressive scenarios could in theory involve rate reductions across the board.

Advancing Multiple Strategic Objectives

Any financial services organization’s strategic direction is likely to vary over time, and objectives change. Having a sophisticated data-driven pricing tool can help achieve the given objective. Such objectives may include:

Whatever the objective, being able to determine the impact of any recommended strategy, across all customer segments, is key to success in setting the right goals, achieving those goals, and ensuring fairness in approach.

Any good optimisation engine allows users to set constraints against which the right pricing for the objective function must be determined. There is likely a need for competitive positioning and corporate objectives within the applied constraints. In addition, your pricing engine should allow you to:

Price optimisation is just part of the picture. Optimisation tools can be used to solve for any requirement or blend of multiple requirements, the right price being just one of these. Optimisation can be used to identify not just the best price, but the best associated offer or product structure to meet the expectations of both the customer and the institution. Powerful tools are available that can overlay blended optimisation strategies relating to product, pricing, and marketing decisions.

What an optimisation solution really enables is the future delivery of the right price, for the right product, with the right message, at the right moment, to the right customer. In my next post, I’ll discuss price sensitivity in pricing optimisation.

The post Fair Pricing with Price Optimisation appeared first on FICO.

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