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Rick Biggs
Abstract
As customers become more demanding and expect their banks to deliver more tailored and compelling services, it is more important than ever for financial organizations to ensure they can stand out from the crowd. One of the first areas many consider when working on competitive differentiation is pricing. However, the common belief that ‘competitive pricing’ is synonymous with ‘being cheapest’ all too often means getting caught in the vortex-like pull towards rock- bottom prices that can wreak havoc with profitability.
Introduction
The financial institution that can reach that sweet spot where it can attract customers with offers at higher prices and still win the business is the one that holds the real competitive advantage, raising not only margins but customer satisfaction and share of wallet too.
The key is to optimize prices based on customer preferences and behaviour as well as your own business priorities. This enables you to make informed decisions about offers that encompass not only price points, but also detailed predictions of customer value, sensitivities and behaviours that are revealed through the choices they make at the point of sale. With these Big Data-driven insights to hand, you can give front-line staff the flexibility to respond to and negotiate with customers – including generating a range of alternative deals and individualized bundles of products and services – while ensuring all offers meet business priorities and industry regulations.
Biography
Rick Biggs is Deputy Managing Director EMEA at FICO, having worked there for almost 12 years in total. He is responsible for the DMP/Tools, Analytics and CCS businesses across EMEA.
Previously, Rick was in the Global Analytics team, responsible for full suite of analytical solutions including tools, solutions and models. He has also worked for TDX Group, Callcredit and Experian.
Rick Biggs
Deputy Managing Director EMEA
FICO
Keywords Pricing optimization, Bank profitability, Modelling, Customer-centric, Big Data Paper type Opinion
Although it may sound complex, achieving this level of pricing nirvana is possible. Indeed many financial organizations are already there. Let’s look at how they do it.
Keep it simple, but don’t dumb down
Out-doing competitors means being able to offer pricing at the most granular level – by customer. This requires a significant shift away from traditional methods, demanding that you move from a price-sheet approach to one where optimal pricing comes from analytics-based segmentation of customer populations.
In order to accurately segment these populations and the offers targeted at them, financial services companies need to consider a wide range of data and customer behavioural predictions. For instance, price sensitivity (demand elasticity) models are widely used, but their full impact on customer behaviour often isn’t considered. So, in credit card originations, we need a demand elasticity component in models predicting not only offer take-up, but usage level, delinquency risk, account profitability, and perhaps customer lifetime value. When you add this sophisticated segmentation to broader business objectives, and use them to govern decisions at portfolio, segment and customer level, the possible combinations multiply quickly. Bring in the options of cross- or up- selling and the need to predict how the customer will react to any given action and there are myriad elements that could impact your KPIs. It’s impossible for traditional business rules alone – let alone the human brain – to handle this level of complexity.
Effectively resolving pricing challenges of this magnitude and scope without dumbing them down is where analytic techniques come in. Working backward from the business goal, data analysts can identify the important decision factors and codify relationships between them in mathematical equations (represented by the blue arrows in Figure 1).
These decision models can comprise any number of components, including descriptive models identifying similarities (for example, demographic, behavioural) between customers, and predictive models forecasting their future behaviour. At their heart is a network of action-effect models, predicting likely customer reactions to your possible actions (for example, pricing and other aspects of an offer) and the resulting impact on KPIs.
Figure 1 shows a simplified version of this process but in real life they can be
much more complex. For example, we recently built an optimization model for a client that included several dozen component models and about 30 billion calculations. In decision-making environments of this size, it’s essential to have the tools in place that can quickly and clearly enable you to pull out the insights that will allow you to make informed choices for the benefit of the business.
Make analytics transparent and usable for business experts
The answers delivered by pricing optimization are only as reliable as the input data and models underpinning them, so it’s important not to blindly trust them. In order to know what’s going on ‘under the hood’, business experts need to be able to expose and examine any part of the modelling process and – where their role allows – make adjustments, injecting domain expertise and business judgement into the mathematical process.
For instance, let’s imagine a pricing manager has just received a proposed optimized pricing strategy from a pricing analyst and he’s concerned about the accuracy of the profitability predictions it’s based on. He wants to know how well the cost of funds in the historical data used for modelling aligns with current and projected cost of funds. So, he drills down into components of the optimized strategy to examine the values for this variable and the time period from which the historical data was taken. He can also adjust the time period, and immediately see the results in a simulation of the re-optimized strategy.
Being able to simulate the impact of changing different variables on a proposed strategy is a powerful way for business experts to explore pricing optimizations, make strategy adjustments and evaluate forecasted results prior to deployment. Empower collaboration in the optimization process
Setting pricing strategies generally involves a number of stakeholders from across the organization, and they all need to be able to participate easily in the optimization process.
As depicted in Figure 2, they must be able to input objectives and constraints from their own management perspective into role-specific, workflow-driven interfaces. They should also have the opportunity to review proposed pricing strategies and view optimization process details, results and reports as appropriate for their job.
Pricing processes vary widely among financial institutions, so how this works must be completely open and configurable. A state-of-the-art optimization solution will enable any workflow involving any number of role-based interfaces to be driven from shared repositories for data, analytics and business rules. Such comprehensive, highly customized solutions can be developed and deployed in a fraction of the time usually required for interactive, collaborative data-driven applications.
Integrate the back room with the front line
The collaborative process can be extended as far as needed into the front line so that pricing optimization takes into account not only on what is known about the customer, but what is learned at the point of sale. Mortgage brokers and bank branch managers, for example, can be invited to not only input data, but also adjust the relative importance (“weight”) of various aspects of a deal based on the customer’s stated preferences. This means that they can run a real-time optimization to see a range of alternative offers that reflect each customer’s needs and attitudes.
In this way, your customer-facing employees can have meaningful conversations at the point of sale. They can respond flexibly to what they’re hearing from the customer to restructure and re-price offers, while staying within the parameters of corporate policies and industry regulations.
The advantages of this approach include fewer exception pricing requests and greater take-up of offers. Faster decisions also improve the cost of sales while customers are more satisfied with the process, experiencing efficiency and responsiveness to their individual needs.
Real-time optimization also enforces consistency in point-of-sale actions as financial organizations can ensure their policies are driving and controlling front-
line interactions, while demonstrating to regulators that consumers are being treated fairly.
This approach can be taken a step further by putting the customer themselves in the driving seat and enabling them to find their own personalized, choice-based pricing. For instance, we’re currently working with a North American bank to deploy a pilot project in customer-centric price optimization for its retail lending products. The aim is to meet each customer’s need for new credit or debt consolidation by proposing an individualized bundle of products. The bank intends to deploy the solution for interactions with loan officers in its branches, as well as for self-serve interactions at its website.
Stay agile for market manoeuvrability
Given the dynamic nature of today’s markets, pricing collaboration and optimization need to be asynchronous and ongoing. Stakeholders must be able to make changes at any time. Submitted changes (subject to business-rules- driven workflows and approvals) would then immediately affect all subsequent optimizations.
For example, a risk manager, concerned that the company is writing too many 72-month loans, could adjust pricing parameters to make such loans more expensive or to make the qualification criteria stricter. Based on current inventories and promotions, a sales manager might allow or disallow offers that include up-selling of higher-value products or bundled cross-selling of accessories. Similarly, companies can quickly implement new promotional campaigns and agreements with business partners. They can respond easily to new regulatory requirements, or to sudden changes in competitors’ strategies. It’s also easier to keep an eye on what you might need to do next as customer- centric pricing optimization incorporates an endless feedback loop. All
A South African bank wanted to improve the performance of its portfolio of unsecured personal loans for new and repeat business. It needed to make better originations decisions that would increase take-up rates while reducing bad debt exposure. To maximize loan lifetime profitability, the bank also wanted to reduce prepay rates and know when to target customers with promotions for repeat business.
Solution
The bank optimized its prices and loan amounts. Among the many inputs to the optimized decision strategy, analytics are used to predict when customer behaviours (like early repayment) will occur, helping the bank time marketing to existing customers.
Results
The bank increased its take-up rate and saw average loan amounts go up by 12%. At the same time, it achieved a 14% increase in profit per application and expects a one-year incremental profit of more than $24 million.
participants in the process can very quickly evaluate the operational outcomes of current strategies. They can see how well real and simulated results align, and where gaps indicate the need for improvements or further learning about customer behaviour.
Win the race
To out-price competitors, you need to be quick at identifying the sweet spot where not only price but all aspects of the offer meet the needs and objectives of both customer and business. Given the hefty combination of Big Data; complex internal factors, criteria and stakeholders, and the need to consider real-time customer inputs, more banks than ever are realizing that the best answer is customer-centric pricing optimization.