The overarching theme of the global consumer lending landscape over the past couple of years is focused mainly on one thing: How should lenders look at setting loan and lease pricing and managing portfolio risk in a market that is constantly changing?
Many lenders around the world look to AI-powered pricing analytics to handle market volatility and respond to competitive shifts. However, using complex data analytics tools may sound overwhelming to many business and pricing managers. This blog post is focused on bridging the gap between data science complexity and everyday business tasks, showing you that there is an easier way to analyze your pricing strategies and make data-driven decisions.
The best analytical pricing tools are made for:
modeling risk and profitability
predicting customer behavior
creating optimized pricing strategies
But analytical tools are usually built for analytical teams. Using such tools can seem complex and intimidating for business users, including pricing managers (who are not professional data scientists) and finance, risk, legal and compliance leadership (who are not the day-to-day users of the platform). A sophisticated pricing analytics platform is underutilized as a result, IT teams and data scientists are called to the rescue more often than they would like to, the pricing cycle is slow, and competitive advantage is lost.
When pricing analytics software is not focused on user-friendliness, we see pricing and business managers struggle mainly with these four challenges.
Challenge #1 - Lack of strategic analysis
Pricing managers often struggle with tracking the performance of their business strategies, making it very hard to determine which strategies are working and which are not. This results in a reactive approach to loan pricing that can lead to loss of market share and volumes.
Challenge # 2 - Lack of simulation capabilities
Taking a guess is not always helpful, even if one knows the industry very well. Pricing managers lacking “what-if” simulation functionalities are unable to determine whether to raise prices or lower prices, and if so - by how much and when, and effectively do not have the ability to control the risk profile of their portfolios, given the recent increases in cost of funds and the trend of rising delinquencies.
Challenge #3 - Internal dependencies
When data and models are not easily accessible, it can be very challenging for pricing managers to make informed decisions. Reaching out to IT teams every time there is a need to update rates or involving data scientist teams for every simulation request is not effective, being time and resource consuming.
Challenge #4 - Time to market
If it takes you 30 working days to create a new pricing strategy and update your rates while your competition can do it in three hours, you have a problem! Creating new pricing strategies from scratch, comparing them, and getting them approved by a pricing committee can be very time consuming, especially when dealing with complex data and dynamic markets.
Too much complexity in the pricing analytics systems always leaves a lot of frustration within pricing teams. While it is important to keep your spread target and cost of funds in mind when pricing and deploying consumer loan rates, it is just as important (if not more) to understand how customers and the overall market would react to pricing adjustments.
Does that all mean that you must choose between analytical sophistication and business-oriented solutions? Definitely not.
Pricing is a complex process. It involves multiple teams across an organization. From years of working with banks and lenders such as yourselves, I learned that the key to success rests on two pillars: Collaboration and Separation.
Collaboration enables all the different teams involved in the pricing process to work within a single solution.
Separation, on the other hand, provides dedicated and separated workflows for each team, focusing on three major areas: analytical modeling, pricing, and decision support. These dedicated workflows are represented by different user interfaces that are built for supporting the specific team or part of the pricing and decisioning process. What brings it all together is a user-friendly solution, powered by advanced AI and machine learning.
How does it all come together?
In the analytical workflow, for example, data scientists and analytical teams can upload new data, build, and manage models and pricing methods, thus creating a strong analytical foundation for pricing decisions. When it comes to strategic analysis and decisioning, pricing and business managers use their own workflow that leverages advanced analytics and easily turn it into business insights.
Pricing and business managers need to be able to easily monitor their pricing strategy performance, identify any gaps or new revenue opportunities and have the ability to make informed decisions by tapping into underlying data and models.
Earnix Pricing Accelerator, our business dedicated workflow, is made for supporting these needs. Business and pricing managers can use visual analysis and KPI reports to get a clear insight into pricing strategy performance, compare strategies and identify opportunities for improvement.
They can run simulations on different pricing scenarios based on predictive analytics, providing a clear understanding of how these strategies are likely to perform. The direct connection to data and analytics makes advanced analytics highly accessible for exploring various pricing strategies and their potential impact on their business without the need for extensive analytical knowledge, and reusable templates and out-of-the-box user flows allow the pricing managers to respond quickly - saving time and reducing errors.
Using the Earnix Pricing Accelerator
Let’s deep dive into the day-to-day process and how it will look like using Pricing Accelerator.
Current Situation Analysis
The process usually starts by analyzing the current situation. How is my current strategy doing? Are there any changes in demand or are there any major changes in the mix of customers? Then a new baseline is created to answer these questions and to mainly answer the following question: Given the current and estimated change and assuming my pricing strategy stays the same, what will happen to my business results? What will happen to my position in the market?
This type of analysis can also help in identifying new business opportunities. The next step would be, assuming this is the current market situation and considering the predicted effect on the business, taking a decision going forward - where do I want to be? What are my targets based on predictive analytics? What is the best strategy that will get me there?
Visual analysis dashboards
As shown in Example 1 below, pricing managers can use visual analysis to see a breakdown of their performance in a certain geographical region and notice that a certain region is significantly outperforming the other ones. Looking at this analysis, the pricing manager can now answer questions such as - Is this what I expected? Compared to the previous predictions or to this month’s objectives- Am I where I planned to be?
Multiple reusable dashboards such as this one can be used for every new data set or strategy update, and a direct link to the analytical workflow makes sure that the latest data is always reflected, thus leading pricing managers to make more data-driven decisions.
Next step would be to analyze the effect of recent changes on current performance. For example, isolate the predicted effect of a competitor's move on the total booked volume and net income.
Pricing managers can understand the general decrease in volume trend, as well as estimate the size of decrease and how significant it's expected to be. Using segmented reports, they can perform an even deeper dive and see which segment will be more affected by these competitive moves. Different segmentations can be applied, such as geographical regions, customer segments, loan type, etc. This report can tell pricing manager how to differentiate their strategy across segments and where any missed opportunities may present themselves.
Connecting your goals with predictive modeling
For example, we would like to restore our position in new car loans in a specific region following a competitor’s rate drop. Now it's time to create different pricing scenarios using “what-if” simulations. We can better understand the effects and trade-offs or set objectives and run optimized scenarios that will find the optimal pricing strategy to achieve those objectives. This is a perfect example of leveraging the power of advanced analytics in a business-oriented user workflow.
Presenting to the pricing committee
Different alternatives can be selected from the previously made scenarios to be presented for a discussion in a pricing committee or any similar forum. Example 2 below shows two strategies with two different objectives. Objective 1 is to increase conversion, while Objective 2 is targeting profitability. The trade-offs are clearly presented and based on the underlying profitability and conversion models.
Typical questions in this forum may be centered around these topics. Am I willing to lose market share in order to keep my current spread? And how much volume am I expected to lose if I go with that strategy? Which segments are more likely to reduce volume? and so on.
Now the questions discussed during the pricing committee become more strategic and are supported by the data. As such discussions are often quite dynamic, more scenarios can be created in real time. For example, approve proposed profitability strategy, but give more differentiation to a specific customer segment or a specific region where there has been increasing competition.
These scenarios are created on the spot for a deep dive discussion, with a clear view of the expected business results and trade-offs thanks to the direct access into the underlying analytics.
In summary - Banks and lenders can increase the value derived from analytics by making it more accessible to the business. How?
First, full cooperation. Having a single solution that powers the full process is key. This way there are no handoffs between steps, and everyone’s work is connected through the same system. So, provide your teams with the technical infrastructure that facilitates cooperation.
Second of all, keep it personal. Business managers have their own perspective and needs. They should be able to benefit from having advanced analytics in place even without extensive analytical knowledge.
When complex data and models are transformed into actual business insights, they can support better decision making and allow for a more strategic focus.
What’s in it for you?
Deploy updated prices before the competition can even schedule a meeting between their data scientists and the pricing team.
Discover a code-free SaaS pricing engine to enable business-led deployments without IT intervention.
Deploy rate offers faster and with the understanding of how price adjustments will impact volume, spread, and pull through.
Please reach out to us for more information about Earnix Pricing Accelerator or to schedule a demo. Also, more details can be found here.