The financial services sector is perhaps one of the most data-intensive sectors.
Enormous amounts of structured and unstructured data and complex, large, and diverse data sets are on an exponential increase – especially as customer interactions move online and technological evolutions generate larger amounts of input data.
Utilizing these rich data sets gives the industry a huge opportunity to meet the rapidly evolving, constantly changing, and increasingly demanding customer expectations.
Predictive Pricing – What is it?
Presently, most organizations make their pricing decisions based on research conducted for a large segment. Given the age of personalization and customizations, things need to change. Imagine a scenario where a salesperson has to work on a price or a deal for a specific customer. While working on the deal, she gets a price prompt from the pricing tool in use. This prompt is based on advanced analysis of different variables, historical and real-time data and hence has a higher probability of the customer accepting the deal. This is predictive pricing at work. While the financial services sector can use the data for a host of purposes, it can help this sector improve competitiveness and profitability by driving predictive pricing capabilities. Since pricing demands are inextricably linked with consumer demands, making sub-optimal decisions can lead to tangible losses.
However, making optimal pricing decisions can become complex because of the multitude of variables at play. Aspects like list pricing, special discounts, bundling, special offers, and others need optimization to drive better business outcomes. Organizations also need to remove guess work from the demand and profit forecasting to capably design new pricing strategies.
To replace guesswork from the equation, organizations now need to employ historical data in conjunction with real-time data to develop robust pricing strategies.
While having robust data analytics capabilities become essential to drive pricing strategies, given rising complexities, disruption, competition and with the ever increasing data volumes , it too needs support from new-age technologies such as AI and Machine Learning.
Research shows that the “the annual potential value of Artificial Intelligence in banking at as much as 2.5 to 5.2 percent of revenues, or $200 billion to $300 billion annually”.
The Role of AI and Machine Learning in Driving Predictive Pricing
AI and Machine Learning have been put to active use in the BFSI sector to automate and add intelligence to time-consuming, manual processes. With these technologies in place, organizations can offer more streamlined, personalized, and customized customer experiences.
By combining historical data with real-time data using AI and ML, these organizations can:
- Gain a multi-dimensional view of customers, branches, services
- Improve investment quality by capably conducting margin and pricing analysis
- Understand customer needs and evolving trends by assessing parameters such as churn, retention, acquisition, cross-selling opportunities, etc.
- Identify future fund-raising opportunities
- Sharpen risk assessment and proactively forecast the probability of losses, non-performing exposures and also enable flexible risk reporting capabilities
- Perform detailed competitive analysis
- Enhance decision-making capabilities around issues such as fraudulent transactions, digital wealth management, lending, personalized marketing, and more.
Cross the Final Frontier – Drive the Right Customer Experience
Research shows that those customers who feel loyal towards a bank are 72% times more likely to purchase from the bank while 70% would be willing to recommend the bank.
Improving digital experiences to influence customer behaviour are not negotiable anymore. With the help of technologies such as Artificial Intelligence and Machine Learning, this sector can identify customer needs, preferences, patterns by providing an intelligent 360-degree view of the customer.
Leveraging the right information gleaned from the right variables such as transactional data coupled with other data sources, these organizations can analyze and understand customer preferences and use this knowledge to personalize the offers and services.
By doing this, organizations improve brand loyalty and generate repeat business. For example, corporate banks are using predictive pricing powered by AI and ML to make the international money transfer process more streamlined, intuitive, and intelligent. With these capabilities, banks can make the transaction process smoother by intuitively providing pricing options and currency options. They can intuitively assess and predict customer behavior and guide customer journeys.
Such capabilities help banks not only improve customer experience but create long lasting bonds which in turn help generate incremental revenue.
AI and Machine Learning driven predictive pricing capabilities organizations can experiment with pricing strategies with confidence by identifying the right markets or segments.
The Benefits of Predictive Pricing
Having a strong framework, right technologies and systematic process drive predictive pricing strategies. These can help organizations provide differentiated offerings to attract and retain their customers. Having predictive pricing capabilities can help these organizations enable:
- Dynamic pricing using a real-time engine to score offers and quotations
- Generate and provide appropriate and attractive pricing recommendations and gain explanations to understand the proposed prices
- Improve the balance between conversion projections and margins, measure the impact of different pricing models, and then make proactive adjustments to the same
- Increase competitiveness and make changes to pricing using advanced analytics in response to changes made by competitors
- Improve margins, drive overall revenue, and maximize the number of new customers leveraging dynamic competitive and trend analysis
Along with this, combining billing data with historical data and then analyzing these against the pricing models at play helps in identifying revenue opportunities by providing the best pricing for the customer.
This comprehensive data also helps organizations identify new, upselling, or cross-selling opportunities while also identifying the source of revenue leaks.
The Challenges to Predictive Pricing
The benefits of predictive pricing are quite apparent and while organizations are getting attracted to it, there are certain challenges impeding adoption.
- Managing the data: The primary and the most rudimentary challenge is the inability to capably store, manage, and use the vast volumes of historical data in conjunction with real-time data.
- Finding the right fit: The absence of a pricing solution that integrates into the existing tools ecosystem easily is a hurdle to cross. Organizations need solutions that fit in with their existing frameworks and tools ecosystem and generate price recommendations.
- Getting the data right: Organizations need regular access to dependable price-related data and information into an audience segment’s willingness to pay. They also need insights into volume transactions and when these happen and why to incentivize these opportunities better. A robust predictive pricing solution goes a long way in helping organizations organize and analyze their data appropriately and intelligently and complement it with the right workflows and processes that drive profitable pricing strategies.
- Comprehensible insights: Generating the right insights is essential. But predictive pricing can only get to work when the end-user can easily understand these insights. Providing intuitive, easy-to-use dashboards along with management tools coupled with explanations helps organizations understand the rationale behind price recommendations.
In Conclusion
Most financial services organizations want to improve financial monitoring, make the right investment predictions, improve risk management and decision-making, enhance the precision of real-time approvals, ensure robust fraud detection, and provide customers with highly specialized and targeted financial advice.
However, only when organizations power their pricing strategies with technologies like AL and ML-that they can evaluate all the relevant touchpoints, analyze all structured and unstructured data, and deliver an elevated service experience.
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