Five Areas Where Big Data Drives Innovation in the Bill Pay Industry..

As the Bill Pay Industry Motors On…

The traditional model of service providers relying on call centers and face-to-face interactions with their customers to gauge their satisfaction are long past. With the advent of PSD2, the regulatory authorities themselves may be more open to new business models in the Bill Pay space.

With the explosion of data being collected from mobile applications, location based devices & social media, Bill Pay providers can monetize on their years of historical data by opportunistically combining the above and providing Analytics in the below five strategic areas –

1. Ensuring the best possible & timely Customer Payment Experience –Younger customers are typically very happy in leveraging online channels like mobile phones, web applications to make their payment instead of using paper based mailing. Using online channels to process payments also results in higher degrees of both end customer and service provider satisfaction, as it is quicker in terms of funds transfer, availability and is also less error prone. Leveraging Big Data to understand which of your customers prefer mobile channels (based on lifestyle & behavioral preferences) and helping them download service provider mobile applications can accelerate mobile payment adoption modes. Another key use case is to understand which customers typically pay just before or after the deadline thus incurring late fees – another source of customer dissatisfaction. Again, understanding customer payment modes & trends can help increase customer satisfaction here. The ability to reach out to a customer at the best possible mode that they prefer (via mobile app, or, a text message, or, a phone call) can also help address customer dissatisfaction with services.
2. Provding a Unified View of Customer Across Multiple Service Accounts – Creating a single customer profile or view across multiple household services & interactions, payment history across those can provide an ability for Service Providers to understand the total Customer Lifetime Value (CLV) of a single customer. Creating this profile can also help drive the business value in the following areas.

• What mode of contact do they prefer? And at what time? Can Customers be better targeted at these channels at those preferred times?
• What is the overall Customer Lifetime Value (CLV) or how much profit we are able to generate from this customer over their total lifetime?
• By understanding CLV across populations, can Service Providers leverage that to increase spend on marketing & sales for products that are resulting in higher customer value?
• Which of my customers are targets for promoting Green Services and Products?
• What Features are customers currently missing?
• How can Service Providers we increase cross sell and up-sell of products & services?
• Does this customer fall into a certain natural segment and if so, how can we acquire most customers like them?

           Five Ways for Bill Pay Providers to Monetize their Data Assets

3. Improving Customer Satisfaction – Creating a single customer profile or view across multiple household services & interactions can provide an ability for Service Providers to understand the total Customer Lifetime Value (CLV) of a single customer. Creating this profile can also help drive the business value in the following areas – Customer Satisfaction, Customer NPS (Net Promoter Score), Customer Mood & Willingness to adopt new services, Customer Retention etc.

4. Analytics As A Service to interested 3^rd Parties

The ability of consumers to make their household services payments can serve as a reliable indicator of household economic health as well as a sign of their willingness to adopt new products and services. This data can be anonymized at an individual consumer level, analyzed using machine learning and be provided as a service to various stakeholders – Other businesses like Retailers, the Government & the Regulatory Authorities.

Concrete examples include –

• Combining Social data, demographic data with bill pay data & other credit data can help the Government gauge the direction of the economy. Obviously the more data that can be merged into this model (e.g. mortgage payment data etc) can help with its overall accuracy
• Allowing Retailers to analyze consumer mobile usage data, bill pay data, credit records as well as use external data (social media etc) to predict what products they may like etc and to target promotions & card offers etc

A final note on the overall scope of Predictive Analytics in this usecase-

• Obtaining a real-time Single View of the Customer (typically a customer across multiple channels, product silos & geographies) across years of account history
• Customer Segmentation by helping businesses understand customer segments down to the individual level as well as at a segment level
• Performing Customer sentiment analysis by combining internal organizational data, clickstream data, sentiment analysis with structured sales history to provide a clear view into consumer behavior.
• Product Recommendation engines which provide compelling personal product recommendations by mining realtime consumer sentiment, product affinity information with historical data etc.
• Market Basket Analysis, observing consumer purchase history and enriching this data with social media, web activity, and community sentiment regarding past purchase and future buying trends.

5.Service Provider Analytics

Service Providers can themselves access this data to help with the various areas of their operations –

• Improve new Consumer Acquisition by creating client profiles and helping develop targeted leads across a population of individuals
• Instrument and understand Risk at multiple levels (customer churn, client risk etc) in real time
• Financial risk modeling across multiple dimensions (?)
• For Providers with multiple products & services (e.g Cable, Voice and Internet), Basket Analysis based on criteria like behavioral preferences, asset allocation etc – i.e “what products & services are typically purchased in tandem”
• Run in place analytics on customer lifetime value (CLV) and yield per customer
• Suggest Next Best Action for a given client and across a pool of customers
• Provide multiple levels of dashboards ranging from the Descriptive (Business Intelligence) to the Prescriptive (business simulation as well as optimization)
• Help with Compliance and other reporting functions

CONCLUSION…

Bill Pay is a specialized area of the payments industry. However, the massive amounts of historical customer & service data that players possess can be advantageously leveraged to provide value added services and ultimately drive new business models.