Machine Learning (ML) and Artificial Intelligence (AI) sounds like science fiction—the stuff that you only see in movies. But the technology is here, it’s already proving itself in the market, and it’s being built with business in mind.
Today, we’ve all seen or heard about systems designed to intelligently trade stocks and shares, or built to deliver an autonomous driving experience. In fact, most of us interact with AI on a daily basis. For example, reaching out on line to customer service; using a search engine, shopping on Amazon, or asking Alexa about the weather.
Machine Learning is premised on the notion that rather than teaching computers everything they need to know about the world and how to carry out instructions, it might be possible and more efficient to teach them to learn for themselves.
Some areas of Artificial Intelligence and Machine Learning are a natural evolution of Business Intelligence where instead of providing data in the form of a report that a person analyzes and uses to draw conclusions, Machine Learning algorithms actually learn from the data and provide predicted outcomes or conclusions. These outcomes can then be put into motion by automated processes designed to act according to these outcomes.
AI/ML is the process of learning and improving on past experiences—it uses the data fed to it to learn patterns so it is able to make statements, decisions or predictions with a degree of certainty based on past experiences. “Learning” is enabled with a feedback loop which senses or is told whether its decisions are right or wrong then modifies the approach it takes in the future.
Given a healthy volume of data to learn with, the power of AI can be awesome. In fact, just like with Business Intelligence, the quality of the results a Machine Learning program can produce is dependent on the volume and quality of the data it has to learn with.
As mentioned before, we are already seeing AI and ML being leveraged for businesses in areas like customer service, with machine-driven, natural interactions with customers. We also see it used heavily in marketing and advertising with incredibly sophisticated systems designed to detect the age, sex, and political and social orientations of individuals simply by analyzing facial expressions, body language, clothing, etc.
Sophisticated AI and ML can be leveraged within financial applications as well. So what are some ways that you can leverage billing data to help a machine “learn” something that can benefit your company?
Here are some areas that BillingPlatform is exploring:
- Customer churn prediction: The ability to learn from your invoicing, payment and usage data about signs that a customer is beginning to churn. The platform can then leverage these signals and automate targeted customer communications about under utilized products, alternative products, incentives and offers all in an effort to proactively encourage retention. It can also alert customer managers to potential churn risks and also predict company revenue and help direct business focus.
- Pricing and Quoting assistance: Incorporating ML into the configure-price-quote (CPQ) process to help sales people optimize pricing and product combinations for better chances of closing deals as well as better choices for long-term customer retention. The ML algorithms can learn from past successes and failures in the sales process as well as tap billing and payment data for churn trends to provide deep insight in the form of concrete suggestions to transform the quote and sales process.
- Advanced collections strategies: Leveraging AI and platform automation to define and execute optimum collections strategies for different customer types based on industry, region, as well as payment and spending patterns, among other things. The use of AI and ML combined with platform automation capabilities like workflow and web service integrations, can completely automate collections processes right down to the direct dialing of customers to discuss and agree on a payment plan and path to resolution for delinquent accounts.
This next prototype out of the Innovation Lab will focus on leveraging Amazon Sagemaker, AI/ML technology to develop and train a Machine Learning algorithm to predict customer churn. For this initial phase, we will focus on universal parameters like aging, open balances, usage volume and revenue levels. We may choose to expand the training data to include information related to Rating methods such as Subscription, Usage, one-time fees. etc.
If you are interested in leveraging this lab prototype and possibly adding in your own, specific product data, please submit a request to email@example.com.