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Machine Learning Use Case Development
The race is on for companies of all sizes and industries to deliver impactful artificial intelligence and machine learning applications. Fueled by the hype and fear of being left behind, many business leaders are throwing money and resources at implementing AI/ML use cases without having a clear understanding of a specific business problem to solve or business outcome to achieve. Having successfully implemented machine learning solutions at a wide range of customers, Bitwise has seen first-hand the importance of identifying the right use case and building a strategy around identified success metrics to achieve a positive ROI with AI/ML.
Let’s look at five use cases for driving ROI with machine learning, including success metrics, to help identify the right use case for you to start with AI/ML in your organization.
Identifying the Right Use Case for Machine Learning
Finding the right use case to get started sounds easy enough but can often be a challenge, especially with all the noise around the transformational nature of AI/ML technology. To cut through the noise, Bitwise recommends to start small and identify a pain area or gap in your business processes. Even a simple use case can achieve a meaningful ROI, which will provide a strong foundation to build your internal competency and stakeholder buy-in to develop more use cases.
Some of the early use cases like recommendation engines and reducing customer attrition, for example solving merchant attrition using machine learning in the financial industry, have been widely adopted and continue to deliver results. The below use cases provide other examples for implementing machine learning with associated success metrics to help you get the ball rolling.
5 Machine Learning ROI Use Cases
1. Billing Optimization
Complex billing processes can be difficult to manage and provide a poor customer experience. Optimizing billing with machine learning can streamline the process and improve efficiencies and accuracy.
- Cost Savings: 15% reduction in operational costs. This metric reflects the reduction in overall operational costs achieved through streamlined billing processes, directly enhancing profitability.
- Error Rate: Decrease from 2.1% to 0.5%. This measures the reduction in billing discrepancies, ensuring accuracy and enhancing customer trust.
- Cycle Time: Reduced from 30 days to 18 days. This tracks the time required to complete the billing process from initiation to closure, facilitating quicker revenue recognition and improved cash flow.
2. User Support Chatbot
User support is a critical business process that typically requires heavy human interaction. Using machine learning to optimize the support process can help support staff be more efficient and provide a better customer experience.
- Resolution Rate: 85% of queries resolved without human intervention. This metric indicates the proportion of queries resolved by the chatbot augmenting human interaction, showcasing the efficiency and reliability of automated systems.
- Customer Satisfaction Score (CSAT): Increased up to 90%. This score assesses customer approval of interactions, reflecting commitment to providing seamless support.
- Average Handling Time: Reduced to 3 minutes per interaction. This measures the time it takes to handle inquiries, demonstrating the speed and efficiency of augmented customer support.
- Service Efficiency: Optimized support workforce for critical customer support, reducing support cost by 40%.
3. Predicting Wait Times
Industries like Hospitality and Heathcare are often plagued by wait times for their customers. Using machine learning to predict wait times for patrons and patients can provide a competitive edge for organizations where customer service is critical.
- Accuracy of Predictions: up to 95% accuracy level. This metric evaluates the precision of predicted wait times against actual experiences to ensure accurate expectations.
- Customer Satisfaction: Improved by up to 20%. This tracks improvements in customer or patient satisfaction, which correlate with accurate wait time predictions and efficient service delivery.
- Service Efficiency: 30% increase in patient throughput. This assesses how effectively wait time predictions contribute to optimizing workforce allocation and enhancing service speed.
For example, Bitwise helped a national restaurant chain with predicting order wait time using machine learning to provide an improved experience for their online and dine-in customers by showing how long they will have to wait for their order to be served with 87% accuracy.
4. Pricing Optimization
In today’s omnichannel digital landscape, customers have a variety of options to purchase goods and services. Optimizing pricing with machine learning can provide a competitive advantage while maximizing margins.
- Revenue Growth: 10% increase in quarterly revenue. This metric monitors the increase in revenue attributable to strategic pricing decisions, underlining the effectiveness of pricing models.
- Price Elasticity: Sales volume changes by 5% for every 1% price adjustment. This measures how sensitive customer purchase volumes are to changes in pricing, helping to tailor pricing strategies based on consumer behavior.
5. Competitor Analysis
Companies have a trove of data that can lead to valuable insights when asking the right questions. Analyzing the competition using machine learning can help optimize marketing and customer acquisition efforts.
- Customer Acquisition Cost (CAC): Reduced by 12%. This measures the efficiency of the customer acquisition initiatives, especially those driven by insights from competitor analysis, optimizing marketing spend and improving return on investment.
Identify Your AI/ML Use Case to Drive ROI
The examples of billing optimization, user support chatbot, predicting wait times, pricing optimization, and competitor analysis showcase a variety of machine learning use cases but by no means cover the full gambit of what’s possible with AI/ML.
Looking internally at business processes and pain areas can be a great place to start identifying your AI/ML use case with a focus on driving ROI. For example, we’ve found that for some of our customers, errors with data pipelines and data inconsistencies caused problems impacting their critical applications and resulted in cost increases and revenue losses. By implementing proactive monitoring of data platform using machine learning, potential problems can be prevented and avoid costly delays in time-to-production.
Ready to take the next step? Let’s talk about your AI/ML objectives and explore how Bitwise can help you identify the optimal use case and success metrics to put you on the path to achieving ROI and driving adoption in the organization.
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