Artificial Intelligence and Machine Learning Solutions
Charter Global can help you find innovative ways with 100% PCI, PII, and other compliances. We do this by analyzing your business data and building suitable AI/ML (Artificial Intelligence or Machine Learning) models to improve business value using your own historical data.
Myth: Why ML? We have data analysts.
Organizations have data analysts for drawing conclusions and projections for many years. Data Scientists aim to find hidden patterns in the data and put it to the advantage of the business. Using historical data, data scientists can identify which parameters are contributing significantly to a decision (or prediction) & which are not. ML models can be built to predict the new customer’s behavioral pattern in real-time based on historical data. If effectively used, such real-time predictions can add significant business value.
From Business Problem to ML Problem
The below steps identify if a problem needs to be solved using AI/ML technologies.
Machine Learning Process
AI / ML Model Selection
Depending on the problem statement, our ML data scientists pick an appropriate set of models and choose the best performing model.
How accurate is it?
It depends on the data and variations in it. In a nutshell, there may still be a few real-world scenarios that a model may not predict accurately, but we can tune and train the model to overcome those issues iteratively.
Call Center POS Application: Issues and AI/ML Solutions
| Goal |
| Call Center POS Application: Use Artificial Intelligence / Machine Learning solutions to improve revenue and reduce costs. |
| Sub goals |
| Though first 2 sub goals are generic, those are important in nature for any successful AI/ML solution. |
| Sub goal # 1 |
| Make a data driven decision without losing ownership of data |
| Sub goal # 2 |
| Protect confidentiality of customer information |
| Sub goal # 3 |
| Reduce Average Handle Time (AHT) |
| Sub goal # 4 |
| Improve conversion rate |
| Solutions and benefits |
| Subgoal # 1 |
| Solution: To use stand-alone, on-premises, open-source, Customized AI/ML solutions (data needs to be shared if we opt for popular Cloud/ML-based solutions).
Benefit: Data is not shared with external entities. |
| Subgoal # 2 |
| Solution: AI/ML Model does not use any PCI or PII data fields
Benefit: If we use a customer data record as an input to the model, we cannot tie it back to the corresponding order or customer in the system. |
| Subgoal # 3 |
| Solution: Identification of potential buyer / non-buyer using Artificial Intelligence/Machine Learning model recommendations.
Benefit: Improved bottom line due to Scientific and real-time decision making based on buying patterns of past customers |
| Subgoal # 4 |
| Solution: Identification of service provider using Artificial Intelligence/Machine Learning model recommendations.
Benefit: Improved conversion rates and Agents pitching plans with conviction. |
Call Center POS Application: Sample Deployment Scenario
AI/ML model will provide RESTful API to the Sales Application. For an incoming caller, the required data is provided to the AI/ML model which predicts if the caller is a buyer or not. This result is shared with the Sales agent.
