ROI is a powerful tool for business when narrowed down and focused on crafting a tactic for a business operation. Most organizations don't have a clear understanding of how artificial intelligence can help their business. After identifying specific use cases, the next step is determining how to measure the results of a solutions and define the potential return of investment of the artificial intelligence initiatives will be.
Defining Key Performance Indicators (KPIs)
Defining KPIs is typically mandatory in machine learning training due to the metrics that you’re trying to optimize. But, in the case of segmentation as an example, you’ll need to know what each segment is going to be used for and the criteria that needs to be met. The defined metrics may also differ by segment. For example, increasing average spend for the best customer segments and improving retention rates for average customer segments.
Set a Benchmark for Comparison
After performance metrics are defined, the next step is determining how to measure the impact of solutions to define what the potential ROI of machine learning, and AI will be. Many service providers offer proforma development included with AI development services for projected ROI. Proforma is designed to validate key assumptions and update them as the solutions are performed or tested.
Have a Benchmark for Comparison
After performance metrics are defined, maintaining an authentic control group or holdout groups are keys to analyzing the impact that the AI model is contributing. For decades, marketing has used the success of customer experiences as benchmark comparisons, and the principles still hold their credibility today. With the evolution of technology in business, the segments and treatments are now personalized on one-to-one levels.
Monitor Overtime
Algorithms should be updated on a regular basis as more training data becomes more available. This can be easy or very difficult when it comes to implementing machine learning models because models can drastically change over time. It is crucial that all models are closely monitored to ensure goals are being met and metrics are preserving data changes. Be sure to have regularly scheduled audits in place to monitor the changes in core data.
At the core of it all, AI should understand AI. Data sets are made of both large and small data and self-learning algorithms should operate so that they outsmart humans, providing a foundation for a business case. The return on invested of an AI technology can be measured best by benchmark comparisons. When focused correctly, ROI is a powerful tool to help power any business activity.
