How do you apply optimal stopping to make a business data-driven decision?

Optimal stopping is a decision-making strategy that helps you determine the best time to decide after gathering and evaluating enough data. In business, this approach can help you make more informed decisions and increase the likelihood of success in various scenarios, such as product launches, investments, or marketing campaigns. 

Let’s use a real-world example: Imagine you’re the owner of a small e-commerce company that sells custom T-shirts and wants to decide which design to promote next month. You have ten designs to choose from, and you can gather data about their popularity by running small-scale social media ad campaigns for each design. 

Here’s how you can apply optimal stopping to make a data-driven decision: 

1. Determine the stopping point: First, you need to decide when to stop gathering data and make a decision. In the context of optimal stopping, there’s a rule of thumb called the 37% rule. In this case, you should evaluate about 37% of the options before deciding. Since you have ten designs, you’ll gather data for the first four designs (10 * 0.37 = 3.7, rounded up to 4). 
2. Collect and evaluate the data: For each of the first four designs, run a small-scale social media ad campaign, collect data on key metrics (e.g., clicks, engagement, conversions), and compare their performance. Let’s say Design 1 is the best-performing option among the first four designs. 
3. Continue gathering data with a decision threshold: Now, evaluate the remaining six designs individually. If you find a design that performs better than Design 1 (the best-performing option from the first four), stop gathering data and choose that design. If none of the remaining designs outperforms Design 1, choose Design 1 as the winner. 

By applying optimal stopping, you gain the following benefits: 

1. Efficient resource allocation: By not spending time and resources on evaluating all ten designs, you can allocate those resources to other important business tasks or decisions. 
2. Increased likelihood of success: Since you’ve based your decision on data, you can be more confident that the chosen design will resonate with your target audience and lead to a successful marketing campaign. 
3. Reduced decision fatigue: Optimal stopping helps you avoid the endless cycle of evaluation and analysis, which can lead to decision fatigue and procrastination.

Remember, optimal stopping is just one of many decision-making strategies. Depending on the context and the type of decision, you might need to consider other factors or use different approaches. However, optimal stopping can be a helpful tool in many data-driven decision-making processes.