August 2021CIOAPPLICATIONS.COM9you may evaluate the risk and benefit (e.g., safety and productivity) of working from home versus going to the office(using experience or historical data) and then determine the best action by balancing risk and benefit.The three types of analytics areclosely related and dependent on each other, as described below. From Descriptive to PredictiveLearning about what happened in the past (descriptive analytics) is often a prerequisite to predicting the future (predictive analytics).While descriptive analytics such as a summary of past prices, past sales data, revenue and profitability provide essential reports to run your business,learning about the past enables us to identify the trend and pattern, understand what happened, and possibly why something happened. Such knowledge allows data scientists to predict future outcomes (or a range of possible outcomes).As an illustration, suppose you are sellinga special kind of juice in a local shop, you may gather historical data on prices and weekly sales and display it in a graph to understand the association between price and sales (descriptive analytics), see Figure 2. To learn about how sales may vary with price (the demand curve), you may fit a regression line as in Figure 2, which is: S =1867-234P, where S = weekly sales, and P = unit price, assuming no other factors are interfering with the relationship. Such a model can predict sales level at any price point (predictive analytics).From Predictive to PrescriptivePredictive analytics is typically required as an input to prescriptive analytics,as knowing something about the future (predictive analytics) can help us explore the benefits of alternative options which supports selection of the right action (prescriptive analytics). Evaluating the potential outcomes of options often requires cause-and-effect assessment, known as causal inference in the literature. Continuing with the example above,suppose you want to set the best price for yourjuicein order to maximize profit, which is a prescriptive analytics (decision-making) problem,you can use predictive analytics to first establish the relationship between sales and price using historical sales data. While the best method to understandthecause-and-effect relationship is through a randomized experiment (A/B testing), we assume all you havein this example is a few data points from some past weeks.The regression line in Figure 2 already provides estimates of sales at various prices which is a form of predictive analytics. The relationship implies that, for every dollar price decrease (increase), you expect to have 234 more (less) units to sell weekly. Since you aim at maximizing profit, you would determine the optimal P such that the profit function is maximized. Assuming the cost of making a cup of juice is $1; your profit function can be expressed as estimated sales multiplied by unit profit, which isf(P)= S(P-1)= (1867-234P)(P-1),as plotted in Figure 3. We can determine the most profitable price by reading off from the chart(or through calculus) which comes out to be about $4.5 per cup, resulting in a weekly profit of $2,849. ConclusionThis article introduces the three types of analytics (descriptive, predictive, prescriptive), and illustrates them with a simple price-setting example. The link between predictive and prescriptive analytics is established throughthe cause-and-effect assessment of alternatives. In practice, establishing any cause-and-effect relationship may not be as straightforward (due to potential outliers and confounders).More advanced analytics are often required to understand the causal effect and derive the optimal solution via prescriptive analytics.Further discussion of the data scientist toolbox is available in Lo (2020). Figure 3 Profit as a function of unit priceFigure 2 Weekly juice sales as a function of unit priceFigure 1 Three Types of Analytics
< Page 8 | Page 10 >