In this note, let’s define analytics as the analysis of data in order to take actions. (This is a narrow definition of analytics, but one that is useful here.) If you don’t have day to day work experience with analytics, it is easy to have the mistaken impression that analytics is only about data and statistical models.
Although understanding data and developing statistical models is certainly an important component of an analytic project, this is just one aspect of analytics. This aspect includes cleaning data, enriching data, exploring data, developing features, building models, validating models, and iterating the process. From a broad perspective, this is a process in which the input is data and the output is a statistical model. When most people think of modeling, this is what they think of. For many analytic projects, this is just a small part of what is required for a successful engagement.
The second aspect of analytics is what I am concerned with in this note. This is the aspect of analytics concerned with:
- developing an appropriate score for a statistical model;
- using the score to define useful actions;
- determining which measures are best for evaluating the effectiveness of these actions;
- tracking these measures (often with a dashboard) and making sure that that they advance the strategic objectives of the company or organization.
One way to remember this is using the mnemonic SAMS for Scores, Actions, Measures and Strategies.
For example, with a response model, often a threshold is used. If the score from the response model is above the threshold, an offer is made (this is the action); if not, no offer is made.
Here are some examples of SAMS:
| Model | Score | Action | Measure | Strategy |
|---|---|---|---|---|
| on-line response model | likelihood to respond to an offer | display the offer to the visitor that has the highest likelihood of response and available inventory | revenue per day generated by the web site | increase revenue from a website by improving targeting of offers |
| fraud model | likelihood that a transaction is fraudulent | approve, decline, or obtain more information | detection and false positive rates | reduce costs and improve customer experience by lowering fraud rates |
| data quality model | likelihood that a data source has data quality problems | if the score is above a threshold, manually investigate the data to check whether there is in fact a data quality problem | detection and false positive rates | improve operational efficiencies by detecting data quality problems more quickly |
A successful analytics projects requires a careful study of what actions are possible; of the possible actions, which can be deployed into operational systems; and, how the systems can be instrumented so that the data required to compute the required measures is available.
For more information, see: blog.rgrossman.com
