Understand What Could Happen Insights
Learning Objectives
- Understand predictions and improvements
- Navigate to a story’s What Could Happen insights and explore them.
- Identify the best predicted future outcome for your scenario.
- Identify the factors behind a prediction.
Note
The instructions in this unit assume that you have successfully created an Einstein Discovery story. Refer to the instructions in "Create a Story," in the first unit in this Trailhead module.
About What Could Happen Insights
While other story insights show historical data from past activities, What Could Happen insights calculate statistically probable outcomes of future events. Einstein Discovery conducts predictive analysis to predict future outcomes, and prescriptive analysis to suggest ways in which to improve those predicted outcomes. Predictions and improvements come from the kind of regression and machine-learning analysis that data scientists conduct using advanced analytics and AI tools. Use What Could Happen insights to interactively perform “what if” analyses on your data.
A prediction is just that: an anticipated outcome based on Einstein Discovery’s statistical analysis of your data. An improvement is a suggested action, based on prescriptive analytics, that a user can take to improve the likelihood of a desired outcome.
Predictions and improvements aren't guaranteed outcomes. However, they can help you investigate and understand factors to help you improve those outcomes.
What Could Happen insights show you the most important details behind the prediction for a single goal, which in our case is CLV.
Note
What Could Happen insights allow you to get predictions and improvements using the data in your story’s dataset. Einstein Discovery also enables you to use predictions and improvements in a production environment. For instructions on how to use predictive models in your org, see Predict Outcomes and Take Actions.
Get Predictions
Let's start by seeing how Einstein Discovery can help predict CLV by industry.
Einstein displays the What Could Happen screen.
In the left panel, you see a list of columns in your dataset. The Model Feature label simply means that the column is represented in the predictive model that Einstein Discovery uses to generate predictions. These are your explanatory variables. The columns are sorted by the correlation percentage, which indicates how much the data in that column correlate with the outcome variable. In our case, we see that Division correlates most highly with CLV, followed by Type and Rating.Each column has a list of values to choose from. To see a prediction, click the dropdown and select a value from the list.
2. For Division, select Naval.
Einstein shows the predicted CLV, when Division is Naval, to be 20995.67. It also shows us that this selection has a negative effect on the average CLV (lowers it by 267).
Note
3. Try selecting other divisions until you find the one that contributes to the highest predicted CLV.
When Division is Raw Materials, the predicted CLV is 24926.15, a positive factor that’s 721.6 higher than the average. That’s 3930.48 higher than when Division is Naval. Quite a difference!
4. To learn what’s behind this prediction, scroll down to the waterfall chart.
This chart shows you the baseline CLV, the impact when Division is Raw Materials, the expected impact of other fields, and the overall predicted outcome. To see statistical details, hover over the bars in the chart.
You can select combinations of field values to see how the interaction among these factors affects the predicted CLV. Let’s see what happens.
5. Try selecting values for Type until you find the one that contributes to the highest predicted CLV when Division is Raw Materials.
Get Improvements
Einstein Discovery can recommend actions you can take to change a predicted outcome. Actionable variables drive improvements in Einstein Discovery. An actionable variable is a variable that people can control or influence, such as deciding which marketing campaign to use for a particular customer, or which shipping method to recommend to a customer.
If you click the Actionable button next to a column, Einstein displays actions you can take, if any, to improve the outcome in the Top Improvements box.
Conclusion
In this module, you learned how Why it Happened insights give you a deeper understanding of the complex relationships in your existing data. You saw how related and unrelated factors affected the observed outcome, then used What Could Happen insights to make better decisions about future business actions.