Understand Why It Happened Insights
Learning Objectives
- Navigate to a story's Why it Happened insights and explore them.
- Understand how combinations of factors affect the outcome.
- Understand how unrelated factors affect the outcome.
About Why It Happened Insights
Your Story’s Outcome Variable and Goal
When you configured the story, you told Einstein Discovery to maximize the CLV variable in AcquiredAccount. CLV is the outcome variable in your story, and maximizing CLV is your goal. All the insights in this story show you how different variables and combinations of variables help explain variations in CLV. The top insights in the list reflect the most statistically significant variations in the outcome variable.
Select the Why it Happened Insight Type
On the Insight Navigation bar, click Why It Happened.
In Search story insights, type Division in the search bar, then scroll down and select Division - Naval.
Einstein Discovery refreshes the insights list.
- Global Average represents the mean value for CLV across all divisions (including Naval).
- Division is Naval represents the average CLV value for the Naval division.
Hover over the Global Average bar to get more information.
The Global Outcome has a global mean value of 20,136 and a global count of 10,000. What does this data tell us? That the average CLV across all divisions (including Naval) is $20,136, and there are 10,000 rows (also called observations) in the dataset.
Understand the Division Is Naval Insight
Hover over the Division is Naval bar to see more information.
We can learn a lot about Naval customers from this information. Let’s look at the numbers in the following order so that we can understand the building blocks first.
- Frequency is 3.3%. Customers in our Naval division make up only 3.3% of customers overall. How unfortunate, because our Naval customers have a higher than average CLV. Perhaps it's time to try to acquire potential Naval customers? Or perhaps we realize that the Naval market is small and we focus on other divisions?
- Conditional Frequency is 1 (100%). In our case, 100% of the records in the category Division is Naval are in the Naval division. Perhaps this information seems obvious.
- Coefficient is -267. What does this coefficient value tell us? That the CLV for Naval division would be $267 lower than the mean if there were no other factors involved. This number tells you that the simple fact that division is Naval influences the CLV for the Naval division.
- Precluded Sum is 4,596. The impact for the average customer includes the impact of customers who are in the Naval division and the impact of those customers who are not. Einstein Discovery calculates the impact that customers who are not in the Naval division have on the CLV of customers who are in the Naval division. In our case, the impact of removing all the effects for divisions that are not Naval is to increase CLV by $4,596.
- Impact is 4,338. This number represents the net impact. Impact considers the effect of simply being a Naval customer and the percentage of overall customers that are Naval. Impact also adds in the impact of other customers that are not Naval. What is it telling us, in our case? Without the other factors in the Related to and Unrelated categories, Naval customers would have a CLV of $4,338 more than average. That’s a significant number! Why aren’t we realizing that potential? In the next sections, we find out.
We are done with the first-order analysis in the Division is Naval category. Now let’s look at another category.
Understanding the Unexplained Section
Looking at Unexplained phenomena sounds mysterious. Really, it's just the comparison between the predictions made for all observations in the requested subset, and their overall average, compared with the observed average. The bar shows whether the average for unexplained factors was higher or lower.
Hover over the Unexplained bar to display details.
The difference between the actual average CLV (calculated from the dataset), and the predicted average CLV (from Einstein Discovery’s predictive model), is $481.