Time Estimate

# Understand Why It Happened Insights

## Learning Objectives

After completing this unit, you’ll be able to:
• 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

#### Note

The instructions in this unit assume that you have successfully created an Einstein Discovery story. Refer to the instructions in "Create a Story," the first unit in this Trailhead module.

Why It Happened insights help you take a deeper look into the exact factors that led to an outcome.

#### Note

The why in Why It Happened refers to a high correlation - not necessarily a causal relationship. A correlation is simply the association—or “co-relationship”—between two or more things. In Einstein Discovery, correlation describes the statistical association between explanatory variables and the outcome variable. However, keep in mind that correlation is not causation. Correlation merely describes the strength of association between variables, not whether they causally affect each other

Use Why It Happened insights to drill deeper into the various factors that contributed to your story’s goal. These insights are based on a statistical analysis of your dataset and augmented by AI and machine learning. Einstein Discovery uses waterfall charts to help you visualize 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 and shows us a waterfall chart of CLV values, including values for Global Average and Division is Naval. What can the chart tell us about why customers who are part of the Naval division are different from the average customer?

• 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.
The chart shows that the Naval division in our company has a much higher CLV than the mean! Are Naval customers intrinsically better? Maybe they are basically the same as other customers but there are underlying correlations that increase CLV. Maybe it’s a little of both. Let’s find out.

#### Note

Don’t worry if the images here differ slightly from the screens you see in Einstein Discovery. The interface elements are usually the same, but some of the details—including the data they show—can differ slightly.

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

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.

#### Note

Here’s one way you can use this number. Suppose that the number is high, like 1,000. In that case, the effect of being a Naval customer, with no other factors considered, is that the CLV is \$1,000 higher than average. Awesome! But you see that the observed outcome is much less than \$1,000. This information indicates that Naval customers have the potential to be valuable, but that something else is dragging this number down.

• 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 Unrelated Categories

We got some useful information from categories that are related to the Naval division. Now let’s look at the Unrelated category. But why do we look at information that is unrelated? Good question. The information in this section is “unrelated,” meaning that it is not specific to customers in the Naval division. This section shows us factors that have positive or negative effects on all customers. This section also accounts for how frequently a factor occurs for Naval customers, relative to customers in general. Let’s get more specific.

• If a good thing happens more frequently for Naval customers than it does for all customers, the effect is positive.
• If a good thing happens less frequently for Naval customers than it does for all customers, the effect is negative.
• If a bad thing happens more frequently for Naval customers than it does for all customers, the effect is negative.
• If a bad thing happens less frequently for Naval customers than it does for all customers, the effect is positive.

In other words, Einstein Discovery is sophisticated enough to show how bad things happening less often have a positive impact. To see more examples, let's look at our chart.

Hover over the Unrelated Small Contributors bar to display details.

You can see that all the other factors (a total of 299 small contributors) contribute an extra \$232 in CLV.

You quickly realize the power of the Unrelated section. It gives you deeper information about why something happened. In other words, it gives you more power to credit (or give constructive feedback to) the right people.

We’re done looking at the Unrelated factors. Now let's move on to the Unexplained section.

## 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.