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Understand What Is The Difference Insights

Learning Objectives

After completing this unit, you’ll be able to:
  • Navigate to a story’s What is the Difference insights and explore them.
  • Understand the relationships between variables.
  • Use these insights to help maximize customer lifetime value (CLV).

Understand What Is The Difference Insights

Note

Note

The instructions in this unit assume that you have successfully created an Einstein Discovery story according to the steps in "Use Stories to Get the Big Picture," the first unit in this Trailhead module.

In Einstein Discovery, What Is The Difference insights are comparative insights that help you better understand the relationships between explanatory variables and the goal (target outcome variable) in your story. These insights, based on a statistical analysis of your dataset, help you figure out which factors contribute to the biggest changes in the outcome variable. Einstein Discovery uses waterfall charts to help you visualize comparisons in What Is The Difference insights.

By isolating an explanatory variable, you can see and learn how it relates to the whole, and how it compares to another explanatory variable. For example, you can compare sales performance for manufacturing customers to your overall sales performance. In addition, you can compare sales performance between manufacturing and distribution customers. Finally, you can add a filter to focus on a smaller slice of your data (such as a particular sales region).

Find the Best Customers Using CLV

In this unit, we use What Is The Difference insights to explore the story you created previously (see "Create a Story" in the "Einstein Discovery Classics" module). Recall that the goal of this story is to maximize customer lifetime value (CLV). CLV is a metric that predicts the profitability over the entire lifetime of the company’s relationship with a customer.

Select the What Is The Difference Insight Type

On the Insight Navigation bar, click the down arrow in the upper right, and then click What Is The Difference.

On the Insight Navigation bar, click down arrow, then click What Is The Difference.

The Insights Navigation bar displays this category but no graph. To see a graph, you must first select a variable.

Compare an Explanatory Variable with the Global Average

In our example story, the global average represents the CLV of all data in the dataset. It is useful to compare the CLV of a single variable with the global CLV average. To select a variable, for Relating to (select a variable) on the left of the Insights navigation bar, select Industry - Shipping.

From Relating to (select a variable), select an explanatory variable to compare.

When the calculations are complete, you see the most statistically significant insights in a waterfall chart.

Compare CLV for Industry = Shipping with the global average.

CLV appears at the top of the graph as a reminder that we configured this story to maximize CLV as our outcome variable.

Note

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.

At the top of the chart, the gray bar labeled Industry is Shipping (Average) shows us the average CLV when Industry is Shipping. To see more details, hover over the gray bar.

Hover over the gray bar to see numbers for the average CLV when Industry is Shipping.

When our story maximizes CLV as the outcome variable, a red bar in the chart shows a condition that reduces CLV from the average. Hover over a red bar in the graph.

A red bar represents a condition that lowers CLV below the global average.

In this example, when Industry is Shipping and BillingState is Texas, CLV is -89, or 89 below average. Notice that the corresponding insight description on the left is highlighted in gray.

When our story maximizes CLV as the outcome variable, a green bar in the chart shows a condition that increases CLV from the average. Hover over a green bar in the graph.

A green bar represents a condition that raises CLV above the global average.

In this example, when Division is Standard Hardware, CLV is 190 above average. Notice that the corresponding insight description on the left is highlighted in gray.

The corresponding insight description is highlighted when you hover over a bar in the chart.

Note

Note

If our story goal was to minimize CLV, the green and red colors would be reversed.

The blue bar at the bottom of the chart shows the Global Average (Outcome), which represents the average CLV for all data in the dataset (20,136).

A blue bar represents the average CLV for all data in the dataset.

Compare Two Variables

Next, we add a second explanatory variable and compare the two. On the Insights navigation bar, go to Between (select a variable) on the right and select Industry - Technology.

Select a second variable to compare with the first.

When the calculations are complete, you see a waterfall chart comparing the two industries.

Compare technology CLV with shipping CLV.

At a glance, this chart shows that CLV for Industry is Technology (Average) outperforms Industry is Shipping (Average) in many ways. For example, when accounts are rated hot, Technology has a better lifetime CLV than Shipping. But in Texas, Shipping has a much better CLV than Technology.

Hover over the gray bar at the top of the chart that shows Industry is Shipping (Average).

Details from hovering over a gray bar in the chart.

Hover over the blue bar at the bottom of the chart that shows Industry is Technology (Average).

Details from hovering over a blue bar in the chart.

Comparing the actual CLV numbers confirms that the average CLV for Technology is higher than for Shipping.

Add a Filter

Optionally, you can add a filter to further focus your analysis on a subset of the data. On the far right side of the Insights navigation bar, click Search story insights and choose Type - Consulting.

Select from Search story insights to filter the chart.

When the calculations are complete, you see a waterfall chart comparing the two industries with only Consulting data.

Chart shows filtered data.

In this example, Small Terms and Division is Standard Hardware show the highest correlation for maximizing CLV when Type is Consulting.

Conclusion

In this module, you continued your work as the VP of operations for a major automotive supplier. You dug deeper into the story you created in the Einstein Discovery Data Integration module. You learned how to interpret several of the insights that Einstein Discovery uncovered in your data. You looked at descriptive insights that detailed what happened, and at comparative insights that show what is the difference when comparing variables. The story was filled with insights about your data. Exploring the story helped you discover relationships between the CLV of your accounts and other variables that can influence CLV.

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