Use Stories to Get the Big Picture
- Open a story.
- Describe the order in which insights are presented in a story.
- Name the types of insights that a story gives you.
In this module, we do a deep dive into a story to learn about different types of insights and how to interpret them. Imagine that you’re the VP of operations for a major automotive supplier, where you’ve developed a reputation for ace detective work. The executive team was so impressed when you solved the case of the shrinking margins in the Einstein Discovery Basics module. They’re back, asking you for more help. Now they want more information about the customer lifetime value of accounts.
What is Customer Lifetime Value?
Customer lifetime value (CLV) is a metric that predicts the profitability over the entire lifetime of the company’s relationship with a customer. Looking at CLV can help you find the group of customers who are potentially the most profitable. That way, more marketing resources can be allocated to them.
What is a Story?
- helps you uncover relationships between a business-relevant metric (outcome variable) and the explanatory variables that are potential influencers of that metric
- has a goal of minimizing or maximizing the business metric (for example, maximizing margin or minimizing costs)
- contains answers, explanations, and recommendations arranged into an organized presentation with a logical flow and related sections
- is filled with insights about your data and the variables you're interested in
Let's create a story to work with.
Before you work through this Trailhead module, sign up for a free Analytics-enabled Developer Edition org. This org is a safe environment where you can practice the skills you’re learning.
Signed up? Great! Let's jump right in!
Before we can create the story used in this module, we need the data to analyze. Download the CSV file called AcquiredAccount.csv and save it to your computer.
The CSV file has 11 columns: Account Id, BillingState, Division, Industry, Ownership, Rating, Type, AccountScore, StartDate, CloseDate, and CLV. The CSV file contains one row of information for each of the 10,000 different companies that our auto parts manufacturing company does business with. Here is what the first few rows of the CSV file look like:
The next step is to get the data from the CSV file into an Einstein Analytics dataset.
- In your new DE Org, switch to Lightning Experience (if you have not already done so).
- Click the App Launcher icon (), search for Analytics Studio, and then click the tile to launch Analytics Studio.
- Click Datasets.
- Click Create and then select Dataset from the dropdown.
- Choose CSV File as the source for your new data.
- In the file-selection window that opens, find and select (or drag and drop) the AcquiredAccount.csv file you downloaded, and then click Next.
- Accept the defaults and click Next.
- Accept the defaults and click Upload File. Einstein Analytics creates a dataset and imports the data from the CSV file.
Now, you’re ready to create a story from this dataset. Begin by telling Einstein Discovery which variable to maximize. In this module, we focus on maximizing the CLV variable.
- Hover over the dataset, click the dropdown, and click Create Story. Analytics Studio launches the Story Setup wizard.
- In the first screen, for The field, select CLV as the outcome that you want to Einstein to analyze.
- Accept all other defaults and click Story Type.
- In the Story Type screen, click Insights & Predictions, then click Setup Options.
- In the Setup Options screen, accept the default (Automated) and click Create Story.
When it’s done preparing the story, Einstein Discovery shows it to you.
The story presents the insights that Einstein Discovery uncovered for you. The first insights you see are What Happened insights, which are descriptive insights that tell you more about what happened according to the data in the dataset.
Order of Insights in the Story
In the list of insights, the first insights that you see are the ones that explain, statistically, the most variation in the outcome variable. The insights that appear later, as you scroll through the story, explain variables that, statistically, account for less of the variation in the outcome variable. At the beginning of an analysis, we don't know cause and effect. The insights show us statistically significant patterns that give us clues about where to dig in for more detail. Of course, you are free to explore the insights in any order you want.
Other Types of Insights
- Why It Happened
- Predictions & Improvements
- What is the Difference (in the Predictions & Improvements dropdown)
All of these types of insights are covered in this module as well as in the “Einstein Discovery Story Insights” module.
Dive Into Our Story Insights
Now that you have and overview of stories and insights, let's dive in to see how to get the most out of each type of insight in your story. The next unit explores What Happened insights.
Rights of ALBERT EINSTEIN are used with permission of The Hebrew University of Jerusalem. Represented exclusively by Greenlight.