MicroStrategy Academic Program

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1 MicroStrategy Academic Program Creating a center of excellence for enterprise analytics and mobility. PREDICTIVE ANALYTICS AND R APPROXIMATE TIME NEEDED: 1 HOUR

2 TABLE OF CONTENTS TABLE OF CONTENTS 2 SET UP SHOP 3 Downloading and Installing MicroStrategy Desktop 3 Downloading and Installing R on Windows 4 Downloading and Installing R on Macintosh 6 Locate supporting files from Dropbox location 6 Install additional libraries 6 WORKSHOP STORYLINE 7 LAUNCH DESKTOP, CONNECT TO, CLEAN AND MODEL DATA 7 Connect to your data 8 Data modeling 9 Data processing 10 EXPLORE THE DATA AND VISUALIZE INSIGHTS 13 Pairwise correlation 15 Data creation 20 Trend analysis 22 Model building and evaluation with K-Means clustering 24 Stepwise logistic regression 32 BUILD YOUR DOSSIER 38 Visualization 1 38 Visualization 2 42 Visualization 3 44 Insert a title and the company logo 46 Conclusion 49 Copyright All Rights Reserved. microstrategy.com 2

11 for Mac or Windows 64-bit at https://www.microstrategy.com/us/get-started/desktop 2.

3 SET UP SHOP Downloading and Installing MicroStrategy Desktop 1. If you have not already, begin by downloading MicroStrategy Desktop v10.11 for Mac or Windows 64-bit at 2. Populate the fields and click Download Now. 3. Choose the appropriate link based on your computer for either Windows or Mac. Copyright All Rights Reserved. microstrategy.com 3

4 4. To install MicroStrategy Desktop, follow the appropriate procedure below when using a Macintosh or a Windows based computer. For Windows: 1. Extract desktop-windows zip found in C:\Users\<yourusername>\Downloads. 2. Open the folder MicroStrategy Desktop Double-click DesktopSetup.exe. Follow the steps to complete the installation. For Macintosh: 1. Double-click MicroStrategyDesktop_10.11_Mac.zip found in MacintoshHD:\Users\<yourusername>\Downloads. 2. Double-click MicroStrategyDesktop_10.11_Mac.dmg. Click to agree with the license. 3. Drag the MicroStrategy Desktop.app in to the Applications folder and follow the steps to complete the installation. MicroStrategy Desktop is now ready to go. Downloading and Installing R on Windows 1. For this workshop, download the most recent version of R for Windows from the URL below: 2. On the web page, click the link to download R for Windows as shown. 3. On the message that appears, click Run. Copyright All Rights Reserved. microstrategy.com 4

5 4. Click Yes in the User Account Control message. 5. Select English and click OK. 6. Click Next until you see the screen below. 7. Make sure the No option is selected and click Next. 8. Click Finish. Your setup of R is completed. Copyright All Rights Reserved. microstrategy.com 5

6 Downloading and Installing R on Macintosh 1. For this workshop, download the most recent version of R for Windows from the URL below: 2. On the web page, click the link to download R pkg. 3. Double-click the downloaded package and follow the directions on screen to complete the installation. Version of MicroStrategy Desktop includes the tools to provide a deep integration with the R product you just installed. This brings within MicroStrategy all the power R offers in term of data analysis, with the ease of the MicroStrategy Desktop interface. Locate supporting files from Dropbox location The location will be provided by the instructor. Supporting files include: One dataset called R World.xlsx One image file called Kiara fashion Logo.jpg One.txt file called All R Metric Expressions.txt One.txt file called data Wrangle Script.txt One.jpg image called PairwiseCorr.jpg Save these files to your computer desktop. Install additional libraries 1. Launch the R application. A terminal-like window opens. 2. Enter the following two commands install.packages("xml", repos=" install.packages("ellipse", repos=" Each command will install a library in your R application. Copyright All Rights Reserved. microstrategy.com 6

WORKSHOP STORYLINE Kiara Fashion, a national retailer, has recently acquired another regional chain and added its offerings to their existing product line.

To do so, they have run both store flyers and mailers, but the conversion rate has remained low, leading some to question the acquisition decision.

7 WORKSHOP STORYLINE Kiara Fashion, a national retailer, has recently acquired another regional chain and added its offerings to their existing product line. The marketing team has been trying to capitalize on the acquisition by offering this new, enhanced product line to the customer base. To do so, they have run both store flyers and mailers, but the conversion rate has remained low, leading some to question the acquisition decision. The Vice President of Marketing has instructed you, the campaign analyst, to figure out how to optimize marketing campaigns to get better results. You will achieve this by first analyzing the customer base to identify the most valuable customers. Then analyze past purchases data in conjunction with customer data to determine which customers are most likely to buy the product. The insights gained from the campaign analysis will be used to acquire a new set of customers who exhibit similar characteristics. Your work will culminate into a presentation-ready dashboard build with MicroStrategy Desktop which you present to the VP. LAUNCH DESKTOP, CONNECT TO, CLEAN AND MODEL DATA 1. Launch MicroStrategy Desktop by double-clicking its icon. You could take a look at existing dossiers or visit the MicroStrategy Community to exchange ideas in the Resources area below. For now, we will create our own dossier 2. Get started on your New Dossier by clicking the corresponding button. Copyright All Rights Reserved. microstrategy.com 7

like spreadsheets, to big data sources, relational databases, and cloud-based applications.

8 3. Click the Contents icon to hide that panel for now. Connect to your data 1. From the Datasets panel click New Data. TIP: MicroStrategy Desktop allows you to quickly and intuitively connect to almost any data source, from a tabular file like spreadsheets, to big data sources, relational databases, and cloud-based applications. The Data Sources Wizard appears where you can live connect to sources or import data into memory. Copyright All Rights Reserved. microstrategy.com 8

2. Click the option to import a File from Disk. Then choose the file by clicking Choose Files. 3. Locate and open the file named: R World.xlsx. 4. The dataset now shows as one of your chosen files.

9 2. Click the option to import a File from Disk. Then choose the file by clicking Choose Files. 3. Locate and open the file named: R World.xlsx. 4. The dataset now shows as one of your chosen files. Click Prepare Data to begin modeling the data. Data modeling 1. Select the check boxes to choose all sheets in the file (or all tables in the dataset) before clicking Select. Copyright All Rights Reserved. microstrategy.com 9

10 2. After a moment, you are presented with a preview of the data. Here you can review a sample of the data in the dataset, the mapping of the columns, metrics, attributes and the data type of each column. As you can see, MicroStrategy automatically and intelligently joins the ID columns from different tables. TIP: Data Blending allows you to combine data from multiple, diverse sources into a single Dossier. This is particularly useful when you are trying to analyze data that is related but maintained separately. TIP: Data enrichment features give users the opportunity to enrich the data without any extra work. Users can add hierarchical levels to time and geographic columns and add geo-coding to geographic levels to display information via Maps in MicroStrategy. Data processing The dataset you are trying to analyze needs to be massaged before you can find insights. With the Data Wrangling feature, you can quickly clean up the data. 1. On the Preview screen, click the arrow beside the Customer Profile Data dataset as show below. Copyright All Rights Reserved. microstrategy.com 10

11 2. From the menu, click Wrangle. TIP: The Data Wrangling tool creates a script of all the actions you perform on the dataset. This script can be saved to your machine, uploaded at a later date, and applied to a new dataset to significantly speed up the process of preparing data for analysis. 3. In the Data Wrangling window, click the button highlighted below to import a script that one of your coworkers built previously using Data Wrangle, to find and replace Na and Null in certain columns with a No value. 4. Click Browse at the bottom left of the window that appears, and find the Wrangle Script.txt file from the supporting files. 5. Navigate to the file Wrangle Script.txt and click Open. Copyright All Rights Reserved. microstrategy.com 11

12 6. Click Apply in the Import History Script Window to execute the script that now appears. 7. The procedures now show in the Script window. Click Apply at the bottom right. 8. Now click Finish to finalize the data processing and data modeling. Copyright All Rights Reserved. microstrategy.com 12

Click the Save icon in the toolbar to save your progress. 2.

13 Your data is now imported and ready to analyze. EXPLORE THE DATA AND VISUALIZE INSIGHTS 1. Click the Save icon in the toolbar to save your progress. 2. Save your work as Campaign Analysis Dossier. 3. Display the Contents Panel and hide the Gallery as shown below. Copyright All Rights Reserved. microstrategy.com 13

4. Click the vertical ellipsis ( ) in the Datasets panel and select

Rename Chapter 1 to Test Discovery by clicking on the vertical

Then type in the new name and press Enter. 6.

14 4. Click the vertical ellipsis ( ) in the Datasets panel and select Table View. 5. Rename Chapter 1 to Test Discovery by clicking on the vertical ellipsis next to it and clicking Rename. Then type in the new name and press Enter. 6. Right-click Page 1 and Rename it to Data Assessment. 7. From dataset 3. Campaign Coverage Data, double-click Duration Last Contact to add it as a metric on the grid. 8. Click the arrow in the metric in the grid visualization, hover over Aggregate By, and click Average. Copyright All Rights Reserved. microstrategy.com 14

15 9. Repeat the last step to add Standard Deviation. Your grid should appear as follows. 10. Click Save to save your progress. Pairwise correlation Correlation measures the strength of a relationship between two metrics. If a correlation is greater than 0.8 we say that two metrics are highly, positively correlated. If correlation is less than 0, we call two metrics negatively correlated. Pairwise Correlation measures how two variables behave with respect to each other. The key statistic is the correlation coefficient, also known as r, a number that captures both the direction and the magnitude of the relationship as a number that ranges from -1 to +1. The output is a correlation plot and a table that contains the correlations of the variables when taken in pairs. 1. Begin by inserting a new page into your chapter by clicking the vertical ellipsis next to Test Discovery, then click Insert Page. Copyright All Rights Reserved. microstrategy.com 15

2. Right-click the newly inserted page and rename it Pairwise Correlation. 3.

Socio Economic Data, right-click the Monthly Consumer Conf Idx metric and

Click Switch to Formula Editor in the lower left of the Metric Editor where

16 2. Right-click the newly inserted page and rename it Pairwise Correlation. 3. From Dataset 5. Socio Economic Data, right-click the Monthly Consumer Conf Idx metric and select Create Metric. 4. Click Switch to Formula Editor in the lower left of the Metric Editor where you can paste in the R metric expression for the Pairwise Correlation from the text file provided called All R Metric Expressions. Copyright All Rights Reserved. microstrategy.com 16

5. Copy the Pairwise Correlation expression (the first in the text file, use the first expression for PC and the second one for the Mac) and paste it into the formula window of the Metric Editor, as

17 5. Copy the Pairwise Correlation expression (the first in the text file, use the first expression for PC and the second one for the Mac) and paste it into the formula window of the Metric Editor, as shown below. Note: For Mac users, the formula that contains the path to the actual R script must be modified to reflect your user name. The path will look like: /Users/yourname/Documents/MicroStrategy R Integration Pack/RScripts. Replace yourname with your actual user name. 6. Click Validate to confirm the validity of the formula. Rename the metric to Pairwise Correlation and click Save. TIP: Conceptually, think of the R Analytic as a black box with an R Script inside. MicroStrategy doesn t need to know much about the R Script in the black box, it only needs to know how to pass data in and, after the script has executed in the R environment, how to consume the results. 7. From the dataset 5. Socio Economic Data, drag and drop the following attributes and metrics into their corresponding sections of the Editor panel: Customer ID Month Last Contacted Pairwise Correlation Monthly Consumer Conf Idx Monthly Consumer Price Idx Qtrly Emp Variation Rate Quaterly Num Of Emp US FED 3mnth Rate Copyright All Rights Reserved. microstrategy.com 17

Using the Finder or File Explorer, search for the file PairwiseCorr.jpg and copy it to your desktop. 10.

18 TIP: Once you see the OK message in the Pairwise Correlation column, it means that the R script has been executed successfully. If it does not work, verify the path to the R script in the metric definition. 8. Click Save in the toolbar to save your progress. Pairwise Correlation will generate a JPEG image. 9. Using the Finder or File Explorer, search for the file PairwiseCorr.jpg and copy it to your desktop. 10. Click the vertical ellipsis for the Test Discovery chapter, select Insert Page, then rename it Pairwise Correlation Visualization. The Contents panel should now appear as follows. 11. In the new page, click the icon highlighted in red below to insert an image. 12. Click the vertical ellipsis at the top right of Visualization 1, then select Delete. Copyright All Rights Reserved. microstrategy.com 18

19 13. Click the Browse button and open the JPEG you just generated by using the pairwise correlation analytic script. 14. Open the JPEG generated, located in: Your desktop as you copied it in a previous step, or Included with your exercise files TIP: To get the correct URL for the image, simply open the image in a web browser and copy and paste the URL. 15. Analyze the image in Presentation Mode by clicking the following red icon in the top right of the screen. Copyright All Rights Reserved. microstrategy.com 19

20 16. Return to Design Mode by clicking the pencil icon in the top right. Data creation 1. Click the vertical ellipsis for the Test Discovery Chapter and select Insert Page. Copyright All Rights Reserved. microstrategy.com 20

2. Right-click the new page and rename it to Data Creation. 3. Continuing in the new Data Creation page, right-click any metric in the Datasets panel and select Create Metric.

1*[monthly consumer conf idx])+(0.95*[us FED 3mnth rate]) 5. Validate the formula. The Metric Editor should appear as shown below. Click Save to create the derived metric.

21 2. Right-click the new page and rename it to Data Creation. 3. Continuing in the new Data Creation page, right-click any metric in the Datasets panel and select Create Metric. Make sure you select Formula Editor. 4. In the Formula window, type in this formula and name the new metric Economy Factor. (0.91*[Qtrly Emp variation rate])+(0.53*[monthly consumer p rice idx])+(0.1*[monthly consumer conf idx])+(0.95*[us FED 3mnth rate]) 5. Validate the formula. The Metric Editor should appear as shown below. Click Save to create the derived metric. TIP: The Economy Factor is created as the sum of the products of each metric s correlation co-efficient, with respect to the Quarterly Num Of Emp metric. For example, the co-efficient of Qtrly Emp Variation Rate is 0.91 as shown in the image above. We will use Economy Factor for further analysis. Copyright All Rights Reserved. microstrategy.com 21

6. Create a grid visualization on the Data Creation page using the following objects located in the Editor panel within the 5. Socio Economic dataset.

22 6. Create a grid visualization on the Data Creation page using the following objects located in the Editor panel within the 5. Socio Economic dataset. Customer ID Month Last Contacted Economy Factor Monthly Consumer Conf Idx Monthly Consumer price Idx Qtrly Emp Variation Rate US FED 3mnth Rate 7. Click Save to save your progress. Trend analysis Trend analysis is a method of time series analysis that involves comparing the same metric (such as monthly sales revenue figures) over a period of time (attribute) to detect general patterns in a relationship between associated variables. Copyright All Rights Reserved. microstrategy.com 22

Click to display the Visualizations Gallery on the right.

23 1. Insert a new page and rename it Trend Analysis. Your Contents panel will appear as shown below. 2. Drag and drop Month, Loyalty Tier, and Revenue to the Editor panel as shown. 3. Click to display the Visualizations Gallery on the right. Select Line Chart from the gallery to convert your grid into a line chart 4. Move Loyalty Tier to the Color By area and make sure that Revenue and Month are in the Vertical and Horizontal areas, respectively. Copyright All Rights Reserved. microstrategy.com 23

24 5. Click Save to save your progress. Model building and evaluation with K-Means clustering K-Means clustering is an algorithm that groups items (customers) into k distinct clusters so that items within the same cluster are more similar to each other than other items (customers) within different clusters. This method aims to partition n items into k clusters, in which each object belongs to the cluster with the nearest mean. The output of this analysis is a metric with a Cluster Number. Copyright All Rights Reserved. microstrategy.com 24

1. Insert a new page and rename it Segmentation.

function list. 3. In the Segmentation page, create the grid as shown below.

25 1. Insert a new page and rename it Segmentation. Your Contents panel should look similar to the image below. 2. Right-click Net Margin from 4. Month Level Customer Data and select Aggregate By, then select Average from the function list. 3. In the Segmentation page, create the grid as shown below. From dataset 2. Past Purchase Data Customer ID Loyalty Tier From dataset 4. Month Level Customer Data Revenue Avg(Net Margin) (located at the bottom) Copyright All Rights Reserved. microstrategy.com 25

26 4. Right-click any metric in the Datasets panel and select Create Metric. Make sure to click Switch to Formula Editor. 5. Copy the K-Means metric expression from the text file called All R Metric Expressions and paste it into the formula window. Note: For Mac users, the formula that contains the path to the actual R script must be modified to reflect your user name. The path will look like: /Users/yourname/Documents/MicroStrategy R Integration Pack/RScripts. Replace yourname with your actual user name. Copyright All Rights Reserved. microstrategy.com 26

6. Validate the script. Rename the new metric Cluster and click Save in the Metric Editor to create the analytic. 7. For reference, the kmeansclustering.

27 6. Validate the script. Rename the new metric Cluster and click Save in the Metric Editor to create the analytic. 7. For reference, the kmeansclustering.pdf file contains documentation on this type of analysis. This can be found in Windows in C:\Program Files\MicroStrategy\Desktop\R Integration Pack\RScripts or for a Mac /Users/yourname/Documents/MicroStrategy R Integration Pack/RScripts. 8. Add the newly created Cluster analytic to the grid. 9. Click Bubble Chart from the gallery to convert the grid into a bubble chart. 10. Rearrange the objects in the Editor panel as shown in the image below. Move Revenue to the Vertical area, and Avg(Net Margin) to the Horizontal area, Color by Cluster, Break by Customer ID, then by Loyalty Tier. Copyright All Rights Reserved. microstrategy.com 27

13. Adjust the sliders to select the ranges as shown below.

28 11. Right-click any of the circles/data points in the visualization and choose Select Color Ranges. 12. Change the Color option to Red-Yellow-Green and the Based On option to Value. 13. Adjust the sliders to select the ranges as shown below. The colors represent three different clusters. Click Apply, then OK. Copyright All Rights Reserved. microstrategy.com 28

29 As shown below, you can now visualize the results for three well separated clusters. We can clearly find that records belonging to Cluster 2 represent the most valuable customers, as they have the highest Revenue and Average Net Margin. 14. Don t forget to Save. 15. Now that we have identified the high value customers (Cluster 2), displayed in yellow, we can inspect these records further by Selecting the area around these circles. Click in the visualization and drag to draw the selector around the yellow cluster. Copyright All Rights Reserved. microstrategy.com 29

16. Right-click one of these selected points and choose

one circle in the top right of the visualization, well

30 16. Right-click one of these selected points and choose Group, then select Customer ID. 17. Label this group High Value Customers, then click Save. You should now see the group High Value Customers shown as one circle in the top right of the visualization, well separated from the other circles. Copyright All Rights Reserved. microstrategy.com 30

31 18. You can hover over the circle at the top to study characteristics for the High Value Customers. 19. On the Datatsets panel, locate the newly created derived attribute Customer ID(Group). Right-click and select Edit Groups. 20. Rename the attribute as Value Segments. Click and rename All Others to Medium & Core Value Customers. Then change the section for Display All Other Elements to a consolidated group. Copyright All Rights Reserved. microstrategy.com 31

32 21. Click Save in the Group Editor. Then click Save in the dossier to keep your progress. Stepwise logistic regression Stepwise Logistic Regression is a variant of classical Logistic Regression in which variables (metrics) are only included in the model if they have a significant effect on the variable (metric) we are trying to predict. The Stepwise Logistic Regression script uses backward elimination based on the Akaike Information Criterion (AIC). By using backward elimination, all variables are initially included in the model and are removed one by one, until only the significant variables remain. The output metrics are probability values, in percentages. Copyright All Rights Reserved. microstrategy.com 32

1. Insert a new page from the Contents panel and rename it

Search the Day Of Week Last Contacted attribute in the

3. Having located the attribute, right-click and select

33 1. Insert a new page from the Contents panel and rename it Buyer Propensity. 2. Search the Day Of Week Last Contacted attribute in the Datasets panel TIP: By using the Search, you reduce the number of elements visible. This makes it easier to see the addition of the new metrics. 3. Having located the attribute, right-click and select Duplicate as Metric. A count metric is created and shows in the Datasets panel. Copyright All Rights Reserved. microstrategy.com 33

Repeat the previous steps for the following attributes: Duration Of Last Contact Type Mode Of Contact Month Last Contacted Num Of Contact Campaign Type Outcome

34 4. Right-click the new Count metric and change the Aggregate By option to Maximum. TIP: When duplicating an attribute as a metric, MicroStrategy by default takes aggregation as Count. 5. Repeat the previous steps for the following attributes: Duration Of Last Contact Type Mode Of Contact Month Last Contacted Num Of Contact Campaign Type Outcome Last Campaign Job 6. In the Buyer Propensity page, add the attribute Customer ID to the rows. Add all of the newly created max derived metrics as well as Target Buy to the metrics drop zone. Use the image below as a guide for the order of the columns. Copyright All Rights Reserved. microstrategy.com 34

Copy and paste the Stepwise Logistic expression from the All R Metric Expressions

35 7. Locate the Target Buy metric in the Datasets panel. Right-click and select Create Metric. Click Switch to Formula Editor. 8. Copy and paste the Stepwise Logistic expression from the All R Metric Expressions text file into the formula window inside the Metric Editor. Copyright All Rights Reserved. microstrategy.com 35

36 Note: For Mac users, the formula that contains the path to the actual R script must be modified to reflect your user name. The path will look like: /Users/yourname/Documents/MicroStrategy R Integration Pack/RScripts. Replace yourname with your actual user name. 9. Validate the formula. Rename the metric to Propensity to Buy and then click Save to create the analytic. 10. The StepwiseLogistic.pdf file contains documentation on this function. Documentation and the R Scripts off-the-shelf are found in Windows in C:\Program Files\MicroStrategy\Desktop\R Integration Pack\RScripts or on a Mac /Applications/MicroStrategy Desktop.app/Contents/R Integration Pack/RScripts/ 11. Add the Propensity to Buy metric to the grid visualization. Then right-click it and change the number formatting to Custom (10.56%) using the Custom number format option as shown below. Then click OK. Copyright All Rights Reserved. microstrategy.com 36

12. Right-click the Propensity to Buy column

Right-click the recently created Propensity to Buy metric and select Create Metric. 4. Click Switch to Formula Editor in the Metric Editor.

38 BUILD YOUR DOSSIER Visualization 1 1. Click the vertical ellipsis to the right of the Test Discovery Chapter, and click Insert Chapter. 2. Click the vertical ellipsis next to the newly created Chapter 1 and Rename it to Campaign Performance Analysis. 3. Right-click the recently created Propensity to Buy metric and select Create Metric. 4. Click Switch to Formula Editor in the Metric Editor. 5. Enter the formula ([Propensity to Buy]*100) in the Formula window. 6. Name the metric Propensity Score. Copyright All Rights Reserved. microstrategy.com 38

It will get added automatically to the Size By. 10. Right-click Propensity Score in the Color By drop zone and select Thresholds. 11.

39 7. Click Validate. Then click Save in the Metric Editor to create the metric. 8. Choose the Heat Map from the Visualization Gallery. 9. Add Customer ID to the Grouping and add Propensity Score to the Color By drop zones as follows. It will get added automatically to the Size By. 10. Right-click Propensity Score in the Color By drop zone and select Thresholds. 11. Change the Based On field to Value. Then adjust the intervals to 20, 40, 60, 80 as shown in the image below. Copyright All Rights Reserved. microstrategy.com 39

The higher the score, the higher the likelihood to buy, so lower values are red and higher ones are green. 13.

40 12. Click Apply to view the changes, then click OK to confirm. The Heat Map shows the likelihood to buy for each customer, colored by the Propensity Scores. The higher the score, the higher the likelihood to buy, so lower values are red and higher ones are green. 13. Insert a filter by clicking the filter icon in the toolbar above the visualization, as shown below. 14. Drag and drop the Value Segments derived attribute into the newly inserted filter. Copyright All Rights Reserved. microstrategy.com 40

15. Click Select Target that appears in the filter. 16.

A blue border will appear around the heat map visualization once selected. 17.

With the filter still selected, navigate to the Format panel, and change the Style of the filter to Radio

41 15. Click Select Target that appears in the filter. 16. Click the heat map visualization to set the filter to target that visualization. A blue border will appear around the heat map visualization once selected. 17. Click Apply to finish creating the filter. 18. With the filter still selected, navigate to the Format panel, and change the Style of the filter to Radio Buttons. TIP: Each time a filter is applied to a visualization, MicroStrategy automatically re-runs the R script to compute the derived metric on the filtered dataset. 19. Select the visualization again and navigate back to the Editor panel. Copyright All Rights Reserved. microstrategy.com 41

20. Add Age, Income USD, and Max(job) to the Tooltip drop zone of the Editor panel to include additional information on each customer when hovering over the Heat Map.

42 20. Add Age, Income USD, and Max(job) to the Tooltip drop zone of the Editor panel to include additional information on each customer when hovering over the Heat Map. TIP: MicroStrategy allows only metrics in the Tooltip bar across different visualizations. 21. Select High Value Customers to be shown in the Heat Map by making that selection in the filter just created. 22. The Heat Map updates. Double-click in the title of the visualization, currently Visualization 1, and rename it to Value at Buy Propensity Segmentation. 23. Click Save in the toolbar to maintain your progress. Visualization 2 This visualization will help identify target groups for campaigns based on propensity scores across high value segments derived from relevant classification models. The campaign analyst can use this visualization to decide whether to contact customers, based on identifying key parameters. Copyright All Rights Reserved. microstrategy.com 42

Double-click the title of the visualization and rename it Campaign KPI Analysis. 4. Choose the Sequence Sunburst visualization from the Gallery. 5.

43 1. Click the bar chart icon in the toolbar to insert a new visualization, as shown below. 2. To focus on this visualization, click the outward facing arrows at the top right of the newly inserted Visualization 1 to expand it. 3. Double-click the title of the visualization and rename it Campaign KPI Analysis. 4. Choose the Sequence Sunburst visualization from the Gallery. 5. Add the following attributes in the Editor panel to the Attributes drop zone: Life Stage Code Ethnicity Loyalty Tier Buy Decision Flag Mode Of Contact Duration Of Last Contact Type 6. Include Income USD in the Metric drop zone. Move your cursor to the center of the visualization and hover from the core to the outer layers of the sunburst. Copyright All Rights Reserved. microstrategy.com 43

The marketing team can use this report to customize strategies across different campaigns to achieve higher

44 7. Click the inward facing arrows at the top right of the Campaign KPI Analysis visualization to minimize it. Visualization 3 This visualization reveals drivers/kpis that impact customer decisions. The marketing team can use this report to customize strategies across different campaigns to achieve higher conversion rates. 1. Select the page Trend Analysis from our Test Discovery chapter. Copyright All Rights Reserved. microstrategy.com 44

Observe the forecasted results shown by the thin black line of best fit expressed in the form of a polynomial equation. 4.

45 2. Click the Format tab. Select Trend Lines from the dropdown. Select the check box for Enable Trend Line. Then select Enable Forecasting. Set Forward to 2 to project two months out. Your setting should look like the image below. 3. Hover your cursor over the trend line. Observe the forecasted results shown by the thin black line of best fit expressed in the form of a polynomial equation. 4. Rename the visualization Trend Analysis. 5. Right-click the Trend Analysis page in the Contents panel and select Duplicate Page. Copyright All Rights Reserved. microstrategy.com 45

Navigate to Page 1 of the Campaign Performance Analysis chapter and confirm the visualization was moved successfully.

46 6. In the Contents panel, drag the Trend Analysis page below Page 1 of the Campaign Performance Analysis chapter. 7. On the line graph, click the vertical ellipsis at the top right of the visualization. Select Move to, then select Page Navigate to Page 1 of the Campaign Performance Analysis chapter and confirm the visualization was moved successfully. You can then delete the Trend Analysis page by right-clicking it directly. 9. Click the Save icon to keep your progress. Insert a title and the company logo Copyright All Rights Reserved. microstrategy.com 46

47 1. Click to add a text box and an image container. Click and select each of the icons shown in red below. Make certain the image is above the text box. You may need to drag one above the other. 2. Click the image container and then click Browse to navigate to the company logo provided in the supplementary files, called Kiara Fashion Logo, just like you browsed to the PairwiseCorr JPEG earlier. 3. Now that the image has been added, click the Format panel and change the Fill color to Black. 4. Click the Text Box and type Campaign Performance Analysis. Copyright All Rights Reserved. microstrategy.com 47

48 5. Use the Format panel to size it to 18. Change the text color to white and the Fill color to Black, as shown below. 6. Save your progress. 7. Adjust and align the objects in your dashboard as shown in the image below. Copyright All Rights Reserved. microstrategy.com 48

49 Conclusion In this dossier, value segments are cross-examined with buyer propensity segments. There is value-at-buy propensity segmentation, compound segmentation, and propensity of buying. Such a cross-examination can aid the targeting efforts based on each customer s value. Your dossier will help campaign analysts create differentiated customer service strategies, identify target groups for marketing campaigns, and help with resource allocation and prioritization. Copyright All Rights Reserved. microstrategy.com 49

50 GO BEYOND DATA DISCOVERY In this workshop, we utilized MicroStrategy Desktop to access, visualize, and analyze your data. To move beyond self-service data discovery, the MicroStrategy Academic Program offers schools and universities full access to the MicroStrategy platform suite, on the cloud. Those in academia or universities can leverage our full breadth of enterprise benefits through our cloud offering that is tailored for the classroom, as well as free classes and workshops for larger groups of students. Instructors can leverage the full MicroStrategy platform on the cloud to teach classes about broader enterprise analytics and mobility topics including automation, distribution, mobile BI, predictive analytics, big data access and more. Click here to learn more about MicroStrategy on AWS for the Classroom. With our platform, you can: Publish dossiers made in Desktop or in the Enterprise Platform to a shared and personalized Library Collaborate with colleagues in real time on any device or browser, to ask questions, or draw attention to trends. Provide enhanced security so users can see personalized data and insights View and interact with your data on mobile applications on tablets or smartphones Process and view real time data with write back capabilities

MicroStrategy Academic Program

MicroStrategy Academic Program Creating a center of excellence for enterprise analytics and mobility. DATA PREPARATION: HOW TO WRANGLE, ENRICH, AND PROFILE DATA APPROXIMATE TIME NEEDED: 1 HOUR TABLE OF