sales insight for retail 3

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Document Version: 2.0 – FinalDate: February 28, 2019

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Sales Insight for Retail 3.0User Guide

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Sales Insight for Retail 3.0User Guide – Version: 2.0 – Final

February 28, 2019© 2019 SAP SE or an SAP affiliate company. All rights reserved. 2

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Document History

Version Status Date Change

2.0 Final February 28, 2019 Document updated to reflect the latest changes.

1.0 Final September 24, 2014 Final version

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Table of Contents

1 Getting Started ................................................................................................... 61.1 Before You Start ......................................................................................................................... 61.2 What Is SAP Sales Insights for Retail?......................................................................................... 61.3 Key Concepts ............................................................................................................................. 6

2 SAP Sales Insights for Retail Basics ................................................................... 82.1 Logging on to the Application....................................................................................................... 82.2 Getting to Know the Application ................................................................................................... 82.3 Key Metrics (KPIs) ...................................................................................................................... 9

3 Setting the Scope of the Analysis ..................................................................... 113.1 Setting the Time Frame ............................................................................................................. 113.2 Selecting Product Sets .............................................................................................................. 123.3 Selecting a Store Set................................................................................................................. 143.4 Searching for Products and Stores ............................................................................................ 163.5 Setting Options for the Key Item List and Affinity Calculation ...................................................... 17

3.5.1 Options for Key Item List ............................................................................................ 173.5.2 Options for Affinity Insight ........................................................................................... 18

3.6 Setting Additional Transaction Filters ......................................................................................... 21

4 Performing the Analysis .................................................................................... 224.1 Genie Insights ........................................................................................................................... 224.2 Value Driver Tree ...................................................................................................................... 224.3 Key Item List ............................................................................................................................. 26

4.3.1 Development Over Time for Key Item List ................................................................... 294.4 Affinity Insight ........................................................................................................................... 31

5 Saving Results for Further Analysis .................................................................. 35

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Table of Figures

Figure 1: New Computation Menu ......................................................................................................................... 11

Figure 2: Setting the Time Frame .......................................................................................................................... 12

Figure 3: Selecting Two Product Sets for Affinity Insight Computation ..................................................................... 13

Figure 4: Selecting Tags for Products .................................................................................................................... 14

Figure 5: Selecting a Set of Stores ........................................................................................................................ 15

Figure 6: Selecting Tags for Stores ........................................................................................................................ 16

Figure 7: Additional Search Settings for Products ................................................................................................... 17

Figure 8: Options for Key Item List ......................................................................................................................... 18

Figure 9: Options for Affinity Insight ....................................................................................................................... 19

Figure 10: Equation to Calculate Support ............................................................................................................... 20

Figure 11: Equation to Calculate Expected Support ................................................................................................ 20

Figure 12: Equation to Calculate Delta Support ...................................................................................................... 20

Figure 13: Header Area in Value Driver Tree Workspace ....................................................................................... 22

Figure 14: Value Driver Tree ................................................................................................................................. 23

Figure 15: Value Driver Details .............................................................................................................................. 24

Figure 16: Bar Chart for Value Driver Tree ............................................................................................................. 25

Figure 17: Header Information for Key Item List ..................................................................................................... 26

Figure 18: Key Item List: Table-Based View ........................................................................................................... 27

Figure 19: Sorting, Filtering, Grouping, and Column Selection in Key Item List ........................................................ 27

Figure 20: Bar Chart Showing Long Tail Effect ....................................................................................................... 29

Figure 21: Development over Time ........................................................................................................................ 30

Figure 22: Price Details for Key Items .................................................................................................................... 30

Figure 23: Total Basket Contents by Revenue for Key Items .................................................................................. 31

Figure 24: Header Information for Affinity Insights .................................................................................................. 32

Figure 25: Heat Map for Affinity Insight .................................................................................................................. 33

Figure 26: Affinity Insight Tree Map ....................................................................................................................... 34

Figure 27: Affinity Insight Table View ..................................................................................................................... 34

Figure 28: Managing Snapshots ............................................................................................................................ 35

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1 Getting Started

1.1 Before You Start

Before you start working with SAP Sales Insights for Retail, make sure that the following prerequisites are met:

· One of the following web browsers is installed on your machine:

o Microsoft Internet Explorer 10 / Microsoft Edge

o Mozilla Firefox 70 or above

o Google Chrome 70 or above

· You have received the URL to call up the application.

· You are authorized to access the application.

1.2 What Is SAP Sales Insights for Retail?

SAP Sales Insights for Retail is tailored to meet your needs as retail category managers and merchandisingmanagers. This highly flexible reporting tool allows you to analyze your point-of-sales data on market basket leveland determine product affinities.

For example, you can analyze the following:

· Absolute numbers of market baskets that contain products from two selected sets of the product hierarchy

· Average multiplicities of products in market baskets

· Change of multiplicities during promotion

· Causal relationships between products

· Total market basket sales or profits associated with specific products

· Most successful and least successful products or product groups for the selected stores and time frame

· Root causes for changes in revenue and profit for the selected stores and products

SAP Sales Insights for Retail is based on SAP HANA®, SAP's in-memory database. This technology allows you toperform real-time computations on extremely large data sets.

1.3 Key Concepts

Concept Explanation

Affinity A quantitative relationship that is determined on the basis of two products orproduct sets purchased together in one market basket.

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Concept Explanation

Analyzing the affinity allows you to discover co-occurrence relationships amongproducts or product groups in a set of market baskets. The affinity is calculated byapplying certain metrics and filters on the point-of-sales data in the database.

Heat map The graphical representation of data, where the individual values contained in amatrix are visualized as colors.

Line item A distinct SKU that is purchased in one sales transaction.

Market basket A sales transaction comprising the goods purchased by a certain customer at acertain point in time at a certain location.

Metric A key performance indicator (KPI) that is included in or can be directly derivedfrom point-of-sales data.

Product set Any combination of items from any level of the product hierarchy.

Snapshot A set of filters and analysis results that is saved for later reference.

Stockkeeping Unit(SKU)

A distinct item that is offered for sale. The SKU is the lowest level in the producthierarchy.

Value driver tree The graphical representation of a metric concept that splits value-based metricsinto their sub-metrics to show the source of the value added.

The value driver tree shows how KPIs change over time and how they affect eachother. This allows you to quickly analyze root causes for revenue and profitchanges in any set of stores and on any level of the product hierarchy.

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2 SAP Sales Insights for Retail Basics

The following sections help you to familiarize yourself with SAP Sales Insights for Retail. They provide informationabout the available workspaces as well as options for selecting and configuring the calculation and display of the datayou want to analyze.

2.1 Logging on to the Application

To open the SAP Sales Insights for Retail workspace, proceed as follows:

1. Open your web browser and enter the URL provided to you by your local IT department.

The SAP HANA log-on screen appears.

2. Enter the user name and the initial password provided to you by your local IT department.

3. Change the initial password (if required).

4. Choose Log On.

2.2 Getting to Know the Application

SAP Sales Insights for Retail allows you to analyze your point-of-sales data on market basket level. The applicationfocuses on identifying quantitative relationships between different products or product groups.

You select the data you want to analyze by setting the scope for a computation, for example, time frame, products,and stores to be included in the analysis.

NoteThe screenshots in this document are intended to provide an example of the user interface. The actualoptions available to you may differ. This depends on the data in your database as well as on the system set-up.

Available Workspaces

You can use the following workspaces to analyze your point-of-sales data:

· Genie InsightsThis workspace gives you an overview of notifications related to computation results, for example, significantincreases or decreases.

· Value Driver TreeThis workspace allows you to identify what drives an increase or decrease in business performance for theselected stores and products or product groups by comparing profit-related KPIs in two different time frames.

· Key Item List

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This workspace helps you to find the most successful or least successful products or product groups in theselected stores or in a certain area of your assortment. You can also use the key item list to identify the stores orregions that are most or least successful at selling a selected set of products.

· Affinity InsightThis workspace enables you to analyze the composition of market baskets, for example, by determining howmany customers purchased a certain combination of products.

For more information about the individual workspaces, see Performing the Analysis.

Parameters for Computation

When you start a new computation for the value driver tree, key item list, or affinity insight, you must first set thescope of your analysis. After setting the scope, you trigger the computation.

When you set the scope, you use the following parameters:

· Time frame

· Products

· Stores

· Transaction filters

Additional options are also available for key item list and affinity insight computations. For details about how to setthe scope for your analysis, see Setting the Scope of the Analysis.

2.3 Key Metrics (KPIs)

Metrics are key performance indicators (KPIs) that are included or can be derived from point-of-sales data. SAPSales Insights for Retail is based on the idea of calculating and visualizing different metrics based on a defined set ofmarket baskets.

The following table lists the metrics that the system calculates when you trigger the analysis.

NoteThe calculation depends on the workspace that is currently being displayed. If you are in the Affinity Insightworkspace, for example, the system only calculates the metrics that are relevant for this workspace.

Metric Definition Used in Workspace

Average basket profit The average profit resulting from a transaction thatcontains at least one item of the selected productsets.

Key Item List

Average basket revenue The average revenue resulting from a transaction thatcontains at least one item of the selected productsets.

Key Item List

Average basket line items The average number of items included in the basketsthat contain at least on item of the product or productgroup.

Key Item List

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Metric Definition Used in Workspace

Average buying frequency The average number of times a customer purchasesthe selected products in the selected stores in thespecified time frame.

Value Driver Tree

Baskets (also: number oftransactions)

The number of distinct sales transactions that containat least one item of the selected product set.

Affinity Insight

Key Item List

Value Driver Tree

Distinct buyers (also:number of customers)

The number of individual customers that purchasedany item in the selected product set. Customers areidentified by their loyalty card ID. If this is not present,the system considers each transaction as unique.

Key Item List

Value Driver Tree

Gross margin The gross income expressed as a percentage of thenet sales.

Gross margin = (Sales revenue - cost of sales) x 100÷ sales revenue.

Value Driver Tree

Profit The surplus remaining after all costs are deductedfrom the total revenue generated.

Profit = Gross margin x revenue

Affinity Insight

Key Item List

Value Driver Tree

Promotion share The total revenue generated by products onpromotion divided by the revenue generated by allproducts.

Value Driver Tree

Revenue The income generated from the sale of goods beforeany costs or expenses are deducted.

Affinity Insight

Key Item List

Value Driver Tree

Score A numeric expression that is calculated based on therating of a metric on a scale from 0 to 100. The finalscore is the weighted average value resulting from thevalues calculated for the individual metrics.

Key Item List

Share The number of times (in percent) items of bothproduct sets A and B appear together in distincttransactions relative to the total number of distincttransactions.

Affinity Insight

Share of A in basketscontaining B

The share of transactions that include product set Arelative to the number of transactions that includeproduct set B.

Affinity Insight

Share of B in basketscontaining A

The share of transactions that include B relative to thenumber of transactions that include A

Affinity Insight

Unit sales The total number of units (SKUs) of a product that aresold in distinct transactions.

Affinity Insight

Key Item List

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3 Setting the Scope of the Analysis

You set the scope of your analysis when you start a new computation. The parameters for setting the scope of yourcomputation are available for the Value Driver Tree, Key Item List, and Affinity Insight workspaces. You can adjustyour settings at any time using the New Computation menu at the left in the application.

Figure 1: New Computation Menu

The available settings may vary depending on the type of computation that you are performing. For example, tocalculate product affinities, you must select two product sets A and B. In contrast, the metrics for the key item list andthe value driver list are calculated for one product set only.

To make settings, you use the New Computation menu at the left. This opens a dialog in which you can do thefollowing:

1. Set a time frame for the analysis.

To generate a value driver tree, you set two time frames to be compared.

2. Select the relevant products.

To analyze product affinities, you select two product sets. To calculate the metrics for the key item list or thevalue driver tree, only one product set is necessary.

3. Select a set of stores.

4. Set additional transaction filters as required.

5. Choose Compute to start the analysis

When the computation is finished, the respective results are shown automatically in the relevant workspace.

The following sections provide more detailed information about the individual steps for performing a computation.

3.1 Setting the Time Frame

Specifying a time frame for your analysis allows you, for example, to evaluate seasonal effects of sales or the effectsof promotional campaigns over a certain period of time.

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Figure 2: Setting the Time Frame

NoteThe start and end dates that you enter are included in the analysis. For example, you enter a time frame thatstarts on February 1, 2019 and ends on February 3, 2019. In this case, the system takes into account thedata available for these three days.

If you want to generate a value driver tree for the selected product set, you must set two time frames that arecompared with each other. In this case, another selection area for the comparison time frame is provided.

NoteWhen you use the date picker to set the time range, use a single click to select the dates for the desiredrange.

When you have set a time range, the number of days in the range is shown and the dialog uses color coding to giveyou an idea of the computation time necessary. The larger the time period, the more computation time required.

· If your time period is greater than 60 days, then red is used.

· If the time period is greater than 30 days, the number of selected days is shown in yellow.

· Short periods involving smaller amounts of data are shown as green.

If you are comparing two time periods, you can choose the clock button to obtain a suggested period. Forexample, if you have selected a time range of 8 days for the first time period, choosing this button for the second timeperiod automatically sets it to the eight days preceding the start of the first range selected.

3.2 Selecting Product Sets

After you have set the time frame for your analysis, you select one or two product sets, depending on the type ofcomputation that you want to perform:

· To calculate product affinities in the Affinity Insight workspace, you select two product sets A and B.

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· To generate a key item list or a value driver tree, you select one product set. In this case, Set B is deactivated inthe dialog.

Figure 3: Selecting Two Product Sets for Affinity Insight Computation

For example, you can select items on product category level, such as food or clothing, or you can select brands of acertain product category.

You can select single items in the product hierarchy or include their subordinate items:

Button Effect

Adds one single item on any level of the producthierarchy to your selection.

Adds the subordinate items on the hierarchy levelbelow.

Instead of drilling down the levels of the hierarchy, you can search for products or product categories using names orIDs. For more information about using the search field, see Searching for Products and Stores.

If tags have been assigned to products or product categories, you can further narrow down your selection byselecting one or multiple tags.

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Figure 4: Selecting Tags for Products

When you select items or tags in a product set, the area at the lower right in the selection dialog immediately showsyou how many products you have selected out of the entire hierarchy. Color coding here also gives you an indicationof whether your selection might be too small or large.

3.3 Selecting a Store Set

When setting the parameters for a computation, you can select stores on various levels of the store hierarchy. Forexample, you can select all stores located in a certain region or country. You can also select individual stores.

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Figure 5: Selecting a Set of Stores

For more information about using the search field, see Searching for Products and Stores.

You can further narrow down your selection by filtering the available stores using one or multiple tags that arecurrently assigned to them (if available).

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Figure 6: Selecting Tags for Stores

As with the product selection, the area at the lower right in the selection dialog immediately shows you how manystores you have selected out of the entire store hierarchy. Color coding here also gives you an indication of whetheryour selection might be too small or large.

3.4 Searching for Products and Stores

When you're selecting products and stores, you can also use the search field to find relevant items. Note thefollowing about the search function:

· You can use the percentage sign wildcard or enclose your search term in double quotations to find similar terms.

· You can right-click the column header in the product or tag hierarchy for products or stores to apply an additionalfilter.

Instead of drilling down the different levels of a product or store hierarchy, you can search using the name or the ID ofthe product or store. You can even search for a list of products or stores. For example, to search for multiple IDs ornames, separate your entries using semicolons:

ExampleYou type 0002;0003 in the search field for stores and press Enter . The system shows all stores with theID number 0002 and all stores with the ID number 0003.

You can also copy a list of IDs for products or stores directly from a spreadsheet and paste them into the search field.The application automatically handles them as separate search terms and it is not necessary to enter separatingsemicolons.

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Additional search settings are available for selecting products. You can choose between the general producthierarchy and the marketing product hierarchy. For each selection, you can decide whether you want to excludeinactive product or include the promotion ID.

Figure 7: Additional Search Settings for Products

3.5 Setting Options for the Key Item List and AffinityCalculation

When you are in the Key Item List or Affinity Insight workspace, you can use additional options to configure thecalculation and display of the analysis results.

3.5.1 Options for Key Item List

When you make settings for a key item list computation, you can make settings that affect the data displayed:

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Figure 8: Options for Key Item List

You can make the following settings here:

1. You specify the level of aggregation. For example, this might be by product, by brand, or by store.

NoteWhich levels are available to you and how these are named depends on your system configuration.

2. You determine which metrics in the list are most important by rating them on a scale from 1 to 100 in the ScoreAdjustable Weights section.

Based on your rating, the system calculates the score for all metrics. The overall score that is displayed in thekey item list is the weighted average value created from the score for the individual metrics.

3. You specify whether to display the items with the highest score or those with the lowest score in the key item list.

o To display the items with the highest score, you select the Show Results with the Highest Scores option.

o To display the items with the lowest score, you select the Show Results with the Lowest Scores option.

3.5.2 Options for Affinity Insight

When you make settings for an affinity insight computation, you can also make settings that affect the data displayed:

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Figure 9: Options for Affinity Insight

Calculate Metrics at SKU Level

You can specify that product affinities are calculated at SKU level in addition to the hierarchy levels that you selectedwhen specifying the product sets.

ExampleYou select set A = Beverages and set B = Bread. If you do not activate the calculation at SKU level, thesystem analyzes how many transactions contain at least one item from the beverages category and one itemfrom the bread category. If you activate the calculation at SKU level, the analysis is performed for anypossible combination of SKUs in these categories.

When the results are displayed in the Affinity Insight workspace, you can use the dropdown to switchbetween displaying the results for the selected product category level (Levels) or on SKU level (Products).

Delta Support for Affinity Heatmap

You can activate delta support for the computed heatmap. If this is activated, the heatmap results are based on thedifference between the metrics for support and expected support. You can use this setting to make sure that thedetected correlations between two sets of products are significant.

The following elements are used to calculate this, using two product sets, A and B:

· Count(S): This is the total count of distinct transactions (also referred to as “market baskets”) in the selectedtransaction set.

· Count(A): This is the total number of distinct transactions in which one of the SKUs from A appears.

· Count(B): This is the total number of distinct transactions in which one of the SKUs from B appears.

· Count(AB): This is the total number of distinct transactions in which both one SKU from A and one SKU from Bappear.

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ExampleAssume that there are 100 market baskets and two product sets. Product set A contains beverages andcheese, while product set B contains milk. The value for count(S) is 100. The value for count(A) is thenumber of market baskets that contain at least one beverage or cheese product. The value for count(B) isthe number of market baskets that contain at least one milk product. The value for count(AB) is thenumber of baskets that contain at least one beverage or cheese product and at least one milk product.{ XE"Affinity Metrics:Support" }

The number of times that a pair consisting of product A and product B appears together in distinct transactions,relative to the total number of distinct transactions, is called support. This is calculated as follows:

Figure 10: Equation to Calculate Support

In the dashboard, this metric is called Support (%).

The expected support for a pair of products A and B is calculated as follows:

Figure 11: Equation to Calculate Expected Support

In the dashboard, this metric is called ExpSupport (%).

The difference between the actual support and the expected support is called delta support. This is calculated asfollows:

Figure 12: Equation to Calculate Delta Support

In the dashboard, this metric is called DSupport (%).

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Use Subcategory as Levels

You can automatically use subcategories as levels in the heatmap. Then you don't need to set them explicitly whenyou're creating your computation.

3.6 Setting Additional Transaction Filters

After selecting stores and product sets, you can further limit the scope of your analysis by setting additionaltransaction filters. For example, the available point-of-sales data may include information about the customersegment or the time of day at which the products were purchased.

NoteWhich transaction filters are available depends on the information included in your point-of-sales data as wellas your system configuration. If no filtering options are available, additional configuration may be necessary.If in doubt, contact your system administrator.

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4 Performing the Analysis

This section of the user guide describes the available workspaces that you can use to analyze your point-of-salesdata.

4.1 Genie Insights

This workspace is the first one shown after you have logged on to SAP Sales Insights for Retail. This area gives youan overview of notifications to alert you of important computation results. The alerts are grouped according to variouscategories, such as significant product variation. The relevant thresholds for alerts are set during implementation.

To see the computation details, choose Expand Group.

You can provide feedback about whether a notification was useful.

This workspace also allows you to set up and start a new computation for value driver tree, key item list, or affinityinsight.

4.2 Value Driver Tree

The Value Driver Tree workspace allows you to quickly analyze the root causes for changes in revenue and profit forthe selected store and product set. The value driver tree shows how the profit-related metrics develop over time andhow they affect each other. In addition, you can display a bar chart that breaks down the amount of profit according tothe vendor company.

The header area in the workspace shows the settings selected for the computation.

Figure 13: Header Area in Value Driver Tree Workspace

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To change the settings, choose Edit. You can then create a new computation using the adjusted settings. When thecomputation is complete, the workspace opens to show your results.

You can use the History dropdown at the right to access the last ten computations that you have performed in theworkspace.

Value Driver Tree

In the graphical representation of the value driver tree, the profit is split into various sub-metrics.

Figure 14: Value Driver Tree

The display is based on the following hierarchy:

Profit· Gross margin

o Gross margin of products that are not on promotion

o Gross margin of products that are on promotion

o Promotion share

· Revenue

o Average basket size

o Items per basket

o Price per item

o Number of transactions

o Number of customers

o Average buying frequency

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The color coding shows you immediately whether and to what extent a metric is trending upwards or downwards:

· Green means that the trend shows an increase of more than 3%.

· Red means that the trend shows a decrease of more than 3%.

· Gray means that the upward or downward trend is below 3%.

You can click one of the squares in the hierarchy to get a detailed view of the increase or decrease:

Figure 15: Value Driver Details

You can change the display of the tree using the buttons provided:

Button Effect

Switches display of the value driver tree to top tobottom or left to right.

Expands the area to full screen.

Re-centers the value driver tree.

Bar Chart

In addition to the actual value driver tree, you can also display a bar chart that breaks down the data according to arange of available metrics such as profit, revenue, or unit sales and a selected level, such as product or brand.

The bar chart compares old and new amounts for each selected combination of metric and level.

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Figure 16: Bar Chart for Value Driver Tree

Additional functions are available at the upper right that allow you to do the following:

· Show or hide the legend

· Zoom in or out

· Expand to full screen

· Export the results to a spreadsheet

NoteNote that when you export data to a spreadsheet, any filters that you have set in the view are not taken intoaccount. You can apply filters after exporting the entire data set.

The buttons at the right also allow you to choose between the following bar graph modes:

· Comparison

· Variation

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· Table view

In the table view, you can right-click on column headers to apply additional filters and sorting criteria.

4.3 Key Item List

The Key Item List workspace allows you to quickly identify the most successful and the least successful products orproduct groups. The system creates the key item list based on the time frame, products, and stores you selected.

The header area in the workspace shows you the settings selected for the computation.

Figure 17: Header Information for Key Item List



You can show the calculation results as a table, as a scatter plot chart or as a bar chart.

Table-Based View

The sequence in which the items are displayed in the table depends on how you rate the metrics in the key item listoptions as described in Setting Options for the Key Item List and Affinity Calculation.

To check the current weights, choose the button highlighted in the diagram below. Products for which a significantprice variation occurred in the selected time frame are denoted using blue color coding in the first column at the left.You can use the View dropdown to choose a subset of columns to be shown.

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Figure 18: Key Item List: Table-Based View




You can sort and filter the columns by clicking on the column headers. You can also select the column headers to bedisplayed here:

Figure 19: Sorting, Filtering, Grouping, and Column Selection in Key Item List

You can also drag and drop column headers to focus on the data that interests you.

Which columns are available depends upon what was specified during implementation.

Scatter Plot Chart for Key Items

Using the dropdowns, you can select the metrics that are displayed on the axes of the chart. You can select any ofthe metrics that are available in the table-based view.

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Each dot represents one product or product group. You can click the individual dots to display detailed information.



· Zoom in or out


Bar Chart for Key Items

In the bar chart for key items, you can choose a metric by which to sort the top items in your analysis.

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Figure 20: Bar Chart Showing Long Tail Effect

Each bar represents one product or product group. You can click the individual bars to display detailed information.



· Zoom in or out


4.3.1 Development Over Time for Key Item List

For each item shown in the table-based view of the key item list, you can obtain information about development overthe selected time frame. For example, you might want to find out details about the relationship between the price andthe number of units sold.

To obtain this information for a key item, click an entry in the table.

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Figure 21: Development over Time

You can use the dropdown lists to set the axes and aggregation levels for the selected item. Various metrics whosedevelopment over time you want to analyze, such as price details, total basket contents by revenue, and statistics.

Price Details

When you're looking at the development of key items over time, you can also take a closer look at the price details.This graph shows you the average actual and optimal price positions for the items and automatically computes theprice elasticity:

Figure 22: Price Details for Key Items

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You can click on the dots shown in the graph to obtain detailed explanations. You can also view this data in tableform.

Total Basket Contents by Revenue

This view gives you detailed insight into how particular items in the basket contribute to revenue.

Figure 23: Total Basket Contents by Revenue for Key Items

You can click on each segment shown to obtain details. This gives you an idea of items that are bought together.

Statistics

You can also display a range of statistics about the computed data:

· Units per basket

· Total basket revenue

· Similar products

· Correlations with other product

· General correlations - here you can hover over the correlation value to obtain more details about the analysisresults.

4.4 Affinity Insight

The Affinity Insight workspace allows you to find product affinities, for example, by calculating how often products ofthe two selected product sets are sold together.

The header area in the workspace shows you the settings selected for the computation.

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Figure 24: Header Information for Affinity Insights



You can visualize the calculation results as a heat map, a tree map, or a table. The diagrams show you the numberof baskets.

In each visualization, additional functions are available at the upper right that allow you to do the following:


· Zoom in or out



Heat Map

Using the dropdowns, you can specify whether to display the results for the top product levels or for the top productpairs (SKUs).

The items in product set A are displayed on the x-axis and the items of product set B are displayed on the y-axis ofthe heat map.

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Figure 25: Heat Map for Affinity Insight

You can display the heat map for the following metrics:

o Baskets

o Share

o Share of A in baskets containing B

o Share of B in baskets containing A

The color-coding allows you to identify patterns and exceptions in the analysis results. The more intense the shading,the higher the affinity is.

o Red means that a combination is "hot." That is, items of both product sets are sold together with a certainfrequency in the same distinct baskets.

o Blue means that a combination is "cold." That is, the items of both product sets are not or only rarely soldtogether.

o Gray means that the selected metric is not relevant for the heat map.

Tree Map

In addition to the heat map, you can display the affinity metrics as a tree map. Using the dropdown, you can specifythe metric for which the chart is displayed.

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Figure 26: Affinity Insight Tree Map

You can display the tree map for the following metrics:

o Baskets

o Units

o Revenue

o Profit

Table View

You can also display the affinity metrics in a table view.

Figure 27: Affinity Insight Table View

User Guide PUBLIC



5 Saving Results for Further Analysis

You can use snapshots to save results that you would like to use again later. A snapshot is a set of settings andanalysis results. The snapshot feature is helpful if you want to compare the results of the current session with theresults of a previous session. You can also use it for storing settings that you use regularly.

Creating Snapshots

To save the current results of the current session as a snapshot, proceed as follows:

1. Choose the Manage Snapshots button at the upper right corner.

This opens the workspace for managing snapshots.

2. Choose New Snapshot.

A dialog box appears.

3. Enter a name for the snapshot you want to save.

4. Specify whether you want the snapshot to be visible to others.

5. Choose Save.

Your snapshot is then shown in the overall list of available snapshots.

Managing Snapshots

To retrieve a saved snapshot, choose Manage Snapshots. Then choose the relevant option to load the snapshotsettings and results:

Figure 28: Managing Snapshots

You can now compare this snapshot with the current analysis results.

NoteWhen you load your snapshot, it replaces your current settings and results.

Snapshots also help you to run an analysis more quickly. If you use similar settings on a regular basis, you cansimply load the relevant snapshot, adjust as required, and then trigger a new computation.

User Guide PUBLIC



You can also use this table to do the following:

· Check the settings used in a snapshot

· Rename a snapshot and change its visibility setting

· Delete a snapshot

NoteYou can only modify and delete your own snapshots.

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