## Sales Potential

## The Sales Potential Analysis helps to create the best possible floor plan that could significantly increase sales by properly allocating space between categories. It also allows you to determine the potential increase in sales that can be achieved by increasing the space by a certain value.

In what situation can you use the Sales Potential analysis?

- If you want to know the potential increase in sales when you increase the space by a certain value.
- You want to identify the categories with the highest sales potential within the benchmark and determine if these categories have enough space in the selected store.
- If you want to know what percentage of the best products fit in the space dedicated for a category.
- If you want to know what percentage of the best products from a given category can be placed if you increase the space.
- If you need to know what are the sales of the products in planogram in a given store.

Where can you find the Sales Potential analysis?

This analysis can be found in the** Sales Potential **tab in the bottom panel of the Store Editor.

To make the **Sales Potential** working, you first need to set up sales data for the store. You can make the settings in the menu **Store / Sales Data Settings **or by using the keyboard shortcut **Ctrl (Cmd)+Option+W.**

Setting up sales data for a store is described in more detail in the Sales Data Settings in the Store Editor manual.

#### Criteria Settings

This manual explains all the criteria that can be set for this analysis. If you are looking for an explanation of a particular criterion, just click on the relevant link:

**Name****Old Floor Plan Date****Analyze Focused Fixtures****Facing Multiplication****Minimum Display Width****Ignored Layers****Available Products Only****Categories / Parent Category / Analyze Placed Categories**- ABC Analysis
**Planogram States****Planogram Type**

##### Name

You can fill in the name of the criteria.

##### Old Floor Plan Date

Here you can select the historical floor plan with which you want to compare the current floor plan.

The floor plan usually changes over time. Some categories will get smaller, others will get larger, and some categories will stay the same. You can see the change in space compared to the old floor plan in the Linear Space column for each category.

The results of the analysis then show whether or not the change in space was the right decision. From this analysis, we can clearly see that by allocating the space between categories correctly, we can increase sales.

If there is no date selected, the Linear Space column shows the current cumulation of all shelf widths of the category and the analysis does not compare two floor plans, but analyses the active floor plan and its space and shows what the potential of the store is with regard to this space.

This is a possibility thanks to which you can easily evaluate, for example, the impact of a store rebuilding as you can analyse your decision about the changes.

##### Analyze Focused Fixtures

If you want to analyze only a certain part of the floor plan, not the whole, you need to target that part first. Subsequently, if you check the Analyze Focused Fixture checkbox in the analysis, Quant will only count the focused area.

##### Facing Multiplication

Multiples of pieces displayed in width to be taken into account in the analysis.

The analysis calculates how many products in a category that have non-zero sales can fit into the linear space of that category. This value is calculated based on the width of all products with non-zero sales and the value of the linear space. However, sometimes products may be displayed 2 pieces wide instead of one, or fixture may have blockers that also take up space from the total linear space. So if you need to create a margin in width, you can use this criterion.

##### Minimum Display Width

This is the minimum display in width that the product should occupy. This can be useful if we don't know the dimensions of some products or if they are too small and we want to define a minimum for them.

##### Ignored Layers

If you want to ignore the space of a floor plan layer, you need to mark this layer it in this setting. For example, during sales or Christmas. In this case, just select the layer you want to exclude from the analysis in the Ignored Layers criterion.

##### Available Products Only

If you check this attribute, Quant will only analyze products that are available at this store (as set in the store properties).

CategoriesIn the analysis you need to set which categories you want to see the report for. You can do this in three ways:

- By setting it in the
**Parent Category**row - this will analyze all categories that are direct subcategories of the selected parent category. - By setting it in the
**Categories**row - here you can select any categories you want to include in the analysis. - Settings in the
**Analyze Placed Categories**row - if this checkbox is checked, Quant will analyze all categories that are placed in the floor plan of the given store.

ABC Analysis

These are criteria for evaluating products based on ABC analysis. Here you will find two criteria:

**A Level Share**= threshold for**the best products of the category**. If the A Level Share is 70%, then the best products of the category that represent a total of 70% of sales (turnover, profit...) will be considered as A products.**B Level Share**= threshold for**the second best group of products**. If the B Level Share is 20%, then the B products will be the second best products in the category, which represent the next 20% of sales (turnover, profit...).- The remaining products making up the last 10% of sales will be classified as
**C products**.

##### Planogram States (Prepared, Waiting, Implemented)

To be able to calculate what sales are made by the placed products, you need to select a planogram state from which Quant calculates product sales.

If the fixture has planograms in multiple lifecycle states at the same time and you check all three states in the criteria, Quant selects a planogram from one state according to these priorities: Prepared>Waiting>Implemented.

##### Planogram Type (Project, Standalone)

If you have designed planograms both in the project and as standalone planograms, you can choose which planograms to calculate product sales from. This refers to the columns in the table labeled (Store Planogram) and (Benchmark Planogram).

Save the criteria that you want to use more often in your **library** so that you can only open them the next time you make the report and don't have to set them up again.

#### Analysis Interpretation

The main purpose of the analysis is to estimate the largest potential sales growth as a percentage in the average store. This can be achieved by identifying the best-selling products, expecting that each product will take up the minimum amount of space in order to fit as many of the best-selling products as possible into the allocated space for that category. The analysis estimates how many of the best-selling products of a given category can fit into the available space in the store and therefore calculates the economic impact of changes within the floor plan.

The analysis can identify the best products in a benchmark store. If the selection of products at the selected store is correct, these exact products should appear in the product list of the specific store analysed. If not, you can easily see what changes you should make in the display of the store's assortment.

In specific cases, the **Space&Sales analysis** may not accurately reflect the situation. Let's explain it on a specific example. Let's imagine that a given category contains 100 products, but with respect to sales, 2 products stand out considerably and make up, for example, 80% of the category's sales. So in the Space&Sales analysis, this category would be assigned a higher percentage of space to match the sales share, but a user with knowledge of the category would have to evaluate that it would not make sense to assign that much space to this category because there would not be enough products to place in the planogram or it would not make sense to place low selling products there. So in this particular case, the **Sales Potential** analysis will better evaluate the situation and despite assigning little space to this category, we will see that this category will have a high sales potential, so there will be no distortion in the results as in the first case.

The explanation of the individual columns of the analysis is given only for the sales value Sold Pieces, other sales indicators such as profit or sales can be interpreted in the same way.

**Linear Space**: the cumulation of the space of all shelves in width for a given category. If the Old Floor Plan Date is specified, this is the change in the space in width for the category.

The Spice category has 2 fixtures that are 70 cm wide in the store. Both fixtures have 4 shelves. Linear Space = 2*70*4 = 5,6 m.

On the 1.2. 2023, the Spice category had only 1 fixture with a width of 70 cm and 4 shelves = 2.8 m of space. We have increased the space for this category and added one more same fixture and on the 1.2.2024 the space for this category is 5.6 m. If the analysis criteria specify an Old Floor Plan Date of 1.2. 2023, the analysis will show a difference of 5.6-2.8 = 2.8 m

**Number of Products**: Number of products in the category.

##### Store sales indicators

Sales indicators that have a label **(Store)** in brackets show the sales values for the specific store you are analyzing. In **Sales Data Settings**, this is the sales data set that you set in the **Store Specific Sales Data **property.

**Sold Pieces (Store)**: How many units of products of a given category were sold at a given store for a given period.**Sold Pieces (Store Potential)**: The number of units that could be sold at a given store if the space allocated for this category is maximized.

What is the calculation?

Suppose we have 1 m of space for category X. Quant creates a virtual planogram that has 1 shelf of length 1 m. It fills this space starting with the best product (most units sold) and adds up the width of these products until it reaches 1 m. The sales of all these products that fit in this space constitute the potential of the store. In our example in the table below, this is 330 units. Therefore, if this store has 1 m of space available for this category and if we use the full potential of this space, we will sell a total of 330 units from this category.

**Sold Pieces % (Store Potential)**: How much of the store's sales will be covered if we fill the allocated space for the category.

We currently have a 5.6 m space for the Spice category in the store and with this space the store potential is 2486 units. That is how many units will be sold if we make full use of this space and display the best products in it. We can put so many products in this space that we cover 80.95 %. If we increase the space by one fixture to 8.4 m, we can put more products and sales will increase to 2734 units, which will cover 89.03 % of sales.

**Sold Pieces (Store Potential / Space)**: How many units are sold on average per 1 m width at this store. This indicator will help us to evaluate the best category for which we get the most sales per meter on average.

On average 443,93 units of products from Spice category are sold per 1 m of space. Calculation: 2486/5.6 = 443,93

**Sold Pieces (Store Planogram)**: How many of the units of products displayed in the planogram for the category were sold at the store. Quant takes the planogram from the lifecycle state selected in the criteria.

##### Benchmark sales indicators

Sales indicators that have the label **(Benchmark)** in brackets show the sales values for the benchmark store and allow you to see, for example, the comparison between the sales of the particular store you are analyzing against your selected average of other stores. In **Sales Data Settings**, these are the sales data sets that you set in the **Benchmark Sales Data **property. If you select multiple sales sets here, you can check the **Calculate Average Benchmark Sales** checkbox to average the values. You can also view the values as the average number of units sold over a certain number of days if you check the **Calculate Average Days** checkbox and fill in this value in the **Number of Average Days **property.

Benchmark sales indicators can serve you very well if you want to compare your store's data against the average of your selected stores and see if the store is below or above average in the given indicators.

**Sold Pieces (Benchmark)**:**Sold Pieces (Benchmark Potential)**: The number of pieces that could be sold at the benchmark store if the space allocated for this category is maximised.**Sold Pieces % (Benchmark Potential)**: What part of benchmark sales will we cover if we fill the allocated space for the category.**Sold Pieces (Benchmark Potential / Space)**: How many units are sold on average per 1 m of width at the benchmark store.**Sold Pieces (Benchmark Planogram)**: How many units of the products displayed in the planograms for the category were sold at the benchmark stores. Quant takes the planogram from the lifecycle state that you check in the criteria. The planogram published for that store (products from that store) is taken into account, but in this case, the benchmark sales data is counted, not that store's.