Purpose
The purpose of the article is to upskill your knowledge of the ranging rules available in the DotActiv Software. Selecting the best-ranging rules can be difficult but it is a very important aspect of ensuring the correct range is selected for each Cluster/Store.
The article outlines why we make use of ranging rules and explains each of the ranging rules available with an example of when to use each rule.
Why We Use Ranging Rules
Using rules during the ranging process for selecting products in a specific store helps ensure that the right products are available to meet the store’s unique needs and customer preferences. Here are some key reasons why rules are applied:
- Customer Demographics: Rules help tailor the product range to the preferences, buying behaviours, and demographics (age, income, lifestyle) of the store’s target customer base. This ensures the store stocks what customers are likely to purchase.
- Regional Preferences: Different regions have varying tastes and preferences. Rules can account for local or cultural product demand, ensuring that each store carries products that appeal to the local market.
- Space Constraints: Stores have limited shelf space, so rules prioritize high-demand or high-margin products, ensuring the store maximizes profitability within available space.
- Supplier Relationships: Rules help incorporate agreements with key suppliers, ensuring that products from preferred suppliers are included in the range while balancing price points and quality.
- Category Strategy: Each store may focus on specific product categories or tiers (e.g., budget, premium, or mid-range). Rules ensure the product selection aligns with the overall business strategy for the category.
- Seasonality and Trends: Certain products are more relevant during specific times of the year (like back-to-school supplies or summer wear). Rules help adjust product ranges to meet seasonal trends and timely demand.
- Store Format: Different stores have different formats (supermarket, convenience store, speciality store), and the ranging rules help ensure the right assortment of products based on the size and type of store.
- Sales and Profit Data: Historical sales data and profit margins often guide product selection. Rules-based on these insights ensure that only profitable, high-turnover items are included.
- Brand Strategy: Retailers may have guidelines to promote certain in-house brands or a specific product mix, and rules ensure this brand strategy is followed during the product selection.
In short, rules during the ranging process provide structure, helping ensure that stores offer the most relevant, profitable, and customer-focused products.
Ranging Rules For Sustainability
Eco-Packaging
Rule Information/Description
Points are tallied up per product based on all the values for the packaging-based sustainability fields as follows:
Plastic Grade: +5 points if value is 1 or 2. -5 points if value is between 3 and 7
Recycled Material %:
+15 points if value is above 50, else
+10 points if value is above 25, else
+5 points if value is at least 5
Overpackaging %:
-15 points if value is above 50, else
-10 points if value is above 25, else
5 points if value is greater than or equal to 5
Recyclable: +5 points if Yes
Compostable / Biodegradable: +5 points if Yes
Reusable: +5 points if Yes
Made using Recycled Materials: +5 points if Yes
Example
Utilize the packaging-based sustainability fields within the Ranging rule to help score and rank products based on their environmental impact. For example, if I am evaluating two different bottled water products for a store’s shelf space, I would tally points based on each product’s packaging attributes.
For Product A, which has a plastic grade of 1, 60% recycled material content, 10% overpackaging, and is fully recyclable, I would assign the following points: +5 for the plastic grade (value of 1), +15 for having over 50% recycled materials, -10 for 10% overpackaging, and +5 for being recyclable. The total score would be 15 points, indicating a relatively sustainable option.
For Product B, with a plastic grade of 4, a 30% recycled material content, 60% overpackaging, and no recyclable components, the score would be: -5 for the plastic grade (value of 4), +10 for 30% recycled materials, -15 for over 50% overpackaging, and 0 for non-recyclable packaging. The total score for Product B would be -10 points, making it a less sustainable choice compared to Product A.
This method helps prioritize products that align with sustainability goals, assisting in product selection for improved environmental impact across the category.
Product Sustainability
Rule Information/Description
Points are tallied up per product based on all the values for the product-sustainability fields as follows:
Is Refill: 5 points if Yes
Locally Produced: 5 points if Yes
Organic-Certified: 5 points if Yes
Vegan: 5 points if Yes
Unethical Production: -25 points if Yes
Example
When configuring a ranging rule like the one outlined, the goal is to prioritize products that align with specific sustainability criteria while penalizing those that do not meet ethical standards. This rule assigns positive points to products based on attributes such as being refillable, locally produced, certified organic, and vegan. Each of these fields contributes 5 points if the product meets the criterion, encouraging the inclusion of sustainable options in the range. However, a product marked as involving “Unethical Production” is heavily penalized with a deduction of 25 points, discouraging its selection. For example, a locally produced, organic-certified, and vegan product would accumulate 15 points (5+5+5), increasing its likelihood of being included in the range. Conversely, a product identified as being produced unethically would lose 25 points, likely resulting in its exclusion from the range. This method helps to drive sustainable product curation based on pre-defined ethical values.
For instance, imagine a category planner is working with a range of skincare products. Product A is a locally produced, organic-certified, and vegan-friendly facial cream, meaning it meets three of the sustainability criteria. This product would earn 15 points (5 for being locally produced, 5 for organic certification, and 5 for being vegan), making it a strong candidate for inclusion in the product range. On the other hand, Product B, which is also vegan and organic-certified but flagged for unethical production practices, would accumulate only -15 points (5 for being vegan, 5 for organic certification, but a 25-point deduction for unethical production). Despite its sustainability features, Product B would likely be excluded from the range due to the significant penalty for unethical production. This example demonstrates how the rule balances positive sustainability attributes with ethical considerations to shape the final product assortment.
Ranging Rules For Sales
Sales
Rule Information/Description
Get total sales for the period set for each relevant market.
Per product, get a sales average per market per period.
Per product, also get another average sales value using only market / period combinations which have a sales value
We then get an average per product as an average of those two values. This lowers the negative score impact on having ‘gaps’ in the data
Products are then ranked in descending order of this average value and assigned points in each 10% band as per variables
Example
Sales as a ranging rule are used to select a range based on the sales of each product. The more sales accumulated the more points are allocated to the product in terms of the entire category. The points allocation can assist in reducing, eliminating and including products in the category range.
Sales (ND)
Rule Information/Description
Gather all the sales for the period set for applicable stores
Pull in all other applicable ranged stores / SKU combinations without any sales
Per product and per period, for each store we don’t have any sales for, fill in the gaps with the product’s average sales per store for that period
Then pull all of our sales averaged per product per period ranked them in descending order and assigned points in each 10% band as per variables
Example
To effectively implement the Ranging rule within Dotactiv’s software, we start by aggregating all sales data for the defined period across the applicable stores. This initial step involves collecting and consolidating sales figures for each store to create a comprehensive view of performance. Next, we identify any store/SKU combinations where no sales data is recorded. For these gaps, we substitute the missing sales data with the product’s average sales across stores for the same period, ensuring a more complete dataset. Once the data is filled in, we calculate the average sales per product for each period. This dataset is then ranked in descending order based on sales figures. To facilitate easier analysis and decision-making, we categorize the products into 10% bands based on their sales performance, assigning points according to their position within these bands. This process ensures that the product assortment is optimized based on actual sales performance and historical averages, enhancing inventory and category planning decisions.
Sales (Weighted)
Rule Information/Description
Gather all the sales for the period set for applicable stores
Pull in all other applicable ranged stores / SKU combinations without any sales
Per product and per period, for each store, we don’t have any sales for and for which the product is currently clustered,
fill in the gaps with a % of the total product group|||s sales for the period equal to that store % contribution of the total sales for the period for that product
Then pull all of our sales per product per period ranked them in descending order and assigned points in each 10% band as per variables
Example
First, the software gathers all sales data for the designated period across applicable stores. It then identifies store-SKU combinations that have not registered any sales during this period. For each store lacking sales, the system estimates potential sales based on the store’s contribution to the overall product group sales. Specifically, the software calculates this contribution percentage and uses it to fill in the missing sales data by applying the same percentage to the total sales of that product group.
Next, the software ranks the sales data for each product in descending order based on the total sales for the period. It then assigns points to each product within predefined 10% bands, reflecting their relative performance. This ranking and point assignment process helps to prioritize products and ensure that the assortment is optimized according to each store’s performance and contribution. By filling in gaps and ranking products in this manner, the Ranging rule supports more accurate and effective product assortment decisions.
Simple Sales
Rule Information/Description
Gets total sales for the period set.
Averages the sales per market per period.
Data is ranked in descending order and assigned points in each 10% band as per variables
Example
The rule begins by calculating the total sales for a specified period, providing a comprehensive overview of performance. It then averages these sales figures across different markets during the same timeframe, allowing for a comparative analysis. The data is subsequently ranked in descending order, enabling the planner to identify the top-performing products. Each product is assigned points based on its sales performance, divided into 10% bands. For example, the top 10% of products might receive 10 points, the next 10% would get 9 points, and so on, down to the lowest 10% receiving 1 point. This point allocation helps in understanding product performance relative to others in the category, facilitating more informed decisions in product ranging and assortment planning.
Internal Market Sales Rank
Rule Information/Description
Gets total sales for the period set for the internal markets selected.
Averages the sales per market per period.
Data is ranked in descending order and assigned points in each 10% band as per variables
Example
In the Dotactiv software, the Ranging rule function is crucial for analyzing sales performance across different internal markets. For instance, suppose we are evaluating the total sales for the past quarter across various markets. The system first aggregates the total sales figures for each market within the specified period. Next, it calculates the average sales per market by dividing the total sales by the number of periods. This averaged data is then ranked in descending order to identify which markets are performing best to worst. To provide a clearer comparison, the markets are segmented into 10% bands based on their sales performance. Each market is assigned points according to the band it falls into, with the top 10% receiving the highest points and the bottom 10% receiving the lowest. This ranking system allows us to quickly pinpoint high-performing markets and identify areas needing improvement, enabling more informed decision-making and strategic planning.
External Market Sales Rank
Rule Information/Description
Gets total sales for the period set from the External Market table for the selected market.
Averages the sales per period.
Data is ranked in descending order and assigned points in each 10% band as per variables
Example
Utilize the software’s ranging rule by first setting the desired period and market in the External Market table to gather the total sales for the specific market during that time frame. The software would then average the sales per period, providing a clear view of the performance trends over time. Once the data is collected, it is ranked in descending order, meaning the products with the highest sales appear at the top. The software automatically assigns points based on a 10% band system, where the top-performing 10% of products receive the highest points, followed by the next 10%, and so on. This method helps in making informed decisions regarding product assortment, ensuring the best-performing items receive the focus they deserve in the category plan.
For example, the planner uses this rule to view the best-performing SKUs based on sales within the retailer as well as within the market to ensure that all the best-performing SKUs in the market will be on the planogram.
Ranging Rules For Units
Units
Rule Information/Description
Get the total units for the period set for each relevant market.
Per product, get a unit average per market per period.
Per product, also get another average unit value using only market / period combinations which have a sales value
We then get an average per product as an average of those two values. This lowers the negative score impact on having ‘gaps’ in the data
Products are then ranked in descending order of this average value and assigned points in each 10% band as per variables
Example
Units as a ranging rule are used to select a range based on the units sold of each product. The more units that are sold the more points are allocated to the product in terms of the entire category. The points allocation can assist in reducing, eliminating and including products in the category range.
Units (ND)
Rule Information/Description
Gather all the units for the period set for applicable stores
Pull in all other applicable ranged store / SKU combinations without any sales
Per product and per period, for each store we don’t have any units for, fill in the gaps with the product’s average units per store for that period
Then pull all of our units averaged per product per period ranked them in descending order and assigned points in each 10% band as per variables
Example
In the Dotactiv software, the Ranging rule is used to optimize product assortment across stores by ensuring that all applicable store-SKU combinations are accounted for, even if some products haven’t been sold in certain stores. Here’s how it works: First, the system gathers all product units sold for the specified period for each applicable store. Next, it identifies and pulls in other store-SKU combinations that have no sales data for that period. For each store where no units have been sold for a particular product, the software compensates by filling in the missing data with the product’s average sales units per store for that period. After addressing these gaps, the software calculates the average units sold per product across all stores for that period. It then ranks these products in descending order based on their average units. To refine the assortment further, the products are grouped into 10% bands according to their performance, and each band is assigned a score or points based on predefined variables. This approach ensures that the product assortment is both comprehensive and data-driven, allowing for more accurate and effective ranging decisions.
Units (Weighted)
Rule Information/Description
Gather all the units for the period set for applicable stores
Pull in all other applicable ranged store / sku combinations without any sales
Per product and per period, for each store we don’t have any units for and which the product is currently clustered,
fill in the gaps with a % of the total product group|||s units for the period equal to that store % contribution of the total units for the period for that product
Then pull all of our units per product per period ranked them in descending order and assigned points in each 10% band as per variables
Example
To apply the Ranging rule in Dotactiv’s software, start by collecting all the units sold during the specified period for the relevant stores. Next, identify any store/SKU combinations that exist in your range but have recorded no sales. For each product and period, examine stores with no units sold but where the product is part of the current cluster. To address these gaps, calculate the store’s contribution to the total units of the product by using the store’s percentage share of the total product group’s units for that period. Essentially, this means distributing the product units proportionally based on each store’s contribution to the total.
After filling these gaps, organize the units per product for the period in descending order. Then, rank them and assign points within each 10% band according to predefined variables. This ranking helps in understanding the relative performance of each SKU across stores, ensuring that the product range is optimized and aligned with sales potential.
For example, if a product was sold in 10 stores but one store had no sales, you would estimate the number of units that should have been allocated to that store based on its overall contribution to the product’s total sales. Then, rank all stores based on the units they have sold and assign points accordingly to highlight the most and least-performing stores. This approach helps in refining the product range to better meet sales targets and store performance.
Internal Market Unit Ranks
Rule Information/Description
Gets total units for the period set for the internal markets selected.
Averages the units per market per period.
Data is ranked in descending order and assigned points in each 10% band as per variables
Example
In Dotactiv software, the ranging rule is utilized to analyze sales performance across selected internal markets over a specified period. For example, if a planner sets the period from January to June and selects markets A, B, and C, the system first calculates the total number of units sold in each market for the six-month period. It then averages these unit sales per market to provide a normalized view of performance. Once the average units for each market are determined, the data is sorted in descending order, highlighting which markets have the highest average sales. The top 10% of markets receive the highest ranking points, while the next 10% receive slightly lower points, and so forth, until all markets are assigned points based on their performance bands. This helps in identifying high-performing markets and aids in strategic decision-making by focusing on top performers and understanding market trends.
Simple Units
Rule Information/Description
Gets total units for the period set.
Averages the units per market per period.
Data is ranked in descending order and assigned points in each 10% band as per variables
Example
This rule calculates the total units sold for the specified timeframe and then averages these units across different markets to understand performance trends. For example, if a planner is analyzing sales data from January to March, they would first sum the total units sold during these months, then divide that by the number of markets being evaluated to obtain the average sales per market. The resulting data is then ranked in descending order based on the average units sold. Each product is assigned points according to its position within predetermined 10% bands, allowing the planner to identify top-performing products and those that may need strategic attention. This structured ranking helps in making informed decisions about product placement and inventory management, ultimately optimizing category performance.
External Market Units Rank
Rule Information/Description
Gets total units for the period set from the External Market table for the selected market.
Averages the units per period.
Data is ranked in descending order and assigned points in each 10% band as per variables
Example
Utilize the Ranging rule by first selecting the relevant market data from the External Market table within the software. The system automatically pulls the total units sold for a specific period. Once these units are gathered, it calculates the average number of units sold per period, providing a clearer understanding of product performance. Next, the data is ranked in descending order based on the average units sold, allowing for easy identification of top-performing products. The ranked products are then segmented into 10% bands, with each band being assigned a corresponding point value based on pre-set variables. This process enables strategic product range decisions, ensuring that higher-ranked, better-performing products are prioritized within the category assortment.
For example, the planner uses this rule to view the best-performing SKUs based on sales within the retailer as well as within the market to ensure that all the best-performing SKUs in the market will be on the planogram.
Ranging Rules For Brands
Top Brands
Rule Information/Description
For each product, get average sales and average units per period and calculate a weighted CPI as per variables.
Rank the bands by average CPI
Points are allocated to the products in the top brands (number of brands set in the variables)
Example
In DotActiv, a category planner would use the Ranging rule to optimize product assortment by evaluating each product’s performance. This rule involves calculating the average sales and average units sold per period for each product, which helps assess its demand. A weighted Consumer Price Index (CPI) is then calculated, considering various variables such as price, volume, or demand to gauge product performance across brands. Products are ranked by their average CPI to determine which are the top-performing brands. The planner sets a specific number of brands in the variables, and the products in these top-ranked brands are allocated points, which helps prioritize which products should be included in the assortment. For example, if two brands have a high average CPI based on sales volume and price stability, their products will be given more points, suggesting that they should remain in the assortment, while lower-ranked products might be phased out to improve the overall category performance.
Exclusive Brand
Rule Information/Description
Points are allocated to each product identified as an Exclusive Brand product (Exclusive Brand value(s) are specified in the application variables table).
Exclusive brands revert to brands that are only available at one retailer.
Example
The Ranging rule is used to allocate points to Exclusive Brand products, ensuring that these products receive appropriate shelf space in line with strategic objectives. By setting the Exclusive Brand values in the application variables table, I could identify the relevant products within the assortment.
For example, if a retailer wanted to prioritize their private label products, I would configure the software to assign higher points to these items. This would increase their likelihood of being included in the final planogram, thus aligning the product assortment with the retailer’s goal of boosting the visibility and sales of Exclusive Brands. This process not only helped in better category management but also ensured that shelf space was optimized based on the retailer’s priorities.
House Brand
Rule Information/Description
Points are allocated to each product identified as a House Brand product (House Brand value(s) are specified in the application variables table)
House brands are products that are developed for the retailer to sell in their shop.
Example
In our ranging process, we allocate points to each product identified as a House Brand product to ensure we prioritize these items in our assortment decisions. For instance, if our House Brand products are specified with a value of ‘5’ in the application variables table, each product flagged as a House Brand in our data will receive 5 points. This point allocation helps us in the software to consistently prioritize House Brand products over others when planning our range, ensuring that these products are more prominently featured in our assortment strategy. By using this method, we align with our goal of enhancing the visibility and performance of our House Brand products in the market.
Max Col/Flav
Rule Information/Description
For each product, get average sales and average units per period and calculate a weighted CPI as per variables.
Rank the bands by average CPI
Only within the top brands (number of brands set in the variables):
For each brand, size, Uom and Texture combination, rank the products by the CPI
Allocate points to the top products (number of products set in the variables)
Example
The “Ranging Rule” for calculating weighted CPI is critical in optimizing product assortments. This rule first involves analyzing the average sales and average units sold for each product over a specific period to calculate a weighted CPI (Consumer Price Index) based on predefined variables such as brand, size, UoM (Unit of Measure), and texture. Products are then ranked by their CPI across bands, and only the top brands (determined by a variable set by the user) are considered for further analysis. Within these top brands, products are further evaluated by combinations of brand, size, UoM, and texture, and ranked again based on their CPI. The system allocates points to the top-performing products, ensuring that the most relevant and profitable products, as defined by the number of products set in the variables, are given priority in the assortment. This method helps ensure that shelf space is optimized for products that have the highest potential for sales, considering both the brand and product-specific factors.
For example, if we were ranging soft drinks, the system might calculate a weighted CPI for each brand (e.g., Coca-Cola, Pepsi) based on sales and units, then only focus on the top three brands (as per the set variables). Within these brands, products like Coca-Cola 500ml, Pepsi 330ml, and other variations would be ranked and allocated points according to their CPI. This ensures that the top-performing items in terms of profitability and sales potential make it to the shelf.
Product Label
Rule Information/Description
Points are assigned to products based on the private label value being Exclusive, Private or Supplier (points set in the rule variables)
Example
The Ranging rule related to private label value allows category planners to prioritize products based on their label type. This rule assigns points to products according to whether they are categorized as “Exclusive,” “Private,” or “Supplier.” The rule variables define the point values for each category, with “Exclusive” typically receiving the highest points, as these products are unique to the retailer, driving differentiation and customer loyalty. “Private” label products, often store-branded, also receive significant points due to their profitability and ability to compete with national brands. Lastly, “Supplier” products, which are supplied by external brands, receive fewer points, as they may be more widely available at competing retailers. By setting these variables, category planners can optimize product placement, ensuring that high-priority private label products are more prominently ranged, supporting both business strategy and customer preference.
For example, a category planner might set the points to 10 for “Exclusive,” 7 for “Private,” and 3 for “Supplier” products. In this case, exclusive private-label products would take precedence on the shelf, with private-label products following, and supplier products being placed last. This ensures a tailored offering, boosting customer loyalty and profitability.
Ranging Rules For Profit
Net Profit (SI)
Rule Information/Description
Contribution calculated as the Total Sales minus the Total Cost of Sales
SI Value calculated as the Supplier Income Margin percentage of the Cost Of Sales
Net Profit calculated as the Contribution plus the SI value
Products are then ranked in descending order of this Net Profit value and assigned points in each 10% band as per points variables
Example
In Dotactiv’s software, the ranging rule starts by calculating the Contribution for each product, which is derived by subtracting the Total Cost of Sales from the Total Sales. For instance, if a product’s Total Sales amount to R10,000 and its Total Cost of Sales is R6,000, the Contribution would be R4,000. Next, the SI Value is calculated as the Supplier Income Margin percentage of the Cost of Sales. If the Supplier Income Margin is 10%, the SI Value would be R600 (10% of R6,000). The Net Profit is then determined by adding the Contribution and the SI Value together, resulting in a Net Profit of R4,600 for this product. To rank products, the Net Profit values are ordered in descending sequence, and points are assigned based on their position within each 10% band of the Net Profit distribution. For example, if the top 10% of products fall within the highest Net Profit bracket, they might receive the maximum points, while those in lower brackets receive fewer points. This method ensures that products contributing the most to profitability are prioritized effectively in the ranging strategy.
Total Profitability
Rule Information/Description
Pull the total sales and cost of sales per period for each product for all relevant markets
Calculate Profitability per product as Sales – Cost as a percentage over Sales
Products are then ranked in descending order of the Profitability value and assigned points in each 10% band as per variables
Example
The Total Profitability ranging rule assigns points based on how profitable the product is. The purpose is to maximize the profitability of the range rather than increasing the number of products within the range. This ranging rule will be used if the objective of the category is to ensure that the products positively contribute to the company’s bottom line (profit).
Total Profitability (v2)
Rule Information/Description
Products are assigned points based on the value of the Total Profitability of the product as per the variables.
Example
In Dotactiv’s software, the ranging rule that assigns points based on the Total Profitability of a product is a critical mechanism for optimizing product assortment and shelf space allocation. This rule evaluates each product’s profitability by taking into account variables such as sales volume, profit margins, and overall contribution to category profit. For instance, a product with high profit margins and strong sales performance will accumulate more points compared to a product with lower profitability. By assigning these points, the software prioritizes higher-margin products, ensuring that they are more prominently featured and adequately stocked. This approach not only enhances the profitability of the category but also aligns with strategic goals to maximize return on investment and meet consumer demand effectively. For example, if Product A generates a profit of R500 with a margin of 30% and Product B generates a profit of R200 with a margin of 20%, Product A would receive more points due to its higher total profitability, leading to its preferential placement and visibility on the shelves.
Profit Deduction Percentage
Rule Information/Description
Get total sales and GP % per product.
Take each product sales value down to a specified percentage of that value and divide by product foot space (Height * Width * Current Facings count)
The percentage is calculated as the gross profit percentage minus the specified deduction percentage
Products are ranked in descending order based on that deduced percentage of the selling value and assigned points in each 10% band as per variables
Example
The “Ranging Rule” of Profit Deduction Percentage can be explained as follows: “Get total sales and GP % per product,” the approach revolves around optimizing shelf space allocation using sales performance and profitability. First, each product’s total sales value and gross profit percentage (GP%) are calculated. A specified percentage of each product’s sales value is then taken, which is adjusted by subtracting a predetermined deduction percentage from the GP%. This adjusted sales value is then divided by the product’s foot space, calculated as the product’s height, width, and the current number of facings on the shelf. The result is a deduced selling value per square inch of shelf space. Products are then ranked in descending order based on this deduced percentage, giving priority to high-profit, high-performing items. Points are assigned to each product according to its position in descending bands (10% increments). This ranking helps ensure that space is allocated effectively to products that generate higher sales and profit relative to the space they occupy.
CPI (Consumer price index)
Rule Information/Description
Points are allocated to products in order of their weighted CPI value.
The CPI value per product is calculated using total sales, units and profit for the selected markets and periods, with each fact making use of a variable weighting which can be set by the user.
Example
The weighted CPI value Ranging Rule is to prioritize products for ranging by considering multiple sales metrics.
For example, I would allocate points to products based on their CPI score, which is calculated using a blend of total sales, units sold, and profit margin over a defined period. The software allows me to adjust the weight given to each of these factors, depending on the specific goals of the category. If I want to emphasize profitability, I can assign a higher weight to profit, while still factoring in sales volume and units sold. This calculated CPI value helps me ensure that high-performing products are ranked appropriately in the assortment, supporting both financial goals and consumer demand.
Ranging Rules For Product Performance
Performers in Category
Rule Information/Description
Specified amount of points are allocated to products if they are identified as being either in the top 10%
or bottom 10% for their category based on Sales, Units and Profit respectively
These indicator flags are continuously refreshed in the database based on latest period fact values
Example
The “Ranging Rule” is focused on selecting products specific to the performance of individual products in a specific category. The performance of the individual products is calculated in terms of sales, units, profitability and other Key Performance Indicators. The Ranging Rule is selected when we want to select a range based on the best-performing products within the category.
Performers in Format
Rule Information/Description
A specified amount of points are allocated to products if they are identified as being either in the top 10%
or the bottom 10% for their category within the relevant Cluster based on Sales, Units and Profit respectively
These indicator flags are continuously refreshed in the database based on the latest period fact values
Example
The ‘Performers in Format’ Ranging Rule is used to prioritize products for ranging by considering the top or bottom performers in a category based on their Sales, Units or Profit data for that Cluster.
For example, I would allocate points to products that fall within the top 10% of a cluster, which is calculated using the products’ total sales, units sold, or profit margin over a defined period for a specific cluster. A product might perform differently in one cluster versus another due to the LSM (for example) which means that you might want to range a product in one cluster but not necessarily in all of the clusters of the same category. This rule helps me ensure that high-performing products are ranked appropriately in the assortment for a specific cluster.
Good Better Best
Rule Information/Description
For each product, get average sales and average units per period and calculate a weighted CPI as per variables.
Points are allocated to the top products (based on the CPI and the number of products the user sets in the variables) within each Brand Profile Group
The rule is configured to show a text result.
Possible text values are as follows:
GOOD
BETTER
BEST
Example
Implementing the ranging rule involves a systematic approach to analysing product performance within a category. For instance, let’s consider a product range for a beverage category. First, I would gather data on each product’s average sales and average units sold over the past quarter. Using this data, I calculate the weighted Consumer Preference Index (CPI), which incorporates variables such as sales volume, profitability, and seasonal demand fluctuations. The CPI helps determine which products are performing exceptionally well in terms of consumer preference and sales efficiency.
In the next step, the software ranks the products within each Brand Profile Group according to their CPI scores. For example, if we have a Brand Profile Group for a popular energy drink brand, the software might assign higher CPI scores to products with high sales and strong customer feedback. Based on the variables and the number of top products set in the software, points are allocated to the leading products.
The rule then categorizes these products into one of three text values: “GOOD,” “BETTER,” or “BEST.” For instance, if Product A and Product B in this energy drink group have the highest CPI scores, they might be categorized as “BEST,” indicating their top performance. Products with slightly lower CPI scores could be classified as “BETTER,” while those with the lowest scores might be labeled as “GOOD.” This categorisation allows for strategic decisions regarding product placement, promotions, and inventory management, ensuring that the highest-performing products receive optimal visibility and support within the category.
ROI
Rule Information/Description
Gets total units and average stock per market per period for each product.
Calculate stock turn as units/stock. Calculate ROI as Stock Turn X GP %
Points are allocated based on where the ROI value sits in the following bands:
Value from Value to Points
<=0.5 0
0.51 0.75 1
0.76 1.00 2
1.01 1.25 3
1.26 1.50 4
1.51 1.75 5
1.76 2.00 6
2.01 2.25 7
>=2.26 8
Example
The Ranging rule can be explained by breaking down its purpose and application in optimizing product assortment. This rule assesses product performance across markets by analyzing total units sold and average stock levels for a specified period. Stock turn, a key metric, is calculated as the ratio of units sold to the average stock, indicating how frequently stock is replenished. Return on Investment (ROI) is then determined by multiplying the stock turn by the gross profit percentage (GP%). The ROI value helps assess the profitability of products in relation to their inventory turnover. To evaluate performance, points are allocated based on ROI values according to specific bands, ranging from 0 (for ROI values ≤ 0.5) to 8 (for ROI values ≥ 2.26). For instance, a product with a stock turn of 1.3 and a GP% of 15% would have an ROI of 0.195 (1.3 x 0.15), falling into the first band and earning 0 points. This point-based system allows category planners to easily compare and rank products, facilitating strategic decisions on which items to retain, promote, or phase out from the category based on their contribution to profitability and stock efficiency.
Stock Turn
Rule Information/Description
Gets total units and average stock per market per period for each product.
Calculate stock turn as units / stock
Data is ranked in descending order and assigned points in each 10% band as per variables
Example
In Dotactiv’s software, the Ranging rule is used to assess product performance across various markets by analyzing total units sold and average stock levels for each product over a specified period. The process begins by calculating the stock turn ratio, which is derived by dividing the total units sold by the average stock on hand. This ratio helps in understanding how efficiently stock is being turned over. The products are then ranked in descending order based on their stock turn ratios. Once ranked, products are segmented into 10% bands or percentiles, which helps in categorizing them according to their performance. For example, a product with the highest stock turn ratio would be placed in the top 10% band, indicating it is performing exceptionally well, while a product with a lower ratio would fall into lower bands. This ranking and banding system allows for targeted inventory management and decision-making, ensuring that high-performing products are prioritized and underperforming ones are reviewed for potential adjustments.
Stock Turn (v2)
Rule Information/Description
Gets total units and average stock per market per period for each product.
Calculate stock turn as units / stock
Points are allocated based on where the stock turn value sits in the following bands:
Value from Value to Points
<=0.5 0
0.51 0.75 1
0.76 1.00 2
1.01 1.25 3
1.26 1.50 4
1.51 1.75 5
1.76 2.00 6
2.01 2.25 7
>=2.26 8
Example
In Dotactiv’s Ranging rule, the software analyzes inventory performance by calculating total units sold and average stock levels for each product across different markets over a specified period. This data allows category planners to assess stock turn, defined as the ratio of units sold to the average stock on hand. To evaluate inventory efficiency, points are allocated based on stock turn values, which are segmented into defined bands. For instance, a product with a stock turn of 1.5 would fall into the 1.26 to 1.50 band, earning it 4 points, indicating a healthy turnover rate. Conversely, a product with a stock turn of 0.4 would receive 0 points, signaling potential overstock issues. By categorizing products in this way, planners can make informed decisions on inventory management, optimize stock levels, and enhance overall sales performance.
Time On Shelf
Rule Information/Description
Points are allocated to products where the first sale date indicates that the product is younger than the number of days set in the variables
Example
When applying the “Ranging rule” that allocates points to products based on their first sale date, the focus is on identifying newer products that might need prioritization or specific consideration. In the software, a variable is set to define a threshold for the product’s age in terms of days since its first sale. Products that have a first sale date younger than this threshold are allocated points, which helps in distinguishing and potentially promoting newer items. For example, if the variable is set to 90 days, any product that was first sold within the last 90 days will be awarded points, while older products may not receive such points. This rule ensures that recently launched products are factored into the category strategy, helping with decisions like highlighting them in promotions or giving them prime shelf space to test their performance in the early stages of their lifecycle.
Remaining Ranging Rules
Active Products
Rule Information/Description
Points are allocated to each product with a product status value reflecting that it is an Active product (Active value(s) are specified in the application variables table)
Example
This is to guarantee that points are awarded to products according to their standing in the system, particularly for those that have the classification “Active.”
For example, I would allocate points to each product based on its product status value when configuring the range rules in DotActiv’s program. Products with different statuses, like “Discontinued” or “Archived,” would either receive fewer points or be excluded from the range entirely. Products that are designated as “Active” (defined by the Active value(s) specified in the application’s variables table) would receive the appropriate points. This technique makes sure that only current, relevant products are given top priority during the range planning process, which helps to prioritize and optimize the assortment.
Exclude I Status SKUs
Rule Information/Description
-100 points are allocated to products where the Product status is I
Example
The role involves ensuring that product assortments are optimized according to various business rules and guidelines. For instance, when working with ranging rules in the software, a negative score of -100 is allocated to products where the product status is marked as “Inactive” (I). This rule is essential because inactive products should not occupy valuable shelf space or be part of the active product assortment. By applying this rule, the planner ensures that the category remains relevant and up to date, focusing on items that are actively contributing to sales while preventing inactive products from skewing performance metrics or cluttering the product range. This approach helps in maintaining a clean and efficient product assortment, driving better sales and customer satisfaction.
For example, when the planner needs to create a small drop count planogram or have a congested or high SKU category the software will indicate by the -100 points to not add the SKUs to the range as they are inactive.
Price Ladder
Rule Information/Description
Average Sales are retrieved per market per period.
Within each UOM group, the products are ranked by PricePerUom. Each of these groups is split into 40%/20%/40% bands and a point is assigned to the top products (based on the average sales) in each band.
Example
The Ranging rule helps determine which products should be included in a planogram based on their sales performance and price per unit of measure (UOM). First, the system retrieves average sales data per market and per period, giving insight into how products perform in different locations and timeframes. Within each UOM group, products are ranked based on their price per UOM, allowing for price comparisons within the same measurement group. The products are then divided into three bands: the top 40% (high-price products), the middle 20%, and the bottom 40% (low-price products). Points are assigned to the top-performing products (in terms of average sales) in each of these bands. This method ensures that the best-selling products are considered across all price ranges, helping maintain a balanced assortment that caters to different price sensitivities while focusing on high performers.
An example would be a soft drink category where products are grouped based on price per liter. Products in the top 40% price range (premium brands) may include popular options like imported soft drinks. The middle 20% could feature mid-tier brands, and the bottom 40% would include budget-friendly or store brands. The system would assign points based on sales data, highlighting top-selling products in each price range, ensuring an assortment that reflects both customer preferences and market demand across the price spectrum.
Top Shopper
Rule Information/Description
Gets total top shopper quantity for the period set.
Averages the top shopper quantity per market per period.
Data is ranked in descending order and assigned points in each 10% band as per variables
Example
In Dotactiv’s software, the ranging rule “Gets total top shopper quantity for the period set” is designed to help categorize and prioritize products based on shopper behaviour. This rule first aggregates the total quantity purchased by top shoppers within the specified period, providing a comprehensive view of high-demand products. Next, it calculates the average quantity purchased by these top shoppers for each market during the same period. The data is then ranked in descending order of total quantity, creating a prioritized list of products. To further refine this analysis, the software assigns points to products based on their rank within predefined 10% bands. For example, products in the top 10% band receive the highest points, indicating their top performance. This method allows for a detailed understanding of product performance across different markets and periods, enabling more informed decisions on inventory management and promotional strategies.
Sku Size Limiter
Rule Information/Description
Points are assigned to products where the size is bigger or smaller than the size range set on the variables.
Example
The Ranging Rule that assigns points to products based on size is used to ensure that the assortment is balanced and optimized. When setting up the size range in the software, you define the minimum and maximum size parameters for products in a category. Products that fall within this defined range are preferred, while those that are smaller or larger are assigned points based on their deviation from the ideal range. For example, if the size range for shampoo bottles is set between 250ml and 500ml, a product sized at 300ml would fall within the range and receive no penalty points, while a 600ml bottle, which exceeds the maximum size, would receive penalty points. The higher the deviation from the ideal range, the more points are assigned, which may influence whether that product remains in the assortment, as it may no longer meet the category’s strategic objectives. This rule helps in maintaining an assortment that aligns with consumer preferences and optimizes shelf space.
Supplier Income
Rule Information/Description
Get total sales per product for the applicable markets and selected period
Calculate % contributions per Supplier within the current product grouping
The number of products to recommend set as the range cap for the applicable cluster/category (or the current range count is no cap is set)
If the rule is not being applied a category level, a % of that product count is determined equal to the product group sales contribution to the category
Using the determined total number of products to recommend, use the % contribution per supplier to determine a range count per supplier
Recommendations assigned to the top products per Supplier (based on Sales) equal to the determined product counts
Rule is configured to show as a checkbox.
A tick means that the rule assigned the product any points that are not 0.
Example
The process begins by aggregating total sales for each product across applicable markets over a specified period. Next, the planner calculates the percentage contribution of each supplier within the current product grouping, which helps identify key suppliers driving sales. The planner sets a maximum number of products to recommend for a specific cluster or category or defaults to the current range count if no cap is specified. If the rule isn’t applied at the category level, the planner calculates a percentage of the product count based on each product group’s sales contribution to the category, ensuring that all relevant products are considered. Using the total number of products to recommend, the planner then applies the supplier percentage contributions to determine how many products to recommend from each supplier. Finally, recommendations are assigned to the top-selling products per supplier, ensuring that the range reflects both sales performance and supplier significance. The rule is easily configurable via a checkbox; when checked, it indicates that products have been assigned points based on their sales contribution, ensuring that only those products with a meaningful sales impact are included in the recommended range.