What is ABC Analysis, and Why is it Important in Category Management

ABC analysis in category management is a method of classifying products by priority based on selected criteria, where A represents high priority, B is medium priority, and C means low priority.

ABC analysis is based on the Pareto principle (the 80/20 rule), which states that 20% of efforts produce 80% of results. In category management, this means that 20% of all products generate 80% of sales.

The Advantages of Using ABC Analysis

The purpose of ABC analysis is to structure the assortment and determine the priority products or categories within it.

By dividing the assortment into segments based on their A/B/C status, you can focus and understand how to achieve a particular KPI, and which tasks need to be started right now. For example, which products or categories can increase turnover and profit or how to optimize inventory.

ABC analysis is based on retrospection, that is, on historical data. However, the market is changeable, and it can be influenced by various factors. Therefore, to build a more accurate predictive model, ABC analysis is combined with XYZ analysis, which shows the variability of demand for products.

ABC Analysis: A Step-by-Step Tutorial

Step 1. Set a goal.

Specify the purpose behind conducting the analysis. There are different options:

• optimize the assortment and increase profitability;
• update the supply plan;
• stimulate remnant inventory sales;
• segment products to create special offers for different groups.

Step 2. Select objects for analysis.

These can be products within a category, as well as categories, segments, or brands.

To ensure more accurate results, avoid incorporating new products into the ABC analysis, as there isn’t enough accumulated information about them yet. Also keep in mind that promotional items distort statistics, as they may sell better thanks to special prices. So you need to focus on a wider range of data over time.

Step 3. Define criteria.

The indicators that are usually used for the assortment ABC analysis are:

• number of sales, in units;
• profit, in USD;
• turnover, in USD;
• stock balances, in units;
• advertising expenses, in USD;
• other expenses, in USD.

Choose one of these indicators, the most suitable to the goal of your analysis, as well as the data sample for the relevant period.

Step 4. Use Excel or Google Sheets to enter data.

We have prepared an example of ABC analysis in Google Sheets. In our case, the object is a product, and the analysis criteria is the number of sales.

Enter the values of the calculated parameter (column B).

Step 5. Sort the results in this column from the highest value to the lowest.

Step 6. Calculate the total result.

Step 7. Calculate the share for the first product using the formula:

The share of one product = number of sales of one product / total sales of all products.

In our example, this is column C. The formula will be as follows:

C2 = B2/B$33

Extend the formula to all cells. The total should be 100%.

Step 8. Calculate the cumulative share for each product by adding its share to the share of the previous product.

For the first product on the list, the cumulative share will be the same as in the previous column.

In our example:

D2=C2

And then, cumulatively:

D3=C3+D2

D4=C4+D3

D5=C5+D4 (…)

For the last product on the list, the cumulative share will be 100%.

Step 9. Segment the obtained results into groups.

Group A — focus positions that need to be given more time for deeper analysis based on the set goal. The value of the cumulative share is in the range of 0 to 80%.

Group B — positions with the potential to move into both Group A and Group C. The value of the cumulative share is in the range of 80 to 95%.

Group C — positions that should be eliminated. This may include new items if you are conducting sales ABC analysis because statistics on them have not yet been accumulated. The value of the cumulative share is in the range of 95 to 100%.

How to Apply the Results of the ABC Analysis

After completing the calculations, you can use your final data, for example, to review the inventory situation. To do this, add a column called “Quantity in stock, in units.” Then you can see that, for example, class A items are not in stock based on the quantity sold, while class C items are in excess, and adjust your inventory policy accordingly.

ABC analysis can be very effective as a simple way to focus and prioritize. Especially when you have limited resources: money, time, people, warehouse space, etc.

However, the ABC classification is too simplified because the evaluation criteria are one-dimensional. By prioritizing based on only one indicator, you lose sight of others.

In addition, ABC analysis relies only on historical data, and its results don’t consider market variability. To overcome this inaccuracy, you can implement ABC/XYZ analysis.