How to Model Sales and Profit (A More Accurate Way)
Posted: Thu May 22, 2025 9:33 am
When you have more free time and resources, you will be able to apply a more reliable and accurate modeling system. To make such modeling possible, you need to collect and systematize the following data:
Data on units sold for each article (warehouse inventory unit) broken down by date;
Potential interest in each product over a certain period of time (for example, tracking visitors using Metrica, Google Analytics and other data analysis tools );
Supplier data by article number;
Sales geography for each item;
Data on past promotions for each item;
Customer feedback data for each item
Data on these items from competitors (prices, discounts, their dynamics);
Product data.
Another important factor you need to consider, and kazakhstan phone number list something you really need to know about your business, is your cost structure. This typically includes the purchase price, as well as all the fixed costs associated with each sale. On top of that, you also need to determine your target gross profit.
Excel is very useful for plotting sales versus price and profit versus price, as well as sales/profit versus time. Remember that the values are not constant.
They change over time as the market evolves and internal and external factors change. For example, for some charts to provide useful information, you may need to have multiple prices for each SKU in your data set. This could be a standard price and a discounted price that you can switch between.
Once you have collected enough data, you can calculate simple statistical key metrics that will allow you to analyze the data:
Average sales broken down by item per unit of time, e.g. 1 unit sold on average every 10 days => 3 units sold per month on average;
The difference in sales by SKU, for example if 1 unit is sold every 10 days, this means you have 9 days out of every 10 days where there are no sales.
Using this data and key figures, you can model your sales and profits using:
Normal distribution (be careful, this may not be the optimal choice for pricing models as it can be negative);
Gamma distribution (this method is often quite suitable for pricing models);
Poisson distribution (the method is well suited for pricing models).
Read also our guide to data analysis - How to analyze data: a basic guide .
Data on units sold for each article (warehouse inventory unit) broken down by date;
Potential interest in each product over a certain period of time (for example, tracking visitors using Metrica, Google Analytics and other data analysis tools );
Supplier data by article number;
Sales geography for each item;
Data on past promotions for each item;
Customer feedback data for each item
Data on these items from competitors (prices, discounts, their dynamics);
Product data.
Another important factor you need to consider, and kazakhstan phone number list something you really need to know about your business, is your cost structure. This typically includes the purchase price, as well as all the fixed costs associated with each sale. On top of that, you also need to determine your target gross profit.
Excel is very useful for plotting sales versus price and profit versus price, as well as sales/profit versus time. Remember that the values are not constant.
They change over time as the market evolves and internal and external factors change. For example, for some charts to provide useful information, you may need to have multiple prices for each SKU in your data set. This could be a standard price and a discounted price that you can switch between.
Once you have collected enough data, you can calculate simple statistical key metrics that will allow you to analyze the data:
Average sales broken down by item per unit of time, e.g. 1 unit sold on average every 10 days => 3 units sold per month on average;
The difference in sales by SKU, for example if 1 unit is sold every 10 days, this means you have 9 days out of every 10 days where there are no sales.
Using this data and key figures, you can model your sales and profits using:
Normal distribution (be careful, this may not be the optimal choice for pricing models as it can be negative);
Gamma distribution (this method is often quite suitable for pricing models);
Poisson distribution (the method is well suited for pricing models).
Read also our guide to data analysis - How to analyze data: a basic guide .