Retail store sales forecasting

By Pablo Martin, Artelnics.

Sales forecasting is an essential task for the management of a store. Being able to estimate the quantity of products that a retail store is going to sell in the future allows the owners of these shops to prepare the inventory that they will need.

Machine learning can help us to study and discover the factors that determine the number of sales that a retail store will have in the future.

In this article, we use the information about the sales of a drug store from the last two years to predict the amount of sales that it is going to have one week in advance.

  1. Data analysis
  2. Model training
  3. Testing analysis
  4. Model deployment
  5. Conclusions

Data analysis

The first step of the analysis is to study the data set, which contains the sales information from the drug store. The next time series chart shows the number of sales by month.

Sales per month in a retail store

As we can see, most of the sales are made between March and July. Then, the number of sales decreases till December, when it grows again.

Secondly, we study how the sales are distributed along the month.

Sales by day of month in a retail store

In this case, the days of the beginning of the month are the ones with the major activity. After the middle of the month the sales remain stable.

Lastly, it is also important to take a look at the number of sales by weekday. The next time series chart shows the sales in this shop from Sunday (1) to Saturday (7).

Sales by weekday in a retail store

Sunday is the day preferred by the customers to buy in this retail shop. During the rest of the week, the sales decrease from Monday to Wednesday and increase from Wedenesday to Friday. Saturday is the day with the least number of sales.

The next step is to select and prepare the variables that we are going to use.

The following list shows the input variables, or predictands:

  1. Day of week.
  2. Month.
  3. Day of month.
  4. Promotion.
  5. Previous day promotion.
  6. State holidays.
  7. School holidays.
  8. Previous 1 day sales.
  9. Previous 2 days sales.
  10. Previous 3 days sales.
  11. Previous 4 days sales.
  12. Previous 5 days sales.
  13. Previous 6 days sales.
  14. Previous 7 days sales.

As we can see, the number of inputs is 14.

The only target variable, or predictor, is:

  1. Next 7 days sales

Once the variables are defined, we can calculate the dependencies between all the inputs and the target. The next chart shows the linear correlations between each input and the target variable "Sales".

Correlations between variables and sales in a retail store.

The number of sales of the same weekday of the previous week, the weekday and the state holidays are highly correlated with the number of sales.

Model training

After defining the variables that we are going to use for the analysis, it is time to use Neural Designer in order to build the predictive model for the sales of the store. The next image shows a representation of the neural network that we use for the analysis.

Neural network to forecast sales in retail stores

The information of the date, promos, holidays and sales of the previous week enters to the neural network through the left layer. Then, it is analyzed by the perceptrons in the layer from the middle to find the patterns that determine the number of sales, which is given by the last layer.

Now, the neural network is ready to be trained using the Quasi-Newton algorithm. to find more information about this and other optimization algorithms, you can read 5 algorithms to train a neural network.

Testing analysis

The last step before using the model to forecast the sales is to determine its predictive power on an independent set of data that have not been used before for the training. The next chart shows the linear regression analysis between the scaled output of the neural network and the corresponding scaled targets.

Regression to forecast sales in retail stores

The next table shows the parameters of the previous linear regression analysis.

Regression parameters to forecast sales in retail stores

The intercept and the slope are close to 0 and 1 respectively and there is a correlation between the outputs and the targets of almost 91%. This means the model is predicting well this set of data. As a consequence, the model is ready to be moved to the deployment phase.

Model deployment

Once the model has been tested, it can be used to predict the sales of the shop one week in advance. As an example, we predict the number of sales of this retail shop for the last week of July, without state nor school holidays and knowing that the sales of the previous week from Sunday to Saturday are: 31665, 19169, 17836, 17663, 17513, 18985 and 19042, respectively.

Retail store sales predictions

As we can see, the Sunday of the next week is the day when most of the sales are going to be made. During the rest of the week, the number of sales will remain stable and they will slightly decrease with respect to the previous week.

Conclusions

During this article we have developed a predictive model that can help retailers to determine the number of sales that they are going to make in the future.

By using this model, retailers are able to plan the amount of products that they are going to need and, as a consequence, the system will allow them to increase their profits.

You can use Neural Designer to build predictive models from your data and forecast the sales of your own company or test it using the data set you can find below.

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