05.20.2019 | Oliver Weisse | 0 Comment

# Turn your raw sales numbers into deep sales insights and intelligent forecasts

In the last years technologies like data science, artificial intelligence and predictive analytics have become increasingly popular. There are several applications of these technologies which include targeted marketing, churn prediction or sales prediction.

The academic field behind these technologies is called “machine learning”. The core idea is that the computer (“machine”) learns autonomously from data and generates business insights and leads to data-driven decision making.

To illustrate and demystify the technology, in this article we will walk through the example of sales prediction. We will cover how to get from raw data to valuable predictions with machine learning. The article can be understood by nontechnical readers.

On the platform kaggle.com companies can upload their data and let data scientists compete to build the best machine learning algorithm for their problem. One of the largest Russian software firms, 1C Company, provided their data for competition. The goal of this competition is to predict the sales for the next month. Predicting the sales of store items helps planning how much of each product needs to be kept in the warehouse. Store sales are influenced by many factors, including promotions, competition, school and state holidays, seasonality, and locality. And if it is about predicting from data, machine learning is definitely the right technology.

We have 33 months of sales data and want to predict the sales for the next month. Let us have a look at what we want to predict:

ID shop_id item_id item_cnt_month
0 5 5037 ?
1 5 5320 ?
2 5 5233 ?
3 5 5232 ?
4 5 5268 ?

Given the shop ID and a particular item, we want to predict how many items will be sold in the corresponding month (item count month). Let us now have a look at a sample of our past sales data:

date date_block_num shop_id item_id item_price item_cnt_day
16.06.2013 5 30 11496 399.00 1.0
14.06.2013 5 30 11244 149.00 1.0
06.06.2013 5 30 11388 898.85 2.0
15.06.2013 5 30 11249 399.00 1.0
13.06.2013 5 30 8081 299.00 1.0

Here, one row in the table is one transaction on a particular day. We also have the additional columns date_block_num which enumerates the months and item_price. The column item_cnt_day stands for how many items have been sold in this particular transaction. It can also be a negative value if an item was returned. At the moment, the data is not yet in the format, we need it in. We want to predict monthly sales for a particular item and shop. So, we have to count all the items that have been sold in a month and sum up the item_cnt_day column. In data science jargon these transformations are called groupby-and-aggregate. It is part of the data wrangling process: Transforming the raw data into the desired format. This part can take most of the data scientist’s time. After the transformation our data table looks as follows:

date_block_num shop_id item_id year month item_cnt_month
5 2 30 2013 6 1.0
5 2 482 2013 6 2.0
5 2 491 2013 6 2.0
5 2 835 2013 6 1.0
5 2 839 2013 6 1.0

For example, in June 2013, item 482 was sold twice in shop 2. Let us now try to predict the sales of the next month without any machine learning. We will use the sales of the last month to predict the sales of the current month – this is referred to as the naïve method in statistical forecasting. We have to add the column item_cnt_last_month for that. So, for the row June 2013, shop 2, item 482, we have to search for the entry May 2013, shop 2, item 482 and copy the data from item_cnt_month. This is another example of data wrangling. Now our table looks like this:

date_block_num shop_id item_id year month
5 2 30 2013 6
5 2 482 2013 6
5 2 491 2013 6
5 2 835 2013 6
5 2 839 2013 6

item_cnt_month item_cnt_last_month
1.0 0.0
2.0 1.0
2.0 1.0
1.0 2.0
2.0 0.0

Now we have the variable item_cnt_month that we want to predict in one column and our prediction item_cnt_last_month in the column next to it. We can see that our prediction is not 100% accurate, but we are also not that far off. How do we evaluate how good our predictions are? We have to choose and calculate a so-called scoring or loss function. One possibility would be to just calculate the difference between our prediction and the true sales and take the mean. For our five data points, we are four times off by 1 and once off by 2. So,

In practice we use the root mean squared error (RMSE), where instead of using the absolute deviation as above, we square the deviation and take the square root at the end. This approach penalizes large deviations more. In our example this gives us

Now we know how good our predictions are. The lower the RMSE, the better. There are some commonly used error functions (also called loss functions) such as the RMSE, but in practice they can be anything. For example, it could be how much money you make if you can make a connection between the data science problem and your finances. Practically, in data science the error function is tailored to the specific use case. In the example above, we used only five data points. For the complete last month that we have the true sales data for, we obtain an RMSE of

This was all done with simple data manipulation. No machine learning was included so far. Now let us get to the next step. We will not only use the item counts from last month but also from two months before that. We perform data transformation similar to above and have these two additional features in our data table:

date_block_num shop_id item_id year month item_cnt_month
5 2 30 2013 6 1.0
5 2 482 2013 6 2.0
5 2 491 2013 6 2.0
5 2 835 2013 6 1.0
5 2 839 2013 6 2.0

item_cnt_last_month item_cnt_2_months_ago item_cnt_3_months_ago
0.0 0.0 1.0
1.0 1.0 1.0
1.0 0.0 0.0
2.0 1.0 1.0
0.0 1.0 0.0

We could now take the mean of the item counts of the last three months as a prediction. But are last month’s sales not more important than the sales of three months ago? But how much more important? And could we not use the information from the features “year” and “month”? Maybe there is some seasonality effect and people buy more products in summer. Here is the good news: machine learning will figure out all of that for us! It will learn from the data how to use the information in the features.

There are several suitable machine learning methods such as support vector machines, neural networks, and decision trees. Here, we will introduce decision trees because they are very intuitive and easy to understand.

Have a look at the decision tree that was created with one month of training data. Let us take the first row in our table and see what our prediction would be. The item count last month was 0. Since this is less than 2.5, we take the left path down. The item count two months ago was also 0. Again, we go down the left path and we predict sales of 0.3 for the current month. This is a very simple decision tree with only two levels. In practice the trees are a lot larger. If we train a larger decision tree, we obtain an RMSE of . This is better than just predicting last month’s item count.

The decision tree is created in a way such that the error function (here, the RMSE) is minimized. It is important to note that we cannot test our model on the same data that our decision tree was trained on. Our decision tree was created with data from months 1-4 so that the RMSE is small on these particular months. The RMSE that really counts is the one in month 5 that the algorithm has not seen yet. Otherwise, we could build an extremely large tree that would perfectly predict sales for months 1-4 but would be useless in month 5 because in interpreted too much into the data of months 1-4. In machine learning, this is called overfitting. It is comparable to memorizing all the answers of last year’s exam in university or high school. If this year’s exam is the same as last year’s than your answers would be perfect. But this is rarely the case in reality. The exams differ slightly from year to year, so it is better to really understand the material in order to generalize well.

Decision trees are very intuitive and also have the advantage that we can get insight into which feature is the most important. The higher up the feature is, the more important it is. In our case, this means that the item count last month is more important than the item count two months ago. Not all machine learning algorithms are that easy to interpret. Fortunately, there are other methods which show us how important a feature is. In the next table you see the so-called permutation importance. It shows us how important a feature is for our prediction. As expected, the sales of last month are the most important feature. The item counts from the months before that and the item ID and shop ID also play a role. Surprisingly, the year and month are irrelevant for our prediction. Seasonality seems to not have any influence on the sales – inasmuch as this could be relevant given the four months of training data we have available. That is also a valuable insight.

Now that we built and tested the model on our training data, we can predict the unknown column from our first table:

ID shop_id item_id item_cnt_month
0.0 5 5037 1.200000
1.0 5 5320 0.000000
2.0 5 5233 1.336957
3.0 5 5232 0.960000
4.0 5 5268 0.000000

This is, of course, a simplified version in comparison with a real data science project. It is possible to have a lot more features in the data that can be used by the algorithm. We could also try to use information outside the data set such as how many state holidays the given month had or prevailing weather conditions at the time.

data science forecast machine learning

Oliver Weisse

Oliver holds degrees in physics from HU Berlin with research experience at Uppsala University and Potsdam Institute for Climate Impact Research. He is an experienced data science and machine learning consultant.