Machine Learning for E-Commerce: Sales Prediction

Machine Learning for E-Commerce: Sales Prediction Blog Preview

The retail industry was one of the first to adapt to the rapid implementation of artificial intelligence technologies in life. That is why it has gained such a great advantage among other fields because it has not only rethought its sales system but also the way we buy. 

In order to better understand what awaits the retail sphere in the near future, we have selected for you several trends that can improve this industry very soon.


With the beginning of the pandemic, the concept gained more and more popularity BOPIS (buy online, take away) and BORIS (buy online, return in-store). If you think that the reduction of the pandemic has canceled the function of online shopping, then you are wrong. Applause conducted a study that found 66% of customers will stay with merchants that implement an omnichannel experience into their business. Moreover, customers explained that they would leave the brand if they were not satisfied with the omnichannel experience.

online shopping

AI-Rich Applications

Automating complex processes can help avoid unnecessary costs and labor shortages. Optimizing routine tasks such as tracking orders, communicating with clients, or managing a customer loyalty program can save time and achieve greater accuracy in solving certain issues. A study published by Accenture shows that almost 3/4 of companies are trying to implement artificial intelligence in their service. If you don’t believe words, then I’ll talk to you with numbers. Forrester found that an automated call center solution can have a 3-year ROI of 241% and reduce costs by 31%, which translates into $10.2M in the bottom line.

Outstanding customer experience

Many people are used to buying products through electronic commercial platforms, which opens up a huge potential for introducing virtual or augmented reality into the service. Research published by Shopify showed that AR content had a 94% higher conversion rate than products without it. Virtual or augmented reality allows your customers to save time and contributes to their better experience. The director of product management at Google said in an interview: “As technology continues to improve and usage grows, AR will soon become the universal language of the Internet. It will be best to watch brands push the boundaries to deliver amazing and rewarding experiences for consumers”

Visual search

Back in 2021, Gartner predicted that visual search will have a significant impact on digital advertising in the next 2-3 years. Visual search technology has become more sophisticated and sophisticated over the years. It can be used not only by users but also by retailers who can analyze product visual searches and offer similar products. In addition, for the client, it is a way to find the desired item, which is not so easy to find.

To learn more about trends in e-commerce and retail, read our article.

Why Sales Prediction is Crucial

Let’s understand what commercial forecasting is in general. Forecasting is primarily a calculation of your potential income for a certain period, which will help you make important business decisions.

Why is it so important for every business? First of all, it helps to set goals and measure the performance of your business, which affects all its areas. Thanks to the forecast, you can build effective strategies, allocate resources and finances, and get the income you expect.

Sales forecasts should produce accurate results, which will create an effective cycle of development and growth for the organization for you. It focuses on better understanding the customer, planning inventory and budgets, marketing, and many of the details that make you profitable.

sales forecasts

That is why there are important components that can improve your sales forecasting.

Historical data

First of all, use historical data. Your revenue may fluctuate over time, but knowing and predicting your spending or revenue periods makes it easier for you to adapt to changes or reorient your team to the right workflow. This technique is used by a SaaS company that reduces sales during the holidays at the end of the year.

Key sales and customers

Second, it focuses on key sales and customers. Sales leaders play chess, where they anticipate every move and keep the most important pieces in focus. In this way, you should understand what goals you are aiming for and cooperate with those customers who often use your services, improve the product or service that you have the most popular, or cooperate with partners who are interested in you.

Organization and foresight

You need to consider all the factors and sources that can affect the income of your service. Taking into account all components, external and internal, you can build a clear development strategy and keep your customers with you for a long period

All these factors are the reasons for the fluctuation of your income. Control of all channels and factors, communication with the client, prediction of trends and anti-trends – all this is important if you want to become a leader in the field of your sales. Is it difficult to cope with all this? Yes, if you use mechanical means that will give you inaccurate information or, due to human factors, do not have time to process all channels. Is it profitable to spend resources on work that does not meet your expectations? Of course not.

However, there is a solution that will save you time, money, and resources, calculate accurate data, analyze all channels, predict sales with high accuracy, improve your service, and ultimately increase your income. Such a solution is the introduction of Machine Learning and Artificial Intelligence into your service, i.e. automation.

Machine Learning Techniques for Sales Prediction

Let’s take a closer look at the benefits of Machine Learning in sales forecasting.

  • Use of minimal human effort
  • Better prediction of user behavior
  • Saving money resources
  • Saving money resources
  • Better cash flow
  • Quality inventory management
  • Forecast of future budgets
  • Faster sales cycles

All these are important aspects that affect the success of your business. Therefore, it is worth considering several Machine Learning models that will allow you to improve sales forecasting.

Model fit and prediction

Since there are no models or forecasts that will satisfy all forecasting algorithms and fit exactly your business model, forecasting functions consist of machine learning approaches. here everything depends on the factors that affect your work. They can be a business goal, type of data, quantity or quality of data, forecasting period, etc.

Therefore, we have selected for you several models that are most often found in the business.

  • Regression models
  • Long Short-Term Memory (LSTM)
  • K-Nearest Neighbors Regression
  • Random Forest


ARIMA or Autoregressive Integrated Moving Average Model is a form of regression analysis that uses time series data to measure one variable over another. Its purpose is to predict future trends by distinguishing between entries in the same series.

Autoregressive Integrated Moving Average Model

The components of Arima can be characterized as follows:

Autoregressive (AR) represents a variable that regresses relative to its previous value.

Integrated (I), shows the difference of values that have not yet been processed in order to replace the data difference between their values and previous values.

Moving Average (MA) is the relationship between the observation and the residual error.


The SARIMA model, or seasonal autoregressive integrated moving average, is an extension of ARIMA [Seasonal-ARIMA] but supports one-dimensional time data containing a seasonal component.

In addition to the parameters that already exist in ARIM, such as autoregression (AR), differentiation (I), and moving average (MA), an additional hyperparameter for determining seasonality is added here. This is what adds reliability to the SARIMA model.


Regression models

The regression model is a statistical model that compares the relationship between one dependent variable or several using a line or plane. Such models are used to predict future data in comparison with the past. It is they who help determine trends or price adjustments. In building such a model, it is important to choose the right variables based on the data in order to construct a linear correlation between the target trait and other variables.

Long Short-Term Memory (LSTM)

LSTM, i.e. Long short-term memory, is a type of recurrent neural network that is used mostly in the forecasting of sequential data, due to its ability to learn long-term dependencies. This kind of model is commonly used to study, process, or classify sequential data because it can study long-term dependencies between time steps in the data. Usually, the LSTM model is used in the development of sentiment analysis, modeling, and speech recognition or video analysis.

LSTM model

K-Nearest Neighbors Regression

As for KNN regression, it refers to non-parametric methods, which approximate the relationship between individual variables and continuously derive the average observation in the same area. The size of such neighborhoods can be chosen by cross-validation and minimizing the root mean square error. Among the advantages of the model, its simplicity and good work with nonlinear connections can be highlighted. However, it has disadvantages, which include nearest-neighbor K-regression, where the model has difficulty handling a large number of variables and does not detect values outside the range.


Random Forest

Random forest is an ensemble algorithm of Machine Learning. As you might guess from the name of the temporal model, it is implemented by constructing many branches of one component to another and outputs the mean regression prediction. It is used to forecast time series, which can be univariate or multivariate with the addition of manual seasonal variables. Due to the mean value of the branching model to generate the mean solution, Random Forest provides reliable predictions. Model is often used for business applications designed to predict buyer behavior regarding a particular product or whether or not a customer will repay a loan. Currently, such a model is the most convenient classifier.

Random forest - Machine Learning algorithm

Sales Prediction using ML: Amazinum Use Cases

Knowing which models we can use to create sales forecasts, let’s look at the cases of the Amazinum team that we have developed for our clients.

Project tasks:

  • Maximize product visibility
  • Improve supply chain management
  • Improve supply chain management
  • Provide data predictions to boost advertising ROI
  • Analyze in-stock/out-of-stock products to optimize availability issues
  • Track user behavior
Sales Prediction - Amazinum case


  • Generate personalized recommendations based on user views, ATB (add to bag), and purchase history.
  • Develop and/or modify Kubeflow conveyors for model training, evaluation, load tests, etc.
  • Develop the pipeline using different approaches for different ranges and low values.
  • Develop the graphic on the basis of which the client received the data they needed.


According to the requested information, the client received a graph that showed certain results:

  • analyze personal as well as competitors’ sales performance of the various products under one brand over a specific period of time
  • evaluate the sales performance of multiple brands in a general category or particular product under one brand
  • see the visualization of their personal or competitors’ year or month sales
  • analyze the statistics of product`s sales for one category they get
  • analyze the competitor`s sales for one product for a certain period of time

Benefits of sales prediction for business

Sales forecasting is primarily your weapon among competitors because the one who owns information owns the world. In this way, it allows you to make the right decisions and achieve success.

So what advantages will you get by introducing sales forecasting to your business:

The right goals

For any business, it is always important to set realistic goals and go for them. The information you receive from sales forecasts is the basis for building any plans for the direction of the company and vision for further development. This task is not only for business owners but also for sales managers. Set the right goals – implement them – make your business successful.

Clarity of the budget

What budget forecasting provides is, of course, an objective assessment of costs and revenues. This kind of planning allows you to develop profit plans and plan your income. In the near future of your business, you will be able to manage your resources and expenses better.

Be at the forefront of strategic decisions

The right decisions from personnel to potential marketing activities are the foundation of any business. Such calculations are not possible without forecasting sales. You have to see potential problems and then you can intercept or avoid them. Or, on the contrary, you will be able to assess a positive trend and proactively pick up resources or improve production in one or another area.

Quality external operations

The goal of every company is to satisfy its customers and provide stability to its investors. When all mechanisms, both external and internal, work properly, your company gains authority and retains not only local customers. Therefore, it is important to focus your attention also on external marketing activities, for better interaction between buyers and investors.

Obtaining information

With the help of software aimed at forecasting sales, you can understand numbers which are important information for building a business strategy. For example, you can save time if you know exactly which employee or client you need, having conducted an analysis and prediction beforehand.

Manage resources correctly

Allocation of resources also applies to sales forecasting, because thanks to this you can allocate them between departments or groups for better functioning. In addition, you will be able to improve production quality, regulate stocks, and control cash and budget flows. These features are useful for both marketing
and HP.

Improve the recruitment system

Control an industry that requires additional human resources due to increased demand. Choose a candidate who will definitely be suitable for this.

Resource planning

Sales forecasts also help with hiring decisions. For example, if your sales forecast predicts an increase in demand, companies need to allocate an adequate budget and shift efforts to hiring and sourcing to meet that demand. And on the other hand, if sales are forecast to drop, it may be time to put hiring plans on hold and focus on attracting more business.

Better hiring

Many companies use sales forecasts to allocate resources among different functionaldepartments
and groups

In conclusion

Sales and forecasting is a strategic decision that matters to every corner of your company. Business does not stand still, so the ability to think ahead, anticipate events, and trends, and make the right decisions is what will help you become a leader. Accurate forecasts have implications for the future of the company. And remember, who owns information owns the world.

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Vitaliy Fedorovych

CEO, Data Scientist at Amazinum

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