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How to Achieve up to 98% Accuracy in Forecasting Models: Stylus Case Study


Power BI


Power BI



Stylus is one of the largest retailers of electronics, home appliances, and accessories in Ukraine.


  • to forecast the number of transactions per day; 
  • to calculate the call center workload; to calculate the warehouse workload and provide data for competent logistics.

Initial Data

In order to calculate the workload of the call center managers and competently build logistics, the online retailer needed to prepare a forecast for the next month, i.e. the daily number of transactions and calls received by the call center, taking into account which parts of the country majority of the orders are coming from. Based on the forecasts, the retailer expected to correctly allocate resources, marketing budgets, and make decisions about hiring new employees.

With the help of Web Analytics, it’s possible to measure all eCommerce business processes, from operational work to user behavior on the website. Analytics comes especially handy when it comes to data forecasting and logistic planning.

We analyzed the existing methods of data analysis and settled on three models for forecasting time series:

  • Additive Regression Model
  • ARIMA (autoregressive integrated moving average model)
  • Snaive Model


  • Dedicated Python scripts
  • Machine learning
  • Power BI for visualization
  • Various forecasting models

Why Machine Learning?

Using forecasting models based on machine learning instead of the intuitive calculation methods allows increasing accuracy in:

  • Advertising budget planning
  • Calculation of the call center workload
  • Warehouse workload planning and logistics

Step 1. A Test Forecast

To forecast each metric in the most accurate way, we took a data set from the CRM system and Google Analytics account for the past 2 years. After studying the data, we made test forecasts to predict the number of sessions and transactions for the next three months and two years. This allowed tracking all the abnormal periods that increase the error in forecasting.

A 2-years test forecast

We additionally analyzed the key components, such as trend, weekly & annual seasonality.

Trend analysis showed that a significant increase in sales for the retailer started in November 2018. Weekly and annual seasonality charts allowed us to identify the periods of growth and fall in the number of transactions and predict the frequency of orders by month with an error of 10-15%. To solve the retailer’s tasks, we decided to achieve more accurate indicators, since the everyday data was needed.

Step 2. Achieving More Accurate Transaction Indicators

To achieve more accuracy, we refined forecasting models and created a dictionary with all the holidays, as well as periods of increased demand (Black Friday, Christmas, etc). 

This helped to reveal one more pattern: promo campaigns launched close to the period of high demand impact the dramatic trend changes. At the same time, this fact had almost no impact on the weekly and annual seasonality.

We also excluded from the report the abnormal periods (server being down, Google Analytics counter interruptions and so on) that affect the accuracy of the results.

After all the adjustments were made, we managed to forecast the number of transactions with an error of ~8% per day, and ~2-5% per month *The error of the forecast is growing with the increase of the period forecasted. More than 3 months – the accuracy drops exponentially, up to 80-50%.

A test forecast of transaction numbers using Additive Regression Model

A test forecast of transaction numbers per day based on the two forecasted models

Step 3. Logistics Forecasting

The selection "The other parts of the country"

To distribute the warehouse workload, calculate the number of daily shipments and forecast the delivery of orders, we worked out a data set from the CRM and predicted the total number of orders, dividing them by country parts. We also created a dictionary of locations by region. 

* For some locations, the data was insufficient, so we combined them into a separate sample “The other parts of the country”

Step 4. Visual Representation of the Data Acquired

To make the perception easy, we uploaded all data on the predicted transactions, calls and orders to Power Bi and presented it to the Stylus team in the form of interactive dashboards. 

A forecast of transactions and requests to the call center

Logistics Forecasting

Logistics Forecasting


90-98% The accuracy of the developed forecasting models

~5% The average error of short-term forecasting models (per quarter)

Such high accuracy indicators were achieved thanks to: 

  • The presence of sufficient volume of educational selection (historical data on the metrics of interest for 2 years) 
  • The presence of up to 50 values of a certain metric per time unit (hour, day, month) 
  • A relatively high level of variance during a certain time period

What’s next?

  • Apply models to other metrics (number of users, number of payments)
  • Automate prediction models to automatically retrieve and update data

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