In retail sales forecasting, accurately predicting future sales is crucial for inventory management and strategic planning. Traditional methods like LR often fall short due to the complexity of sales data, which includes seasonality and numerous product families. Recent advancements in machine learning (ML) provide more robust alternatives. This research benefits from the power of ML, particularly Random Forest (RF), Gradient Boosting (GB), Support Vector Regression (SVR), and XGBoost, to improve prediction accuracy. Despite advancements, a significant gap exists in handling complex datasets with high seasonality and multiple product families. The proposed solution involves implementing and optimizing a RF model, leveraging hyperparameter tuning through randomized search cross-validation. This approach addresses the complexities of the dataset, capturing intricate patterns that traditional methods miss. The optimized RF model achieved an R-squared value of 0.945, substantially higher than the initial RF model and traditional LR, which had an R-squared of 0.531. The model reduced the root mean squared logarithmic error (RMSLE) to 1.172, demonstrating its superior predictive capability. The optimized RF model did better than cutting-edge models like Gradient Boosting (R-squared: 0.942), SVR (R-squared: 0.940), and XGBoost (R-squared: 0.939), with more minor mean squared error (MSE) and mean absolute error (MAE) numbers. The results demonstrate that the optimized RF model excels in forecasting retail sales, handling the datasets complexity with higher accuracy and reliability. This research highlights the importance of advanced ML techniques in predictive analytics, offering a significant improvement over traditional methods and other contemporary models.

While mixture density networks (MDNs) have been extensively used for regression tasks, they have not been used much for classification tasks. One reason for this is that the usability of MDNs for classification is not clear and straightforward. In this paper, we propose two MDN-based models for classification tasks. Both models fit mixtures of Gaussians to the the data and use the fitted distributions to classify a given sample by evaluating the learnt cumulative distribution function for the given input features. While the proposed MDN-based models perform slightly better than, or on par with, five baseline classification models on three publicly available datasets, the real utility of our models comes out through a real-world product bundling application. Specifically, we use our MDN-based models to learn the willingness-to-pay (WTP) distributions for two products from synthetic sales data of the individual products. The Gaussian mixture representation of the learnt WTP distributions is then exploited to obtain the WTP distribution of the bundle consisting of both the products. The proposed MDN-based models are able to approximate the true WTP distributions of both products and the bundle well.

The automotive industry plays a critical role in the global economy, and particularly important is the expanding Chinese automobile market due to its immense scale and influence. However, existing automotive sector datasets are limited in their coverage, failing to adequately consider the growing demand for more and diverse variables. This paper aims to bridge this data gap by introducing a comprehensive dataset spanning the years from 2016 to 2022, encompassing sales data, online reviews, and a wealth of information related to the Chinese automotive industry. This dataset serves as a valuable resource, significantly expanding the available data. Its impact extends to various dimensions, including improving forecasting accuracy, expanding the scope of business applications, informing policy development and regulation, and advancing academic research within the automotive sector. To illustrate the dataset's potential applications in both business and academic contexts, we present two application examples. Our developed dataset enhances our understanding of the Chinese automotive market and offers a valuable tool for researchers, policymakers, and industry stakeholders worldwide.

Retail sales forecasting presents a significant challenge for large retailers such as Walmart and Amazon, due to the vast assortment of products, geographical location heterogeneity, seasonality, and external factors including weather, local economic conditions, and geopolitical events. Various methods have been employed to tackle this challenge, including traditional time series models, machine learning models, and neural network mechanisms, but the difficulty persists. Categorizing data into relevant groups has been shown to improve sales forecast accuracy as time series from different categories may exhibit distinct patterns. In this paper, we propose a new measure to indicate the unique impacts of the trend and seasonality components on a time series and suggest grouping time series based on this measure. We apply this approach to Walmart sales data from 01/29/2011 to 05/22/2016 and generate sales forecasts from 05/23/2016 to 06/19/2016. Our experiments show that the proposed strategy can achieve improved accuracy. Furthermore, we present a robust pipeline for conducting retail sales forecasting.

The mean \(\mu_t\) of such distribution is modeled using three components: seasonality (\(\lambda_t\)), an autoregressive term on the latent mean (\(\mu_{t - 1}\)) and an autoregressive sales model. The seasonality component includes a linear trend, in-week seasonality via day of week indicator functions and long term seasonality modeled using Fourier modes. The key point to note is that the prior of such Fourier modes are actually determined by the posterior distribution obtained from the temperature model. Now we write the model above in PyMC. As always, is always good to run prior predictive checks before fitting the model.

The technology behind this is called "predictive analytics" or, in sales terms, "predictive sales analytics". In this article, you will learn how you can tell whether your company needs an ERP system with AI to predict customer behaviour. We will also discuss the advantages of using this technology, how it works, and what is needed for it to work. Predictive sales analytics is a specialized field that aims to make sales forecasts as precisely as possible. Various statistical and mathematical methods are available for this purpose.

Artificial intelligence is reshaping businesses and how they handle innovation. AI may force management to rethink a company's entire innovation process, given rapid technical growth and the displacement of human organizations. The relative balance of AI use is shifting dramatically and permanently in businesses across industries. Each company will have as many possibilities as obstacles due to AI applications. Enterprise AI can alter the enterprise ecosystem in a variety of ways.

If you don't want to run the scripts, the same are available on the Demo website In today's world, data analysis coupled with the power of Machine learning and Artificial intelligence (deep learning) is helping companies solve the most complex of problems. We designed RecOmax as a ready to use platform that will help P&G predict sales of a specific item in a specific store based on historical sales data and complex trend analysis. We aim to build end to end solutions that benefit the client and provide them an edge over their competitors. The build can be divided into 3 main sections. These are the Recommendation engine, the Prediction engine and the Client facing data dashboard (report).

Founded in 2019, by then 17-year-old Oliver Edholm (CEO) and Anton Osika (CTO), Depict.ai is the leading solution in Scandinavia, and increases sales by 4-6%. The giants within e-commerce have been able to build huge in-house teams that leverage AI to deliver clear bottom-line impact. For instance, Amazon is reported to earn 35% of its revenue from their recommendation engine (McKinsey report). However, most e-commerce stores don't have the technical capabilities, nor the large amount of sales data to make existing recommendation systems work. To counteract this imbalance, Depict.ai

Last year, I decided to re-frame my personal brand strategy and launch a new blog. This is going to be fun! My first post is about artificial intelligence (AI) in digital marketing; specifically how the combination of AI & machine learning will shape the future of both industries. It's just not very visible to the mainstream market yet, and there are still many misconceptions about what AI actually is. The digital marketing industry, in particular, has been slow to wake up to this change – as noted by Google Cofounder Sergey Brin: "Current machine learning technology [AI] has already reached a point where it can do very well at specific tasks, such as image recognition… But we are still often limited by the fact that machines don't understand the meaning of the images they look at."