Original data from Predict Future Sales (Kaggle Competition) Translated items_categories.csv, shops.csv, items.csv from Russian to English for easy features engineering and references.

File Information

Translated item description and shop name from Russian to English items.csv - supplemental information about the items/products. item_categories.csv - supplemental information about the items categories. shops.csv- supplemental information about the shops.

Column Description

• ID - an Id that represents a (Shop, Item) tuple within the test set
• shop_id - unique identifier of a shop
• item_id - unique identifier of a product
• item_name - name of item
• shop_name - name of shop
• item_category_name - name of item category

The Global Sales Forecasting Software Market is projected to grow from USD 7.4 billion in 2021 to USD XX billion by 2028, at a CAGR of 10.1% during the forecast period (2021-2028). Growing demand for accurate and precise sales forecasts as well as rising adoption of cloud-based solutions are key factors driving the growth of this market over the coming years.

Sales forecasting software is a tool that helps businesses predict future sales. The software uses historical data and current market conditions to create a forecast. This information can help businesses make decisions about inventory, staffing, and other areas of the business.

On the basis of Type, Global Sales Forecasting Software Market is segmented into Cloud-based, On-premises.

Cloud-based:

Cloud-based Forecasting Software is software that resides on a remote server and can be accessed by authorized users through the internet. It eliminates the need for installing and maintaining software on local computers, which makes it ideal for businesses with multiple locations or those that want to share data among employees. Cloud-based solutions are typically subscription services that charge monthly or annual fees based on usage.

On-premises:

On-premises Forecasting Software is software that resides on the customer's computer network and can be accessed only by authorized users. It offers more customization options and control over data than cloud-based solutions but requires more maintenance and management. On-premises solutions are typically licensed software that charges a one-time fee for use.

On the basis of Application, Global Sales Forecasting Software Market is segmented into Small Business, Midsize Enterprise, Large Enterprise, Other.

Small Business:

Sales forecasting is a critical part of managing any business, but it can be especially challenging for small businesses. It's difficult to predict how much revenue the business will generate and what type of inventory should be kept on hand because there are fewer historical sales records to work with. However, forecasts enable companies to plan by determining staffing levels and ordering supplies based on expected demand. Sales forecasting software helps these small businesses overcome those challenges as well as increase both productivity and profitability.

Midsize Enterprise:

Sales forecasting offers these businesses a way to predict future sales, better manage inventory levels, maintain higher staffing levels during peak months and reduce the risk of over-ordering supplies. These benefits help midsize enterprises make more accurate decisions about how much product should be produced or purchased as well as where those products should go in the warehouse. Midsize enterprises typically have annual revenues between USD 100 million and USD 500 million, making them an attractive target market for software vendors looking at this segment of the industry.

On the basis of Region, Global Sales Forecasting Software Market is segmented into North America, Latin America, Europe, Asia Pacific, and the Middle East & Africa.

North America: Sales forecasting can help businesses make more informed decisions about how much product should be produced or purchased as well as where those products should go in the warehouse. These benefits are especially helpful for organizations that sell their goods and services across North America, which is why it's expected to account for a significant share of this market over the next few years.

Europe: The European market for sales forecasting software is expected to grow at a healthy pace over the next several years as more businesses embrace automation and advanced technology. Sales forecasting can help companies make better decisions about staffing, inventory levels, and other areas of the business that positively impact their bottom line.

Asia Pacific: The Asia-Pacific region offers opportunities for growth in terms of both volume and value thanks to its rapidly growing economy and thriving small business sector. This will be an important area to watch as it develops since those trends bode well not only for this market but also for many others throughout the Asia Pacific during the forecast period.

Latin America: Latin American countries such as Brazil offer major potential due to their burgeoning middle-class population and increasing demand for goods and services. This region is expected to experience healthy growth in the sales forecasting software market over the next decade as businesses become more sophistic

Problem Statement Forecast the future values of a time series dataset representing monthly sales data over a span of 500 months.

Dataset Description The dataset contains two columns:

• Timestamp: Represents monthly intervals from January 2000 to April 2040.
• Sales Amount (in USD): Represents the sales amount for each timestamp.

Context and Methodology

Research Domain:
The dataset is part of a project focused on retail sales forecasting. Specifically, it is designed to predict daily sales for Rossmann, a chain of over 3,000 drug stores operating across seven European countries. The project falls under the broader domain of time series analysis and machine learning applications for business optimization. The goal is to apply machine learning techniques to forecast future sales based on historical data, which includes factors like promotions, competition, holidays, and seasonal trends.

Purpose:
The primary purpose of this dataset is to help Rossmann store managers predict daily sales for up to six weeks in advance. By making accurate sales predictions, Rossmann can improve inventory management, staffing decisions, and promotional strategies. This dataset serves as a training set for machine learning models aimed at reducing forecasting errors and supporting decision-making processes across the company’s large network of stores.

How the Dataset Was Created:
The dataset was compiled from several sources, including historical sales data from Rossmann stores, promotional calendars, holiday schedules, and external factors such as competition. The data is split into multiple features, such as the store's location, promotion details, whether the store was open or closed, and weather information. The dataset is publicly available on platforms like Kaggle and was initially created for the Kaggle Rossmann Store Sales competition. The data is made accessible via an API for further analysis and modeling, and it is structured to help machine learning models predict future sales based on various input variables.

Technical Details

Dataset Structure:

The dataset consists of three main files, each with its specific role:

1. Train:
   This file contains the historical sales data, which is used to train machine learning models. It includes daily sales information for each store, as well as various features that could influence the sales (e.g., promotions, holidays, store type, etc.).

   https://handle.test.datacite.org/10.82556/yb6j-jw41
   PID: b1c59499-9c6e-42c2-af8f-840181e809db

2. Test2:
   The test dataset mirrors the structure of train.csv but does not include the actual sales values (i.e., the target variable). This file is used for making predictions using the trained machine learning models. It is used to evaluate the accuracy of predictions when the true sales data is unknown.

   https://handle.test.datacite.org/10.82556/jerg-4b84
   PID: 7cbb845c-21dd-4b60-b990-afa8754a0dd9

3. Store:
   This file provides metadata about each store, including information such as the store’s location, type, and assortment level. This data is essential for understanding the context in which the sales data is gathered.

   https://handle.test.datacite.org/10.82556/nqeg-gy34
   PID: 9627ec46-4ee6-4969-b14a-bda555fe34db

Data Fields Description:

• Id: A unique identifier for each (Store, Date) combination within the test set.

• Store: A unique identifier for each store.

• Sales: The daily turnover (target variable) for each store on a specific day (this is what you are predicting).

• Customers: The number of customers visiting the store on a given day.

• Open: An indicator of whether the store was open (1 = open, 0 = closed).

• StateHoliday: Indicates if the day is a state holiday, with values like:

  • 'a' = public holiday,

  • 'b' = Easter holiday,

  • 'c' = Christmas,

  • '0' = no holiday.

• SchoolHoliday: Indicates whether the store is affected by school closures (1 = yes, 0 = no).

• StoreType: Differentiates between four types of stores: 'a', 'b', 'c', 'd'.

• Assortment: Describes the level of product assortment in the store:

  • 'a' = basic,

  • 'b' = extra,

  • 'c' = extended.

• CompetitionDistance: Distance (in meters) to the nearest competitor store.

• CompetitionOpenSince[Month/Year]: The month and year when the nearest competitor store opened.

• Promo: Indicates whether the store is running a promotion on a particular day (1 = yes, 0 = no).

• Promo2: Indicates whether the store is participating in Promo2, a continuing promotion for some stores (1 = participating, 0 = not participating).

• Promo2Since[Year/Week]: The year and calendar week when the store started participating in Promo2.

• PromoInterval: Describes the months when Promo2 is active, e.g., "Feb,May,Aug,Nov" means the promotion starts in February, May, August, and November.

Software Requirements

To work with this dataset, you will need to have specific software installed, including:

• DBRepo Authorization: This is required to access the datasets via the DBRepo API. You may need to authenticate with an API key or login credentials to retrieve the datasets.

• Python Libraries: Key libraries for working with the dataset include:

  • pandas for data manipulation,

  • numpy for numerical operations,

  • matplotlib and seaborn for data visualization,

  • scikit-learn for machine learning algorithms.

Additional Resources

Several additional resources are available for working with the dataset:

1. Presentation:
   A presentation summarizing the exploratory data analysis (EDA), feature engineering process, and key insights from the analysis is provided. This presentation also includes visualizations that help in understanding the dataset’s trends and relationships.

2. Jupyter Notebook:
   A Jupyter notebook, titled Retail_Sales_Prediction_Capstone_Project.ipynb, is provided, which details the entire machine learning pipeline, from data loading and cleaning to model training and evaluation.

3. Model Evaluation Results:
   The project includes a detailed evaluation of various machine learning models, including their performance metrics like training and testing scores, Mean Absolute Percentage Error (MAPE), and Root Mean Squared Error (RMSE). This allows for a comparison of model effectiveness in forecasting sales.

4. Trained Models (.pkl files):
   The models trained during the project are saved as .pkl files. These files contain the trained machine learning models (e.g., Random Forest, Linear Regression, etc.) that can be loaded and used to make predictions without retraining the models from scratch.

5. sample_submission.csv:
   This file is a sample submission file that demonstrates the format of predictions expected when using the trained model. The sample_submission.csv contains predictions made on the test dataset using the trained Random Forest model. It provides an example of how the output should be structured for submission.

These resources provide a comprehensive guide to implementing and analyzing the sales forecasting model, helping you understand the data, methods, and results in greater detail.

Grocery Sales Prediction

This dataset provides a rich resource for researchers and practitioners interested in retail sales prediction and analysis. It contains information about various grocery products, the outlets where they are sold, and their historical sales data.

Product Characteristics:

Item_Identifier: Unique identifier for each product. Item_Weight: Weight of the product item. Item_Fat_Content: Categorical variable indicating the fat content of the product (e.g., low fat, regular). Item_Visibility: Numerical attribute reflecting the visibility of the product in the store (likely a promotional measure). Item_Type: Category of the product (e.g., Snacks, Beverages, Bakery). Item_MRP: Maximum Retail Price of the product. Outlet Information:

Outlet_Identifier: Unique identifier for each outlet (store). Outlet_Establishment_Year: Year the outlet was established. Outlet_Size: Categorical variable indicating the size of the outlet (e.g., Small, Medium, Large). (Note: This data may have missing values) Outlet_Location_Type: Categorical variable indicating the type of location the outlet is in (e.g., Tier 1 City, Tier 2 City, Upstate). Outlet_Type: Categorical variable indicating the type of outlet (e.g., Supermarket, Grocery Store, Convenience Store). Sales Data:

Item_Outlet_Sales: The historical sales data for each product-outlet combination. Profit: The profit margin earned on each product sold. Potential Uses

This dataset can be used for various retail sales analysis and prediction tasks, including:

Demand forecasting: Build models to predict future sales of individual products or product categories at specific outlets. Promotion optimization: Analyze the effectiveness of different promotional strategies (reflected by Item_Visibility) on sales. Assortment planning: Optimize product selection and placement within stores based on sales history and outlet characteristics. Outlet performance analysis: Compare the performance of different outlets based on sales figures and profit margins. Customer segmentation: Identify customer segments with distinct purchasing behavior based on product types and outlet locations. By analyzing these rich data points, retailers can gain valuable insights to improve their sales strategies, optimize inventory management, and maximize profits.

Dataset

This dataset was created by KDJ2020

Contents

Dataset

This dataset was created by Ajay Gorkar

Contents

Global Sales Forecasting Software market size was USD 34.65 Billion in 2022 and it is forecasted to reach USD 80.28 Billion by 2030. Sales Forecasting Software Industry's Compound Annual Growth Rate will be 11.1% from 2023 to 2030. Factors Impacting on Sales Forecasting Software Market

Drivers – increasing demand for cloud-based software will propel the sales forecasting software market

The increasing demand for cloud-based services will drive the boost in the market, due to cloud-based services, which are becoming increasingly popular in the current period of digitization. Software that is stored on a distant server and accessible through the internet by authorized users is referred to as cloud-based forecasting software. Recent market innovations including the ability to design customer strategies, formula flexibility, and goal-seeking features are boosting market growth

Restraining Factor:

The high cost of software will hamper the sales forecasting market

The market for sales forecasting software will be constrained by the high cost of software because the cost of gross sales includes all direct expenses spent in producing the goods or services supplied. The expenses of the raw materials used in production and the labor costs of factory employees are elements that will influence the high cost of the market for manufactured goods. Introduction of Sales Forecasting Software

The sales forecasting software is the ultimate tool for predicting future lead volume, sales pipeline software, and close probability. It allows businesses to set realistic sales goals with high levels of accuracy. Cloud-based services are present in the Sales Forecasting Software Market product area, and it is projected that they will significantly accelerate market growth in the near future. The market will rise in the future years as a result of rising acceptance of cloud-based solutions, increased demand for precise and accurate sales forecasts

Dataset

This dataset was created by Luke M

Contents

Analysis of ‘Big Mart Sales’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/akashdeepkuila/big-mart-sales on 12 November 2021.

--- Dataset description provided by original source is as follows ---

Context

The data scientists at Big Mart have collected 2013 sales data for 1559 products across 10 stores in different cities. Also, certain attributes of each product and store have been defined. The aim is to build a predictive model and predict the sales of each product at a particular outlet.

Using this model, Big Mart will try to understand the properties of products and outlets which play a key role in increasing sales.

Please note that the data may have missing values as some stores might not report all the data due to technical glitches. Hence, it will be required to treat them accordingly.

Content

The dataset provides the product details and the outlet information of the products purchased with their sales value split into a train set (8523) and a test (5681) set. Train file: CSV containing the item outlet information with sales value Test file: CSV containing item outlet combinations for which sales need to be forecasted

Variable Description

• ProductID : unique product ID
• Weight : weight of products
• FatContent : specifies whether the product is low on fat or not
• Visibility : percentage of total display area of all products in a store allocated to the particular product
• ProductType : the category to which the product belongs
• MRP : Maximum Retail Price (listed price) of the products
• OutletID : unique store ID
• EstablishmentYear : year of establishment of the outlets
• OutletSize : the size of the store in terms of ground area covered
• LocationType : the type of city in which the store is located
• OutletType : specifies whether the outlet is just a grocery store or some sort of supermarket
• OutletSales : (target variable) sales of the product in the particular store

Inspiration

Sales of a given product at a retail store can depend both on store attributes as well as product attributes. The dataset is ideal to explore and build a data science model to predict the future sales.

--- Original source retains full ownership of the source dataset ---

1) Data Introduction • The Walmart Dataset includes weekly sales, weather and economic indicators, and holiday information for 45 Walmart stores in the United States from February 2010 to November 2012.

2) Data Utilization (1) Walmart Dataset has characteristics that: • The dataset provides a variety of time-series and economic characteristics, including store numbers, dates, weekly sales, holiday status, temperature, oil prices, consumer price index (CPI), unemployment, and more. (2) Walmart Dataset can be used to: • Sales forecasting model development: It can be used to build machine learning regression models that predict future sales by leveraging weekly sales and economic and environmental factors. • Analyzing Promotion and Holiday Effects: Analyzing the impact of holiday and promotion periods on sales can be used to develop marketing and inventory management strategies.

The Predictive Sales Analytics Software market is experiencing robust growth, driven by the increasing need for businesses to enhance sales forecasting accuracy, optimize sales strategies, and improve overall sales performance. The market's expansion is fueled by several key factors, including the rising adoption of cloud-based solutions, the proliferation of big data and advanced analytics capabilities, and the growing demand for personalized customer experiences. Companies are increasingly leveraging predictive analytics to identify high-potential leads, personalize sales pitches, and optimize pricing strategies, leading to improved conversion rates and increased revenue generation. The market is segmented by deployment type (cloud-based and on-premise), by industry (e.g., BFSI, retail, healthcare), and by component (software and services). While the initial investment in predictive sales analytics software can be significant, the long-term return on investment (ROI) is substantial, making it an attractive proposition for businesses of all sizes. Competition is intense, with established players and emerging startups vying for market share. However, the overall market remains fragmented, presenting opportunities for new entrants with innovative solutions. The market is projected to experience sustained growth over the forecast period, driven by technological advancements and increasing adoption across diverse industries. The competitive landscape includes both established enterprise software vendors and specialized analytics providers. While large players like SAP and Dun & Bradstreet benefit from existing customer bases and extensive data resources, smaller, more agile companies are innovating with advanced AI and machine learning techniques to provide specialized solutions. This dynamic environment necessitates a focus on continuous innovation, strategic partnerships, and a deep understanding of customer needs to achieve sustained success in the market. Key trends influencing market evolution include the integration of predictive analytics with CRM systems, the increasing use of AI-powered sales intelligence, and a growing demand for more explainable and transparent AI models. These factors are shaping the future of predictive sales analytics, driving further market expansion and innovation.

Analysis of ‘Volume Forecasting’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/utathya/future-volume-prediction on 30 September 2021.

--- Dataset description provided by original source is as follows ---

Context

Country Beeristan, a high potential market, accounts for nearly 10% of Stallion & Co.’s global beer sales. Stallion & Co. has a large portfolio of products distributed to retailers through wholesalers (agencies). There are thousands of unique wholesaler-SKU/products combinations. In order to plan its production and distribution as well as help wholesalers with their planning, it is important for Stallion & Co. to have an accurate estimate of demand at SKU level for each wholesaler.

Currently demand is estimated by sales executives, who generally have a “feel” for the market and predict the net effect of forces of supply, demand and other external factors based on past experience. The more experienced a sales exec is in a particular market, the better a job he does at estimating. Joshua, the new Head of S&OP for Stallion & Co. just took an analytics course and realized he can do the forecasts in a much more effective way. He approaches you, the best data scientist at Stallion, to transform the exercise of demand forecasting.

Content

You are provided with the following data: price_sales_promotion.csv: ($/hectoliter) Holds the price, sales & promotion in dollar value per hectoliter at Agency-SKU-month level historical_volume.csv: (hectoliters) Holds sales data at Agency-SKU-month level from Jan 2013 to Dec 2017 weather.csv: (Degree Celsius) Holds average maximum temperature at Agency-month level industry_soda_sales.csv: (hectoliters) Holds industry level soda sales event_calendar.csv: Holds event details (sports, carnivals, etc.) industry_volume.csv: (hectoliters) Holds industry actual beer volume demographics.csv: Holds demographic details (Yearly income in $)

Test data Formats Volume_forecast.csv: You need to first forecast the demand volume for Jan’18 of all agency-SKU combination. sku_recommendation.csv: Secondly, you need to suggest 2 SKUs which can be sold by Agency06 & Agency14. These two agencies are new and company wants to find out which two products would be the best products for these two agencies.

Acknowledgements

Thanks to Analytics Vidya and AbinBev for making this data available for us.

Inspiration

Can anyone please forecast for Jan18? I also want to understand the analysis carried out. Thanks.

--- Original source retains full ownership of the source dataset ---

Sales Analytics Software Market Outlook

The global sales analytics software market size was valued at approximately USD 3.4 billion in 2023 and is projected to reach around USD 10.2 billion by 2032, growing at a compound annual growth rate (CAGR) of 12.8% during the forecast period. The growth of this market is being driven by several factors, including the increasing adoption of advanced analytics platforms by organizations to gain competitive advantages and the rising demand for data-driven decision-making processes.

One of the primary growth factors for the sales analytics software market is the increasing emphasis on data-driven decision-making within enterprises. Modern businesses are increasingly leveraging big data and analytics to gain insights into customer behavior, sales trends, and market dynamics. This data-centric approach allows companies to make informed decisions, optimize sales strategies, and improve overall business performance. As a result, the demand for sophisticated sales analytics tools is on the rise, propelling the market forward.

Additionally, the rapid digitization across various industries is significantly contributing to the market growth. With the proliferation of digital channels and e-commerce platforms, companies now have access to vast amounts of data generated from online transactions, customer interactions, and social media activities. Sales analytics software helps organizations to sift through this data, identify trends, and derive actionable insights. This capability is particularly crucial in the retail and e-commerce sectors, where understanding consumer preferences and buying patterns can directly impact sales and profitability.

Another crucial growth factor is the increasing integration of artificial intelligence (AI) and machine learning (ML) technologies within sales analytics solutions. These advanced technologies enhance the predictive and prescriptive capabilities of sales analytics software, enabling businesses to anticipate market trends, forecast sales performance, and personalize customer experiences. As AI and ML continue to evolve, their integration into sales analytics tools is expected to further drive market growth by providing more accurate and actionable insights.

Data Analytics Software plays a pivotal role in the evolution of sales analytics solutions. As organizations strive to harness the power of data, the integration of comprehensive data analytics software becomes essential. These tools not only facilitate the analysis of vast datasets but also enable businesses to derive meaningful insights that drive strategic decision-making. By leveraging data analytics software, companies can enhance their understanding of market trends, customer behaviors, and sales performance, leading to more informed business strategies. This integration is particularly beneficial in industries with complex sales processes, where the ability to quickly analyze and interpret data can provide a competitive edge.

From a regional perspective, North America is expected to hold a significant share of the sales analytics software market. The region's dominance can be attributed to the presence of major technology companies, high adoption rates of advanced analytics solutions, and a robust digital infrastructure. Furthermore, the increasing focus on improving customer experiences and the willingness of enterprises to invest in innovative technologies are likely to sustain the market's growth in this region.

Component Analysis

The sales analytics software market can be segmented by component into software and services. The software segment encompasses various types of analytics software, including descriptive, diagnostic, predictive, and prescriptive analytics tools. These tools help organizations to analyze historical sales data, identify patterns, and predict future sales trends. The growing need for real-time analytics and the ability to integrate these tools with existing CRM systems are driving the demand for sales analytics software.

Within the software segment, cloud-based solutions are gaining significant traction due to their scalability, flexibility, and cost-effectiveness. Cloud-based sales analytics software allows businesses to access data and insights from anywhere, facilitating remote work and collaboration. Additionally, the continuous advancements in cloud technology, such as enhanced security features and increased storage capabilitie

This dataset provides comprehensive insights into US regional sales data across different sales channels, including In-Store, Online, Distributor, and Wholesale. With a total of 17,992 rows and 15 columns, this dataset encompasses a wide range of information, from order and product details to sales performance metrics. It offers a comprehensive overview of sales transactions and customer interactions, enabling deep analysis of sales patterns, trends, and potential opportunities.

Columns in the dataset: - OrderNumber: A unique identifier for each order. - Sales Channel: The channel through which the sale was made (In-Store, Online, Distributor, Wholesale). - WarehouseCode: Code representing the warehouse involved in the order. - ProcuredDate: Date when the products were procured. - OrderDate: Date when the order was placed. - ShipDate: Date when the order was shipped. - DeliveryDate: Date when the order was delivered. - SalesTeamID: Identifier for the sales team involved. - CustomerID: Identifier for the customer. - StoreID: Identifier for the store. - ProductID: Identifier for the product. - Order Quantity: Quantity of products ordered. - Discount Applied: Applied discount for the order. - Unit Cost: Cost of a single unit of the product. - Unit Price: Price at which the product was sold.

This dataset serves as a valuable resource for analysing sales trends, identifying popular products, assessing the performance of different sales channels, and optimising pricing strategies for different regions.

Visualization Ideas:

• Time Series Analysis: Plot sales trends over time to identify seasonal patterns and changes in demand.
• Sales Channel Comparison: Compare sales performance across different channels using bar charts or line graphs.
• Product Analysis: Visualise the distribution of sales across different products using pie charts or bar plots.
• Discount Analysis: Analyse the impact of discounts on sales using scatter plots or line graphs.
• Regional Performance: Create maps to visualise sales performance across different regions.

Data Modelling and Machine Learning Ideas (Price Prediction): - Linear Regression: Build a linear regression model to predict the unit price based on features such as order quantity, discount applied, and unit cost. - Random Forest Regression: Use a random forest regression model to predict the price, taking into account multiple features and their interactions. - Neural Networks: Train a neural network to predict unit price using deep learning techniques, which can capture complex relationships in the data. - Feature Importance Analysis: Identify the most influential features affecting price prediction using techniques like feature importance scores from tree-based models. - Time Series Forecasting: Develop a time series forecasting model to predict future prices based on historical sales data. - These visualisation and modelling ideas can help you gain valuable insights from the sales data and create predictive models to optimise pricing strategies and improve sales performance.

1) Data Introduction • The NeluxTech Retail Dataset is based on daily sales data from various retail and service businesses in Kenya from June 5, 2023, to September 12, 2024, a comprehensive record of purchasing behaviors of several goods and services, including food, stationery, and cyber services, separating loyal and regular customers.

2) Data Utilization (1) NeluxTech Retail Dataset has characteristics that: • It encompasses various industries such as food, stationery, and cyber services, and is refined into a structure that allows customer segmentation and purchase pattern analysis. (2) NeluxTech Retail Dataset can be used to: • Sales forecasting and sales optimization: Daily sales data can be used to predict future sales and to establish sales growth strategies by product and customer. • Customer Segmentation and Behavioral Analysis: Compare and analyze the purchase patterns of loyalty and general customers, and apply them to develop customized marketing and customer retention strategies.

The global sales planning software market is experiencing robust growth, driven by the increasing need for businesses of all sizes to optimize sales performance and improve forecasting accuracy. The market, estimated at $15 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033, reaching approximately $40 billion by 2033. This expansion is fueled by several key factors. The rise of cloud-based solutions offers scalability, accessibility, and cost-effectiveness, attracting both large enterprises seeking sophisticated analytics and SMEs prioritizing efficiency. Furthermore, evolving sales strategies, including the integration of CRM systems and data analytics, demand advanced sales planning tools for accurate forecasting, territory management, and quota setting. The increasing adoption of AI and machine learning within sales planning software further enhances predictive capabilities, enabling businesses to proactively adapt to market changes and optimize resource allocation. Competition is intense, with established players like Salesforce, Oracle, and SAP vying for market share alongside emerging specialized vendors. Market segmentation reveals a strong demand in North America and Europe, with Asia-Pacific exhibiting significant growth potential. However, challenges such as integration complexities with existing systems and the need for ongoing training and support can restrain market growth. The segment breakdown shows a significant share captured by cloud-based solutions due to their inherent flexibility and cost advantages. Large enterprises dominate current spending but SMEs are rapidly adopting these solutions, contributing to market expansion. Geographic analysis suggests North America maintains a substantial market share due to early adoption and technological advancement, but Europe and Asia-Pacific are poised for rapid growth, driven by increasing digital transformation initiatives and expanding business operations in these regions. The competitive landscape remains dynamic, with mergers, acquisitions, and product innovations continuously shaping the market. The future success of sales planning software vendors will depend on their ability to offer robust, user-friendly solutions that seamlessly integrate with existing business systems and deliver actionable insights to enhance sales performance and contribute to overall business growth.

In 2022, click-and-collect sales in the United States were forecast to grow **** percent compared to the previous year. After increasing by more than 100 percent during the first year of the COVID-19 pandemic, click-and-collect retail sales were expected to continue to grow in the near future, albeit at a slower rate.

Sales Forecasting Software Market size was valued at USD 3200.75 million in 2024 and the revenue is expected to grow at a CAGR of 9.45% from 2025 to 2032

Presales Software Market Outlook

The global presales software market size is expected to witness remarkable growth, expanding from USD 1.8 billion in 2023 to approximately USD 4.5 billion by 2032, reflecting a compound annual growth rate (CAGR) of 10.8%. The key growth factor driving this expansion is the increasing demand for enhanced customer relationship management (CRM) solutions, which are integral for businesses aiming to streamline their sales processes and improve customer engagement.

Technological advancements in artificial intelligence (AI) and machine learning (ML) have significantly contributed to the growth of the presales software market. These technologies enable presales solutions to offer predictive analytics, personalized customer interactions, and automated processes, thereby improving efficiency and accuracy in sales forecasting and customer segmentation. AI-driven insights help sales teams to better understand customer behavior, predict future trends, and make data-driven decisions, which are crucial for maintaining competitive advantage in the market.

The growing adoption of cloud-based solutions is another critical factor propelling the market. Cloud-based presales software provides several benefits, including scalability, cost-effectiveness, and remote accessibility, which are particularly advantageous for small and medium enterprises (SMEs) with limited IT budgets. Moreover, the integration of cloud technology facilitates seamless collaboration among sales teams, enabling real-time data sharing and enhanced productivity. As businesses continue to embrace digital transformation, the demand for cloud-based presales solutions is expected to rise steadily.

Additionally, the increasing focus on customer-centric strategies is driving the need for sophisticated presales software. Companies are striving to enhance their customer engagement through personalized and timely interactions, which requires robust CRM tools capable of managing large volumes of customer data. Presales software offers functionalities such as lead management, sales pipeline tracking, and customer analytics, which are essential for executing effective customer engagement strategies. This shift towards customer-centricity is likely to sustain the demand for advanced presales solutions over the forecast period.

The evolution of Sales Engagement Platform Software has been a game-changer in the presales software landscape. These platforms are designed to enhance the interaction between sales teams and potential customers by providing a suite of tools for communication, tracking, and analytics. By integrating with existing CRM systems, Sales Engagement Platform Software allows sales representatives to manage their outreach efforts more effectively, ensuring that every interaction is timely and relevant. This integration not only streamlines the sales process but also provides valuable insights into customer behavior and preferences, enabling sales teams to tailor their strategies accordingly. As businesses strive for greater efficiency and personalization in their sales efforts, the adoption of Sales Engagement Platform Software is becoming increasingly essential.

Regionally, North America is expected to hold the largest market share, owing to the presence of major technology companies and high adoption rates of advanced sales solutions. Europe and the Asia Pacific are also significant markets, with Europe benefiting from technological advancements and regulatory support, while the Asia Pacific region is driven by the rapid growth of SMEs and increasing digitization efforts in emerging economies. The Middle East & Africa and Latin America regions are anticipated to witness moderate growth, supported by increasing investments in IT infrastructure and growing awareness of digital sales tools.

Deployment Type Analysis

The presales software market is segmented into two primary deployment types: on-premises and cloud-based solutions. On-premises deployment refers to software hosted within an organization's own infrastructure, offering greater control over data security and customization. Historically, larger enterprises with substantial IT resources have favored on-premises solutions due to their ability to tailor the software to specific business needs and retain full control over their data. However, the in