Dataset

This dataset was created by Study Mart

Contents

It contains the following files:

The provided data asset is relational and consists of four distinct data files.

1. address.csv: contains address information

2. customer.csv: contains customer information.

3. demographic.csv: contains demographic data

4. termination.csv: includes customer termination information.

5. autoinsurance_churn.csv: includes merged customer churn data generated from this notebook.

All data sets are linked using either ADDRESS_ID or INDIVIDUAL_ID. The ADDRESS_ID pertains to a specific postal service address, while the INDIVIDUAL_ID is unique to each individual. It is important to note that multiple customers may be assigned to the same address, and not all customers have demographic information available.

Size of the data set

The data set includes 1,536,673 unique addresses and 2,280,321 unique customers, of which 2,112,579 have demographic information. Additionally, 269,259 customers cancelled their policies within the previous year.

Note

Please note that the data is synthetic, and all customer information provided is fictitious. While the latitude-longitude information can be mapped at a high level and generally refers to the Dallas-Fort Worth Metroplex in North Texas, it is important to note that drilling down too far may result in some data points that are located in the middle of Jerry World, DFW Airport, or Lake Grapevine. The physical addresses provided are fake and are unrelated to the corresponding lat/long.

The termination table includes the ACCT_SUSPD_DATE field, which can be used to derive a binary churn/did not churn variable. The data set is modelable, meaning that the other data available can be used to predict which customers churned and which did not. The underlying logic used to make these predictions should align with predicting auto insurance churn in the real world.

🇬🇧 English:

This synthetic dataset was designed for those who want to practice customer churn prediction using structured tabular data. It includes 1,000 customer records, each containing features such as age, service tenure, service type, monthly fee, and churn status.

Use this dataset to:

• Build classification models like Logistic Regression, Random Forest, or XGBoost
• Explore churn-related patterns (e.g. short tenure, high price, mobile users)
• Simulate real-world business scenarios without needing real customer data

Features:

• customer_id: Unique customer ID (e.g. C1001 to C2000)
• age: Age of the customer
• tenure: Number of months the customer has been active
• service_type: Type of service used (internet, mobile, tv, bundle)
• monthly_fee: Monthly subscription fee
• churn: Whether the customer has left the service (1 = Yes, 0 = No)

🇹🇷 Türkçe:

Bu sentetik veri seti, müşteri kaybı (churn) tahmini üzerine çalışmak isteyen araştırmacılar ve öğrenciler için oluşturulmuştur. 1.000 müşteriye ait yaş, hizmet süresi, hizmet türü, aylık ödeme ve abonelik durumuna dair sahte ancak gerçekçi veriler içerir.

Bu veri seti sayesinde:

• Logistic Regression, Random Forest, XGBoost gibi sınıflandırma modelleri uygulanabilir
• Churn davranışına etki eden faktörler incelenebilir (örneğin kısa üyelik, yüksek fiyat, mobil kullanıcılar)
• Gerçek müşteri verilerine erişim gerekmeden iş senaryoları çalışılabilir

🧾 Değişkenler:

• customer_id: Müşteri kimliği (ör. C1001 – C2000)
• age: Müşteri yaşı
• tenure: Kaç aydır hizmet aldığı
• service_type: Aldığı hizmet türü (internet, mobile, tv, bundle)
• monthly_fee: Aylık ödeme miktarı
• churn: Hizmeti bırakıp bırakmadığı (1 = Evet, 0 = Hayır)

**New to machine learning and data science? No question is too basic or too simple. Use this place to post any first-timer clarifying questions for the classification algorithm or related to datasets ** !This file contains demographics about customer and whether that customer clicked the ad or not . You this file to use classification algorithm to predict on the basis of demographics of customer as independent variable

This data set contains the following features:

This data set contains the following features:

1. 'User ID': unique identification for consumer
2. 'Age': cutomer age in years
3. 'Estimated Salary': Avg. Income of consumer
4. 'Gender': Whether consumer was male or female
5. 'Purchased': 0 or 1 indicated clicking on Ad

with the intensification of market competition and the complexity of consumer behavior, enterprises are faced with the challenge of how to accurately identify potential customers and improve user conversion rate. This paper aims to study the application of machine learning in consumer behavior prediction and precision marketing. Four models, namely support vector machine (SVM), extreme gradient boosting (XGBoost), categorical boosting (CatBoost), and backpropagation artificial neural network (BPANN), are mainly used to predict consumers’ purchase intention, and the performance of these models in different scenarios is verified through experiments. The results show that CatBoost and XGBoost have the best prediction results when dealing with complex features and large-scale data, F1 scores are 0.93 and 0.92 respectively, and CatBoost’s ROC AUC reaches the highest value of 0.985. while SVM has an advantage in accuracy rate, but slightly underperformance when dealing with large-scale data. Through feature importance analysis, we identify the significant impact of page views, residence time and other features on purchasing behavior. Based on the model prediction results, this paper proposes the specific application of optimization marketing strategies such as recommendation system, dynamic pricing and personalized advertising. Future research could improve the predictive power of the model by introducing more kinds of unstructured data, such as consumer reviews, images, videos, and social media data. In addition, the use of deep learning models, such as Transformers or Self-Attention Mechanisms, can better capture complex patterns in long time series data.

Dataset

This dataset was created by avazjon isoboev

Contents

Predictive Analytics Market size was valued at USD 11.88 Billion in 2024 and is projected to reach USD 33.65 Billion by 2031, growing at a CAGR of 13.9% from 2024 to 2031.

The predictive analytics market is primarily driven by the growing need for data-driven decision-making across industries. As businesses collect more data from various sources, the demand for tools that analyze this information to predict trends, customer behavior, and potential risks is rapidly increasing. Sectors like retail, healthcare, finance, and manufacturing benefit from predictive insights to improve customer experience, optimize operations, and minimize risk.

Additionally, advances in artificial intelligence (AI) and machine learning (ML) are accelerating predictive analytics adoption. These technologies allow predictive models to analyze larger, more complex datasets in real-time, enhancing accuracy and efficiency. The integration of cloud computing and IoT has further expanded the use of predictive analytics, enabling businesses to implement cost-effective solutions and improve scalability.

Telecom CRM Market Outlook

The global Telecom CRM market size is projected to witness significant growth, reaching an impressive value by 2032 from its valuation of USD 8.3 billion in 2023, with a robust CAGR of 10.4% during the forecast period. This growth is primarily driven by the increasing demand for advanced customer relationship management solutions that can handle the complexities of telecom operations, including customer and network management, billing, and revenue management. The adoption of cloud-based solutions is also fueling this growth as companies seek scalable and flexible CRM systems to enhance customer satisfaction and streamline operations.

The surge in demand for telecom CRM solutions can be attributed to the increasing emphasis on improving customer experience and loyalty. Telecom operators and service providers are investing heavily in CRM systems to gain a comprehensive understanding of customer needs and preferences, allowing them to tailor services and improve customer interactions. The ability to consolidate customer data and leverage it for personalized service offerings is a significant growth factor, as it helps companies to not only retain existing customers but also attract new ones in a highly competitive market. Technological advancements in artificial intelligence and machine learning are further enhancing the capabilities of CRM systems in predictive analytics and customer behavior insights.

Another major growth factor is the rapid digital transformation across the telecom industry. This transformation necessitates efficient customer relationship management to handle the influx of data and customer interactions across various digital channels. The integration of CRM systems with emerging technologies like IoT and 5G is providing telecom operators with the tools to manage complex networks and respond swiftly to customer needs. As telecom companies continue to evolve into digital service providers, the demand for sophisticated CRM solutions that can support multi-channel communication and real-time customer engagement is expected to rise.

The telecom CRM market is also witnessing expansion due to regulatory changes and market consolidation. With strict regulations governing customer data privacy and security, telecom companies are investing in CRM solutions that not only comply with these regulations but also enhance customer trust. Additionally, mergers and acquisitions within the telecom sector are driving the need for integrated CRM systems that can support unified customer bases and streamline operations across merged entities. As companies aim to achieve operational efficiency and cost savings through these consolidations, the demand for robust CRM systems is on the rise.

Regionally, the Asia Pacific is expected to dominate the telecom CRM market due to the rapid growth of telecom infrastructure and increasing internet penetration rates. Countries such as China and India are witnessing substantial investments in telecom networks, which in turn is boosting the adoption of CRM solutions. North America and Europe are also significant markets, driven by early technology adoption and strong emphasis on customer service excellence. Meanwhile, the Latin America and Middle East & Africa regions are catching up quickly, with telecom companies in these regions increasingly recognizing the benefits of CRM systems in enhancing customer engagement and operational efficiency.

Component Analysis

The Telecom CRM market by component is divided into software and services, each playing a crucial role in fulfilling the diverse needs of telecom operators. Software solutions within CRM systems are designed to provide a comprehensive platform for managing customer relationships, from customer data integration to analytics and reporting. These software solutions are leveraged by telecom operators to automate workflows, manage customer interactions, and provide actionable insights through data analytics. As telecom companies aim to enhance customer experiences, the demand for sophisticated CRM software that supports these capabilities is increasing.

In addition to software, services constitute a vital component of the telecom CRM market. Services include consulting, implementation, training, and support, which are critical for ensuring that CRM solutions are effectively integrated and utilized within telecom operations. Telecom operators often require expert guidance in customizing CRM software to meet their unique business needs and to ensure seamless deployment across their networks. The implementation and support services are essential for min

The fitting effect of four models on the test set.

Predictive Analytics in Banking Market Outlook

The global predictive analytics in banking market size is projected to witness significant growth, from USD 8.5 billion in 2023 to an estimated USD 24.7 billion by 2032, growing at a robust CAGR of 12.5% during the forecast period. This remarkable growth is driven by the increasing adoption of big data technologies and the need for real-time insights in banking operations. Predictive analytics in banking has become a pivotal tool for financial institutions aiming to enhance operational efficiency and customer service by leveraging data-driven insights to anticipate trends, customer behavior, and potential risks.

Several growth factors are propelling the expansion of the predictive analytics market within the banking sector. One significant factor is the burgeoning volume of data generated by customers as they interact with banking services through digital channels. Banks are now harnessing this vast data repository to gain insights into customer preferences and behaviors, which can support personalized banking solutions. The integration of artificial intelligence and machine learning algorithms in predictive analytics tools allows banks to analyze customer data more accurately, leading to improved decision-making processes. Furthermore, regulatory requirements for risk management and fraud prevention have necessitated the adoption of predictive analytics, as they enable banks to identify potential threats proactively and comply with stringent financial regulations.

The increasing need for improved risk management is another critical driver of the predictive analytics in banking market. Financial institutions face myriad risks, ranging from credit and market risks to operational and compliance risks. By employing predictive analytics, banks can anticipate and mitigate these risks more effectively, reducing potential losses and safeguarding their financial stability. Predictive models can assess the likelihood of default on loans, analyze market trends, and monitor internal processes to detect anomalies or inefficiencies. This capability has become even more crucial in the face of economic uncertainties and volatile financial markets, where timely and informed decision-making is paramount.

Customer management is also a vital area where predictive analytics is making a significant impact. Banks are increasingly focusing on enhancing the customer experience by offering tailored products and services. Predictive analytics enables banks to segment their customer base, predict future behaviors, and develop targeted marketing strategies. For instance, by analyzing historical transaction data, banks can anticipate customer needs and proactively offer relevant products, such as loans or investment opportunities. This not only improves customer satisfaction and loyalty but also drives revenue growth through cross-selling and up-selling.

Component Analysis

The predictive analytics in banking market is segmented by component into software and services. The software segment encompasses a range of analytical tools and platforms that facilitate data processing, visualization, and predictive modeling. These software solutions are integral to the functioning of predictive analytics, as they provide the necessary infrastructure for data integration, model development, and result interpretation. The demand for advanced analytics software is on the rise, driven by the need for real-time decision-making capabilities in banking operations. Moreover, continuous advancements in software technology, including AI and machine learning, are enhancing the predictive accuracy and efficiency of these tools, further bolstering market growth.

On the other hand, the services segment includes consulting, implementation, and support services that complement the software offerings. As banks strive to integrate predictive analytics into their operations, the role of service providers becomes crucial. These services ensure the successful deployment and integration of analytics solutions, addressing challenges such as data quality, system compatibility, and user training. Consulting services, in particular, assist banks in identifying appropriate use cases for predictive analytics, aligning them with strategic business goals. As banks increasingly recognize the value of data-driven insights, the demand for specialized services that facilitate the seamless adoption of predictive analytics is expected to grow significantly.

The convergence of software and services is essential for the optimal utilization of predictive analytics in banking. While software provi

Customer churn refers to the phenomenon where customers discontinue their relationship or subscription with a company or service provider. It represents the rate at which customers stop using a company's products or services within a specific period. Churn is an important metric for businesses as it directly impacts revenue, growth, and customer retention.

In the context of the Churn dataset, the churn label indicates whether a customer has churned or not. A churned customer is one who has decided to discontinue their subscription or usage of the company's services. On the other hand, a non-churned customer is one who continues to remain engaged and retains their relationship with the company.

Understanding customer churn is crucial for businesses to identify patterns, factors, and indicators that contribute to customer attrition. By analyzing churn behavior and its associated features, companies can develop strategies to retain existing customers, improve customer satisfaction, and reduce customer turnover. Predictive modeling techniques can also be applied to forecast and proactively address potential churn, enabling companies to take proactive measures to retain at-risk custos.

1.Age Distribution and Churn Rate:

What is the distribution of ages among your customers? Is there a relationship between age and churn rate?

2.Gender Analysis:

What is the gender distribution of your customers? Is there any noticeable difference in churn rates between genders?

3.Tenure and Churn:

How long, on average, have your customers been with your service (tenure)? Is there any pattern between tenure and churn?

4.Usage Frequency:

How frequently do customers use your service, on average? Does usage frequency affect churn rates?

5.Support Calls and Churn:

What is the average number of support calls made by customers? Is there any correlation between support calls and churn?

6.Payment Delay:

What is the typical payment delay among customers? Does payment delay influence churn behavior?

7.Subscription Type and Contract Length:

What are the different subscription types and their proportions? Do customers with different subscription types have different churn rates? How does contract length relate to churn?

8.Total Spend and Churn:

What is the average total spend of customers? Is there any correlation between total spend and churn?

9.Last Interaction:

How recently did customers interact with your service? Is there any connection between the recency of the last interaction and churn?

The size and share of this market is categorized based on Network Management (Traffic Analysis, Fault Management, Performance Management, Configuration Management, Service Quality Management) and Customer Experience Management (Sentiment Analysis, Churn Prediction, Customer Feedback Analysis, Personalization, Predictive Customer Service) and Operational Efficiency (Automated Workflows, Resource Allocation, Predictive Maintenance, Cost Optimization, Process Automation) and Security Solutions (Fraud Detection, Intrusion Detection Systems, Threat Intelligence, Data Privacy Management, Network Security) and Data Analytics (Big Data Processing, Real-time Analytics, Predictive Analytics, Data Visualization, Business Intelligence) and geographical regions (North America, Europe, Asia-Pacific, South America, Middle-East and Africa).

Contact Center Analytics Market size was valued at USD 1.88 Billion in 2024 and is projected to reach USD 8.84 Billion by 2031, growing at a CAGR of 18.6% during the forecast period 2024-2031.

Global Contact Center Analytics Market Drivers

The market drivers for the Contact Center Analytics Market can be influenced by various factors. These may include:

Growing Focus on Customer Experience: Companies are placing more emphasis on using contact center analytics to obtain insights into customer interactions and improve overall experience as they realize how important it is to deliver great customer service.

Growing Adoption of AI and Machine Learning: The market is progressing thanks to the integration of AI and machine learning technologies in contact center analytics solutions, which offer sophisticated speech and text analytics, sentiment analysis, and predictive analytics.

Demand for Real-Time Insights: Contact center analytics solutions that provide real-time monitoring and actionable insights are in greater demand as a result of the necessity to immediately resolve customer complaints and optimize operations in real-time.

Operational Efficiency: Companies want to increase agent productivity and optimize contact center operations. For the purpose of maximizing staff management, resource allocation, and performance monitoring, contact center analytics offers useful measurements and KPIs.

Regulatory Compliance Requirements: Adoption of contact center analytics solutions is fueled by the need to comply with laws like GDPR, CCPA, and PCI-DSS, which need strong analytics capabilities to guarantee data security, privacy, and industry standards.

Growing Volume of Customer Interactions: To handle, evaluate, and extract insights from these enormous volumes of data, analytics solutions are becoming more and more necessary as customer interactions spread over several channels, including as voice, email, chat, and social media.

Trend toward Cloud-Based Solutions: Businesses are choosing cloud-based contact center analytics platforms over conventional on-premises systems due to their scalability, flexibility, and affordability.

Emphasis on Proactive consumer Engagement: Contact center analytics helps companies to recognize patterns, anticipate consumer needs, and interact with customers in a proactive manner, which increases customer satisfaction and loyalty.

Integration with CRM and Other Systems: Contact center analytics can be integrated with CRM programs and other business software to improve data visibility and provide a more thorough picture of customer interactions and behavior.

Differentiation and Competitive Advantage: Businesses see contact center analytics as a strategic tool for differentiating themselves from the competition, streamlining operations, and providing individualized experiences.

The Customer Engagement Solutions market is experiencing robust growth, projected to reach $19.16 billion in 2025 and exhibiting a Compound Annual Growth Rate (CAGR) of 13.1%. This expansion is fueled by several key drivers. Firstly, the increasing adoption of cloud-based solutions offers scalability, cost-effectiveness, and enhanced accessibility for businesses of all sizes. Secondly, the rising demand for personalized customer experiences is pushing organizations to invest in advanced analytics and AI-powered tools to understand customer behavior and tailor interactions accordingly. Furthermore, the growing need for omnichannel engagement, enabling seamless interactions across various platforms (web, mobile, social media, etc.), is a major catalyst for market growth. While the on-premises deployment model still holds a significant share, the shift towards cloud solutions is accelerating rapidly. The market is segmented into solutions (software, platforms) and services (implementation, maintenance, support), with solutions holding a larger market share currently due to the initial investment required in establishing robust systems. Competition is intense, with major players like Salesforce, Microsoft, and Genesys vying for market share through continuous innovation, strategic partnerships, and acquisitions. North America currently dominates the market, followed by Europe and APAC, although APAC is expected to witness the fastest growth over the forecast period due to increasing digitalization and a burgeoning tech-savvy population. Restraints include the high initial investment costs for some solutions and the complexity of implementing and integrating these systems into existing infrastructure. Despite these challenges, the long-term outlook for the Customer Engagement Solutions market remains highly positive. The increasing reliance on data-driven decision-making, coupled with the evolving customer expectations for personalized and seamless experiences, will continue to drive demand for sophisticated solutions. The market will witness significant innovation in areas such as AI-powered chatbots, predictive analytics for proactive customer service, and improved integration capabilities across various channels. Companies focusing on delivering tailored solutions that address specific industry needs and offering comprehensive service packages are expected to gain a competitive edge. The continuous evolution of technologies such as artificial intelligence, machine learning, and the Internet of Things (IoT) will further fuel innovation and growth within the sector. The market's trajectory indicates a significant opportunity for businesses to capitalize on the demand for enhanced customer engagement and improved operational efficiency.

The technological development in the new economic era has brought challenges to enterprises. Enterprises need to use massive and effective consumption information to provide customers with high-quality customized services. Big data technology has strong mining ability. The relevant theories of computer data mining technology are summarized to optimize the marketing strategy of enterprises. The application of data mining in precision marketing services is analyzed. Extreme Gradient Boosting (XGBoost) has shown strong advantages in machine learning algorithms. In order to help enterprises to analyze customer data quickly and accurately, the characteristics of XGBoost feedback are used to reverse the main factors that can affect customer activation cards, and effective analysis is carried out for these factors. The data obtained from the analysis points out the direction of effective marketing for potential customers to be activated. Finally, the performance of XGBoost is compared with the other three methods. The characteristics that affect the top 7 prediction results are tested for differences. The results show that: (1) the accuracy and recall rate of the proposed model are higher than other algorithms, and the performance is the best. (2) The significance p values of the features included in the test are all less than 0.001. The data shows that there is a very significant difference between the proposed features and the results of activation or not. The contributions of this paper are mainly reflected in two aspects. 1. Four precision marketing strategies based on big data mining are designed to provide scientific support for enterprise decision-making. 2. The improvement of the connection rate and stickiness between enterprises and customers has played a huge driving role in overall customer marketing.

Customer Experience Management Market Outlook 2032

The global customer experience management market size was USD 11.4 Billion in 2023 and is likely to reach USD 32.12 Billion by 2032, expanding at a CAGR of 12.2% during 2024–2032. The market growth is attributed to the increasing need for businesses to enhance customer satisfaction and improve loyalty.



Customer Experience Management (CEM) market is experiencing a considerable increase in its value and significance. Businesses worldwide recognize the unparalleled power of delivering an exceptional customer experience. This evolution in the marketplace has sparked global investments in CEM solutions, with companies steadily acknowledging its impact on consumer loyalty, retention, and revenue.



Advanced technologies such as artificial intelligence and machine learning allow businesses to understand their customers' behaviors and patterns in-depth. Customization and personalization, two major trends, are simplified and scaled through AI-backed data analytics, thereby enhancing the overall customer experience. The integration of digital platforms into everyday consumer life fuels the need for companies to manage customers' interactions across multiple channels consistently.

Impact of Artificial Intelligence (AI) in Customer Experience Management Market

Artificial Intelligence transforms the customer experience management market by streamlining operations, enhancing customer interactions, and providing predictive analysis for innovative business solutions. The integration of AI tools such as voice assistants, chatbots, and machine learning algorithms results in instant customer responses, consequently boosting overall satisfaction.



AI empowers businesses with data-driven insights about customer behavior and preferences which, in turn, forms the foundation for personalized marketing strategies fostering increased customer engagement and loyalty. AI's impact on customer experience management leads to improved service delivery, retention, and growth.

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The size and share of this market is categorized based on Social Media Analytics (Sentiment Analysis, Engagement Metrics, Influencer Analysis, Content Performance, Audience Insights) and Customer Feedback Analytics (Survey Analysis, Review Analysis, Net Promoter Score (NPS), Churn Prediction, Customer Satisfaction) and Brand Monitoring (Brand Sentiment, Competitor Analysis, Share of Voice, Crisis Management, Brand Health Tracking) and geographical regions (North America, Europe, Asia-Pacific, South America, Middle-East and Africa).

The global AI in Social Media Market size was around $1.62 Billion in 2023 and is predicted to grow to around $17.35 Billion by 2032 at a CAGR of 30.12%.

The US marketing analytics market, a significant segment of the global industry, is experiencing robust growth, fueled by the increasing adoption of data-driven decision-making across various sectors. The market's substantial size, estimated at $X billion in 2025 (assuming a proportional share of the global market based on US economic influence and digital marketing maturity), is projected to expand at a Compound Annual Growth Rate (CAGR) exceeding 12.73% through 2033. This growth is driven by several key factors. Firstly, the proliferation of digital channels and the resulting explosion of marketing data necessitate sophisticated analytics solutions for effective campaign management and ROI optimization. Secondly, the rising adoption of cloud-based marketing analytics platforms offers scalability, cost-efficiency, and enhanced accessibility for businesses of all sizes. Thirdly, the increasing demand for personalized marketing experiences pushes businesses to leverage advanced analytics to understand customer behavior and preferences, leading to targeted campaigns and improved customer engagement. Furthermore, the burgeoning need for real-time data insights to rapidly respond to market changes and optimize marketing strategies further contributes to this growth. The US market's segmentation mirrors global trends, with cloud deployment dominating due to its inherent advantages. Key application areas include online marketing, email marketing, and social media marketing, reflecting the omnipresence of these channels. Major end-user sectors like retail, BFSI (Banking, Financial Services, and Insurance), and technology are leading adopters, leveraging analytics to improve customer acquisition, retention, and profitability. While the competitive landscape is crowded with established players like IBM, Microsoft, Salesforce, and Adobe, the market also presents opportunities for specialized niche players focusing on specific industry verticals or advanced analytical techniques. The continued innovation in areas like artificial intelligence (AI), machine learning (ML), and predictive analytics will likely shape future market growth, particularly in areas like customer journey mapping and predictive modeling for marketing campaign optimization. The US market's robust growth trajectory suggests significant investment opportunities and underscores the critical role of marketing analytics in the ongoing digital transformation across various industries. Recent developments include: June 2023 - Moody’s Corporation and Microsoft have announced a new partnership to deliver next-generation data, analytics, research, collaboration, and risk solutions for financial services and global knowledge workers. Built on a combination of Moody’s robust data and analytical capabilities and the power and scale of Microsoft Azure OpenAI Service, the partnership creates innovative offerings that enhance insights into corporate intelligence and risk assessment, powered by Microsoft AI and anchored by Moody’s proprietary data, analytics, and research., July 2022 - Neustar, a TransUnion company, announced a partnership with integrated data platform Adverity to allow marketers to connect all their data effortlessly to boost marketing and brand effectiveness. To better optimize marketing spending and boost return on investment (ROI), marketers need a comprehensive data strategy as data-driven marketing becomes more complex. Through this relationship, companies and agencies can more accurately assess the marketing effectiveness of various online and offline platforms, such as the walled garden and television ecosystems., December 2022 - Vi Labs, an Enterprise-AI for digital health, acquired Motus Consumer Insights, a member acquisition analytics, site selection, and marketing BI firm. Through the acquisition, Vi's robust AI-powered customer engagement and retention solution will be combined with the premier platforms for customer acquisition and site selection in the market. Vi's mission to use the power of data and AI to support people living active and healthy lifestyles worldwide is only accelerated by this deal.. Key drivers for this market are: Increase in Social Media Channels, Increasing Need to Utilize Marketing Budgets for an Effective ROI; Adoption of Cloud Technology and Big Data. Potential restraints include: Increase in Social Media Channels, Increasing Need to Utilize Marketing Budgets for an Effective ROI; Adoption of Cloud Technology and Big Data. Notable trends are: Adoption of Cloud Technology and Big Data is Expected to Drive the Market Growth.

Analysis of ‘Food Demand Prediction Dataset’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/gauravsahani/food-demand-prediction-dataset on 28 January 2022.

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

Context

Demand forecasting is a key component of every growing online business. Without proper demand forecasting processes in place, it can be nearly impossible to have the right amount of stock on hand at any given time. A food delivery service has to deal with a lot of perishable raw materials which makes it all the more important for such a company to accurately forecast daily and weekly demand. This being a reason to come up with this dataset!✌️

Content

Given the following information, the task is to predict the demand for the next 10 weeks for the meal combinations, which are:

• Historical data of demand for a product-center combination (Weeks: 1 to 145)
• Product(Meal) features such as category, sub-category, current price and discount
• Information for fulfillment centers like center area, city information, etc.

Inspiration

This dataset is targeted towards beginners, its e expected to explore more and get as many insights regarding the data and come up with mostmost accurate predictions! Thank you!😄

Please leave a Like!, if you liked the dataset!👍

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