data mining crm case study

Data Mining in CRM

• By Alex Wilson
• Published January 5, 2019
• Updated January 5, 2019
• 12 mins read

Data mining is a powerful tool that allows companies to extract valuable insights from their customer relationship management (CRM) data. By analyzing large amounts of customer data, companies can identify patterns, trends, and correlations that can help improve their marketing strategies, customer service, and overall business performance.

Key Takeaways

• Data mining helps extract valuable insights from CRM data.
• It enables companies to improve marketing strategies and customer service.
• Data mining can uncover hidden patterns, trends, and correlations.
• It requires advanced analytical techniques and algorithms.

Data mining involves sifting through massive amounts of data to uncover hidden patterns and discover new insights. It uses advanced analytical techniques and algorithms to extract meaningful information from CRM databases.

One of the key benefits of data mining in CRM is the ability to improve marketing strategies . By analyzing customer data, companies can identify their target markets more accurately and tailor their advertising and promotional efforts accordingly. This leads to greater ROI and reduces wastage of resources.

Uncovering Customer Trends

Data mining allows businesses to identify customer trends by analyzing historical data. Companies can study customer behavior and preferences to understand what products or services are most popular among their target audience. By understanding these trends, companies can make data-driven decisions and optimize their offerings to cater to the demands of their customers.

For example, a data mining analysis may reveal that a particular product is most popular among millennials. Armed with this knowledge, the company can devise targeted marketing campaigns to reach this specific demographic.

Data Mining Techniques

Data mining uses various techniques to extract insights from CRM data. Some common techniques include:

• Cluster analysis: Groups similar data points together to identify segments or clusters within the customer base.
• Association rule mining: Identifies interesting relationships between different products or items in an organization’s database.

Data Mining Applications

Data mining has numerous applications in CRM and beyond. Some key applications include:

• Customer segmentation: Divides customers into different segments based on their demographics, behavior, or preferences.
• Churn prediction: Helps identify customers who are likely to leave or stop using a company’s products or services.

Data Mining Case Studies

In conclusion, data mining is a vital component of effective CRM strategy. By analyzing and interpreting CRM data, companies can gain valuable insights into customer behavior, preferences, and trends. This enables businesses to enhance their marketing, customer service, and overall performance. With advanced techniques and algorithms, data mining allows companies to make data-driven decisions and improve their competitive advantage in the market.

Common Misconceptions

One common misconception about data mining in customer relationship management (CRM) is that it is an invasion of privacy. Many people believe that data mining involves the unauthorized gathering of personal information to be used for nefarious purposes. However, in reality, data mining in CRM is the process of analyzing large sets of data to gain valuable insights and improve customer experiences.

• Data mining in CRM is done with the consent of the customers involved.
• The data collected is usually aggregated and anonymous.
• Data mining helps companies understand customer behaviors and preferences to provide better services.

Another misconception is that data mining in CRM only benefits the company, without offering any value to the customers. This belief often stems from the fear that data mining is solely used to target customers with advertisements or manipulate their buying behaviors. Nevertheless, data mining in CRM actually aims to enhance customer satisfaction by tailoring products and services to their specific needs and preferences.

• Data mining enables companies to personalize offerings and improve customer experiences.
• By analyzing customer data, companies can identify and address pain points in their products or services.
• Data mining helps companies understand customer feedback to make necessary improvements.

There is a misconception that data mining in CRM is a one-time process that provides immediate results. Some people believe that companies can simply gather data, run data mining algorithms, and instantly discover valuable insights. However, data mining is typically an ongoing and iterative process that requires time and effort to generate meaningful results.

• Data mining requires continuous data collection and analysis.
• Companies need to refine their models and algorithms for accurate and relevant insights.
• Data mining in CRM is a long-term strategy to improve customer relationships and business outcomes.

Many individuals also mistakenly think that data mining in CRM is solely about predicting customer behaviors and making assumptions based on statistical models. While prediction is an essential aspect of data mining, it is not the only goal. Data mining also involves exploration and discovery of patterns and relationships within the data to find hidden insights that can drive business decisions.

• Data mining uncovers meaningful patterns and correlations in customer data.
• It allows companies to identify untapped market segments or target new customer groups.
• Data mining assists in identifying trends and forecasting future market developments.

Lastly, one common misconception is that data mining in CRM is a standalone solution that can solve all customer-related issues. Although data mining plays a critical role in CRM, it is just one component of a comprehensive customer relationship management strategy. Data mining should be integrated with other CRM practices, such as personalized marketing campaigns and customer support initiatives, to provide a holistic approach to customer relationship management.

• Data mining works in synergy with other CRM components to enhance customer experiences.
• Effective CRM requires a combination of data mining, customer engagement metrics, and personalized communication.
• Data mining is a tool to assist in decision-making but should not replace human interactions with customers.

Data mining is the process of extracting information and patterns from large datasets to gain insights and make data-driven decisions. In Customer Relationship Management (CRM), data mining plays a vital role in uncovering valuable customer behavior patterns, preferences, and trends. This article explores various aspects of data mining in CRM through a series of visually engaging tables.

Customer Segmentation Based on Age

This table highlights the distribution of a company’s customer base across different age groups. By analyzing this data, marketers can tailor their marketing strategies and product offerings to cater to the unique preferences of each age segment.

Customer Lifetime Value (CLV)

CLV represents the predicted net profit a company expects to earn over the lifetime of a customer. This table showcases the CLV breakdown by different customer segments:

Product Affinity Analysis

This table demonstrates the association between different products purchased by customers, revealing product combinations that are often bought together. Marketers can use this information to offer cross-selling recommendations to customers and optimize product placement strategies.

Customer Churn Rate

This table showcases the percentage of customers who canceled their subscriptions or stopped purchasing from the company over a specific period, highlighting the importance of retaining customers and implementing effective churn prevention strategies .

Campaign Effectiveness by Channel

Understanding the performance of marketing campaigns across different channels is crucial. This table showcases the conversion rates for various marketing channels, providing insights to optimize resource allocation in future campaigns.

Customer Satisfaction by Product Category

This table illustrates the average customer satisfaction scores for different product categories. By assessing satisfaction levels, companies can focus on improving areas with lower scores and fine-tune their products to meet customer expectations.

Customer Complaint Resolution Time

This table showcases the average time taken by the customer service team to resolve customer complaints, ensuring efficient issue resolution and higher customer satisfaction.

Customer Engagement by Communication Channel

This table presents the count of customer engagements across different communication channels, such as phone calls, emails, and live chats, helping companies understand preferred methods of communication for effective customer interactions.

Data mining in CRM provides valuable insights into customer behavior and preferences, enabling companies to enhance customer experience, maximize sales, and improve overall business performance. By leveraging data mining techniques and analyzing the information presented in tables like the ones above, businesses can make informed decisions and create targeted marketing strategies that foster lasting customer relationships. It is through the power of data that companies can gain a competitive edge in the dynamic world of customer relationship management.

Data Mining in CRM – Frequently Asked Questions

What is data mining in crm.

Data mining in Customer Relationship Management (CRM) refers to the process of extracting useful patterns, insights, and knowledge from large datasets to improve customer targeting, marketing strategies, and overall relationship management. It involves analyzing customer behavior, preferences, and interactions to drive informed decision-making for better business outcomes.

Why is data mining important in CRM?

Data mining plays a crucial role in CRM as it helps businesses gain a deeper understanding of their customers, identify valuable patterns, and make data-driven decisions. By uncovering hidden insights from large datasets, data mining allows companies to tailor their marketing efforts, personalize customer experiences, optimize sales strategies, and enhance customer satisfaction and loyalty.

What are the benefits of data mining in CRM?

Data mining in CRM offers several benefits, including:

• Improved customer segmentation
• Enhanced customer targeting
• Personalized marketing campaigns
• Better prediction of customer behavior
• Increased customer satisfaction and loyalty
• Optimized sales and revenue
• Reduced customer churn
• Identifying cross-selling and upselling opportunities
• Improved decision-making based on data insights
• Competitive advantage in the market

What are some common data mining techniques used in CRM?

Common data mining techniques used in CRM include:

• Association rule mining
• Classification and regression
• Clustering analysis
• Text mining and sentiment analysis
• Forecasting and predictive modeling
• Social network analysis
• Anomaly detection
• Decision trees
• Neural networks
• Sequential pattern mining

What challenges are associated with data mining in CRM?

Some challenges associated with data mining in CRM are:

• Data quality and completeness
• Data integration and compatibility
• Privacy and security concerns
• Choosing appropriate data mining techniques
• Interpreting and validating results
• Managing large and complex datasets
• Ensuring ethical use of customer data
• Keeping up with evolving technologies
• Skills and expertise required for data mining
• Implementing data mining in CRM systems

How can data mining improve customer segmentation in CRM?

Data mining helps improve customer segmentation in CRM by analyzing customer data and identifying distinct groups or segments with similar characteristics, behaviors, and preferences. It allows businesses to divide their customer base into meaningful segments, enabling targeted marketing campaigns, personalized offers, and tailored communication strategies that resonate with each segment’s specific needs and preferences.

Can data mining help predict customer behavior in CRM?

Yes, data mining can help predict customer behavior in CRM. By analyzing historical customer data, purchase patterns, demographic information, and other relevant variables, data mining techniques such as predictive modeling and machine learning algorithms can be used to identify patterns and trends that may indicate future behavior. This allows businesses to anticipate customer needs, predict churn, identify cross-selling opportunities, and make proactive decisions to improve customer satisfaction and loyalty.

How can data mining be used to improve customer experience in CRM?

Data mining can be used to improve customer experience in CRM by analyzing customer data and generating insights that enable personalized experiences and targeted interactions. By understanding customer preferences, purchase history, browsing behavior, and other relevant data, businesses can offer personalized recommendations, tailored promotions, optimized pricing, and relevant content that aligns with individual customer needs and desires, enhancing overall customer satisfaction and experience.

What role does data mining play in CRM analytics?

Data mining plays a vital role in CRM analytics by providing the necessary techniques and tools to extract valuable insights from raw data. Data mining algorithms and analytics enable businesses to analyze large volumes of customer data, discover patterns, trends, and correlations, and transform the data into actionable insights. These insights drive strategic decision-making in CRM, guiding marketing campaigns, sales strategies, customer service improvements, and overall business performance.

How can businesses ensure ethical and responsible use of data mining in CRM?

Businesses can ensure ethical and responsible use of data mining in CRM by:

• Obtaining customer consent and following privacy regulations
• Maintaining data security and confidentiality
• Anonymizing or aggregating data to protect individual identities
• Being transparent about data collection and usage practices
• Providing customers with control over their data and preferences
• Regularly assessing and auditing data mining practices
• Ensuring data mining is aligned with company values and policies
• Training employees on responsible data handling and usage
• Engaging in open dialogue with customers about data mining
• Collaborating with ethical and trusted data mining partners

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Data Mining Case Studies & Benefits

• Key Takeaways

Data mining has improved the decision-making process for over 80% of companies. (Source: Gartner).

Statista reports that global spending on robotic process automation (RPA) is projected to reach $98 billion by 2024, indicating a significant investment in automation technologies.

According to Grand View Research, the global data mining market will reach $16,9 billion in 2027.

Ethical Data Mining preserves individual rights and fosters trust.

A successful implementation requires defining clear goals, choosing data wisely, and constant adaptation.

Data mining case studies help businesses explore data for smart decision-making. It’s about finding valuable insights from big datasets. This is crucial for businesses in all industries as data guides strategic planning. By spotting patterns in data, businesses gain intelligence to innovate and stay competitive. Real examples show how data mining improves marketing and healthcare. Data mining isn’t just about analyzing data; it’s about using it wisely for meaningful changes.

The Importance of Data Mining for Modern Business:

Data mining has taken on a central role in the modern world of business. Data is a major issue for businesses today. Making informed decisions with this data can be crucial to staying competitive. This article explores the many aspects of data mining and its impact on decisions.

• Unraveling Data Landscape

Businesses generate a staggering amount of data, including customer interactions, market patterns, and internal operations. Decision-makers face an information overload without effective tools for sorting through all this data.

Data mining is a process which not only organizes, structures and extracts patterns and insights from this vast amount of data. It acts as a compass to guide decision makers through the complex landscape of data.

• Empowering Strategic Decision Making

Data mining is a powerful tool for strategic decision making. Businesses can predict future trends and market behavior by analyzing historical data. This insight allows businesses to better align their strategies with predicted shifts.

Data mining can provide the strategic insights required for successful decision making, whether it is launching a product, optimizing supply chain, or adjusting pricing strategies.

• Customer-Centric Determining

Understanding and meeting the needs of customers is paramount in an era where customer-centricity reigns. Data mining is crucial in determining customer preferences, behaviors, and feedback.

This information allows businesses to customize products and services in order to meet the expectations of customers, increase satisfaction and build lasting relationships. With customer-centric insights, decision-makers can make choices that resonate with their target audiences and foster loyalty and brand advocacy.

Data Mining: Applications across industries

Data mining is transforming the way companies operate and make business decisions. This article explores the various applications of data-mining, highlighting case studies that illuminate its impact in the healthcare, retail, and finance sectors.

• Healthcare Case Studies:

Data mining is a powerful tool in the healthcare industry. It can improve patient outcomes and treatment plans. Discover compelling case studies in which data mining played a crucial role in predicting patterns of disease, optimizing treatment and improving patient care. These examples, which range from early detection of health risks to personalized medicines, show the impact that data mining has had on the healthcare industry.

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• Retail Success stories:

Retail is at the forefront of leveraging data mining to enhance customer experiences and streamline operations. Discover success stories of how data mining empowered businesses to better understand consumer behavior, optimize their inventory management and create personalized marketing strategies.

These case studies, which range from e-commerce giants and brick-and-mortar shops, show how data mining can boost sales, improve customer satisfaction, transform the retail landscape, etc.

• Financial Sector Examples:

Data mining is a valuable tool in the finance industry, where precision and risk assessment are key. Explore case studies that demonstrate how data mining can be used for fraud detection and risk assessment. These examples demonstrate how financial institutions use data mining to make better decisions, protect against fraud, and customize services to their clients’ needs.

• Data Mining and Education:

Data mining has been used in the education sector to enhance learning beyond healthcare, retail and finance. Learn how educational institutions use data mining to optimize learning outcomes, analyze student performance and personalize materials. These examples, ranging from adaptive learning platforms and predictive analytics to predictive modeling, demonstrate the potential for data mining to revolutionize how we approach education.

• Manufacturing efficiency:

Data mining is a powerful tool for streamlining manufacturing processes. Examine case studies that demonstrate how data mining can be used to improve supply chain management, predict maintenance requirements, and increase overall operational efficiency. These examples show how data-driven insights can lead to cost savings, increased productivity, and a competitive advantage in manufacturing.

Data mining is a key component in each of these applications. It unlocks insights, streamlines operations, and shapes the future of decisions. Data mining is transforming the landscapes of many industries, including healthcare, retail, education, finance, and manufacturing.

Data Mining Techniques

Data mining techniques help businesses gain an edge by extracting valuable insights and information from large datasets. This exploration will provide an overview of the most popular data mining methods, and back each one with insightful case studies.

• Popular Data Mining Techniques

Clustering Analysis

The clustering technique involves grouping data points based on a set of criteria. This method is useful for detecting patterns in data sets and can be used to segment customers, detect anomalies, or recognize patterns. The case studies will show how clustering can be used to improve marketing strategies, streamline products, and increase overall operational efficiency.

Association Rule Mining

Association rule mining reveals relationships between variables within large datasets. Market basket analysis is a common application of association rule mining, which identifies patterns in co-occurring products in transactions. Real-world examples of how association rule mining is used in retail to improve product placements, increase sales, and enhance the customer experience.

Decision Tree Analysis

The decision tree is a visual representation of the process of making decisions. This technique is a powerful tool for classification tasks. It helps businesses make decisions using a set of criteria. Through case studies, you will learn how decision tree analyses have been used in the healthcare industry for disease diagnosis and fraud detection, as well as predictive maintenance in manufacturing.

Regression Analysis

Regression analysis is a way to explore the relationship between variables. This allows businesses to predict and understand how one variable affects another. Discover case studies that demonstrate how regression analysis is used to predict customer behavior, forecast sales trends, and optimize pricing strategies.

Benefits and ROI:

Businesses are increasingly realizing the benefits of data mining in the current dynamic environment. The benefits are numerous and tangible, ranging from improved decision-making to increased operational efficiency. We’ll explore these benefits, and how businesses can leverage data mining to achieve significant gains.

• Enhancing Decision Making

Data mining provides businesses with actionable insight derived from massive datasets. Analyzing patterns and trends allows organizations to make more informed decisions. This reduces uncertainty and increases the chances of success. There are many case studies that show how data mining has transformed the decision-making process of businesses in various sectors.

• Operational Efficiency

Data mining is essential to achieving efficiency, which is the cornerstone of any successful business. Organizations can improve their efficiency by optimizing processes, identifying bottlenecks, and streamlining operations. These real-world examples show how businesses have made remarkable improvements in their operations, leading to savings and resource optimization.

• Personalized Customer Experiences

Data mining has the ability to customize experiences for customers. Businesses can increase customer satisfaction and loyalty by analyzing the behavior and preferences of their customers. Discover case studies that show how data mining has been used to create engaging and personalized customer journeys.

• Competitive Advantage

Gaining a competitive advantage is essential in today’s highly competitive environment. Data mining gives businesses insights into the market, competitor strategies, and customer expectations. These insights can give organizations a competitive edge and help them achieve success. Look at case studies that show how companies have outperformed their competitors by using data mining.

Calculating ROI and Benefits

To justify investments, businesses must also quantify their return on investment. Calculating ROI for data mining initiatives requires a thorough analysis of the costs, benefits, and long-term impacts. Let’s examine the complexities of ROI within the context of data-mining.

• Cost-Benefit Analysis

Prior to focusing on ROI, companies must perform a cost-benefit assessment of their data mining projects. It involves comparing the costs associated with implementing data-mining tools, training staff, and maintaining infrastructure to the benefits anticipated, such as higher revenue, cost savings and better decision-making. Case studies from real-world situations provide insight into cost-benefit analysis.

• Quantifying Tangible and intangible benefits

Data mining initiatives can yield tangible and intangible benefits. Quantifying tangible benefits such as an increase in sales or a reduction in operational costs is easier. Intangible benefits such as improved brand reputation or customer satisfaction are also important, but they may require a nuanced measurement approach. Examine case studies that quantify both types.

• Long-term Impact Assessment

ROI calculations should not be restricted to immediate gains. Businesses need to assess the impact their data mining projects will have in the future. Consider factors like sustainability, scalability, and ongoing benefits. Case studies that demonstrate the success of data-mining strategies over time can provide valuable insight into long-term impact assessment.

• Key Performance Indicators for ROI

Businesses must establish KPIs that are aligned with their goals in order to measure ROI. KPIs can be used to evaluate the success of data-mining initiatives, whether it is tracking sales growth, customer satisfaction rates, or operational efficiency. Explore case studies to learn how to select and monitor KPIs strategically for ROI measurement.

Data Mining Ethics

Data mining is a field where ethical considerations are crucial to ensuring transparent and responsible practices. It is important to carefully navigate the ethical landscape as organizations use data to extract valuable insights. This section examines ethical issues in data mining and highlights cases that demonstrate ethical practices.

• Understanding Ethical Considerations

Data mining ethics revolves around privacy, consent, and responsible information use. Businesses are faced with the question of how they use and collect data. Ethics also includes the biases in data and the fairness of algorithms.

• Balance Innovation and Privacy

Finding the right balance between privacy and innovation is a major ethical issue in data mining. In order to gain an edge in the market through data insights and to innovate, organizations must walk a tightrope between innovation and privacy. Case studies will illuminate how companies have successfully balanced innovation and privacy.

• Transparency and informed consent

Transparency in the processes is another important aspect of ethical data mining. This is to ensure that individuals are informed and consented before their data is used. This subtopic will explore the importance of transparency in data collection and processing, with case studies that highlight instances where organizations have established exemplary standards to obtain informed consent.

Exploring Data Mining Ethics is crucial as data usage evolves. Businesses must balance innovation, privacy, and transparency while gaining informed consent. Real-world cases show how ethical data mining protects privacy and builds trust.

Implementing Data Mining is complex yet rewarding. This guide helps set goals, choose data sources, and use algorithms effectively. Challenges like data security and resistance to change are common but manageable.

Considering ethics while implementing data mining shows responsibility and opens new opportunities. Organizations prioritizing ethical practices become industry leaders, mitigating risks and achieving positive impacts on business, society, and technology. Ethics and implementation synergize in data mining, unlocking its true potential.

• Q. What ethical considerations are important in data mining?

Privacy and consent are important ethical considerations for data mining.

• Q. How can companies avoid common pitfalls when implementing data mining?

By ensuring the security of data, addressing cultural opposition, and encouraging continuous learning and adaptation.

• Q. Why is transparency important in data mining?

Transparency and consent to use collected data ethically are key elements of building trust.

• Q. What are the main steps to implement data mining in businesses?

Define your objectives, select data sources, select algorithms and monitor continuously.

• Q. How can successful organizations use data mining to gain a strategic advantage?

By taking informed decisions, improving operations and staying on top of the competition.

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Improvement of crm using data mining: a case study at corporate telecom sector.

Sanjib Kumar Routray, Sanjit Kumar Dash, Sasmita Mishra . Improvement of CRM using Data Mining: A Case study at Corporate Telecom Sector. International Journal of Computer Applications. 178, 53 ( Sep 2019), 12-20. DOI=10.5120/ijca2019919413

As Customer relationship management (CRM) is a well established concept and its practice to enable the realization of successful Telecommunication system, data mining techniques is developed for improving the customer relationship management part mainly in Corporate Telecom Sector. Considering the existing methodology, a well established methodology with data mining is needed for development of good integrated approach with growth in time and space complexities. The aim is to find the strategic point on the essential part of Telecommunication industry by exploring the techniques of data mining. Then the focus is on presenting a new methodology in case of mobile services on the perceptions of customers of Telecommunication basing on applicability of data mining techniques to CRM databases by generating Association rules from frequent item sets on the proposed approach F-MFPG (Fast Modified Frequent Pattern Growth) by using FFIM (Fast Frequent Item sets Mining) Algorithm under concept of data mining and predicting the profit of Corporate Telecom Sector and predicting the churn for retention of customers for efficient managerial decision for reaching the ultimate goals by proposing a suitable classification techniques in data mining algorithms.

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• Alawin, Ayman., Mohmmad, Al., aitah, ma., Balqua, Al (2014) “ Proposed ranking for point of sales using data mining for Telecom Operations” In the proceeding of International Journal of Database management system,Vol-6.
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• Dahiya, Kiran., Talwar, Kanika (2015) “Customer Churn Prediction in Telecommunication Industry using Data mining technique-A Review” In the proceeding of International Journal of Advanced Research in Computer Science and Software Engineering, Vol-5, Issue-4.
• Heling, Jiang., Yang, An., Yan, Fengyun.,Hang, Miao (2016) “Research on pattern Analysis and data Classification methodology of Data mining and knowledge Recovery”, In the proceeding of International journal of Hybrid Information Technology, IEEE, Vol-9( pp.179-186).
• Khan, M N., Rahman, Abdur (2017) “An Assessment of data mining based CRM techniques for enhancing profitability” In proceeding of Association of Modern Education & Computer science, (pp.30-40).
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• Senkamalavalll, R., Dr. Bhubaneswari,T (2013)“Data mining techniques for CRM” In the proceeding of International journal of Scientific & Engineering Research, Vol-4, Issue-4, (pp. 509-513).
• Tejaswini, Hilage, Kulkarni, R.V (2012)” Review the literature of Data Mining” In the proceeding of IJRRS, Vol-II ( PP.107-114).

Index Terms

Computer Science Information Sciences

Customer relationship management Fast Modified Frequent Pattern Growth Fast Frequent Item sets Mining Association Classification True Positive False Positive and Telecommunication

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A Case Study of Customer Relationship Management using Data Mining Techniques

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Customer relationship management (CRM) is a combination of people, processes and technology that seeks to understand a company's customers. It is an integrated approach to managing relationships by focusing on customer retention and relationship development. CRM has evolved from advances in information technology and organizational changes in customer-centric processes. Companies that successfully implement CRM will reap the rewards in customer loyalty and long run profitability. However, successful implementation is elusive to many companies, mostly because they do not understand that CRM requires company-wide, cross-functional, customer-focused business process re-engineering. Although a large portion of CRM is technology, viewing CRM as a technology-only solution is likely to fail. Managing a successful CRM implementation requires an integrated and balanced approach to technology, process, and people. 1. Introduction In the mid-twentieth century, mass production techniques and mass marketing changed the competitive landscape by increasing product availability for consumers. However, the purchasing process that allowed the shopkeeper and customer to spend quality time getting to know each other was also fundamentally changed. Customers lost their uniqueness, as they became an “account number” and shopkeepers lost track of their customers’ individual needs as the market became full of product and service options. Many companies today are racing to re-establish their connections to new as well as existing customers to boost long-term customer loyalty. Some companies are competing effectively and winning this race through the implementation of relationship marketing principles using strategic and technology-based customer relationship management (CRM) applications. CRM technology applications link front office (e.g. sales, marketing and customer service) and back office (e.g. financial, operations, logistics and human resources) functions with the company's customer “touch points” (Fickel, 1999). A company's touch points can include the Internet, e-mail, sales, direct mail, telemarketing operations, call centers, advertising, fax, pagers, stores, and kiosks. Often, these touch points are controlled by separate information systems. CRM integrates touch points around a common view of the customer (Eckerson and Watson, 2000). Figure 1 demonstrates the relationship between customer touch points with front and back office operations. In some organizations, CRM is simply a technology solution that extends separate databases and sales force automation tools to bridge sales and marketing functions in order to improve targeting efforts. Other organizations consider CRM as a tool

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Understanding Data Mining With the Help Of Case Studies On Data Mining In Market Analysis

Data | 0 comments

The modern generation of the 21st century witnessed a massive growth of businesses and companies around the globe. Every other day you would find a business lifting its head into existence. The arrival of so many businesses together leveled up the game of the competition. The competition is for the sense of existence in the market. One of the major technologies that gained immense popularity and is growing rapidly among businesses is Data mining.

Data mining technology assists the various companies to look into the compilation of huge quantities of data they brought together and use them to develop collaboration and improve relationships to boost its efficiency. It helps them gather accurate and detailed information about customers, which helps them strategize an influential plan and make better decisions.

Let us first understand what Data Mining is before we delve deep into the case studies in data mining applications or the various data mining case study examples.

What Is Data Mining?

Data mining is a mechanical tool used by companies that helps extract all the information from a compilation of data. Such information helps in making predictions and acting as per the same. Various statistical and mathematical calculations are used to remove the cover from relations and trends among the huge quantities of data stored in the company database. Data mining is a perfect combination of statistics, data warehousing, artificial intelligence technology, and machine learning.

Statistics resembled the starting point of data mining. Regression analysis, standard deviation, and variance are the statistical functions that act as tools in facilitating people’s study of relationships between data and their reliability. Statistics is one of the pillars of data mining technology, as most data mining actions function according to it.

Data warehouses saw their birth in the 1970s when they used large mainframe systems and the COBOL techniques of programming to store data. All these saw the arrival of big databases that we now know as data warehouses. These warehouses are for the management, retrieval, and storage of data. Megabytes and terabytes of data get data management systems reflecting sophistication. Such storage is an integral part of data mining as it helps the company to manipulate organized data.

Artificial intelligence is another basic pillar of data mining like data warehouses and statistics. The beginning of artificial intelligence in the 1980s gave a set of algorithms with a design to help the computer learn by itself. The passage of time helped massive development that led to algorithms becoming data manipulation tools, with applications in large sets of data.

Data mining collaborated with artificial intelligence instead of applying an early-defined hypothesis that helped generate relationships between the data. Artificial intelligence helped analyze the data and find collaborations between the data, thus developing models to help the developers assume diverse relationships.

Artificial intelligence paved the way for machine learning. Experts define machine learning as the machine’s capability to improve its performance after assessing its earlier results. Machine learning comes right after artificial intelligence as it works towards bringing together the study of trial and error with the help of statistical analysis. It provides an opportunity for the software to learn things by itself and makes all the data without any external help.

Tasks Of Data Mining

Classification

Classification refers to a procedure of finding out a model that explains the classes and concepts of data. The primary objective of classification is to predict the class objects whose identities are still behind the curtain. This derivation of this model works on the analytical results of the training sets of data.

You can understand it better with the following example;

• Political parties are assigning the voters in their known buckets.
• Including new customers into an existing customer group.

When we talk about statistical modeling, regression analysis comes first in discussion. It refers to a statistical process for estimating the relationship among the diverse variables. Regression comes with a lot of analyzing and modeling techniques for many variables. Here the spotlight is on the relationship between a dependent variable and one or more than one independent variable.

Following are some examples;

• Predictions concerning the unemployment rates for the following year.
• Making estimates about the insurance premium.

Detection Of Anomaly

Anomalies refer to the problems and issues within the software. This is a process of identifying the events, items, or observations that do not fall in line with the patterns or items expected in the dataset.

Example: Fraud transactions in your credit card account.

Time Series

A time series refers to a series of data points that are in a list, graph, or index, depending on the order of time. Commonly speaking, time-series signifies the action sequence taken along successive points of time, placed at regular intervals. Therefore, it is a discrete sequence of data.

Example: Production forecasting, forecasting of sales, etc.

Clustering refers to sorting the objects into diverse groups, where each group would consist of objects having similar characteristics. The features of the objects in one group will differ from the objects in another group. Following is a static example of clustering;

• Searching the customer segments in a company, depending on their transactions, customer calls, and web.

Analysis Of Association

Association is one of those data mining functions that finds out the possibility of an item recurring while in a collection. Association rules explain the rules of relationship between the items that co-occur.

Example: Search for the various opportunities for cross-selling for a retailer, as per his transaction history.

Expanding And Exploring Business

As we already know, data mining refers to a process in businesses where large chunks of data get explored to find meaningful rules and patterns. Companies can use data mining to gain that competitive edge over their fellow companies and push their business to better heights.

History Of Data Mining

You might feel that data mining is a new concept. This term might be a new one, but the concept has been around for quite a few years. Classical statistics, artificial intelligence, and machine learning together led to the development of data mining . Everything began in the 1960s when the concept of data collection surfaced. It refers to the storage of data and information in computers. Tapes, disks, and computers were the technology available during that time.

Next, we saw the arrival of the concept of Data access in the 1980s. The concept of data access brought the introduction of relational databases and structured languages for the query. Both these helped in educating us humans more about data. Dynamic availability of data at a record level came with Data access.

Decision reports and Data warehousing came in the 1990s, which unveiled the procedure of management and retrieval of centralized data. This came with the following characteristics;

• Maintenance of a central address for keeping all the data concerning the organization.
• It will help you in analyzing the data and concentrating on the specific characteristics.
• Dynamic delivery of data at multiple levels.

The present data mining is all about making predictions and generalizing the patterns.

Influential Events And Personalities In Data Mining

Data mining saw the inception of its mention in the hands of John Herry Holland in 1975, who wrote the book “Adaptation in Natural and Artificial Systems.” This book was a thesis on genetic algorithms. However, the term “Data Mining” came into the limelight in the 1990s, with its mention in the database community for the first time. Moving further, William S . Cleveland brought forward Data mining as an independent concept in 2001. Data mining gained its epitome of prominence in February 2015, when The White House of the United States Of America hired D.J. Patil as their data scientist.

Methods Of Analysis In Data Mining

There are different methods of analytical observation in Data mining. They are as follows;

Artificial Neural Networks

These are the non-linear models of prediction that have a stark resemblance with the neural networks in biology when it comes to structuring.

Genetic Algorithms

Genetic algorithms refer to optimizing that utilizes the combination of genetics, natural selection, and mutation based on the concept of natural evolution.

Induction Of Rules

The application of all the rules found via extraction is the ones found from the significance of statistics.

Decision Trees

These are structures with the shape of trees that help in showing a set of decisions. These decisions help in generating rules for classifying a dataset.

• Chi-square Automatic Interaction Detection (CHAID), Classification and Regression Trees (CART).
• Classification and Regression tree works towards segmentation of the dataset by creating a spill that goes two ways.
• Chi-square Automatic Interaction Detection uses the chi-square tests to create splits in multiple directions.

Method Of Nearest Neighbour

The nearest neighbor technique works towards classifying each record within a dataset. It depends on combining the classes of the k-records that have the highest similarity with the dataset. (K=1)

Visualization Of Data

• Data visualization refers to the interpretation of complex relationships via visuals within data with multi-dimensions.
• One of the substantial examples is the usage of graphical tools for the illustration purposes of data relationships.

Also Read: Top 10 Data Mining Techniques for Business Success

Application Of Data Mining

You can establish data mining for your company via the feature of modeling. Modeling refers to the act of building a model that has an application to a particular situation, following which you can use that model in a different situation that does not have any existing model. Such models help you in making predictions regarding the patterns.

Data Mining In The Field Of Marketing

Companies garnered huge benefits and returns in their marketing area when considering data mining and working on its advancement. Companies use all the data gathered via data mining to push their efforts towards tailoring their discount coupons, and gift vouchers and working on their sales and advertisements to target their customers.

When you devise your marketing strategy through such mined data, you can devise a better marketing strategy and increase your sales effectiveness. Your company will also save a lot of money depending on such information.

Case Studies On Data Mining

Let us now go through the various case studies of data mining applications that will help us understand the importance of this procedure in various companies and businesses;

Case Study No.1: Target

The first data mining case study example is that of the company named ‘ Target .’ Target vouchers use data mining for tailoring their discount coupons. They hope that sending such discount coupons to the customers will make them buy their products regularly. The strategists of Target assume this as an effective mechanism to prevent the customers from changing their loyalties to other brands or companies.

• When most customers are vulnerable to changing brand loyalties , due to varied choices and changing preferences. Like in tech sphere, innovative features like longer battery life and a more advanced camera system can be some factors.
• The stores like Target try to utilize such time to exploit the opportunity and lure customers into making purchases from their brand. Not just temporarily, but they try to engage them till the very end.

The company puts all its data into useful, which they collect while you are in its store for purchase or on its online website for buying something. They also collect such data by purchasing it from other companies. Duhigg states that Target has been collecting data for decades through the customers who walk into their stores regularly. They assign distinctive, unique codes to diverse customers from time to time.

This unique code is the Guest ID number within the working of Target, which keeps track of everything they purchase. Later, they use this very data to analyze the tastes and preferences of the customers and take effective steps to boost their marketing strategy.

Andrew Pole, an analyst, started his analysis of the “Pregnancy prediction model” by going through the history of the company’s baby shower registry. He tried to use this data to predict a woman’s changes regarding their shopping habits when they are expecting a baby. He formulated a list of 25 items based on this information to determine whether a woman is pregnant or not. This model successfully predicted whether a customer was pregnant or not but also assumed the date of delivery.

Case Study No.2: Amazon

Here we are going to talk about Amazon . The case study of Amazon is one of the best case studies on data mining in market analysis. Amazon tries to use all the data they mined so that they can improve their customer service. The data mined consists of the customer’s name, home address, and personal details. Such data also consists of the customer’s preferences and the issues they are dealing with to find a solution.

They try to collect all the data regarding the customer from the various departments of the firm. Once they have all the necessary data, they synchronize and compile them for sending them to the human representative. The human representative then uses the data to have a great personalized conversation with the particular customer.

The employees of the customer service department of Amazon have all such needful information in their hands. This adds to making the conversation with the customer a lot more convenient. The employees of customer service have enough information about you which helps them make the conversation personal. However, there are no worries about the conversation being creepy.

Also check: Amazon AWS data partner marketplace.

Case Study No.3: Starbucks

Starbucks is one of the leading coffee shops with innumerable branches around the globe. Their case study will be a perfect example of the case studies on data mining in market analysis. Starbucks indulges in data mining to determine the perfect locations for setting up its stores. Tactics of Data mining and modeling assist the numerous Starbucks locations within proximity.

They try to analyze the data based on the locations, the population composition of the location, and traffic in the streets around to predict whether setting up a store there will be successful or not. Starbucks seeks assistance from a data platform named ArcGIS, developed by a company named Esri. They help them gather all the necessary information about the concerned location, demographic structure, the presence of Customer homes around, work, and other outings. All these data supplement the monitoring and boosting of their sales.

This particular company named Esri gathers a lot of data from Starbucks and, after ingrained analysis, positions them on platforms that are easily understandable for the employees there.

Case Study No.4: Usage Of Association Rule Mining In the Systems Of Recommendation

Recommender systems gained immense popularity among various fields of the industry at the current time. Music, movies, books, search queries, research books, social tags, etc., are widely-known fields. These recommendation systems assist enterprises by combining ideas from intelligent systems, information retrieval, and machine learning to make assumptions regarding the customer’s behavior. Recommender systems have two distinct approaches for their functioning;

Collaborative Filtering

The method of collaborative filtering indulges in collecting and analyzing a huge chunk of information regarding the user’s preferences, behavior, activities, etc. this will help them predict the like of a user in accordance with the other users. One of the approaches here is the usage of the Apriori algorithm.

Here you would know how the Apriori algorithm is used to squeeze out data concerning association rules from the user profiles. PVT is one of the prominent examples in this regard. PVT is one of those recommender systems that recommend various TV channels to the viewers, depending on their viewing experience. Channels with both positive and negative reviews work under the management of PVT. It treats TV viewers as transactions and the program ratings as itemsets.

One can use the Apriori algorithm to find out a set of rules and attached confidence levels between the programs. The confidence values resemble the similarity scores, and the system uses them to fill a program similarity matrix. There is an initiative to create a bridge between two TV shows. One who watches Splitsville or Roadies will not take an interest in shows like Kaun Banega Crorepati. However, if there is a line drawn between MTV Spitsvilla and Kaun Banega Crorepati, it will result in pattern watching.

Case Study No. 5: Model Of Classification For Selection of Targets In Direct Marketing

Historical data of purchase helped develop a prediction model of response along with data mining techniques. This development was to make predictions about whether a customer of the Ebedi Microfinance Bank of Nigeria would revert to a promotional offer or not. Data mining techniques helped develop a prediction model with the help of data regarding the customer’s purchase history. The data found its storage in a data warehouse to assist the decision of the management. The customer’s purchases in history and his demographic dataset helped in formulating a response model.

Then the development of the model took inputs from the following purchase variables;

Recent Purchases

It refers to the number of months from the time of the first purchase till the time the customer made the last purchase. It is one of the most powerful weapons to predict whether the promotional offer will succeed or fail. It is quite a logical observation. The primary statement through this point is that if you made some recent purchases, you are more likely to respond to the offer as against the fact that your last purchase was way back.

Frequency stands for the number of purchases the customer made. This data concerning the number of purchases can be within a definite period or all the purchases to date. This characteristic feature comes second to the factor of recent purchases when it comes to making predictions.

Monetary value

Monetary value resembles the total amount spent by the customer on making purchases from the company. You can draw some resemblance with frequency as such data can have a definite period or the total money spent to date. It is the least favorable tool to predict the steps of the customer.

However, when all three characteristic tools come together, it can sharpen the chances of the prediction being correct.

The customer’s demographic information includes his features and details concerning his sex, postal address, age, occupation, etc. The Bayesian algorithm, rather than the Naïve Bayesian algorithm, was a primary ingredient in constructing the classifier system. The selection techniques of both wrapper and filter features were in an application for selecting the inputs of this model.

The data results revealed that the Ebedi Microfinance Bank of Nigeria could plan effective strategies for marketing their goods and services. They can do it by making a detailed report on the status of customers that will guide their path to making correct decisions regarding the disbursement of funds. Therefore, they can use those funds on useful marketing tactics rather than waste them on failed strategies.

Read More: Case Study: Walmart

The Future Of Data Mining

You can perceive the future of data mining through the following characteristics;

Predictive Analytics

Predictive analytics states that you can achieve “one-click data mining” through a simple and more efficient data mining process.

• You should allow the application of advanced analytics across various subjects.
• The area that will bring the highest revolution will be medicine. The researchers can analyze prediction to determine the factors associated with a particular disease and what medicine might work wonders on the affected patient.

Distributed Data Mining

Distributed data mining refers to the act of mining data spread across different locations. Combine the facilities of local data analysis and a global data model to get the best results out of data mining.

Hypertext Or Hypermedia Data Mining

This type of data mining includes hyperlinks, texts, marked texts, and any other form of information related to hypermedia. It has the following techniques;

• Classification.
• Clustering.
• Semi-structured learning.
• Analysis of Social Networks.

Multimedia Data Mining

The data from Multimedia data mining includes multimedia like videos, images, animation, audio, etc. This form of data requires a separate representation compared to traditional data.

Spatial or Geographical Data Mining

Data mining consisting of space or geography includes the analytical information concerning satellite images, natural resources, and all data from topography. All of the data comes from diverse locations, with most of them being pictures.

Things That Bother About Data Mining

There are a few drawbacks or rather concerns that experts have regarding data mining. They are as follows;

Assurance Of Privacy

Data mining is gaining immense popularity and momentum among various industries. This results in the collection of more information about every individual. When such accurate and personal information comes to the public domain, the chances of exploitation increase by manifolds. Some people or applications can use this information to meet their narrow desires. So you can very well understand that data mining allows easy accessibility of data, thus exposing it to the ills of exploitation. People can steal your identity and use it to fraud someone else.

Issues With User Interface

The experts dealing with data mining studies are skeptical about whether visualization tools can expose the true knowledge any data holds. The chances of people understanding any visual data and discovering the true meaning are difficult at times.

Problems Concerning The Performance Of Data Mining

When we talk about data mining tools, numerous statistics, and other analytical methods had a design for discovering smaller quantities of data. Such tools might fail to accommodate the rising gravity and depth of information.

There is no guarantee that the collection of data and its mining will mitigate the risks in the future. Therefore, data mining is still not a flawless procedure.

The above information can help you better understand data mining through the various case studies in data mining applications. Data mining can be a boon for various industries as that will help them secure more relevant information about the customers and boost all strategies for garnering better customer experiences.

When we talk about the few drawbacks of data mining, various companies’ advanced technology and software developments can mitigate the risks. Moreover, no company will sacrifice its brand name for the sake of some cheap benefits. You can get the best data solution services from http://bizprospex.com , which can help you with their AML Sanctions List , PEP List , Data Appending , and Skip Tracing services to gather the best and most authentic data available.

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Learning from Data Streams in Evolving Environments pp 209–221 Cite as

Process Mining for Analyzing Customer Relationship Management Systems: A Case Study

• Ahmed Fares 3 ,
• João Gama 3 , 4 &
• Pedro Campos 3 , 4  
• First Online: 29 July 2018

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Part of the book series: Studies in Big Data ((SBD,volume 41))

Process Mining aims to discover and evaluate As-Is processes from sets of sequential events, by examining different instances of the same process and building models that can detect patterns and behaviors. In the meanwhile, organizational perspective is being considered in Process Mining by taking advantage of the ability to extract social networks that represent different kinds of relations between resources performing the process. The case study tries to describe how Process Mining could be applied in order to detect and improve “Customer Relationship Management” process and extract some kind of social networks that represent the relations between the employees(resources) of National Institute of Statistics of Portugal (INE) using event logs.

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Acknowledgements

This work was supported by the research project TEC4Growth—Pervasive Intelligence, Enhancers and Proofs of Concept with Industrial Impact/NORTE-01-0145-FEDER-000020, , North Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, and through the European Regional Development Fund (ERDF) and the ERDF European Regional Development Fund through the Operational Programme for Competitiveness and Internationalization—COMPETE 2020 Programme within project POCI-01-0145-FEDER-006961, and by National Funds through the FCT Fundao para a Cincia e a Tecnologia (Portuguese Foundation for Science and Technology) as part of project UID/EEA/50014/2013.

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Ahmed Fares, João Gama & Pedro Campos

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Fares, A., Gama, J., Campos, P. (2019). Process Mining for Analyzing Customer Relationship Management Systems: A Case Study. In: Sayed-Mouchaweh, M. (eds) Learning from Data Streams in Evolving Environments. Studies in Big Data, vol 41. Springer, Cham. https://doi.org/10.1007/978-3-319-89803-2_9

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