Modern technology makes it possible to pull information from virtually anywhere. With the help of data mining software, businesses can learn more about their customers and gather insights to shape the future of their brand, products and services. When used correctly, data mining software can lead to hidden gems of intelligence to boost innovation.

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We’ve gathered some of the best advice from industry experts to help you get the most out of data mining software, including:

• Pinpointing your goals before jumping in
• The importance of data cleanliness
• Using data mining software to better understand customers
• Uncluttering old data to make room for new data

In this article, you’ll learn:

• What is data mining software?
• What industries use data mining software?
• 25 ways to uncover customer insights with data mining software
• Frequently asked questions

What is data mining software?

Data mining software is a powerful tool that helps organizations extract valuable insights and knowledge from large sets of data. This software uses a variety of techniques, including machine learning and artificial intelligence, to analyze and process data in order to discover patterns, trends, and relationships.

One key feature of data mining software is its ability to process and analyze large volumes of data quickly and efficiently. This allows organizations to uncover hidden patterns and correlations that may not be readily apparent through traditional data analysis methods.

What industries use data mining software?

Data mining software is used in a wide range of industries. Here are a few examples of how various industries make use of data mining software to improve their operations:

• Retail: Retailers use data mining software to analyze customer purchasing patterns, optimize pricing strategies, and forecast demand for products. This helps them make informed decisions about inventory management, product promotions, and customer segmentation.
• Finance: Financial institutions use data mining software to detect fraudulent activities, predict stock market trends, and assess credit risks. By analyzing historical data and identifying patterns, financial organizations can make smarter investment decisions and improve risk management practices.
• Healthcare: In the healthcare industry, data mining software analyzes patient records, identifies patterns in disease outbreaks, and predicts patient outcomes. This helps healthcare providers improve patient care, optimize treatment plans, reduce healthcare costs, and improve patient experience.
• Marketing: Marketers use data mining software to analyze customer behavior, segment target audiences, and personalize marketing campaigns. By understanding customer preferences and purchasing patterns, marketers can create more effective promotional strategies, increase customer engagement, and deliver a powerful brand experience.
• E-commerce: E-commerce companies use data mining software to analyze customer browsing behavior, personalize product recommendations, and optimize pricing strategies. By analyzing online transaction data, e-commerce companies can enhance the shopping experience, increase conversion rates, and improve customer satisfaction.
• Manufacturing: Manufacturers use data mining software to optimize production processes, predict equipment failures, and improve supply chain management. By analyzing manufacturing data, companies can identify opportunities to increase efficiency, reduce downtime, and enhance product quality.

This list is not all-inclusive. Many other industries leverage data mining software to gain insights and drive decision-making. Below, we’ll explore more of the ways businesses can utilize data mining software to improve business performance in the contact center and beyond.

25 ways to uncover customer insights with data mining software

1. Before you start, outline your goals. “Before any data is touched, extracted, cleaned, or analyzed, it is important to understand the underlying entity and the project at hand. What are the goals the company is trying to achieve by mining data? What is their current business situation? What are the findings of a SWOT analysis? Before looking at any data, the mining process starts by understanding what will define success at the end of the process.”

- Alexandra Twin, What Is Data Mining? How It Works, Benefits, Techniques, and Examples, Investopedia; X/Twitter: @Investopedia

2. Make data protection a priority. “Like any process that deals with sensitive data — including personal data — your number one concern should be to ensure that all data you're collecting and using has been provided with explicit consent and in full compliance with any applicable privacy laws. This also includes making sure the data is secure throughout all stages of the process, including collection, storage, analysis, all the way to data deletion.

Organizations also need to implement internal rules to specify what the data can be used for and how it can be analyzed and implemented – and make sure that the insights taken from data mining themselves don't infringe on privacy policies. As a rule of thumb, being transparent, honest, and ethical with data should be your top priority.

Some companies may want to hire staff specialized in data science and security to oversee all data management and analysis procedures, which can be a big help to ensure data protection and user privacy throughout the entire process. They can also deploy specialized tools to achieve the best results.”

- Luna Campos, A Complete Guide to Data Mining and How to Use It, HubSpot; X/Twitter: @HubSpot

3. Data cleanliness before usage. “Data miners spend the most time on this phase because data mining software requires high-quality data. Business processes collect and store data for reasons other than mining, and data miners must refine it before using it for modeling. Data preparation involves the following processes.

• Clean the data: For example, handle missing data, data errors, default values, and data corrections.
• Integrate the data: For example, combine two disparate data sets to get the final target data set.
• Format the data: For example, convert data types or configure data for the specific mining technology being used.”

- What is Data Mining?, AWS; X/Twitter: @awscloud

4. Real-time guidance tools can lead to more effective customer service. “Real-time guidance uses artificial intelligence (AI) and machine learning (ML) to monitor conversations, most often between customers and customer service agents in the contact center. Based on pre-determined parameters, such as scripting compliance, customer statements or competitive mentions, real-time alerts can help agents more effectively navigate through customer interactions. The right tools also enable supervisors to monitor agent performance, empowering them support agents through conversations, such as escalations, as needed.”

- How real-time agent guidance enhances customer experience, CallMiner; X/Twitter: @CallMiner

5. Make sure data is complete. “Data mining relies on the actual data present, hence if data is incomplete, the results would be completely off-mark. Hence, it is imperative to have the intelligence to sniff out incomplete data if possible. Techniques such as Self-Organizing-Maps (SOM’s), help to map missing data based by visualizing the model of multi-dimensional complex data.

Multi-task learning for missing inputs, in which one existing and valid data set along with its procedures is compared with another compatible but incomplete data set is one way to seek out such data. Multi-dimensional preceptors using intelligent algorithms to build imputation techniques can address incomplete attributes of data.”

- Gopinadh Gullipalli, 12 Data Mining Tools and Techniques, Invensis Technologies; Twitter: @Invensis

6. Use data mining to understand your customers better. “Netflix analyzes not just ratings and viewing history, but also pause points, rewind/fast-forward frequency, and even completion rates. This paints a detailed picture of your viewing preferences and engagement levels. Netflix constantly tests different recommendation algorithms and personalizes user interfaces based on individual preferences. This ensures you see the most relevant content and keeps you coming back for more.”

- Kavindu Rathnasiri, Data Mining Success Stories: How Hidden Gems Uncover Real-World Value; LinkedIn: Kavindu Rathnasiri

7. Don’t feel like you need to go big. “When starting with data mining, it’s best to begin with small datasets. Small datasets are beneficial for beginners because they are easy to manage, and they can be used to practice and experiment with various data mining techniques.”

- Muhammad Sameer Hussain, Data mining hacks 101: Listing down the best techniques for beginners, Data Science Dojo; X/Twitter: @DataScienceDojo

8. Establish methods to detect anomalies. “There will be times when the ability to recognize data doesn’t paint a clear picture, so businesses need to have methods in place to detect anomalies. For example, a business identifies a pattern with a product where 75% of sales are coming from people between the ages of 30-40. Then suddenly one month, there is a huge boost in the number of 18-30 year-olds who are buying that same product. That would be an anomaly that would merit investigation.

This is an extreme scenario. Most anomalies will not be this large, which is why it’s important for a business to be able to effectively identify them. In the example above, data would be gathered about these specific purchases during this specific timeframe. The goal would be to understand why it happened and if it can be replicated.”

- 7 Most Important Data Mining Techniques to Make an Impact on Your Business, Data Entry Outsourced; X/Twitter: @EasyDataEntry

9. Use data to identify current trends…and pivot accordingly. “Shoppers’ buying decisions are influenced by multiple factors, whether it’s social media influencers, celebrity endorsements, viral videos, events, or exposure to up to 5,000 advertisements every single day. With so many external influences driving up—or down—the interest in specific products or brands, it’s hard to keep track of it all.

“Your search data is a reflection of what’s happening in the world and the impact it has on shopper behavior. If you want to become better at selling online, you have to identify and react to quick shifts in interest around certain products. That means authorizing changes to your homepage to imply a sense of urgency around key items, or crafting newsletters dedicated to an emerging fashion trend.”

- Dana Naim, Six ways to boost conversions with search intent data, Digital Commerce 360; X/Twitter: @DC360_Official

10. Remember that tools need human collaboration. “Many companies rely on automation for the entire process of data mining, including preparation. However, depending wholly on machines and algorithms can be risky at times. Therefore, human intervention is important. The combined effort of ML algorithms and humans can bring more accuracy in data and enhance its quality.”

- Effective Data Preparation for Data Mining Success: Top Tips and Tricks, WisdomPlexus; X/Twitter: @WPlexus

11. Use data mining software that monitors social media posts. “Large airlines like Delta, monitors tweets to find out how their customers feel about delays, upgrades and in-flight entertainment. When a customer tweets negatively about his lost baggage, the airline forwards to their support team. The support team sends a representative to the passengers destination presenting him a free first class upgrade ticket on his return along with the information about the tracked baggage promising to deliver it as soon as he or she steps out of the plane.”

- Rob Petersen, 20 companies do data mining and make their business better, BarnRaisers; X/Twitter: @RobPetersen

12. Catch customers at risk for churning. “Mobile service providers use data mining to design their marketing campaigns and to retain customers from moving to other vendors.

From a large amount of data such as billing information, email, text messages, web data transmissions, and customer service, the data mining tools can predict ‘churn’ that tells the customers who are looking to change the vendors.

With these results, a probability score is given. The mobile service providers are then able to provide incentives, offers to customers who are at higher risk of churning. This kind of mining is often used by major service providers such as broadband, phone, gas providers, etc.”

- Sruthy, Data Mining Examples: Most Common Applications Of Data Mining 2024, Software Testing Help; X/Twitter: @VijayShinde

13. Develop customer segmentation models that can continuously adapt. “Customer behaviors and needs aren’t static, and the customer journey and experience has to adapt over time to reflect this. For example, the dramatic shifts within the last few years have created new needs as human experiences have changed. From office work to remote working, from eating out to dining in, from new artificial intelligence-driven technology to a return to the basics, your market knowledge needs to evolve as each of your customer segments do.

Developing customer segmentation models that can incorporate new information and segment data, and then adapt accordingly, is key. The most successful segments for one season might be completely different the next — and what existing customers are looking for might not be the same, as society and technology evolve. Understanding the why behind your ideal customers’ choices will help you to weather external storms and reach new segments more effectively.”

- What is customer segmentation analysis, and how can it help?, Qualtrics; X/Twitter: @Qualtrics

14. Walmart relies on data mining for efficient business operations. “By analyzing historical sales data and real-time customer demand, Walmart can predict future inventory needs with greater accuracy. This reduces the risk of stock outs and overstocking, saving money on storage and logistics while ensuring shelves are well-stocked for customers.

Based on demand and competitor pricing, Walmart can adjust prices in real-time, offering competitive deals on popular items and maximizing profits on slower-moving products.”

- Kavindu Rathnasiri, Data Mining Success Stories: How Hidden Gems Uncover Real-World Value; LinkedIn: Kavindu Rathnasiri

15. Sentiment analysis tools identify how customers really feel. “Sentiment analysis is often driven by an algorithm, scoring the words used along with voice inflections that can indicate a person’s underlying feelings about the topic of a discussion. Sentiment analysis allows for a more objective interpretation of factors that are otherwise difficult to measure or typically measured subjectively, such as:

• How fast the individual is speaking (rate of speech)
• Changes in the level of stress indicated by the person’s speech (such as in response to a solution provided by a customer support representative)
• The amount of stress or frustration in a customer’s voice

In customer service and call center applications, sentiment analysis is a valuable tool for monitoring opinions and emotions among various customer segments, such as customers interacting with a certain group of representatives, during shifts, customers calling regarding a specific issue, product or service lines, and other distinct groups.

Sentiment analysis may be fully automated, based entirely on human analysis, or some combination of the two. In some cases, sentiment analysis is primarily automated with a level of human oversight that fuels machine learning and helps to refine algorithms and processes, particularly in the early stages of implementation.”

- What is sentiment analysis? Examples, best practices, & more, CallMiner; X/Twitter: @CallMiner

16. Use basket/affinity analysis to improve product layouts and recommendations. “This technique analyzes products/services bought by a customer, which can then be applied to improve product layouts in brick-and-mortar stores, or related product recommendations for online retail outlets. The basket refers to the collection of items selected by one consumer during their shopping expedition.

The technique assumes that future customer behavior can be predicted through past performers, i.e. their preferences and purchases. This can be applied by more than just retail stores, such as:

• Online e-commerce stores can evaluate credit card data to find patterns that can highlight fraud incidents, as well as tailor reward cards with optimal limits, interest rates and terms for consumers
• Telephone use identify which customer segments respond to telephone calls to action on websites and identity reasons why this is the case, and how the CTAs can be improved to get more people calling the business.”

Jack Dawson, 5 Ways you can use Data Mining to gain Competitive Advantage for your Online Store, Dataloq; X/Twitter: @Datafloq

17. Identify duplicate or similar data. “A fundamental data mining problem is to examine data for “similar” items. An example would be looking at a collection of Web pages and finding near-duplicate pages. These pages could be plagiarisms, for example, or they could be mirrors that have almost the same content but differ in information about the host and about other mirrors. Other examples might include finding customers who purchased similar products or finding images with similar features.

Distance Measure is simply a data mining technique to deal with this problem: finding near-neighbors (points that are a small distance apart) in a high-dimensional space. For each application, we first need to define what “similarity” means. The most common definition in data mining is the Jaccard Similarity. The Jaccard similarity of sets is the ratio of the size of the intersection of the sets to the size of the union. This measure of similarity is suitable for many applications, including textual similarity of documents and similarity of buying habits of customers.”

- James Le, 10 Data Mining Techniques Data Scientists Need for Their Toolbox, Built In; X/Twitter: @BuiltIn

18. Mine feedback data to boost innovation. “Whirlpool Corporation is one of the world’s leading major home appliance companies. Innovation takes formidable priority in the company business and marketing efforts. Consumer feedback plays a great role in their innovation strategies.

Whirpool has a million reviews being added globally in a month. This data is collected from 40 different websites and six different form factors. The types of data range from emails to different contact forms with sales representatives.

Whirpool uses data mining software that analyzes all this data and answers vital questions such as how are the products performing in the market, which features are consumers like the most, how is the customer purchasing experience, and etc.

In addition to these insights, data analytics also helps the entire organization speak a common language and perform a comprehensive competitive product analysis to see where they are in terms of competition. Converting this data to insights allows the company to find different paths and ideas for innovations.”

- 7 Real-World Examples Of Data Mining In Business, Marketing, Retail, Intellspot; X/Twitter: @intellspot

19. Invest in data mining software to improve knowledge and product development. “Mining techniques can do an automatic analysis of businesses and help them to create a seamless process from topline to bottom line. The data that is lying across the departments is extracted, coordinated and stored in a virtual warehouse.

This virtual warehouse becomes a powerhouse of insights and a stepping stone of business decisions that can take your organization many light-years ahead in business expertise, knowledge and product development.”

- Murtaza Amin, 8-Ways Data Mining Can Improve your Business, HackerNoon; X/Twitter: @hackernoon

20. Get rid of old data to make room for new insights. “Storing prepared data is essential, but for how long? Make sure you are not storing outdated data as it will no longer benefit your business. This will also make space free for the latest datasets. Ensuring standard formatting is also crucial while storing data to understand its relevance. ”

- Effective Data Preparation for Data Mining Success: Top Tips and Tricks, WisdomPlexus; X/Twitter: @WPlexus

21. Understand how data mining, data analytics, and data warehousing work together. “While each discipline has its distinct focus, they are interrelated and complement each other in leveraging data effectively. Data mining helps identify patterns that may go unnoticed, while data analytics provides insights based on those patterns. These insights can then be used to make informed decisions or drive further analysis.

Data warehousing is the backbone of these processes by providing a centralized repository for storing and managing large datasets. It ensures that the necessary data is readily available for mining and analysis.

In today’s era of big data, where organizations have access to vast amounts of information from various sources such as social media, IoT devices, and streaming data, leveraging these three disciplines is crucial for staying competitive.”

- Tyler Garrett, Data Mining: A Complete Guide and Techniques, Dev3lop; X/Twitter: @itylergarrett

22. Remove noisy data with a technique called “binning.” “Binning is a technique where we sort the data and then partition the data into equal frequency bins. Then you may either replace the noisy data with the bin mean, bin median or the bin boundary.”

- Caston Fernandes, Get rid of the dirt from your data — Data Cleaning techniques, Medium: Caston Fernandes

23. Use data to create custom products for customer segments. “Data mining is also perfect for creating custom products designed for market segments. In fact, you can predict which features users may want…although truly innovative products are not created from giving customers what they want.

Rather truly innovative products are created when you look at the data from your customers and spot holes customers are demanding be filled. When it comes to creating that product, these are the elements that must be baked into the product.

• Fulfill an obvious need
• Offer something utterly unique
• Set to enter the market with a unique name
• Attractive design
• Serves a broad market
• Can be sold in generations
• Create an impulse-purchase price
• Cost to make is low enough to make a profit”

- 10 Ways Data Mining Can Help You Get a Competitive Edge, Neil Patel; X/Twitter: @neilpatel

24. Use regression analysis to identify the most important factors. “Regression analysis is about understanding which factors within a data set are most important, which can be ignored, and how these factors interact. With this technique, data miners are able to validate theories such as ‘when a lot of snow is predicted, more bread and milk will be sold before the storm.’

While this seems obvious enough there are a number of variables that need to be verified and quantified for the store manager to make sure enough stock is available. For example, how much is ‘a lot’ of snow? How much is ‘more milk and bread’? Which types of weather forecasts tend to cause consumer action and how many days before the storm will consumers start buying? What is the relationship between inches of snow, units of bread, and units of milk?

Through regression analysis, specific inventory levels of milk and bread (in units/cases) can be recommended for specific levels of snow forecasted (inches), at specific points in time (days before the storm). In this way, the use of regression analysis maximizes sales, minimizes out-of-stock instances, and helps avoid overstocking which results in product spoilage after the storm.”

- What Is Data Mining? A Beginner’s Guide (2022), Rutgers University Bootcamps; X/Twitter: @RutgersU

25. Produce visuals for complex datasets. “Data visualization is a powerful tool for exploring and understanding complex datasets. It allows analysts to represent data visually using charts, graphs, and plots, making it easier to identify patterns, outliers, and relationships.

For instance, a scatter plot can help visualize the correlation between two variables, while a histogram can show the distribution of a single variable. By using interactive visualization tools like Tableau or Python libraries like Matplotlib and Seaborn, analysts can create compelling visualizations that aid in data exploration.”

- Techniques And Tools For Effective Data Mining, Faster Capital; X/Twitter: @FasterCapital

From uncovering potential risks to inspiring new market opportunities, data mining drives multiple #business solutions. We share how to get the most of your #contactcenter data from @Datamation, @DataScienceCtrl and more: https://t.co/tsOiddDwcn pic.twitter.com/yPUxNKEqJm

— CallMiner, Inc. (@CallMiner) October 9, 2019

Now is the best time to tap into the potential of data mining software to gain valuable insights into your customers and your operations to drive continuous improvement. CallMiner is a conversation intelligence solution that analyzes 100% of customer interactions to deliver unprecedented insights and drive your business forward. Schedule a demo today to learn more.

Frequently asked questions

How can data mining be effective?

Data mining is effective for many use cases when users understand how to set objectives, clean and prepare data, and evaluate and improve data. Modern data mining software can help with each of these processes, but human data mining skills are just as important for successful data mining.

Is Python a data mining software?

Python isn’t a data mining software but a programming language that many data mining software uses, along with R. pandas, scikit-learn, and KNIME are examples of Python-based data mining software.

Which data mining software is best?

The best data mining software depends on your needs. For example, CallMiner gathers structured and unstructured data from customer phone calls and other modes of contact to capture customer feedback, which is ideal for contact centers. Meanwhile, KNIME is a popular tool for mining laboratory data in life science fields.