The global market value for data mining tools will triple over the next five years, growing from $9.27 billion in 2021 to $28.5 billion in 2027 – a compound annual growth rate of (CAGR) of 20.62%. Companies across industries are increasingly adopting data mining as a guiding component in their decision-making processes.
In this guide, you’ll learn about the basics of ecommerce data mining and the methods and techniques to apply for the greatest ROI.
Key Takeaways
- Data mining helps businesses turn their raw data into valuable insights.
- Data mining is an analytical process for identifying meaningful patterns and recurring cycles in large data sets.
- Ecommerce companies can apply the CRISP-DM method to begin productive data mining initiatives.
What Is Data Mining?
Data mining refers to the process of analyzing large data sets to discern patterns and define relationships between different kinds of data points. This contributes to business intelligence and helps leaders make informed, strategic choices.
The premise behind data mining is that past trends in an organization’s data may contain predictive insights about future events. The larger the data sets and the longer the periods analyzed, the more accurate data mining can be.
Data mining is a core component of data analytics and the organizational practice of data science. Specifically, it is an example of a set of analytical techniques called knowledge discovery in databases (KDD). KDD is a selective, transformative, and interpretive process concerned with extracting insights from already captured data sets rather than determining what kinds of data a system captures or ignores.
The Value of Data Mining
Organizations can mine historic data that spans years or consists of incoming streams of large data sets. The intended application of the extracted insights largely determines the appropriate scope of data mining operations. Practical data mining applications include:
- Marketing
- Sales
- Customer support
- Human resources
- Finance
- Supply chain management
In enterprise-level contexts, data mining can be applied to more specialized cases such as fraud detection, cybersecurity, safety analysis, and risk management.
CRISP-DM: Applying Data Mining to Ecommerce
According to IBM, one of the most widely applicable data mining methods is called CRISP-DM, which stands for cross-industry standard process for data mining. This method is well-suited for ecommerce companies looking to begin data mining initiatives.
CRISP-DM consists of a six-phase cycle.
1. Business Understanding
Data mining operations must begin by identifying the problems the process should solve. Without clear objectives, businesses may waste time and resources extracting accurate but non-useful insights.
2. Data Understanding
This step involves connecting data mining goals to the necessary sources and assets. For example, goals like capturing new market share or improving profitability will draw on sales, marketing, and competition data. Alternatively, an initiative targeting distribution efficiency would require different data sets such as geolocation, average delivery times, and fuel costs over time.
3. Data Preparation
Data is prepared for review. Data preparation is a three-step process:
- Extract: Pull data from selected sources.
- Transform: Perform operations such as deduplication, nullifying, and error correction.
- Load: Upload transformed data into a relational database.
4. Modeling
This step involves choosing the best statistical and mathematical techniques for a specific operation. Applying the wrong method – such as averaging values with wild outliers – can yield misleading conclusions.
5. Evaluation
In the evaluation phase, analysts test the efficiency of their chosen models for answering the kinds of questions the data mining operation targets. In some cases, models work appropriately with prepared data but deliver answers that require additional refining to be helpful in decision-making processes.
6. Deployment
Once organizations have tested preparation and modeling techniques and found them satisfactory, they must deploy mined data to the right users. This may involve preparing visual presentations for the C-suite for strategic planning or feeding mined data into multiuser systems like CRMs to guide teams in day-to-day operations.
Types of Modeling in CRISP-DM
There are five kinds of data mining modeling techniques with broad applications in ecommerce businesses:
- Classification analysis: Assigns categories to data points based on matching with a list of criteria.
- Association rule learning: Attempts to link values in one data set with values in another, such as promotion redemptions with changes to average order value (AOV).
- Anomaly or outlier detection: Inverts pattern recognition to identify data points that analyses should not include, such as zero or nil values in data passing into additional mathematical operations.
- Cluster analysis: Resembles classification analysis but without predetermined categories. Cluster analysis is useful in identifying new segments that may not be obvious to human analysts.
- Regression analysis: Tests sequences of hypotheses to reveal which data points in a pattern or learned association are most important. For example, extreme weather events may drive sales for specific products. Still, the degree of increase may vary widely depending on other factors such as geographic region or scarcity of other products.
Valuable Applications for Mined Data in Ecommerce
Mined data applications for ecommerce companies will depend largely on individual needs. However, these four use cases will provide a solid foundation for data mining initiatives across the board.
1. Basket Analysis
This connects sales probabilities between products. It helps businesses determine how likely a customer who buys one product is to buy various others.
2. Sales Forecasting
Sales forecasting combs historical sales, financial, economic, and market data for recurring predictable patterns that can guide budgeting, pricing, and other sales-related decisions.
3. Database Marketing
Businesses mine their marketing databases in platforms such as CRMs to uncover consumer behavior patterns, enabling more targeted marketing and lead generation.
4. Inventory Planning
Inventory intelligence drives bottom-line value for ecommerce companies. Without data-driven monitoring for inventory operations, out-of-stocks, unnecessary storage overages, and supply chain disruptions can quickly eat into profits.
Pricing and Inventory Optimization for Ecommerce with Hypersonix
With end-to-end simplicity – from codeless deployment to clear, actionable recommendations – Hypersonix ProfitGPT is the unrivaled choice for AI-driven ecommerce profit and inventory optimization. The platform constantly monitors internal and external data sources to deliver real-time predictive and prescriptive recommendations with the added security of anomaly detection.
