Association Analysis In Anaplan

Imagine selling a greater number of products than usual. Imagine selling products that were not getting sold earlier. Imagine selling products that you never guessed would get sold alongside the conventional ones. Imagine increasing your revenue than you normally generate.

This mere imagination can be turned into reality through increasing the sale of homogenous products in relation to buying behavior of customers. And this can be achieved through carrying out Association Analysis in Anaplan.

Association Analysis is a data mining technique that is used to identify relationships among disparate items based on their simultaneous occurrence in a transaction or event. It is also known as market basket analysis. This technique is popularly used in e-commerce, retail and other sectors to discover business opportunities in product bundling, up-selling and cross-selling.

Anaplan can be very useful in providing a cloud-based planning platform that can help businesses to discover consumer behaviour patterns which in turn can be used to improve their sales strategies. And, ANAPLAN’s powerful association tool can be brought into use, in this regard.

Are you looking to unlock the hidden potential of your data and gain actionable insights to fuel your business strategies? Then, welcome to this blog. Whether you are an Anaplan user or just curious about the potential of association analysis, this blog offers an exciting journey into a world of valuable data-driven decision-making.

By reading this blog, you will discover the Apriori algorithm and how it forms the backbone of association analysis in Anaplan. Gain a comprehensive understanding of how the algorithm works and how it can be applied to your datasets to extract meaningful insights.

This blog is an attempt to explore how association analysis works in ANAPLAN and how businesses can use this tool to boost growth and profits.

 

Working of Association Analysis in Anaplan

The Apriori algorithm, a well-known data mining tool that employs a bottom-up methodology to identify frequent item sets and association rules, serves as the foundation for association analysis in ANAPLAN. The following phases can be used to breakdown the association analysis process in ANAPLAN:

  1. Data Preparation: In association analysis, the initial step is to identify the transactional data that will be evaluated. To accomplish this in ANAPLAN, transactional data can be imported from a variety of sources, including ERP systems, CRM systems, and e-commerce platforms.
  2. Itemset Generation: The process of creating itemsets, which are collections of items that commonly appear together in transactional data, comes next. ANAPLAN generates itemsets based on user-specified support levels using the Apriori algorithm. In the context of association rule mining, support level refers to the frequency at which an itemset appears in a dataset. It is a measure of how often a particular itemset appears in the data relative to the total number of transactions.For example, if a dataset contains 100 transactions, and a particular itemset appears in 20 of those transactions, then the support level for that itemset is 20/100, or 0.2.

    The support level is used as a parameter in the Apriori algorithm, which is a popular algorithm used for finding frequent itemsets in a dataset. By specifying a minimum support level, the algorithm can efficiently identify itemsets that appear frequently enough in the data to be considered for further analysis.

  3. Association Rule Generation: After the itemsets have been created, ANAPLAN uses the Apriori algorithm to produce association rules, which are sentences that explain the connections between various items. Based on user-defined confidence thresholds, these rules are created.Let’s consider an association rule “Milk, Bread -> Eggs”. It means that customers who buy both “Milk” and “Bread” together are likely to buy “Eggs” as well. In this rule, “Milk” and “Bread” are on the left-hand side (antecedent), and “Eggs” are on the right-hand side (consequent).
  4. Rule Evaluation and Visualization: Last but not least, ANAPLAN analyses the association rules and visualizes the outcomes using graphs, tables, and charts. Businesses can use this information to understand customer behaviour and spot cross-selling, up-selling, and product bundling opportunities.
    • Analyze Association Rules: Anaplan analyzes the association rules generated in the previous phase and filters out rules that do not meet the user-defined confidence threshold.
    • Rule Evaluation: Anaplan evaluates the remaining association rules by calculating various metrics such as lift, support, confidence, and conviction.
    • Rule Ranking: Anaplan ranks the association rules based on their importance, using the metrics calculated in the previous step.
    • Visualization: Anaplan presents the results of the rule evaluation in the form of various visualizations such as tables, graphs, and charts. This helps users to easily understand and interpret the insights generated by the algorithm.
    • Drill-down Analysis: Anaplan allows users to drill down into the data and perform further analysis on the association rules. This enables users to explore the data in more detail and gain deeper insights.
    •  Report Generation: Anaplan allows users to generate reports based on the results of the analysis. These reports can be customized to meet the specific needs of the user and can be exported in various formats such as PDF, Excel, or PowerPoint.

 

Advantages of Association Analysis In Anaplan

Business organizations aiming to enhance their sales tactics and spur growth might gain from association analysis in ANAPLAN in a number of ways. Among these advantages are:

  • Improved Customer Insights: By determining the connections between various items, association analysis in ANAPLAN enables businesses to better understand consumer behaviour and preferences. Businesses may benefit from this as they work to enhance their product lines and marketing approaches.
  • Increased Sales: Companies can boost their sales and profitability by spotting cross-selling, up-selling, and product bundling opportunities.
  • Better Inventory Management: Association analysis can also help businesses optimize their inventory management by identifying which items are frequently sold together and which items are slow-moving.

 

Business Use Cases

Let’s take a look at some business use cases where Association Analysis in Anaplan can be helpful.

  • Association analysis can be used by firms to determine which goods are usually purchased together in the retail sector. If customers routinely buy hot dogs and ketchup together, for instance, a firm can provide a discount on both products to entice customers to buy the two items together.
  • Association analysis can be used by organizations in the e-commerce sector to determine which products are frequently seen or bought together. For instance, a company may recommend supplementary items, such as socks or shoe care products, to clients who consistently buy a particular brand of shoes, to encourage customers to make extra purchases.
  • Association analysis can be used in the healthcare sector to determine which medical procedures are frequently performed in tandem. For instance, if patients who receive a particular medical procedure also frequently receive a certain medication, healthcare providers can optimize their treatment plans to ensure that patients receive both treatments together.

Thus, we can see through the above use cases, how by utilizing the powerful Association Analysis technique in ANAPLAN, businesses can gain valuable insights into consumer behavior and identify opportunities for growth and profitability.

 

Conclusion

Association analysis is a powerful data mining technique that can assist companies in understanding client behaviour and enhancing their sales tactics. Businesses can discover cross-selling, up-selling, and product bundling opportunities as well as enhance their inventory management with the use of ANAPLAN’s robust association analysis tool. By leveraging association analysis in ANAPLAN, businesses can drive growth, increase sales, and improve their bottom line.

Author Details

Himanshu Ghongade

Passionate about data analysis with experience in building business and driving revenue in IT consultancy, IT sales and US Mortgage domain; possess keen business acumen in analyzing and understanding business requirements, customer-value maximization, and developing new business processes and revenue streams. A refined understanding of business dynamics and updated market knowledge. Well-versed with R-programming and many statistical techniques and applications- Anaplan, Oracle, XLMiner, Tableau, Minitab, SPSS and Advanced Excel. Avid reader; possess keen business acumen in analyzing and understanding business requirements. A strong communicator with analytical skills.

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