Market basket analysis (MBA) is a technique that retailers use to discover which products customers tend to buy together. You can think of this as the “customer bought also viewed” feature on Amazon.
MBA has been used for decades, but it wasn’t until machine learning became more commonplace in retail that it’s ability to predict trends from large datasets could be fully realized. Below are 24 tips and tricks I’ve learned after working with MBA for over 10 years in retail.
| Takeaways |
|---|
| 1. Conduct thorough research for Market Basket Analysis and its applications. |
| 2. Be aware of the potential challenges and misleading data in Market Basket Analysis. |
| 3. Use appropriate data cleaning and preprocessing techniques to ensure accurate results. |
| 4. Utilize statistical measures like support, confidence, and lift for meaningful insights. |
| 5. Understand the significance of Market Basket Analysis in understanding consumer behavior. |
Eliminate Duplicate Transactions
Click on the Duplicate Transactions tab in your report. This will take you to a page that shows all of your duplicate transactions, along with some information about each transaction (e.g., whether or not it was an error).
You can use this page to view details about each transaction and make notes accordingly.
If you want to remove any duplicates from the report, select one of the rows on this page and click Remove Selected Rows at the bottom of the page (you’ll see it if there are any selected).
A dialog window will pop up asking if you want to remove only those records from this report or from all reports in Market Basket Analysis; choose whichever option works best for what you’re working on!
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Confirm The Order Of Purchases
If a customer purchases a bunch of products in one order, then another group of products in a separate order, and then returns to buy yet another group of items, the market basket analysis will be skewed.
You want to make sure the data you’re working with is accurate and can be trusted.
To confirm that each customer purchased the items in the right order:
Look at each customer’s previous purchase before their most recent purchase (the “previous” item). If that item is also on their most recent purchase list, then they probably made both purchases together as one transaction.
This may not always be true because sometimes customers buy two or three items separately but still use one item from their previous purchase as part of their current one.
For example, if an e-commerce site sells coffee mugs along with coffee beans and filters (which are all packaged together).
These three items would likely appear next to each other on an invoice even if they were purchased separately by different people using different payment methods or during different shopping sessions over time.
Check whether there are any duplicate product IDs between the two lists; this means it’s likely those purchases were made at roughly the same time.
You can also check other fields like quantity sold or retail price across multiple orders which could indicate whether they were part of a single transaction
Remove Cancelled Or Returned Orders
It’s important to remove cancelled or returned orders from your analysis. If you don’t, you may get misleading results. When an order has been canceled by the customer, it will appear in the data set with a status of “canceled” and have no quantity sold.
When an order has been returned by a customer and later re-entered into Market Basket Analysis (MBA) as a new order (meaning they didn’t use their previous information), that new order will be counted in MBA as well.
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Remove Trial And Sample Products
Trial and sample products are not part of the basket. When looking at sales data, you may find that a certain product isn’t included in the market basket and is included on other days.
This is because it’s a trial or sample product, which means it’s being tested out and doesn’t have a price attached to it yet.
Trial and sample products are not used in the analysis. To ensure accuracy, we need to filter out these types of purchases so that we can get an accurate picture of customer behavior when they purchase groceries for regular use.
Trial and sample products are not purchased for regular use. These items may be on sale or given away as part of a promotion or giveaway event (like at a trade show).
But they’re typically not purchased as part of normal shopping trips since they’re often expensive compared with similar options sold at local stores nearby!
Make Sure You Have Sufficient Quantities Of Purchased Products
Make sure you have enough quantities of purchased products.
Check the data for missing or incomplete transactions.
Make sure the quantities match up with the amount of products purchased (e.g., if a customer bought eight items, they shouldn’t also have an incomplete transaction).
Check the data for missing or incorrect prices.
Check the data for duplicate transactions (i.e., if a customer buys one item twice). It’s possible that this is just an error on your part, but it could also indicate fraud it’s worth looking into!
Check if your data is ordered by date rather than by amount so you can see how people are spending their money over time. If there’s no discernible trend here, it may be too hard to tell if there’s anything unusual going on with your sales numbers!
Ensure You Have Enough Data For Your Analysis
You need to have a sufficient number of transactions to make an accurate analysis. This will vary depending on the type of business, but in general, it is best to have at least 20% and up to 50% of your total sales for a given time period in order to get reliable results.
Make Sure Each Product Has Enough Data Associated With It
This goes without saying if you sell only one or two products but if you sell hundreds or thousands of different items, this can become difficult!
You should ensure that every item has enough data associated with it so that the results are not skewed by outliers (such as one customer buying hundreds of cans of beans).
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Do Not Forget To Consider Data Quality Issues
The quality of your data is one of the most important factors in conducting an analysis. Data quality issues can affect the accuracy, validity, reliability, relevance and integrity of your analysis. In this section we will look at each one of these issues and how they can affect your study results.
Accuracy: Data that are inaccurate do not represent reality accurately (it does not have true values).
For example: If a salesperson has entered the wrong prices for products then this would be an inaccurate entry since it does not represent the actual price that was paid by customers during their shopping experience at Walmart store.
Validity: Data that are valid are collected in such a way that they measure what they were supposed to measure (it measures what its meant to measure).
For example: Suppose you want to know how many customers visited the Walmart store during holidays but instead of capturing only the number of visits by customers you include both online and offline purchases made by them in your study as well.
This would result in invalid results as you cannot say which channel accounted more traffic than another because both channels were measured together instead of separately i..e offline visits vs online purchases
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Work With Professionals With Experience In Market Basket Analysis And Misleading Data
When you’re working with professionals who have experience in market basket analysis and misleading data, they can help you get a better understanding of your business.
It’s important to work with someone who has a lot of experience in this area because they will be able to provide more insight into the problem at hand. The following are some reasons why it’s better to work with such professionals:
They will be able to tell you if there is anything wrong with your data or if there is any misleading information about your product or service that needs to be corrected.
They can help identify any trends that may exist among consumers based on how long it takes them to buy different items from your store.
This kind of information is critical for determining which products should be placed together on shelves or table tops so customers can easily see them when shopping at grocery stores and other retailers.
They understand how market basket analysis works but also know when something doesn’t seem right about certain parts of the process (like incorrect calculations) which makes them great at finding ways around these problems too!
Use These Tips To Ensure Accurate Market Basket Analysis And Misleading Data
Market basket analysis is a common way to understand purchasing behavior, but it can be misleading. If you’re doing a market basket analysis and notice something odd, there are some things you need to look out for. These tips will help you avoid making mistakes:
Look at the country’s gross domestic product (GDP) – if the GDP of your country is relatively low compared with other countries’ GDPs, then it will be hard for your customers to afford expensive items.
In this case, don’t analyze their purchases based on expensive items alone there may be other factors at play here!
Look at the average annual income per capita – if your average annual income per capita is high enough so that most people in your country have disposable cash left over after paying bills each month (and have enough saved up in case they ever lose their job).
Then they’ll probably spend some money on luxury goods every once in awhile even if they don’t buy them regularly! So keep an eye out for any interesting trends as well as just buying habits when analyzing data like this one; otherwise you might miss an important pattern!
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Conclusion
In this article, we explored some of the main ways that market basket analysis can be used to draw deeper insights from your data.
By looking at the items people purchase together, you can get a better idea of what products work well together and how those relationships are impacting sales.
You can also use market basket analysis to understand the importance of different items in your inventory and discover new opportunities for price optimization or designing more effective marketing campaigns.
Using machine learning algorithms like frequent itemset mining, association rule learning, and collaborative filtering will allow you to take this analysis further than traditional methods like Apriori.
Further Reading
Using Market Basket Analysis in Management Research: A research paper highlighting the application of Market Basket Analysis in management studies.
A Comprehensive Guide on Market Basket Analysis: An in-depth guide providing comprehensive insights into Market Basket Analysis techniques.
Understanding Consumer Behavior with the Market Basket Analysis: Explore how Market Basket Analysis can be used to gain valuable insights into consumer behavior.
FAQs
What is Market Basket Analysis?
Market Basket Analysis is a data mining technique that identifies relationships between products frequently purchased together.
How is Market Basket Analysis used in retail?
In the retail industry, Market Basket Analysis is used to optimize store layouts, cross-sell products, and develop effective marketing strategies.
What are the key metrics used in Market Basket Analysis?
Support, Confidence, and Lift are the primary metrics used in Market Basket Analysis to measure the strength of item associations.
Can Market Basket Analysis be applied to non-retail datasets?
Yes, Market Basket Analysis can be applied to various datasets beyond retail, such as website user behavior, healthcare patient patterns, and more.
How can Market Basket Analysis help in recommendation systems?
Market Basket Analysis can enhance recommendation systems by suggesting complementary or related products to users based on their historical purchases.
Costantine Edward is a digital marketing expert, freelance writer, and entrepreneur who helps people attain financial freedom. I’ve been working in marketing since I was 18 years old and have managed to build a successful career doing what I love.