Understanding Market Basket Analysis and Product Recommendations




Market basket analysis uses point-of-sale data to select the combination of products that sell best together. Product recommendations are then offered to the customer according to what is in their basket. Utilising machine learning means businesses can predict which products are purchased most often with the items in their basket, leading too…


Increased Sales

Increased Customer Satisfaction


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The Role of AI


Artificial Intelligence (AI) aids in correlating the relational data needed to offer customers the right complementary goods for their basket. AI is the extra muscle that provides an edge on identifying and predicting the perfect combinations as it is able to identify the ever-changing shifts in market trends, along with dependent variables such as time, location and demographic.


AI is able to collect and analyse vast amounts of customer data, which can then greatly speed up the content creation process and personalise each users experience. AtomX Digital has AI led capabilities to show sharp, relevant and focussed content at an appropriate step within the customer journey, to persuade them to buy bundles and complementary goods.


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Market Basket Analysis Example


Consider a site such as Amazon; when you search any item you wish to buy, you are presented with a list of frequently bought items just below the product you are viewing. For example, if you are to search for a mobile phone, the items frequently bought may include complementary items such as a case, headphones or screen protector.


Not only will this benefit the business by increasing sales, but this also provides the customer with a convenient way to purchase items they need there and then, without needing to return to make separate purchases. Amazon will also go beyond just recommending related products; for example, they may bundle certain items for a reduced price.


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How Does Market Basket Analysis Work?


The model around Market Basket Analysis is based on Association Rule Mining. Association Rules can be used to measure and analyse transactional or basket data. These take the following form:


IF {Phone} THEN {Case, Headphones, Screen Protector}


The most recognised algorithm that generates these rules is the Apriori algorithm. This identifies the most common individual items in a dataset, it then continuously increases the set, depending on how often the items appear. The algorithm then makes it possible to analyse the dataset by their ranking of importance by calculating the “Support and “Confidence” of each rule.


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Conclusion


With the utilisation of Market Basket Analysis and Product Recommendations, businesses are able to better understand the relationships between the products customers purchase and are then able to offer a suitable selections for them, boosting customer satisfaction and increasing sales. Analysing this data is key to understanding the needs of your customers, which can then be harnessed for marketing strategies such as cross-selling and targeted campaigns. With the digital transformation now reaching across all verticals, businesses are realising the potential for more efficient and intelligent solutions. Businesses who have not yet recognised this opportunity will now be playing catch-up to perform as well as their competitors who begin to implement the benefits of MBA and Product recommendations by offering lower prices, bundles, and more.


Here at AtomX Digital, we work to understand your unique organisational business objectives. By combining this with your customer needs and current AI capabilities, we leverage our own in-house Machine Learning capabilities to provide real time customer insights, predictive services and help establish an innovation culture within your organisation.


Please get in touch with us if you are looking to increase your digital sales, improve your customer satisfaction, and take the next step in your organisation's journey.








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