Product Clustering On The Basis Of Purchase And Browsing Behavior

Product behavior is one of the most reliable and useful aspects to know the products and predict their future selling possibilities. Studying product behavior helps marketers to understand what influence and impression does a product leaves on the customers and motivates them to buy. Through a better understanding of a product’s behavior, a business can identify which products can sell, to whom and which won’t.

An ideal product clustering is a combination of product behaviors that can give more accurate and actionable insights. However, the types of behaviors acknowledged for product clustering are primarily two of those used for customer segmentation. The significant product behaviors for an eCommerce store that can be grouped together for better insights are:

  1. Purchase Behavior
  2. Browsing Behavior

Let’s get ahead and know what each of these behaviors means in the context of products and how they help to analyze products to increase their sales.

Purchase Behavior

Purchase Behavior provides the information related to purchases of products. Products are browsed and looked upon often but the products which are bought give the most important behavioral insights. This is why, purchase behavior gives an upper hand in addition to the analysis of browsing behavior.

Relying upon the purchase history of products, many more products of similar nature can be figured. The purchase history can also be accessed for Predictive Analysis to figure out other similar products in which the customers might be interested in.

The analysis of purchase behavior helps to understand many other aspects that affect the future purchases of a product. For example: Purchase behavior helps to understand Product Affinity (a way to recognize the liking of a product based upon the previous purchases), which can be used for personalization and effective campaigning. Consideration of Product Affinity enables an organization to track purchase patterns, product behavior and offer cross-sell opportunities to increase revenue.

Furthermore, purchase behavior assists in making the product clusters by the next level analysis of buying patterns like Seasonality and Discount Sensitivity of products.

For example: If a product is bought much more in a specific month or season of the year, then it is reasonable to offer that product with special offers at that time.

Similarly, Discounts and Special offers make up an integral part of success for most of the modern-day retailers. We know that different products have different sensitivity to discounts. However, an analysis of purchase history can help to identify the discount sensitivity of each product.

For example: when revenue generation is the primary concern, products with higher margins can be promoted with bigger discounts which tend to sell more with higher discounts.

Products with relatable browsing and purchase behavior patterns can be grouped together to form meaningful product clusters. Similarly, purchase aspects like seasonal purchase behavior and discount sensitivity can add more value for forming the same.

Browsing Behavior

Browsing behavior denotes the browsing activity on a product. Unlike customers, products aren’t capable of browsing but they are browsed by visitors or customers, which provides the data for browsing behavior of products.

In simple words, browsing behavior is the browsing activity of customers on a particular product. Like the number of times it is browsed. For example, if a product is browsed 100 times by different customers in one month, then it is the browsing behavior of that product.

Analyzing the browsing behavior of products is quite important and insightful as it focuses on tracking and understanding the finest details of the product’s visibility, demand, and saleability.

The more information an online store has about its products, the better it’ll be able to serve them to those who want them. When behavioral aspects of the products are understood, the site can offer effective personalized on-site experience and targeted campaigning to interested customers.

If a product is browsed a lot of times then there are other products too which can be deemed similar to the browsed one on the basis of similar behavior. Figuring out such similarly behaving products help to form effective product clusters based on browsing behavior.

It’s obvious now which behaviors are necessary for a business to analyze in order to increase customer engagement and sales. But it remains out of human potential to keep track of all these behaviors without an analytical tool. 

If there’s no analytical tool involved, then it would be impossible to know which product was visited on the store and which products made up to the sales. It is possible to have all these insights using a little technical help, yet it’s not possible to analyze and update the product clusters with ever-changing data with respect to time.

It is definitely important to have an automated solution to analyze the behavioral data to make effective product clusters and market accordingly to grow the business. After the product behavior is tracked and analyzed, rule-based clusters are meant to be created in order to campaign the similarly behaving products.

In Enalito, the rule-based clustering of products is quite similar to the rule-based segmentation of customers. Similarly, the methodology adopted for clustering products as per their behaviors is also the same as that of customer segmentation. The marketing automation implements rule-based hierarchy and applies the RFM methodology for effective clustering in a way that can handle everything from collecting data, its analysis, creation of product clusters to creating campaigns that can really sell.

To understand the marketing automation and RFM methodology for effective clustering, refer to these articles. The matter is in the context of customer segmentation, although the automation based on rule-based clustering and RFM methodology share the same data points, scoring, valuation, and analysis to effectively form product clusters.

  1. Marketing automation utilizing rule-based segmentation (clustering)
  2. Using RFM methodology for rule-based (dynamic) segmentation {clustering}

Once it is clear how marketing automation utilizes rule-based clustering and how RFM methodology is used to figure the values and scores to form meaningful product clusters, it is necessary to understand how product clusters are represented by Enalito. Let’s get ahead to know the grid-based format of product clusters that help retailers to get insights into product behavior and know what products to campaign and in the best manner.

Related Articles