In an e-commerce database, what is the ideal method to cluster products so that grouped items can be offered at discounted prices alongside individual ones? Which generalized solution is most effective?
Hey everyone, I’ve been thinking a lot about this since I first encountered clustering in one of my projects. I believe combining data from user behavior, product attributes, and even seasonal trends can help create groups that really resonate with customers. For instance, blending both the static features like brand or category with dynamic ones like recent popularity can yield clusters that are far more useful for promotional tactics. I’m really curious if anyone has tried a hybrid solution that involves both traditional statistical methods and a bit of machine learning. How do you balance the computational complexity with the need for real-time updates on promotional offers? It would be great to hear about the kinds of trade-offs you’ve run into and any alternative approaches you might suggest!
The approach I found most effective involved starting with a robust feature engineering process combined with unsupervised learning techniques. In my experience, transferring raw product data into meaningful numerical representations enabled clear clustering. I integrated sales trends, product attributes, and even customer behavior to identify natural groupings. This allowed for dynamic adjustments based on changes in market conditions while maintaining cost efficiency. Rather than relying solely on static filters, working with algorithmic clusters provided flexibility in promotions and more targeted discounting strategies.
hey i tried combining association rule mining with purchase trends. it gave me groups that went beyond basic attributes. im still refining real-time updtes but experimenting with apriori algo alongside clustering has shown promising results for dynamic promotions. might be worth a shot if u haven’t already!
Hey everyone, I’ve been mulling over this question too. While others have looked into blending static and dynamic features or using association mining, I’ve been curious about leaning into graph-based clustering as an alternative. Imagine constructing a network where products are nodes and edges represent co-purchase activities or shared attributes. Then, applying community detection techniques can uncover clusters that don’t necessarily show up with traditional unsupervised learning methods. What do you think about such an approach? Do you see value in exploring more relational data beyond what standard features capture? I’m really interested in hearing if anyone has experimented with this idea or if there are pitfalls I should be aware of when dealing with noisy or sparse connection data. Looking forward to your thoughts!