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This is a very general question. I looked through the feature lists of many e-commerce analytics packages. It struck me that while they offer impressive-lookin
by jwr 16y ago
This is a very general question.
I looked through the feature lists of many e-commerce analytics packages. It struck me that while they offer impressive-looking graphs and gather huge amounts of data, the "analytics" part seems to be very simple. They mostly present you the data that has been measured.
I am in a situation where I will want to provide e-commerce analytics as part of a more complete solution. But I would like to approach this from a different angle: provide digested data, with insight into patterns that could lead to increased profits for online stores.
What do you think could be extracted from e-commerce data that would be of value to online stores? I'm looking for information that is difficult to extract (e.g. requires a tech advantage), but can be extracted automatically or with minimum human curation.
An example of an interesting direction: e-commerce analytics companies offer "customer segmentation" views. But they are all just simple data views: you can segment based on location, time of visit, etc. I think doing a factor analysis on customer buying patterns would be much more interesting and would provide real value to the store by automatically extracting "customer types" from data. One could then market directly to these customer groups.
So, to rephrase, I'm looking for ideas/pointers on what kinds of analyses would be difficult to create, but very useful for e-commerce.
- noelwelsh 16y agoI'm agreed that most so-called analytics has little value. Your customer segmentation idea is a good one. You could also approach this as a classification problem, where you attempt to classify customers as purchasers or non-purchasers for each product or product group. Another idea is to look at high and low value pathways through a site. If you can identify pathways that don't lead to purchases that might suggest where design effort should be spent.
- bravura 16y agoI am currently working on this problem with one client. We are doing different sorts of market analysis, by analyzing products. In particular, we are looking at optimizing profits by choosing product titles more effectively, as well as optimizing profit by choosing price points more effectively. This can be used purely automatically, as black box decisions, or this information can be presented in a visualization interface for a human analyst. We've also talked about several other ways of turning historical sales data into actionable business intelligence. One thing we haven't looked at is doing market analysis based upon customer profiles. This is an interesting idea, and I have some thoughts on how to do it. Essentially, finding different ways to cluster users, and then "explaining" who these customer segments. The explainability is based upon figuring out the most important features they have in common, and exposing these features in plain English.