Feature engineering turns raw user activity and profile data into focused signals that help clustering reveal useful customer groups. The work requires judgment: retain behaviors tied to product engagement, remove distracting information, and revisit choices as model results expose new problems. section 4.1.2
A practical feature-engineering process for clustering includes:
- Choose behaviors that reflect the question, such as recommendation reliance, visits, purchases, add-to-cart activity, checkout frequency, and profile edits. section 4.1.2
- Enrich behavioral records with useful profile context while avoiding overreliance on static demographics. section 4.1.2
- Remove weak signals, handle duplicates and missing values, correct invalid ranges, and transform inconsistent records into a uniform format. section 4.1.2
- Iteratively refine, scale, and assess features using domain expertise so clusters reflect meaningful and interpretable dimensions rather than merely detectable patterns. section 4.1.5
Table 4.1 Examples of data points for clustering
| Features |
|||||
| user_id |
purchased_items |
purchase_value |
last_active |
n_visits |
|
| Examples |
abhj3k |
2 |
908 |
2024-04-30 08:36:24 |
48 |
| shj67d |
0 |
0 |
2023-12-26 12:56:24 |
24 |
|
| i963gh |
12 |
673 |
2024-05-15 23:22:11 |
156 |
|
Use the sample data table to distinguish raw customer records—purchases, value, activity time, and visits—from the behavioral signals that can be selected or summarized for clustering. section 4.1.2
