Target audience segmentation divides users into groups with shared behavior, characteristics, or needs. Clustering can reveal these groups before the team knows exactly what to look for, supporting personalization, marketing, and feature decisions. section 4.1
Useful segmentation starts with representative data and improves iteratively. Granular events such as clicks and profile updates must be aggregated into meaningful user-level measures, while the team tests which behaviors reflect engagement, value, or churn risk. section 4.1.2
A practical segmentation workflow includes:
- Explore behavioral and demographic data with clustering to identify coherent groups. section 4.1
- Aggregate events into measures such as purchases, purchase value, visits, recommendation clicks, and checkout frequency. section 4.1.2
- Inspect anomalies rather than forcing every user into a segment; unusual activity may reveal bots or other data-quality issues. section 4.1.6
- Train a classification model to assign new users to established clusters as the product scales. section 4.1.6
From segments to experiences
A platform can use distinct behavioral groups to guide interventions: indecisive users receive limited-time offers, conservative users receive education about recommendations, and high-value champions receive loyalty benefits and early sales access. section 4.2.4
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 |
|
Inspect example clustering features, including purchased items, purchase value, last activity, and visit count. section 4.1.2
