The European Journal of Social & Behavioural Sciences
Online ISSN: 2301-2218
European Publisher
Customer Segmentation With Machine Learning for Online Retail Industry
Table 3: Customer Segments with Business View
| No | Recency | Frequency | Revenue | Customer | Strategy |
| 1 | Low | Low | Low | Inactive Rare Low Spender | Ignore |
| 2 | Low | Low | Mid | Inactive Rare Mid Spender | Ignore |
| 3 | Low | Low | High | Inactive Rare High Spender | Stimulate |
| 4 | Low | High | Low | Inactive Frequent Low Spender | Stimulate |
| 5 | Low | High | Mid | Inactive Frequent Mid Spender | Stimulate |
| 6 | Low | High | High | Inactive Frequent High Spender | Stimulate |
| 7 | High | Low | Low | Active Rare Low Spender | New Customers with low potential-Ignore |
| 8 | High | Low | Mid | Active Rare Mid Spender | New Customers who can become Silver or Gold, take care, welcome promotion |
| 9 | High | Low | High | Active Rare High Spender | New Customers, potential to become Gold or Platinum, focus to retain, welcome promotion |
| 10 | High | High | Low | Active Frequent Low Spender | Loyal customers with low value, Silver, Reward |
| 11 | High | High | Mid | Active Frequent Mid Spender | Loyal customers with mid value, Gold, Reward |
| 12 | High | High | High | Active Frequent High Spender | Loyal customers with high value, Platinum, Reward |