In as we speak’s fast-paced e-commerce world, customized advertising and marketing isn’t only a nice-to-have — it’s important for retaining prospects and staying forward. Nykaa, one in all India’s main magnificence and cosmetics retailers, does this brilliantly by combining RFM (Recency, Frequency, Financial) evaluation with Ok-Means Clustering to know their prospects higher and tailor their outreach.
On this submit, I’ll break down how Nykaa leverages RFM (Recency, Frequency, Financial) evaluation and Ok-Means clustering to supercharge buyer loyalty — and the way you are able to do it too.
Nykaa caters to over 20 million prospects, providing an enormous catalog of magnificence and wellness merchandise. However right here’s the true problem:
- How do you personalize messaging for such an enormous buyer base?
- Who’re your loyalists vs. your low cost consumers?
- How do you maximize ROI from campaigns?
That is the place data-driven segmentation utilizing RFM + clustering turns into a superpower.
RFM stands for:
- Recency — How lately a buyer bought
- Frequency — How typically they bought
- Financial — How a lot they spent
Every buyer is scored based mostly on these values, making a structured profile that reveals patterns.
| Phase | Avg Recency (days) | Avg Frequency | Avg Spend (₹) |
|-----------------|--------------------|----------------|----------------|
| Loyalists | 15 | 8 | 12,000 |
| One-Time Patrons | 140 | 1 | 1,500 |
| Huge Spenders | 22 | 3 | 18,000 |
| Dormant Customers | 190 | 2 | 2,000 |
RFM permits Nykaa to maneuver away from one-size-fits-all messaging and as an alternative group customers based mostly on precise shopping for habits.
👉Professional Tip: For those who’re implementing RFM, make sure you normalize the scores earlier than feeding them into clustering algorithms. This step ensures no single metric disproportionately influences the outcomes.
As soon as RFM knowledge is prepared, Nykaa applies Ok-Means clustering to search out behavior-based buyer teams.
- Preprocess Information: Normalize RFM values utilizing Min-Max or Z-score scaling.
- Discover Optimum Clusters (okay):Use the Elbow Technique to find out one of the best worth for okay. (Nykaa selected okay = 5.)
- Apply Ok-Means Algorithm
from sklearn.cluster import KMeans
from sklearn.preprocessing import MinMaxScaler
import pandas as pd# Instance RFM knowledge
rfm_data = pd.DataFrame({
'Recency': [15, 140, 22, 190],
'Frequency': [8, 1, 3, 2],
'Financial': [12000, 1500, 18000, 2000]
})
# Normalize
scaler = MinMaxScaler()
rfm_normalized = scaler.fit_transform(rfm_data)
# Apply Ok-Means
kmeans = KMeans(n_clusters=5, random_state=42)
rfm_data['Cluster'] = kmeans.fit_predict(rfm_normalized)
print(rfm_data)
To validate the standard of the clusters, Nykaa makes use of the next metrics:
| Metric | Worth | That means |
|----------------------|-------------|----------------------------------|
| Silhouette Rating | ~0.67 | Sturdy cluster separation |
| Davies-Bouldin Index | ~0.4 | Decrease = higher clusters |
| Inertia Drop | Sharp at okay=5| Supreme variety of clusters |
These metrics verify that the clusters are significant and actionable.
Listed here are a few of the personas recognized post-clustering:
| Cluster Title | RFM Profile | Nykaa’s Technique |
|------------------|--------------------------------|------------------------------------------------|
| Excessive Rollers | Latest, Frequent, Excessive Spend | Premium suggestions, luxurious launches |
| One-Timers | Low Frequency, Low Spend | First-time reductions, assessment prompts |
| Dormant Customers | Previous Recency, Low Frequency | Reactivation campaigns, flash gross sales |
| Champions | High R, F, M | Loyalty tiers, early entry, shock presents |
| Discount Hunters | Low Spend, Excessive Frequency | Sale notifications, budget-friendly bundles |
After segmenting their buyer base and focusing on them individually, Nykaa noticed main enhancements:
| Metric | Earlier than | After |
|-------------------|------------|-------------|
| Open Fee | 15% | 42% |
| Conversion Fee | 2.1% | 7.4% |
| Avg Order Worth | ₹850 | ₹1,120 |
| Repeat Buy | 1.3x/12 months | 2.6x/12 months |
Outcome: Personalization practically tripled conversions and doubled repeat purchases.
Right here’s a step-by-step blueprint:
- Collect not less than 12 months of buy knowledge
- Calculate Recency, Frequency, and Financial metrics
- Normalize your knowledge
- Apply Ok-Means clustering
- Analyze clusters → map them to personas
- Launch customized campaigns for every group
- Observe KPIs like open price, AOV, and conversions
Nykaa’s method is an ideal instance of how machine studying + buyer knowledge = loyalty at scale.
By combining RFM evaluation with Ok-Means clustering, Nykaa has constructed a robust engine for hyper-personalized advertising and marketing. This technique not solely improves buyer engagement but in addition reduces churn and will increase ROI on campaigns.
This method is scalable and efficient — any e-commerce model can replicate it.
Depart a remark or join — I’m glad to share the whole implementation.
