The Fuzzy Clustering model groups users in homogeneous groups based on the buying behavior of specific products or categories. For each customer, the Fuzzy Clustering model finds the top three clusters and their associated probabilities.
Fuzzy clustering enables customers to be part of multiple groups and calculates the probability of belonging to each cluster. The output for each scored user is the name, id, and probability for the first, second, and third most likely clusters.
Treat customers in the same cluster differently based on their probability of
belonging to that cluster. For example, you can run two different campaigns
for the customers with 0.98
and 0.25
probability.
Target customers that belong to multiple clusters. For example, you can
create a menswear-accessory
bundle deal that consists of the customers with
0.60
probability in menswear
and >=0.25
probability in
accessories
.
Engage with your customers better without overspending. You can create surgical discounting or pricing tests. For example, you can give a lower discount for product X (or similar products) to the customers that belong to cluster(s) aligned with product X. If a customer already likes product X, you don’t need to give a high discount.
Improve retention, loyalty, and lifetime spend by encouraging customers to buy different products. You can create tests by providing higher discounts for product X (or similar products) to customers in clusters that are either negatively indexed towards product X or do not feature product X at all. When you convert such customers to purchase product X, you help improve retention and gain incremental revenue without impacting margin. As such customers would not have purchased these products prior to your tests, the benefits are substantial.
Use Metrics to identify the most common one-time purchased product. Based on that, you can create campaigns to encourage one product purchasers to buy other products, thereby improving their retention and lifetime spend.
A customer may not strongly belong to any specific cluster and instead have all the cluster probabilities almost equal. For example, you may want to treat your customers in the same cluster differently if one belongs in that cluster with .98 probability and the other with .25 probability.
Some marketing mediums are expensive. Therefore, you can use the fuzzy
clustering model to further filter a cluster. Fuzzy clustering provides a way
to group such customers. For example, you may want to target the customers with
>=0.8
probability of belonging in a cluster. Alternatively, you can include
more customers or target the customers whose first or second most likely
cluster is a certain cluster.
The Fuzzy Clustering model predicts the behavior of buyers.
One-time buyer
.Non buyer
.For each contact, the model calculates the purchase behavior features in a specific category.
The model binds historical data multiple times using a range of values for clusters. It also generates various graphs and metrics, and uses them to determine the clusters that can be used for scoring. The goal is to have stable and meaningful clusters. that exhibit reasonable statistical difference in the purchase behavior.
The name and probability of the first, second, and third most likely clusters are displayed in the CDP user interface.
CDP displays the cluster names for the top three clusters in 360 > 360 Profiles. In addition, CDP displays the cluster names and probabilities for the top three clusters in Actions > Campaigns, Actions > Campaigns+, and Analytics > Metrics.
To learn more about how the Fuzzy Clustering model can enhance your marketing workflow, contact us.