The Product Clustering model is an unsupervised learning model that groups customers based on the type of products they buy or do not buy. In other words, this model groups customers based on their buying behavior of specific products or categories.
The model uses the k-means algorithm and iteratively assigns customers to the nearest cluster based on their euclidean distance from the centroid of the clusters.
Use cases
The following use cases describe how Acquia product-based clusters help you produce relevant and personalized touches, thereby increasing customer engagement and reducing marketing costs:
More targeted product or brand campaigns
When creating a product-focused campaign, quickly identify and target the customers or clusters that have previously purchased these products. Such campaigns help to increase affinity toward the products that customers already prefer.
When launching a new product, identify a current similar product, locate the customer cluster aligned with the existing product, and target customers in those clusters in the launch campaign. This ensures a high response rate because you target customers with similar product affinities.
Deepen product affinity and increase promotion spend effectiveness
Engage customers efficiently without overspending.
Create surgical discounting or pricing tests. For example, you might test giving a lower discount for product X or similar products to customers that belong to cluster(s) that are aligned to product X. If a customer already likes product X, the customer does not need the incentive of a high discount to buy product X (or products similar to it).
Broaden customer’s purchased product mix
Improve retention, loyalty, and lifetime spend by encouraging customers to buy different products.
Create tests 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 improve retention and gain incremental revenue without cannibalizing margin. The benefits are large because these customers, in all likelihood, would not have purchased these products prior to your tests.
Use product clusters to identify single-product buyers
Use Metrics to identify the most common one-time purchased product. Using this information, you can create campaigns to encourage single-product buyers toward buying other different products, thereby improving the retention and lifetime spend.
Target audience
The Product Clustering model predicts the behavior of buyers.
A buyer is a customer with a transaction in the past three years.
The model is trained on the customers with more than two transactions in a particular time frame.
The customers with more than two transactions in a particular time frame are scored by being assigned to their top three clusters and probabilities.
The customers with one transaction are labeled as
One-time buyer.The customers with no transactions are labeled as
Non buyer.
How does the model work?
For each customer, the model:
Considers the number of transactions (not total revenue or number of transaction items) in a time frame per product category at a given level.
Applies balancing to product categories based on:
Importance of the product category. For example, customers might frequently buy “Carry Bag”, but it may not be important to the client.
Popularity of the product category. For example, customers might frequently buy chewing gums at the checkout. However, it can be a known fact to the client and may not be deemed necessary or useful to surface in the product cluster.
The output for each scored customer consists of the product cluster id, such as Menswear, Womenswear, Accessories, that the customer is most closely associated with.
Using the Product Clustering model
CDP displays the output from the Product Clustering model in:
360 > 360 Profiles
Actions > Campaigns and Actions > Campaigns+
Analytics > Metrics
To learn more about how the Product Clustering model can enhance your marketing workflow, contact us.