Today’s forward-thinking retailers are seeking relevant, agile and intelligent solutions to drive their targeted marketing efforts. They recognize data as a strategic asset and leverage it to make critical business decisions and strengthen their competitive advantage.

However, despite the increase in retail channels and modes to identify potential buyers, many retailers haven’t been able to quickly adapt to changing times. Their customer segments remain broad and ill defined.

One of the major obstacles to identifying the right customers in a targeted marketing campaign is an organization’s inability to predict future purchase behaviors. Advanced clustering can help resolve this issue.

Clustering is a powerful technique for identifying data with similar characteristics. Advanced clustering techniques can be used to group customers based on their historical purchase behavior, providing retailers with a better definition of customer segmentation on the basis of similar purchases. The resulting clusters can be used to characterize different customer groups, which enable retailers to advertise and offer promotions to these targeted groups. In addition to characterization, clustering allows retailers to predict the buying patterns of new customers based on the profiles generated.

Today’s business intelligence technologies enable retailers to have targeted relationships with their customers through the use of customer intelligence systems that capture data on three levels:

  • Basic customer information, such as customer profile/ID, location and purchase price
  • Product information, such as segment, brand, product hierarchy and product size
  • Transaction information, including volume sold, invoice details, date and product ID

While classic techniques, such as k-means, have been readily used in the past, they do not yield proper results when clustering a large dimension and highly sparse (approximately 99%) dataset. In addition, the newer variants, such as ROCK (Robust Clustering Algorithm for Categorical Attributes), RSKC (Robust and Sparse K-means Clustering) and Skmeans (Spherical K-means), suffer from heaping and/or instability problems. These classic techniques need to be tweaked to handle this special dataset in order to arrive at informative and satisfactory results. This is where CLUTO (Clustering Toolkit) has proven to be very successful.

CLUTO contains partitional, agglomerative and graph-partitioning clustering algorithms, has low computational requirements and has been shown to produce high-quality clustering solutions. Further, results of clustering with CLUTO have been very encouraging when clustering a highly sparse dataset.

A deeper understanding of customer segments is possible by developing a 3D-model of the clusters based on key business metrics, such as orders placed, frequency of orders, items ordered or variation in prices. This business relevance makes it easier for decision makers to identify the problematic clusters that force the retailer to use more resources to attain a targeted outcome. They can then focus their marketing and operational efforts on the right clusters to enable optimum utilization of resources, including:

  • Price Analysis: Clustering is a starting point for deeper price analysis to derive actionable insights and improve the volumes sold based on predicted changes in purchase patterns to changes in prices within each identified cluster.
  • Distribution Analytics: Distributors can gain from product clustering too, as it helps them identify products that can be clustered or bundled together so they can avoid multiple trips and optimize transportation resources.
  • Predictive Insights: Product clustering can provide retailers with predictive abilities, enabling them to map a new customer to already existing product clusters based on identified customer attributes, such as business category, location and services offered.
  • Promotion Analysis: Grouping similar products on the basis of product clustering can help retailers identify product bundles to boost sales and enhance the number of products ordered by a particular customer based on identified similarities in product selection.

While the predictive power offered by product clustering can transform the results of targeted marketing, product clustering is most effective when used within a suite of other retail analytics solutions. The value adds of product clustering is visible in a large dimension and highly sparse dataset. In addition to improving the return on marketing investment (ROMI) in terms of customer profitability, product clustering can help retailers target and transform the dormant customers (customers in the poor category).

The applications of clustering are widespread and can also be applied to groups of similar documents, and items enabling process efficiency in digital research and related areas.

Optimization of scarce marketing resources is becoming very important in this age where customer data is generated in humongous volumes, and the predictive power of clustering can transform the way companies approach targeted marketing in the future.

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