Data Warehouse Case Study VF Case Study

Excerpt from Case Study :



From Supply Chain Efficiency to Customer Segmentation Focus

Because of this focus on supply chain forecasting accuracy and efficiency, the need for capturing very specific customer data becomes critical. The case study portrays the capturing of segmentation data as focused on growing each of the brands mentioned that VF relies on this data to base marketing, location development and store introductions, and pricing strategies on. In reality, the data delivered for these marketing programs and location-based analyses is also providing an agile and scalable platform for VF to more effectively manage and mitigate its supply chain risk as well.

Relying on Alteryx for data analysis as it has superior capability to Microsoft Access and Excel in conjunction with the use of SRC Software for geo-demographic analysis, VF has created a workflow for translating data warehouses into the basis of marketing and supply chain strategies. The strategic goal of getting the right product on the right floor at the right time is further supported by secondary objectives of making data warehouses more efficiently integrated into the VF data warehousing and analysis tools. A secondary objective of more effectively creating an effective retail network is also shown in how the geo-demographic analysis is used for selecting, investing in and launching store locations (Thompson, Walker, 2005). Geo-demographic analysis can illustrate where the best possible income and age demographics exist to support a new store (Lee, Trim, 2006). In addition to these customer-centric measures of performance, geo-demographics can effectively be used to optimize a distribution network to mitigate supply chain costs and inefficiencies (Lewis, Hornyak, Patnayakuni, Rai, 2008).

Another factor that shows how VF is attempting to unify its entire business model with analytics is how focused the organization is becoming on making analytics real-time in nature to measure store, brand and location performance, which is an emerging best practice in the retail industry (Adnan, Longley, Singleton, Brunsdon, 2010). This focus on using geo-demographics to accelerate their entire business model is also seen in how the company is working to streamline the new market forecast or market potential insights as well. The use of geo-demographics to more effectively mitigate risk across customer segmentation, location selection and performance strategies, while developing more effective forecasts is critical to the profitable growth of retailing businesses (Foote, Krishnamurthy, 2001).

Conclusion

At first glance, it appears VF Corporation is being successful with geo-demographics due to its focus on using the insights gained to better manage branding, marketing and location-based analysis. In reality, the use of real-time geo-demographic data generated from their SRC systems are serving to unify its business model by providing greater direction and focus to its supply chain. This in turn creating a demand driven supply network (Lewis, Hornyak, Patnayakuni, Rai, 2008). The result is that VF Corporation is made more competitive using these techniques and translating data warehousing into a competitive advantage over time (Thompson, Walker, 2005).

References

Adnan, M., Longley, P., Singleton, a., & Brunsdon, C. (2010). Towards Real-Time Geodemographics: Clustering Algorithm Performance for Large Multidimensional Spatial Databases. Transactions in GIS, 14(3), 283-297.

Paul Sheldon Foote, & Malini Krishnamurthi. (2001). Forecasting using data warehousing model: Wal-Mart's experience. The Journal of Business Forecasting Methods & Systems, 20(3), 13-17.

Yang-Im Lee, & Peter R.J. Trim. (2006). Retail marketing strategy: The role of marketing intelligence, relationship marketing and trust. Marketing Intelligence & Planning, 24(7), 730-745.

Lewis, M., Hornyak, R., Patnayakuni, R., & Rai, a.. (2008). Business Network Agility for Global Demand-Supply Synchronization: A Comparative Case Study in the Apparel Industry. Journal of Global Information Technology Management, 11(2), 5-29.

Andy Thompson, & Jonathan Walker. (2005). Retail network planning - Achieving competitive advantage through geographical analysis. Journal of Targeting, Measurement and…

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References

Adnan, M., Longley, P., Singleton, a., & Brunsdon, C. (2010). Towards Real-Time Geodemographics: Clustering Algorithm Performance for Large Multidimensional Spatial Databases. Transactions in GIS, 14(3), 283-297.

Paul Sheldon Foote, & Malini Krishnamurthi. (2001). Forecasting using data warehousing model: Wal-Mart's experience. The Journal of Business Forecasting Methods & Systems, 20(3), 13-17.

Yang-Im Lee, & Peter R.J. Trim. (2006). Retail marketing strategy: The role of marketing intelligence, relationship marketing and trust. Marketing Intelligence & Planning, 24(7), 730-745.

Lewis, M., Hornyak, R., Patnayakuni, R., & Rai, a.. (2008). Business Network Agility for Global Demand-Supply Synchronization: A Comparative Case Study in the Apparel Industry. Journal of Global Information Technology Management, 11(2), 5-29.

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