VF Corporation Data Warehouse and Geo-Demographics Case Study

Overview of VF Corporation

VF Corporation (NYSE: VF) is a global leader in the development of branded lifestyle apparel, including women's and men's jeans, outerwear, backpacks, footwear, sportswear, and occupational uniforms. The company operates in six different business segments globally, including contemporary brands, jeanswear, imagewear, sportswear, and outdoor and action sports brands (VF, 2011). The most successful product division is outdoor and action sports, which contributed 38.1% of revenue in 2009, followed by jeanswear at 34.9%. Additional contributions by product division include imagewear at 12% of total revenue, sportswear at 6.9%, contemporary brands at 6.5%, and other services and revenue streams at 1.5% of total revenue in 2009.

What is unique about the company's structure is the division by vertical clothing line dispersed throughout the Asian, Canadian, European, Latin American, and U.S. markets. At the time of the case, VF employed 46,600 people throughout its global operations. At the close of its latest fiscal year, the company had generated revenues of $7.2 billion during FY2009 and a net income of $736 million. This profit figure represented a 21.5% reduction from 2008, as slowed consumer spending and higher textile costs significantly slowed sales growth.

VF Corporation's stock performance illustrates how well the company's shares appreciated relative to competitors over a ten-year period. The competitors included in the analysis are Cherokee (CHKE), Dussault Apparel (DUSS), VOLV (VLOV), and the Warnaco Group (WRC). Comparing the stock performance of these companies over ten years to VFC demonstrates how effective the use of advanced information systems can be in rejuvenating a brand, as seen particularly in the 2008–2010 timeframe.

VF Corporation is well positioned to continue growing both in terms of market share and valuation, as it is the only U.S.-based competitor with such a widely diverse product line and branding position across the full spectrum of the market. VF also employs unique approaches to branding and segmentation based on geo-demographics, as the case indicates. The use of geo-demographics for analyzing large data sets is differentiating the sales and profit performance of retailers (Adnan, Longley, Singleton, & Brunsdon, 2010).

Case Analysis

There are many factors that contribute to VF Corporation's success in standardizing on a series of applications that enable geo-demographic and advanced market analysis. The company's legacy data, burgeoning in size, had begun to dwarf the scalability and performance of its existing personal productivity applications, including Microsoft Access and Excel. Many organizations still rely on these personal productivity tools for data warehousing analysis when more advanced applications are necessary to support strategic initiatives (Weller, 2007).

The case study demonstrates VF Corporation's success in meeting its data management and analysis challenges by placing a clear, overriding objective at the center of its efforts: integrating supply chain management, forecasting, and store expansion to gain greater insights into its customer base. This objective — having the right product on the right floor at the right time — illustrates how VF perceived the problem not as isolated, but as systemic to the entire business model of the company. Using analytics to create a unified, demand-driven supply network that could enable greater levels of business agility became the highest priority goal, which corresponds to successful uses of analytics to streamline retailing operations documented in other studies (Lewis, Hornyak, Patnayakuni, & Rai, 2008). With the focus on optimizing supply chain performance and making VF orders of magnitude more agile than it had been in the past, the company also moved toward anticipating demand through forecasting more effectively than its competitors. This is because, when a demand-driven supply network is created, each source of demand and its variation must be anticipated (Lewis, Hornyak, Patnayakuni, & Rai, 2008).

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 VF's brands; the company relies on this data to inform marketing, location development, store introductions, and pricing strategies. In reality, the data delivered for these marketing programs and location-based analyses also provides an agile and scalable platform for VF to more effectively manage and mitigate supply chain risk.

Relying on Alteryx for data analysis — which has superior capability relative to Microsoft Access and Excel — in conjunction with SRC Software for geo-demographic analysis, VF has created a workflow for translating data warehouses into the foundation 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 more efficiently integrating data warehouses into VF's analysis tools. A secondary objective of building an effective retail network is also evident in how geo-demographic analysis is used for selecting, investing in, and launching store locations (Thompson & Walker, 2005). Geo-demographic analysis can identify 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).

Conclusion

At first glance, it appears VF Corporation is succeeding with geo-demographics due to its focus on using the insights gained to better manage branding, marketing, and location-based analysis. In reality, the real-time geo-demographic data generated from its SRC systems serves to unify the company's business model by providing greater direction and focus to its supply chain. This in turn creates a demand-driven supply network (Lewis, Hornyak, Patnayakuni, & Rai, 2008). The result is that VF Corporation becomes more competitive by applying these techniques and translating data warehousing capabilities into a sustained 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.

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

Lee, Y.-I., & Trim, P. R. J. (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.

Thompson, A., & Walker, J. (2005). Retail network planning — Achieving competitive advantage through geographical analysis. Journal of Targeting, Measurement and Analysis for Marketing, 13(3), 250–257.

VF, Inc. (2011, February 1). Hoover's company records, 112083. Retrieved February 9, 2011, from Hoover's Company Records.

Weller, S. (2007). Fashion as viscous knowledge: Fashion's role in shaping trans-national garment production. Journal of Economic Geography, 7(1), 39.