The Value and Limits of Replacing the Role of Creative Director With Big Data: Struggling to Understand Consumer Tastes and Offer Differentiation
Years after replacing the role of creative director with big data, Gap Inc., along with other global retailers, struggles to understand its target consumer and satisfy the need for scarcity and unique offerings amid an Amazon-driven retail climate.
No longer a pioneer in the middle-tier apparel sector, Gap Inc. found itself in a predicament around its ambiguous brand identity that left consumers confused as to what the retailer stood for. CEO Art Peck replaced the position of creative director in 2017 with his data-driven Product 3.0 strategy for its trendspotting and market-responsive capabilities. Evident from Gap’s struggle with defining its brand identity, predictive analytics alone cannot outperform visionaries in an industry that is an art as much as it is a science.
Amid the digital noise and competitive environment, retailers tap into the pulse of fashion and use quantitative skills for interpreting robust data. Like Peck, many CEOs invest in AI technology to personalize the online shopping experience and reduce the cost of returns. Increasing consumer sentiment and reducing costs in conjunction with sales may improve profit margins. Today retailers still question the value and limits of predictive analytics and whether big data should replace a creative director. Analytical tools handle large volumes of inputs and produce functions that prove the statistical significance of trend forecasts. Though big data helps align product development with consumer demand, retailers must apply data in the context of how fashion reflects a slew of cultural and economic influences. Big data fails to replace human forecasting abilities in accounting for the complexity of consumer attitudes and desired brand image. In today’s Amazon-driven retail climate, retailers must strike a balance between fostering differentiation and emotional connections whilst reaching consumers with speed and precision. Rethinking the importance of a creative director, retailers can implement new solutions for acquiring product-driven consumers in an over-saturated market.
Reliance on Deep Promotional Discounting
Relying on a product-centric vision, Peck implemented a data-driven business model ineffective for sustaining the balance between repeating and pioneering trends. Neither able to create a sense of scarcity for its replenishable pieces nor innovate its fashion-forward apparel, Gap dilutes its brand value with hefty discounts to clear a glut of sitting inventory and drive sales volume. Though retailers try to untrain discount behavior, smart consumers remain patient for prices to lower or switch to competitor sets.
No One-Size-Fits-All Approach
The disparity between sales for Gap, Banana Republic, and Old Navy (see Exhibit 9) may be attributed to Peck’s attempt to blanket the Product 3.0 strategy across all of Gap’s brands — each of which identifies with a unique target customer and product-price offerings. Since 2012, Gap has seen a decline in business performance (see Exhibit 7), such that decreasing marketing expenses failed to translate to bottom-line growth. Although useful for comparing historical measurements, first-party data alone reveals little insight into a brand’s distinct creative direction and product lifecycle.
Revisiting Loyalty Strategies
Third-party sellers, such as Amazon, present opportunities to acquire customers at a low cost; however, retailers risk jeopardizing direct-to-consumer sales. Tiered loyalty programs or buy more, save more promotions allows retailers to gain more control over customer relationships and manage brand equity. Strategic loyalty initiatives can translate to high spending, repeat customers and thus provide more incentives for consumers to self-identify on the retailer’s own channels.
Need for Human Inputs
Big data and predictive analytics efficiently generate algorithms but require an initial manual input of datasets. Efficient planning for the optimal product assortment requires putting fashion into context. Withstanding external pressures, retailers implement sophisticated algorithms, but automation may cannibalize sales if neglecting to input qualitative analyses on desired inventory levels.
Balancing Big Data with Human Forecasting
Outputs generated from analytical tools may statistically differ from forecasts predicted by human forecasters. For example, mean comparisons between pattern, color, and design details — each of which may vary in trend stability — provide little insights into the cyclical nature and explanatory forces of fashion. To keep up with the shift in digital behavior and consumer preferences creative directors should use big data in the context of cultural shifts and economic factors by observing the macroenvironment, microenvironment, and appropriate buying-selling curves. The art and science of forecasting involve processes around landed costs, retail prices, and markups — all of which are subject to change due to variables around the nature of the market plus the product type. Achieving optimal price-value offerings requires a 360-view of consumer demand and competitor insights. A holistic view of the core consumer, competitive market, and price elasticity can help guide retailers toward a nimbler model for changing variables. Maintaining perceptions around brand equity, retailers can manage customer loyalty and sustain higher prices, modifying discounting depth based on changing inventory levels and consumer demand. With a realistic and accurate merchandise plan, retailers mitigate the risks of losing control over perceived brand value and customer relationships.
References
Israeli, A. (2017, July 10). Predicting Consumer Tastes with Big Data at Gap. Retrieved November 08, 2020, from https://hbsp.harvard.edu/product/517115-PDF-ENG
