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Gap Inc. Predicting Consumer Tastes with Big Data Case Study

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Updated: May 24th, 2021

Introduction

Established in 1969, Gap Inc. turned out to be a renowned clothing company before the changing market landscape started undermining its performance during the onset of the 21st century. Leadership changes that sought to enhance this organization’s performance saw Art Peck assume office as the new CEO in 2015 after being promoted from the position of President of Growth, Innovation, and Digital operations.

Peck implemented a digital strategy that used big data analytics to revive the declining sales of clothing products offered by Gap Inc. According to my thoughts regarding this case, Peck’s approach to eliminating the position of creative directors in the company’s fashion brands and replacing them with data analytics to influence product innovation marked a turning point for this company. Specifically, the use of Amazon to boost the expansion of Gap’s distribution network led to increased sales at the global level.

My Thoughts

One major issue I note from this case is that Gap initially failed to keep up with the observed changing customers’ behaviors in the clothing market. I attribute the dropping sales volumes experienced by Gap. since the early 2000s to this company’s inability to take into account the dynamics of clients’ behaviors in the apparel market. According to the case given, Gap failed to put in place an appropriate product assortment mechanism in line with customers’ changing needs and expectations in the fashion sector (Israeli and Avery 5). As a result, competitors such as Zara and H&M could easily gain a considerable share of the apparel market in the United States by offering fast fashion items at low prices compared to the case of Gap.

Another notable element revolves around Gap’s lack of recent innovations that can suit the needs of clients seeking basic and fashion-forward clothing products (Israeli and Avery 3). In my view, the application of traditional methods of predicting customers’ future tastes and behaviors was ineffective for Gap. As a result, clients shifted to rival companies that provided trendy apparel for both basic and fashion-forward items. Gap’s use of conventional market research approaches was ill-informed. Such methods could not allow this company to predict its clients’ future behaviors and deploy factors that could influence their tastes and fashion preferences.

The need for customer engagement emerges as a notable factor for Peck and the management team at Gap. This company can incorporate this element into its product innovation aspect. In my opinion, customer engagement can allow Gap to understand its clients’ changing tastes, which are influenced by the aesthetic element of its products. Any adjustment of the prevailing promotion outlook based on reviews by customers is a step towards Gap’s innovative business practices. According to the case given, the lack of commitment among customers has contributed to this company’s failure to understand their fashion preferences.

In my view, Gap’s management needs to appreciate that customers’ fashion requirements are socially constructed by their collective behaviors and similarity of tastes and preferences (Israeli and Avery 8). Thus, the failure of design directors to consider the impact of social influences on consumers’ fashion tastes undermined Gap’s product innovation goals of matching its products with the changing market needs.

Another notable issue involves this company’s inevitable need for deploying big data to boost product innovation. The earlier mentioned market research strategies adopted by Gap were counterproductive. In my opinion, this company can adopt new analytical methods for assessing customers’ behaviors in the apparel market. Embracing big data to gauge apparel trends and clients’ changing preferences is vital because it influences Gap’s success in its industry of operation (Israeli and Avery 3). Hence, I believe that Peck’s decision to get rid of creative directors while utilizing big data analytics to foster the development of creative apparel to suit clients’ preferences in various segments is appropriate.

Big data analytics offers Gap the opportunity to effectively predict its clients’ future behaviors. This approach considers customers’ current trends and their historical interactions with the company’s website and social media platforms (Israeli and Avery 3). Currently, Peck sees data mining from various records as an effective way of replacing the role of creative directors who have become less impactful on the contemporary apparel design and development field. Consequently, in my view, the use of the Amazon e-commerce platform to broaden the distribution of Gap’s apparel is necessary since it not only bolsters sales but also the mining of relevant data.

Another notable idea entails Gap’s move to sell three brands through Amazon. This approach has the potential of allowing this company to boost its sales by accessing new customers who do not prefer the current distribution network. Furthermore, selling Gap’s brands through Amazon will enable this organization to acquire an entirely new data stream useful towards understanding the shopping habits of existing and potential clients (Israeli and Avery 8). Consequently, Gap will gain a better understanding of the trends, as well as the evolving customers’ behaviors in the modern apparel market.

My Overall Opinion

The adoption of technology in Gap’s market research is crucial towards augmenting its sales in the highly competitive apparel industry. I attribute the failure of this company to keep up with new market trends and customers’ preferences to its reliance on counterproductive approaches to market research and product development. As a result, creative directors in charge of designing items in the three brands offered by Gap could not keep up with consumers’ evolving fashion tastes. These clients had already integrated technology into their shopping behaviors. In my perspective, the adoption of technology will foster the prediction of customers’ behavior and the expected fashion trends, thus allowing Peck and his managers to record improved product innovation to suit clients’ preferences.

Technology has also revolutionized the way businesses operate in the modern market, as denoted by the adoption of online platforms to boost sales and gauge consumers’ purchasing trends and market patterns. Hence, by broadening its e-commerce engagements through online retail companies such as Amazon, Gap will not only increase sales but also mine data for its market analytics purposes. Such an innovative approach is crucial to the success of Gap in the current clothing industry.

Conclusion

The failure of Gap to adapt to the changing patterns in the clothing market has undermined its competitiveness, as denoted by the dropping value of sales in the recent past. Creative directors in the company have failed to take into account the dynamic aspects of the contemporary market, thereby failing to develop brands that suit consumers’ evolving fashion tastes.

As a result, based on my thoughts and the issues I noted from the given case, Peck’s decision to deploy big data analytics in place of creative directors is conceivable since it enhances product innovation in line with customers’ transforming shopping habits and preferences. Overall, Gap may need to utilize technology to realize various market intelligence goals of remaining competitive in an industry characterized by intensified rivalry.

Work Cited

Israeli, Ayelet, and Jill Avery. “Predicting Consumer Tastes with Big Data at Gap.Harvard Business School. 2018, pp. 1-27.

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IvyPanda. (2021, May 24). Gap Inc. Predicting Consumer Tastes with Big Data. Retrieved from https://ivypanda.com/essays/gap-inc-predicting-consumer-tastes-with-big-data/

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"Gap Inc. Predicting Consumer Tastes with Big Data." IvyPanda, 24 May 2021, ivypanda.com/essays/gap-inc-predicting-consumer-tastes-with-big-data/.

1. IvyPanda. "Gap Inc. Predicting Consumer Tastes with Big Data." May 24, 2021. https://ivypanda.com/essays/gap-inc-predicting-consumer-tastes-with-big-data/.


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IvyPanda. "Gap Inc. Predicting Consumer Tastes with Big Data." May 24, 2021. https://ivypanda.com/essays/gap-inc-predicting-consumer-tastes-with-big-data/.

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IvyPanda. 2021. "Gap Inc. Predicting Consumer Tastes with Big Data." May 24, 2021. https://ivypanda.com/essays/gap-inc-predicting-consumer-tastes-with-big-data/.

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IvyPanda. (2021) 'Gap Inc. Predicting Consumer Tastes with Big Data'. 24 May.

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