Predicting Consumer Tastes With Big Data at Gap [PDF]

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ITM Case Study

Predicting Consumer Tastes with Big data at Gap

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INTRODUCTION ●

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ART PECK, CEO & HBS MBA ‘ 79, was struggling to turn around Gap Inc. In the following 2 years of declining sales in an environment where many brick-and-mortar retailers were under pressure. Gap’s troubles were not new to Peck; the company had been struggling to regain its footing since 2000. The way he hoped to improve operations was to eliminate the position of creative director for each of the firm’s fashion brands and to replace them with a more collective creative ecosystem fueled by the input of big data. Peck pushed the company to use the mining of big data obtained from Google Analytics, Google Trends, social media and the company’s own sales and customer database. Peck was upsetting the delicate balance between creativity and commercialization, between designers and merchants. Peck expand the business by online distributing Gap’s brand on Amazon.

Company Overview ●

Gap Inc. was founded in 1990 by Donald and Doris Fisher. It was the largest example of genre, with 135,000 employees and 3659 company-owned and franchised retail locations in 50 countries.

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Gap Inc. managed 5 brands: Gap, Banana, Republic, Old Navy, Athleta and Intermix.

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In 1983, Millard “Mickey” Drexler became CEO of Gap Inc. In his tenure sales grew from $480 million to $14 billion and later in 2000 it grew up to $42 billion.

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In 2002, Drexler left Gap due to declining in sales for eight consecutive quarters because he attempted to inject more fashion into Gap to attract younger shoppers who were migrating to edgier competitors.

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Gap produced hundreds of unique products, each offered in a variety of colors and sizes. Gap offer two types of product that is basics with styles that endured across seasons and more fashion-forward items that captured the spirit of a particular season.

Digital and Big Data at Gap Inc. ● ● ●

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Art Peck heavily invest in digital capabilities to address consumers’ shift to omnichannel shopping. Peck digitized the company’s entire product inventory and introduced retail services, which made it easy for customers to browse, purchase and receive their items seamlessly across channels. Peck promoted data-driven decision making and pushed his team to utilize big data to learn more about customers’ behaviours and thereby deliver a better customer experience. Gap was working with Google and Avametric to develop an augmented reality app that allowed shoppers to test out different looks in order to improve their online and mobile shopping experiences. Gap developed email programs to provide relevant, personalised messages to consumers.

Challenges faced by Peck As a CEO 1. 2.

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3. 4. 5. 6.

Stiff Competition A. H&M and Zara were the competitors Slow growth in core markets A. 3 trillion $ dollar apparel industry B. Change in consumers buying habits Rise of Fast Fashion A. New styles at low price appearing in stores in a weekly basis Frequent & Heavy discount of 40% GAP’s size and ubiquity were transferring from asset to a liability Rise of E-commerce A. Shift from brick and mortar stores to offline channels

PECK’S PRODUCT STRATEGY ● ●

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In 2011: Fired GAP’s head of design, Patrick Robinson. In 2012: Robinson’s replacement, Rebekka Bay, was hired and 3 years later he dismissed Bay. In 2014: Creative Director Marissa Webb, was hired to leverage her sensibility and credibility with younger consumers Webb stepped down in October 2015. Neither Bay nor Webb was replaced. Instead pecks solution was to eliminate the position of creative director. Some retail analyst were skeptical while others were optimistic.

Big Data in Predictive Analysis in Marketing Using data Mining and Machine Learning

Using predictive

Using predictive

Analysis to sell

Analysis for new

Existing

product

products

development

ITM Case Study on Big Data

Predicting Consumer Preferences Traditional ways were inadequate so we need to change and find new ways ●

Preferences are constructed and not revealed and therefore unpredictable.

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Choices are influenced by various factors.

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Changing fashion and trends.

Product 3.0 at Gap “A clear brand vision with a common operating model” The brand vision governs every design, merchandising, inventory and production so that Gap Inc. can identify trends, make them relevant to its customers, test them in stores and respond to demands. Buying more of ITM Case Study on Big Data

those that sell and quickly and moving away from those that don’t with a goal of fewer fashion misses and markdowns.

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In intimation of fast fashion competitors, they wanted to increase its competence at combining spotting trends with reading real-time performance and acting faster on that.

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In place of a creative director, each brand’s vision statement served as a filter so that trends could be incorporated consistent with the brand’s

ITM Case Study on Big Data

image. ●

Product 3.0 relies heavily on the analysis of customer purchase data.

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To implement Product 3.0 , they shifted some manufacturing from asia to the caribbean to receive items faster, by implementing fabric platforming, buying large quantity and holding it in inventory so that design could be quickly created in response to of the moment trends.

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Reinvigorate Gap focused on fixing the product.

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Tightened inventory to reduce the need for deep discounting

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Making Changes in the Distribution Model

Amazon has an undisputed presence in E-commerce Retailing and Gap has took notice of it. GAP has been considering it to partner with it to expand its reach

GAP’s partnership with Zolando and Taobao has been successful, and provide it with lots of marketing insights using their AI systems.

THINGS TO BE VARY OFF- Partnering with E-Commerce platform irk the offline retailers. THe retailers fail to understand that online mode helps in gaining lots of market intelligence.

The Way Forward For success in Clothing Industry following things are important: ● ●

ITM Case Study on Big Data

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Right Product Assortment Gauging Consumer interest and preferences Reducing lead times in providing new variety to customers

For getting the above things right, AI and Big Data collected by E-Commerce sites provide with useful insights that offline retailers lack. Online Retailing provides useful market insights, which will help to survive in the market.