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Gap's Big Data Strategy for Predicting Consumer Tastes

Discuss about the Reflections on Societal and Business Model.

The project is based on “Predicting the consumer tastes with the big data at GAP”. GAP is a clothing as well as accessories retailer which is used of digital strategy for managing their underperforming retail stores. The company is used to collect insights from the online browsing activity of the customers along with engagement into the social media platforms. It helps to understand the customer's buying decisions from the physical stores (Ekbia et al. 2015). The company is used of big data and predictive analytics into their marketing operations to sell of existing products and new development of the product. Gap Inc. can able to discover new products on the platforms (Millington and Millington 2015). It can be created of real human connections which is one of the most important benefits of the social media for the business.

The business case describes the six steps such as define the opportunity, identify the alternatives, gather the data and estimate the timeframe, analyze the alternatives, and assess the risks and communication plan. The company assesses the business opportunities into the retail industry along with diverse the possible strategic responses based on the market intelligence and innovates of trends as well as change into the customer preferences.

The digital marketing strategy aims to develop a performance of GAP Inc. which includes of engagement into the social media, sales the products on online shops, brand awareness as well as attitude. Based on the social media platforms, GAP can interact with the customers as well as an increase in engagement (Akter and Wamba 2016). Better is the website traffic. With the help of the search engine marketing, GAP can able to know about the customers and build a long-term relationship with the customers to maintain a long-term profit. The business objectives of the GAP are as follows:

  • To drive traffic to the website of GAP
  • To increase social media followers of GAP
  • To encourage interaction among GAP as well as its customers
  • To increase sales and brand awareness

In order to achieve the GAP's business objectives, the company is decided to invest in the big data as well as analytics. GAP can generate emails in a consumer-friendly manner to promote their products based on gender as well as age. It is customized, and the email is sent with a special offer on the birthday of a consumer (Kshetri 2014). GAP is targeted of Amazon, Netflix to display their advertising. The digital advertising should be a banner with the GIF picture with the slogan on it. The advertisements are being placed on a side of the website.

The Business Case for Gap

The business opportunity of GAP for further IT investment is as follows:

  • GAP considers next steps towards digital analytics strategy to improve their retailing business
  • GAP assess opportunities along with threats to digital disruption into the retail industry along with diverse the possible strategic responses based on the market intelligence
  • GAP stays forefront into the fashion industry innovation by gauging apparel trends as well as change into the customer preferences
  • GAP directs analytics capability investment dollars

A gap is redefined the digital experiences into the retail industry. To become world's largest retailer, the company gets advantage into their digital strategy such as leading brands, larger customer base as well as a significant base of the technical expertise (Millington and Millington 2015). Gap is responsible for digital experiences over millions of their customers and brands like Gap, Athleta, Old Navy, Intermix and banana republic. The company drives brand online and gains revenue growth throughout coordination of digital marketing activities for their brand (Moser 2015). The impact of big data into the digital marketing of Gap is to understand the customers. The service provider can utilize data in innovative ways at the time of designing of product for the target customers. The target customers for Gap are both female and male.

The manufacturers of GAP have chosen two alternatives for their digital business strategy to sell the products on Amazon. Firstly, the alternative is to become a third party seller on the marketplace of Amazon (Grover and Kar 2017). The manufacturer can control pricing as well as customer relationship. The company chooses to ship the products directly from the warehouse and provide inventory to the Amazon. Secondly, the alternative is to achieve of wholesale model, where the manufacturers can sell the items to Amazon, and then the company decides to sell, price as well as fulfill the requirement of products to the customers (Sugimoto, Ekbia and Mattioli 2016).   

Big data is required to inform the digital marketing strategy, but the data are extracted. Big data are huge data sets that are analyzed to reveal the marketing trends, as well as patterns, assess the human behavior. It is being assumed that:

Assumption 1: The old people are not using the online website for purchasing of products as they are not aware of using the online methods (Matz and Netzer 2017).

Assumption 2: Everybody wants to use of credit cards, and some of them are used of PayPal to provide payment for the purchasing products.

Assumption 3: The Company is aware of the wants and requirements of their customers (Sanders 2016). Therefore, it becomes easier for them track the buying and purchasing decisions of the customers.

Assumption 4: Social media is a profitable venture as it is like word-of-mouth which makes the conversion better and harder to reach their target customers.

Big Data's Impact on Digital Marketing Strategy for Gap

Gap Inc has built out a profitable online business with growth in sales as well as industry-leading capabilities. The company is decided to involve with Amazon; an e-commerce platform enables wide-ranging capabilities which includes cross brand shopping in addition to Omni-channel services (Artun and Levin 2015). The company has taken of two alternatives to grow their retail business into the market such as involved with the third-party seller of Amazon, and the manufacturers can sell the items to Amazon. The following table shows the alternatives of Gap Inc. such as:

Alternative 1:

To become a third party seller on the marketplace of Amazon

Pros

Cons

The pro of selling products on Amazon is that it is trusted and respected e-commerce website; therefore it will automatically raise more sales (Morris 2015). They are customers who are ready to buy as there are many customers who trust Amazon. In order to build the trust of the customers, Amazon is the best site to convince the customers to buy (Loebbecke and Picot 2015). Amazon has a built-in trust which transfers when the customers are making a purchase. Amazon has a larger number of affiliates those refer traffic to the Gap's store when the company is selling items in a niche.

Some of the customers feel that the fees of Amazon are high. Therefore it would become difficult to sell the products throughout the Amazon e-commerce website. The technology is too hard to understand for the customers. Therefore it requires hiring someone who helps to train on how to ship and list the inventory (Hainmueller and Hiscox 2015). Finally, there is no control over branding as Amazon has many sellers, then it is hard for Gap to sell their unique items (Wang 2018). It will be hard for Gap to brand the products.

Alternative 2:

To achieve of wholesale model, where the manufacturers can sell the items to Amazon

Pros

Cons

After achieving wholesale model, it saves money as well as streamlines the purchasing system (Ghosh et al. 2017). It improves the supply chain efficiency that helps the distributors as well as manufacturers to reach the end users. The benefit of the wholesale model is that the distributors are not able to meet with the minimum like when they are buying directly from the manufacturers (Apgar 2015).

Into the wholesale contract, there are minimum quantities that are purchased, and therefore the order is not fulfilled. It requires a manufacturer to increase output as well as a level of production (Mazzei and Noble 2017). Higher production requires more money to be spent on the staffs as well as raw materials, delivery in addition to capital expenditures (Askin and Mauskapf 2017). The wholesale model offers of low prices to the customers those are buying into a bulk.

There are five recommendations for the organization to progress the big data strategy as well as maximize the business values from the data of the enterprise. Following are the recommendations which are suggested as follows: 

Customer-centric outcomes: Gap Inc. should focus on the big data strategies to provide better business values. It starts with analytics strategy with the customers to provide the customers with better retailing services lead to better customer retention (Conrad 2015). The company should require understanding their customers and invest in new technologies along with advanced analytics. 

Big data blueprint: It will help to cover the vision, strategy as well as a requirement of Gap Inc. based on the requirements of customers (Kumar 2017). It provides a basis to develop the roadmap for guiding the organization throughout the practical approaches for development in addition to the implementation of big data strategies.

New product development: Gap should use Netflix for making decisions on new series as well as movies for development of new product (Mohd Suki and Mohd Suki 2015). The company should use of secondary data for anticipation into the market trends.            

With the use of the digital marketing, the customers can access the information anytime as well as from anyplace they need. Most of the people can access the information through the use of technologies (Beck et al. 2015). Gap invests into analytics technology as well as eliminate of traditional creative designers with a collective ecosystem fuelled by the input of big data. The key thrusts of digital analytical strategy involved in the mining of the big data are from the Google analytics. Own sales of Gap as well as customer databases are informed next season's assortment, trends throughout the big data analysis, development of online distribution channels, predictive analytics to target the market along with customer preferences (Chen and Harding 2016). Selling of products of Gap into the website of Amazon are completely new data stream to Gap managers, providing insight into the shopping habits of existing customers when they are not shopping on the company's digital platforms or in their stores, and providing access to new customers not currently attracted by the company’s distribution efforts.

Gap's Alternative Strategies for Selling Products via Amazon

While implementing the digital strategy by the big data, then Gap Inc faces of some of the risks which are categorized as below along with the mitigation plan:  


Risks

Mitigation plan

Competition Risk:

There is intense competition into the national as well as international level in America as well as abroad. There are also diverse retailers those are offering similar merchandise based on price, quality and speed to the market (Khadse et al. 2018). It is possible that the competitors should have better resources and business model to provide better shopping experiences.  

Gap Inc. should consider proper pricing as well as a promotional strategy to increase their sales and productivity in the market. The company should use social media marketing to understand the customers and aware them about their products and services (Lang and Rettenmeier 2017).

Slow growth into core markets:

Gap Inc. competes in $3 trillion global industry that is accounted for 2% of the gross domestic product. The growth of GDP is below the expectations for half of the year due to impact on the retail industry (Torres, Augusto and Godinho 2017). The retail sales annual growth is decreased each year over last of 5 years from 7% to 3%. There is also increase in the pressure of transformation.

As Gap Inc. should decide to advertise their products on Amazon, therefore 40% of the households are the use of Amazon website for purchasing (Sanders 2014). The manufacturing-based is moved to service as well as technological based.

Rise of the e-commerce:

The customers have shifted the purchasing from brick and mortar stores towards the online channels. In the year 2015, clothing becomes best selling online sales category (Baruh and Popescu 2017).  The retailers are running risk which triggers the significant protection of brand situations impacts the sales as well as customer perspective. There is a high volume of customer’s touch points with a rapid expansion of the social media (Millington and Millington 2015). Gap consists of over 3000 physical stores.

The company should increase of disaster recovery plans for maintaining good brand situations into the American marketplace. The company should provide best products to the customers with the best price so that they can able to gain a proper brand reputation (Chen and Harding 2016). There are required to monitor the consumer sentiment.

Rise of the fast fashion:

The competitors are provided lower priced knocked off from the luxury fashions (Sugimoto, Ekbia and Mattioli 2016). The company is lagged of competitors such as Zara to deliver the products to the stores. There are changes fail when the programs are focused on technical excellence of the team (Mohd Suki and Mohd Suki 2015). The probability of failure is increased when people, as well as organized resistance to the changes, are not proactively managed.

The company should define their project objectives properly so that the outsider can able to understand the key requirement as well as goals of Gap Inc (Mazzei and Noble 2017). Communication should be conducted among the project stakeholders and project manager to discuss any problems they are facing at the time of implementing a digital strategy using big data in the organization (Grover and Kar 2017). The technical issues are to be mitigated to prevent the transformational risks.

Heavy and frequent discounting:

 As Gap Inc. is a clothing retailer, therefore their clothing business is commoditized as the customers are viewed lower quality faster fashion offerings as disposable, yields need of lower price as well as heavy discounts (Torres, Augusto and Godinho 2017). The retail analyst is concerned about the overabundance of the promotion of prices at Gap where 40 percent of discounts are common. The customers are looking for unique products and services.

Gap should provide more discounts to their customers so that they are satisfied with both quality and price of the products (Millington and Millington 2015). The company should provide a better quality of products to their customers so that they can not be a matter. Sometimes, the customers are fearful that if a product is of a lower price, then the quality must be poor (Artun and Levin 2015). Therefore, Gap Inc. should implement of better pricing strategy for their products as well as services to meet with customer’s satisfaction level.

Implementation plan

Phase 1:

Development of digital marketing strategies

Phase/milestone description

This phase develops the goal of the tie with the core business drivers. The cost of the resources is determined to track the total budget of the project. The digital marketing strategies are provided required exposure to the business of Gap Inc.

Deliverables

Due date

Accountable person

Project cost plan

Development plan

14th April 2018

Project Manager

Resource needed

The internal resources are allocated to the project to conduct the project work such as project manager, logistics management, retail manager, business management.

Expected level of benefit

Digital marketing transforms the way to reach as well as engage with the customers. With the use of digital marketing, the company can able to increase its sales over some period. It is a digital marketing tactic to become the cost-effective way to market the business (Torres, Augusto and Godinho 2017). With this strategy, the business can able to get more for the marketing spends in America.

Phase 2:

Getting set with the social media

Phase/milestone description

The company has to create a Facebook business page for those who are looking for new as well as reliable sources. The company should update the business page on a weekly basis based on the new products and services available by Gap. The social media site offers a range of promotions whether they are getting more likes their products are more popular among the audiences (Sugimoto, Ekbia and Mattioli 2016). The social media generates returns after few days it takes a campaign to become optimized.

Deliverables

Due date

Accountable person

Social media monitoring report

Social media objectives

Editorial calendar

27th April 2018

Retail Manager

Resource needed

The human resources are allocated to the project to conduct the project work such as project manager, logistics management, retail manager, business management. The non-human resources are social media business page or Facebook page.

Expected level of benefit

Social media platform should increase brand awareness of the company’s products and services among the target customers. Therefore, Gap Inc. can able to discover new products on the platforms. It can the create real human connections which is one of the most important benefits of the social media for the business (Torres, Augusto and Godinho 2017). Social media benefits for growth by increasing website traffic. Participation in the social chats can increase the visibility of the website.

Phase 3:

Use of predictive analytics in Gap Inc.

Phase/milestone description

Gap Inc. is used for predictive analytics for selling of existing products and new development of a product. The company is involved with Amazon, Netflix which is used for predictive analytics to mine the data for generation of personalized product recommendations for the users (Chen and Harding 2016). Using information such as previous orders, product searches, wish lists, shopping-cart contents, returns, and even how long an Internet user’s cursor hovered over an item, Amazon would preemptively ship products to a distribution centre close to the consumer, in anticipation of an incoming order.

Deliverables

Due date

Accountable person

Project Report

Project cost plan

Development plan

30th April 2018

Retail Manager

Resource needed

The possible resources are project manager and business management team along with the retail manager.

Expected level of benefit

Predictive analytics improves efficiency into the production; the company should forecast the inventory along with production rates, while the past data should estimate the potential production failures. It also gains an advantage over the competitors which highlight innovative selling points to promote and enhance leads (Torres, Augusto and Godinho 2017). Finally, predictive analytics provide better marketing campaigns for the company to gain profitability.

Phase 4:

Prediction of customer’s preferences

Phase/milestone description

A consumer's tastes developed within the context of social influences, including the tastes of others around them, their membership in a variety of subgroups, and the prevailing fashions of the time (Sugimoto, Ekbia and Mattioli 2016). Predicting consumers’ future fashion tastes was a difficult proposition. Traditional market research methods, such as surveys, focus groups, and interviews, were often inadequate, as consumers were notoriously poor at predicting their future behaviours (Chen and Harding 2016). Consumers were often unable to imagine changes in fashion, so researching with them was futile.

Deliverables

Due date

Accountable person

Survey data

Interview data

Focus groups

4th May, 2018

Project Manager

Resource needed

The internal resources are allocated to the project to conduct the project work such as project manager, logistics management, retail manager, business management.

Expected level of benefit

Identification of the customer’s preferences benefits the company to know about the customer’s taste and their requirements along with their product and service demands. From the customer's preferences, the company can also be able to know about the brand preferences (Chen and Harding 2016).

Phase 5:

Phase/milestone description

Shifting of distribution model

Gap Inc. historically sold its branded products via its retail stores and digital storefronts, eschewing a wholesale model to sell directly to consumers. The company did not franchise in any country where it operated company-owned stores in an attempt to protect its direct distribution (Sugimoto, Ekbia and Mattioli 2016). The company is created of Omni-channel retail experiences where the e-commerce is part of its shopping ecosystem.  

Deliverables

Due date

Accountable person

Project Report

Project cost plan

Development plan

Distribution channel plan

11th May 2018

Project Manager

Resource needed

The internal resources are allocated to the project to conduct the project work such as project manager, logistics management, retail manager, business management.

Expected level of benefit

The benefit of distribution channels is increasing the quality of the products and services (Chen and Harding 2016). The distribution model is benefits as well as supports to move the product focused sales process towards the selling based on requirements of customers. Mastering the sales as well as analytics is critical towards sales along with marketing optimization (Sugimoto, Ekbia and Mattioli 2016). The entire manufacturing based is moved to service as well as technological based. Social media, as well as digital marketing, is used as a channel to spread the company's information to the customers so that they are more aware of the products as well as services.

References

Akter, S. and Wamba, S.F., 2016. Big data analytics in E-commerce: a systematic review and agenda for future research. Electronic Markets, 26(2), pp.173-194.

Apgar, D., 2015. The False Promise of Big Data: Can Data Mining Replace Hypothesis?Driven Learning in the Identification of Predictive Performance Metrics?. Systems Research and Behavioral Science, 32(1), pp.28-49.

Artun, O. and Levin, D., 2015. Predictive marketing: Easy ways every marketer can use customer analytics and big data. John Wiley & Sons.

Askin, N. and Mauskapf, M., 2017. What makes popular culture popular? product features and optimal differentiation in music. American Sociological Review, 82(5), pp.910-944.

Baruh, L. and Popescu, M., 2017. Big data analytics and the limits of privacy self-management. New media & society, 19(4), pp.579-596.

Beck, T.K., Jensen, S., Simmelsgaard, S.H., Kjeldsen, C. and Kidmose, U., 2015. Consumer clusters in Denmark based on coarse vegetable intake frequency, explained by hedonics, socio-demographic, health and food lifestyle factors. A cross-sectional national survey. Appetite, 91, pp.366-374.

Chen, T. and Harding, J.P., 2016. Changing Tastes: Estimating Changing Attribute Prices in Hedonic and Repeat Sales Models. The Journal of Real Estate Finance and Economics, 52(2), pp.141-175.

Conrad, C., 2015. Predicting Political Donations Using Data Driven Lifestyle Profiles Generated from Character N-Gram Analysis of Heterogeneous Online Sources (Doctoral dissertation).

Ekbia, H., Mattioli, M., Kouper, I., Arave, G., Ghazinejad, A., Bowman, T., Suri, V.R., Tsou, A., Weingart, S. and Sugimoto, C.R., 2015. Big data, bigger dilemmas: A critical review. Journal of the Association for Information Science and Technology, 66(8), pp.1523-1545.

Ghosh, D., Olewnik, A., Lewis, K., Kim, J. and Lakshmanan, A., 2017. Cyber-Empathic Design: A Data-Driven Framework for Product Design. Journal of Mechanical Design, 139(9), p.091401.

Grover, P. and Kar, A.K., 2017. Big data analytics: a review on theoretical contributions and tools used in literature. Global Journal of Flexible Systems Management, 18(3), pp.203-229.

Hainmueller, J. and Hiscox, M.J., 2015. The socially conscious consumer? Field experimental tests of consumer support for fair labor standards.

Khadse, V.P., Akhil, P., Basha, S.M., Iyengar, N.C.S. and Caytiles, R.D., 2018. Recommendation Engine for Predicting Best Rated Movies.

Kshetri, N., 2014. Big data? s impact on privacy, security and consumer welfare. Telecommunications Policy, 38(11), pp.1134-1145.

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Lang, T. and Rettenmeier, M., 2017. Understanding Consumer Behavior with Recurrent Neural Networks. In Proceedings of the 3rd Workshop on Machine Learning Methods for Recommender Systems. https://mlrec. org/2017/papers/paper2. pdf.

Loebbecke, C. and Picot, A., 2015. Reflections on societal and business model transformation arising from digitization and big data analytics: A research agenda. The Journal of Strategic Information Systems, 24(3), pp.149-157.

Matz, S.C. and Netzer, O., 2017. Using big data as a window into consumers’ psychology. Current Opinion in Behavioral Sciences, 18, pp.7-12.

Mazzei, M.J. and Noble, D., 2017. Big data dreams: A framework for corporate strategy. Business Horizons, 60(3), pp.405-414.

Millington, B. and Millington, R., 2015. ‘The Datafication of Everything’: Toward a Sociology of Sport and Big Data. Sociology of Sport Journal, 32(2), pp.140-160.

Mohd Suki, N. and Mohd Suki, N., 2015. Does religion influence consumers’ green food consumption? Some insights from Malaysia. Journal of Consumer Marketing, 32(7), pp.551-563.

Morris, J.W., 2015. Curation by code: Infomediaries and the data mining of taste. European Journal of Cultural Studies, 18(4-5), pp.446-463.

Moser, A.K., 2015. Thinking green, buying green? Drivers of pro-environmental purchasing behavior. Journal of Consumer Marketing, 32(3), pp.167-175.

Sanders, N.R., 2014. Big data driven supply chain management: A framework for implementing analytics and turning information into intelligence. Pearson Education.

Sanders, N.R., 2016. How to use big data to drive your supply chain. California Management Review, 58(3), pp.26-48.

Sugimoto, C.R., Ekbia, H.R. and Mattioli, M. eds., 2016. Big data is not a monolith. MIT Press.

Torres, P., Augusto, M. and Godinho, P., 2017. Predicting high consumer-brand identification and high repurchase: Necessary and sufficient conditions. Journal of Business Research, 79, pp.52-65.

Wang, X., 2018. The role of attitudinal motivations and collective efficacy on Chinese consumers’ intentions to engage in personal behaviors to mitigate climate change. The Journal of social psychology, 158(1), pp.51-63.

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