Does the big data approach work for all three of Gap Inc.'s primary brands: Old Navy, Gap, and Banana Republic? Why or why not? Which brands are better/worse served by this strategy? Why?
I believe that the big data approach does not work for all 3 of Gap Inc.'s primary Brands.
From the descriptions in Exhibit 5, we can see that Banana Republic is a "high-end speciality" brand, that was acquired as a higher price and equality brand (Israeli & Avery, 2017). As Banana Republic's target market are higher-income consumers, I believe that the big data approach won't work for this brand, as big data would be used to incorporate a trend and have it in stores within 3 months (Israeli & Avery, 2017). Exhibit 3 shows that Banana Republic is in the pricey/classic quadrant, showing that within its competitive landscape it is considered a luxury brand, therefore, it is expected to set trends, not follow them. Exhibit 13 shows that Banana Republic requires the creative input that is shown in the Tradition Creative-led Model.
In Exhibit 5, Gap is described as being in the "mass speciality segment" whilst Old Navy competes in the "fast fashion, discount" sector (Israeli & Avery, 2017). I believe that the big data approach would work for both Gap and Old Navy as both these brands are lower priced and mass distributed, therefore I believe they would both be considered to be Fast Fashion. The Merriam Webster dictionary defines fast fashion as "an approach to the design, creation, and marketing of clothing fashions that emphasizes making fashion trends quickly and cheaply available to consumers" (Definition of fast fashion, 2018). Exhibit 3 shows that Gap is located in the middle of all four quadrants, whilst Old Navy is the thriftiest of the 3 Gap Inc. brands and is a mixture of trend and classic.
Gap and Old Navy are ideal for the big data approach as these brands do not need to set trends. The use of big data would reduce Gap & Old Navy's cycle time allowing that to compete directly with other brands in their competitive set, such as Zara, Topshop and American Apparel.
What is your assessment of Product 3.0? How would you improve it?
I found several elements of Peck's approach to Product 3.0 sound business decisions. The areas are Peck's Product 3.0 strategy that I agree with are:
• Using big data and analysis of customer purchase data to drive new product development (Israeli & Avery, 2017).
• Using real-time sales data to highlight which items are popular and should be reordered and which are not (Israeli & Avery, 2017).
• Moving manufacturing to the Caribbean from Asia so that stock can be received faster (Israeli & Avery, 2017).
Exhibit 13 demonstrates that the New Big-Data Based Model helps to maintain brand vision and identity, I do think there needs to be some input from a designer or creative team. Bacon (2018) quotes Sue Varley from Very.co.uk and Joshna Patel of Red Letter Days who both agree that creativity still plays a large role in their marketing campaigns. To me, this demonstrates that Peck's firing of the Creative Director was a poor decision.
To improve Product 3.0 I would have followed the 3 steps that are mention above but have given the Creative Director the final say on all new products.
For which purposes is big data/predictive analytics more or less useful in marketing? As we move into a world filled with more data, what is the role of art versus science in marketing? Under which conditions should “science” rule and under which conditions should “art” rule?
Big Data and predictive analytics is a very useful tool for marketers. As mentioned by Israeli & Avery (2017) in the case study, big data can be used for predicting customer behaviour, inform managerial decision making, matching consumers to products and identifying loyal customers.
Hofacker, Malthouse and Sultan (2016) state that big data could be used for generating insights into consumer behaviour and that Big Data has the potential to help marketers understand each stage in the consumer decision-making process. By using big data, marketers will be able to better position their products to their target market.
Another way big data can be used is personalisation – both for communications with customers as well as showing online customer other products they may like. Strauss and Frost (2009) define personalisation as organisations tailoring their marketing activity to meet the needs or desires of an individual using electronic marketing tools, delivered in a timely manner. Personalisation is becoming a norm in today's society and can be seen on websites such as Amazon, Netflix and Facebook. Jackson (2007) found that personalisation leads to improved customer satisfaction, increased in sales and revenue and higher customer retention.
In this case study, Peck the CEO of Gap Inc. removed creative directors and replaced them with a team driven by hard data. This, in theory, was a move that should have helped streamline Gap Inc.'s design and distribution process. However, I believe that this will have hindered them in some areas. Hill (2018) maintains that "technology helps us do our jobs more efficiently and effectively, but it does not replace the need for creativity". Netta (2018) agrees with Hill, giving ways that technology, i.e. ‘science', can help with creativity, i.e. ‘art'. This includes allowing technology and artificial intelligence to help with training and upskilling of staff, analysing data so that the creatives can understand consumers better and allowing the results to help guide decisions.
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