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denpristay [2]
3 years ago
14

Does this big data approach work for all three Gap Inc.'s primary brands: Old Navy, Gap, and Banana Republic? Why or why not? Wh

ich brands are better/worse served by this strategy? Why?
Business
1 answer:
Greeley [361]3 years ago
8 0

Answer:

We feel that the big data approach is applicable for all three of Gap Inc.’s brands, although the biggest insights from the information collected will come from the brand that has the greatest product diversity. Banana Republic is Gap’s most targeted brand with its expensive price points, so designers already have a relatively good idea of what their customers are looking for. The target customer is upscale, predominantly female, and interested in a classic look. The variability in design for the brand is least among Gap’s, but still significant so the ability to assess the success  

of product offerings in real time is extremely valuable. Similar to the model used by Netflix, withonline shopping Gap does not have to rely on feedback from just the very few customers that choose to comment on and rate products that they purchase.With the introduction of “Product 3.0”, the trends seen at Banana Republic and the Gap were able to cascade down into the less expensive, family brand of Old Navy. This allowed for consumers of all income brackets to be touched. All brands had a clear vision with common ground, being more predictive and demand driven based on data analytics. This sharing of information benefits all of the brands by better streamlining products towards market trends. Since Old Navy and Gap have a greater number of SKU’s, they will have an even greater benefit from big data. But managing, purchasing and inventories for these companies is even more challenging, and having to deeply discount unsuccessful SKU’s can be unavoidable. But minimizing these unsuccessful products can be achieved through big data by analyzing how well a product performs before waiting to receive customer feedback. Gap can emulate fast fashion companies like Zara who keep production runs short, even shorter for unsuccessful runs, and create a sense of urgency from the customer without complicating the customer’s decision process with an inevitable sale

Explanation:

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IRINA_888 [86]
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