Has the ease of buying with just one click ever driven you to make a purchase on Amazon over a preferred website? The success of a product, website or app is dependent on an excellent UI/UX – Amazon thrives here – and increasingly, the same is true for the effectiveness of data analytic applications.
According to an IBM Global CMO Study, which surveyed more than 500 CMOs around the world, 94 percent believe advanced analytics will play a significant role in reaching corporate objectives. However, 82 percent say their organizations are underprepared to capitalize on the data revolution.
Customers today are largely unsatisfied with the personal engagement marketing they receive. Increasingly, they demand engagement at the right time, on the right device and with the right message. Artificial intelligence (AI) may sound futuristic, but this exciting evolution in the personalization we’ve pursued for decades enables modern brands to personalize the shopping experience – in real time – and deliver automated and relevant messages, recommendations and offers at scale, which drive business growth and build engagement.
There are two extreme ends of the retail business model, each with its pros and cons. At one end all stores are virtually identical (think chains like Starbucks, Gap, Subway, etc.), which makes for simpler operations and a consistent, predictable customer experience. On the flip side are chains where the owner operator has a great deal of discretion on assortment, pricing and promotions. For example, retailers like Aeon Group in Japan, ICA in Sweden and Metro in Canada give significant control to store operators.
According to IBM, 2.5 exabytes of data are generated each day. Every click, like, share and mention generates unlabeled data that can’t be dealt with by traditional statistics. Harnessing this data to deliver personalized user experiences can translate into billions of dollars of incremental revenue: This is the province and promise of deep neural networks (DNNs).
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