For enterprise companies, data can be a double-edged sword. On one hand, well-curated and organized data has the potential to unlock great insights, from unexpected customer behaviors to missed market opportunities. On the other hand, poorly handled data can mislead and distract—a problem that only grows with the more data you have at your disposal.
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).
Computerization, the Internet and automation ushered in Industry 3.0, revolutionizing the way we work. Now there’s IoT, cloud computing, 3D printing, autonomous vehicles, mobile, social and Big Data. Welcome, Industry 4.0. In the not-to-distant future supply chain planners will sit in control rooms with a real-time view of inventory at all nodes of the supply chain.
Before a potential customer books a trip, applies for a loan or rents a car, he or she may be exposed to 10 or more touchpoints from a brand. How does the marketer know which ads are most effective? What are the synergies driving conversions? How does the effectiveness of marketing spend across multiple channels change over time?
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