In the era of the connected customer, having a big data strategy to collect, store, organize and analyze the trail of deep customer data is critical to the delivery of timely, personalized customer interactions. Fortunately, with the right technology, infrastructure and analytics in place to unlock the full potential of this data, driving deeper engagement with your connected customers is anything but guesswork.
Consider these 5 ways using big data analytics can help drive your connected customers’ experiences:
In the early days of big data, insights gleaned from email and website clicks re-shaped campaigns, initiated new ones, and led to more personalized experiences – often in the form of product recommendations. Now, new data types and more sophisticated tools, technologies and analytics can uncover deeper, more relevant customer insights, based on behaviors and fact-based predictions. The result is that marketing is able to move from speaking to large customer segments to a “segment of one” and deliver highly targeted, relevant messaging and content – exactly what connected customers expect.
Being data driven isn’t simply about understanding customer purchase histories. It’s about digging into the deep data around behaviors, interests and preferences. The pivot points that push customers to the point of purchase. How, where, when and what messages you deliver are based upon big data analytics across multiple touch points and time frames – not simply intuition and knowledge of experienced decision-makers. Customer experience is improved and more personalized – online, on mobile devices and in-store. Retailers can provide their customers with the convenience of shopping wherever and however they prefer with the assurance that the products they want will be available, thanks to total visibility of inventory across the enterprise. The results are increased customer engagement, satisfaction, and long-term brand loyalty.
In today’s world of new and different data types and vast volumes of data, retailers must consider the “right” platform to store data based upon type, volume and even use. It is critical to develop a big data strategy and architecture to enable an analytic ecosystem. It’s a complete, agile ecosystem where data is readily available and easy to navigate.
Easy access to a wide variety of data enables retailers to efficiently and effectively “connect” data for analytics regardless of where the data is stored – or where it originates. Agility is key.
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