Retailers collect a lot of data. But they haven’t been able to fully optimize it—until recently.
Over the last couple years, a few forces have converged that have created the tipping point for them to utilize Artificial Intelligence (AI)/Machine Learning (ML) in their data stores.
Among them is an explosion of publicly available data that has enabled retailers to combine their proprietary sales data with externally generated data sets, such as demographic information, weather, economic indicators, social media activity, and more. The integration of such information can help them, for example, quickly determine the impact of various external influences on specific categories, SKUs, and stores.
The emergence of the Internet of Things (IoT) networks and their resulting new datasets also have been a force that has enabled retailers enhanced visibility about customer behavior and preferences. For example, sensors in brick and mortar stores are now enabling retailers to collect and analyze customer preferences based on in-store behavior, such as frequency of visits, length and time of visits, dwell time in specific areas of the store, and more.
AI solutions gain multiple enhancements
All these data forms are the essential fuel for enhanced AI/ML applications. That’s because this abundance of data has enabled AI/ML models to be far more accurate than in the past-- and as a result--provide a much better ROI than ever.
Adding to this, the ability to host most AI/ML models in the cloud has improved the cost benefits, scalability, and elasticity of managing, and training data sets. This has helped ease AI/ML implementation and utilization. Such democratization of AI/ML solutions has enabled retailers to better harness the power of all the data they’ve been accumulating—as well as the new external data they now can access.
The combination of these datasets, along with such new cloud capabilities, not only has furthered AI solution adoption in retail organizations, but also enabled more rapid deployment of various solutions and services based on their resulting data analysis. And that can drive better efficiencies in customer engagement, employee empowerment, and operations.
Microsoft offers a powerful AI platform
The Microsoft AI platform—built on Microsoft Azure cloud infrastructure--consists of technology to provide AI at scale, services that offer core AI capabilities through a common set of APIs, and tools for users who want to be hands-on in the creation of their own custom AI models. Additionally, the platform helps ease the process of ingesting data from various data sources, preparing the data, and building predictive models with it.
Purpose-built solutions like Aprimo leverage the Microsoft Azure Cloud platform and its benefits of reliability, scalability, and security. The technology enables Aprimo to use data centers around the world to host a solution in an appropriate location that suits retailers’ privacy needs. Microsoft Azure also enables the Aprimo platform to stay ahead of ever-changing security threats.
One retailer utilizing the customized solution is London-based ASOS is an online fashion retailer on a mission to be the world’s number-one fashion destination for millennials. To support its digital offering, ASOS worked with Microsoft to enable such innovations as real-time product recommendations and instant order updates for 15.4 million customers. The product-driven content experiences was enabled by Aprimo Digital Asset Management.
To remain competitive, retailers need to leverage emerging technology, such as AI/ML and data analysis, to get the right message in front of the right customer at the right time--and measure the outcomes of their efforts. The powerful combination of Microsoft’s data and AI capabilities combined with Aprimo’s customer experience command center platform enables brands to drive better personal and relevant content--and as a result—improve customer engagement.
About the AuthorMore Content by ShiSh Shridhar