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Creating An Omnichannel Experience For Customers By Employing IoT And AI Analytics

  • Written by  Dan Mitchell, SAS

0aaDan Mitchell SASYou’re sold on the value of giving your customers a seamless omnichannel experience. Now how do you make your vision a reality?

Delivering the omnichannel experience customers crave isn’t easy. It requires a complete about-face in the way most retailers conduct business. Each department and channel can no longer do its own thing. The entire organization must coordinate carefully to respond to customers’ actions and preferences in a consistent manner across online, mobile and in-store touch points.  

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To make things more complicated, customers expect to be wowed by exciting new experiences. That might mean installing IoT-optimized digital signs in a showroom that display personalized messages to each customer. It’s driven by analytics and could be specific enough to offer a new mom a coupon for diapers as she walks by. Or it might mean having ice-cold beer on hand during a promotion, even as the cooler door keeps opening and closing all day. This might require that retailers provide information about their plans to their HVAC supplier so they can adjust refrigerator temperature accordingly.

Consolidating data from across the organization and using data analytics to make smart decisions are essential for delivering a seamless customer experience in an omnichannel environment. Retail IT departments can more easily assemble all the puzzle pieces — hardware, software, connectivity, data storage, connectivity and more — needed to implement and keep an omnichannel solution current.

An Omnichannel Analytics Platform

Fashioning an omnichannel customer experience requires retailers to create an ecosystem that shares data and analytics across the enterprise-wide software applications they employ. These applications include various ERP and e-Commerce systems running a wide range of programming languages, including R, Python, Java or Lua. This ecosystem should also make it easier to collaborate with supply chain partners and incorporate value-added technologies from third-party suppliers.

This ecosystem is complex. It encompasses everything from hardware to humans — including software, analytics, storage and connectivity. As a result, it must be open rather than proprietary and deliver interoperability across heterogeneous systems, which may be running either on-premise or in the cloud. Third-party solutions with their own business logic should play seamlessly with the rest of the ecosystem.

The ecosystem also should provide native connections between the analytics platform and third-party products in a way that’s powerful but decoupled to avoid affecting application behavior and performance. It should be flexible enough to give retailers the option to outsource the creation of analytics models and plug the results into the production system.

The Hard Way And The Easy Way

Organizations today have two options for creating an omnichannel analytics ecosystem.

They can create their entire ecosystem from scratch — a complex and risky process. Or they can start with an existing platform and tweak it to suit their needs. Such a platform should be based on a standard design specification — a set of options that enables products of all types to connect and communicate.

To enable the real-time analytics necessary to respond to customers as they interact with various retail channels, the platform should also include the following capabilities:

  • Edge Computing: The platform should enable analytic models to run against data-in-motion with a sub-second response time, close to the applications, online services, devices and IoT sensors creating the data. The analysis initiates alerts and defines which data is pertinent to store and route forward.
  • Flexible Enterprise Computing: Relevant data sets identified at the edge should be transported to the data center or cloud, then combined with additional enterprise data to add context. Advanced analytical techniques, such as visualization, data mining, machine learning and artificial intelligence can be applied to find new insights and create fresh analytic models. The models can be deployed in the cloud or back to the edge as appropriate.
  • Management: A robust infrastructure should connect the edge to the data center/cloud and support the management of analytics at various network layers.

This type of platform provides a cohesive guidance layer that can align the expertise of partners, application providers, data analysis providers and even providers of IoT sensors — to get them and their assets operating effectively to gather, analyze and deliver data and a shared experience. Such solutions are comprehensively tested and documented by expert engineers to ensure faster, more reliable and fully predictable deployment.

As retailers consider how to take the next step in creating an omnichannel experience for customers, they should make life easier for themselves. An open plug-and-play platform and a partner ecosystem can provide a significant leg-up on the journey.


 

Dan Mitchell is the Global Director of Retail and CPG at SAS. Follow him on Twitter @DMitchell_ and LinkedIn www.linkedin.com/in/mitchdan. Follow SAS on Twitter @SASRetail and @SASsoftware.

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