AI is key to realizing true omnichannel customer-centricity

By Benoit Rojare, AI solutions director retail & CPG, Dataiku

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Retailing isn’t what it used to be. The rise of mobile technologies and digital disruption have reimagined the sector from top to bottom. Nowadays, it is critical to give customers what they want, when they want it, and where they want. This is placing increased pressure on retailers to ensure that the promise they are making to the customer is fulfilled completely with a personalized, seamless, and unified experience (both in the online and in the offline worlds). This is what omnichannel retailing is all about.

Realize the impact of omnichannel

Yet delivering good omnichannel customer experiences is difficult to achieve. It requires a common vision, persistence, coordination and cooperation across the business. Something Simon Sinek refers to as, ‘the Infinite Game’ - an infinite mission that businesses strive to achieve incrementally. But there are challenges at every stage from compliance and privacy management, regulation, lack of integrated systems, poor data quality, culture and so much more that create obstacles for achieving a unified view of the customer.

To realize the impact of omnichannel, retailers need to untap new ways to improve the customer experience and optimize operational efficiency. This means maximizing the treasure trove of data at their disposal including everything from; sales inventory, operational reports, consumer insights and more. But just because it’s there doesn't mean it's accessible - and certainly not in real-time. But this is precisely what retailers need to compete.

Unlocking the potential of data

The explosion of data in retail has been happening for some time. We first saw it through marketing and advertising on platforms like Google and Facebook. Today that has been expanded to developments like automated check-outs and in-store sensors. Yet, we’ve really only begun to scratch the surface in terms of possibilities. There are innovations coming through like computer vision being used for image and video recognition.

Juniper Research predicts that transactions using smart checkout technologies like Amazon Go will jump from $2 billion in 2020 to $387 billion in 2025. According to Capgemini, over three-quarters of customers expect to increase the use of touchless interfaces – such as voice assistants and facial recognition and 62% will continue to do so post-COVID. The IDC FutureScape: Worldwide Retail 2022 predictions found that by 2025, 75% of retailers will be fully integrating order and inventory data and optimization against fulfillment. A move that is predicted to improve conversion rate by 10%, customer satisfaction by 50%, and reducing cost to serve by 25%. The kind of results that underlines the critical importance of data and AI.

AI is the only way to deliver hyper-personalization

In a consumer-centric world, AI is the only way to deliver hyper-personalization: its ability to absorb and sort through a lot of unstructured data and use that information to gain more relevance among customers is a critical asset for retailers - and not just the Amazon’s of this world. Retailers of all sizes can use machine learning to analyze a variety of data sources for the basis of AI-based personalization in everything from; delivering product recommendations based on frequently bought items or related products, creating customized web pages and elements to suit a customer’s needs (like Netflix) or unlocking omnichannel experiences by feeding insights from online back into the brick and mortar locations.

Data preparation, features engineering, preprocessing, deploying, and maintaining the workflow and models in production are just some examples of the many things retailers can do in order to deliver highly personalized customer experiences but all of them boil down to AI, and it being as widely democratized in the business as possible. This means businesses must be clear on their data strategy - from identifying the right use cases and tools, right through to having a clear vision of what the objectives are - and ensure both the business and technology teams can collaborate on the same platform. This means retailers must focus on five key areas to unlock the potential of data.

Five key areas retailers must address

1. Ensure data preparation is easy and hassle-free - Data collection is the big issue - according to Capgemini pre-covid, only 1% of retail AI initiatives reach full-scale deployment because - at a time when every penny counts - mass implementation is still considered a risk. To successfully operationalize and scale, data teams need far more than just good data - they also need staff, structure, efficiency, automation, and a deployment strategy; data science tools facilitate these requirements (and much more).

2. Seamlessly connect every source - Retailers have endless data across touchpoints and systems. But operating silos, lack of data ownership, and inconsistent data management create blind spots that make it difficult to generate insights in line with the broader business strategy. Functional areas operate like islands and this cannot happen if real insight is going to be realized.

3. Make legacy technology work - Big retailers have been able to make big investments into machine learning and AI technology to identify and exploit strategic opportunities through data. However, for the majority of midsize or smaller retailers, transforming data into dollars hasn’t been as easy because they do not have the large stacks of resources and expertise that large organizations have access to. It means that getting any new platform or technology to work in harmony with the old is fundamental to ROI.

4. Create a culture of democratization - Retailers need to create change on an organizational level, supporting often hundreds or even thousands of individuals affected by the transformation to being a data-led retailer. It is not enough to just fill the business with data scientists but rather, the focus needs to be on transforming the culture of the enterprise so that data ownership and empowerment goes way beyond one group of specialized individuals, of whom there will never be enough.

5. Enable everyone to get involved with AI transformation - To unlock a data-driven workforce, retailers need to bring together business experts, data scientists and technologists in a multidisciplinary approach focused on solving priority business challenges or capturing new opportunities. It’s about every part of the organization doing their part in harmony toward a common goal.

Opportunities in a consumer-centric world

Making the transition into the age of AI isn’t easy for retailers, but it also isn’t insurmountable. Those organizations that take a step-by-step approach and set themselves up with the right infrastructure for people, processes, and tools can thrive to take advantage of the multitude of opportunities in a consumer-centric world. That’s why AI is key to true omnichannel customer-centricity.

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