Three Ways Mature Retailers Can Set Themselves Apart in AI
A recent Gartner survey revealed that six in ten organizations (59 percent) have deployed artificial intelligence (AI) or machine learning (ML) systems. Within this group, investments are expected to double within the next year.
But this research fails to account for one very important point: value. For so many organizations, the mere presence of AI/ML technology does not equate to meaningful business impact. Likewise, having more applications does not guarantee better results. Put simply, not all AI is equal.
Here are three ways strategic thinking sets apart mature retailers in the AI landscape.
1: Get to know the customer – not the segment
While retailers have used this technology to better understand customers and their behaviors, most still fall short of offering an individual, personalized customer experience. That’s because traditional AI models rely on customer segmentation to generate insights about a specific group, as opposed to recognizing the customer as an individual.
However, with deep learning, a subset of ML, retailers can create a much more precise understanding of each customer. This capability goes beyond simple segmentation and actually formulates a detailed, individual profile with all known customer information in a single vector—sometimes referred to as the “customer genome.” In practice, this means that the retailer can reach each shopper on a personal level, as opposed to targeting various segments.
2: Build an AI platform – not just a product
The true value of AI comes from compounded efficiencies when these models are linked as part of a platform and deployed at scale. The main benefit of an AI platform is speed. Organizing efforts in this way enables up to five times faster, more efficient execution and analysis, which greatly improves the ROI of building and productionizing models. Even more importantly, the speed of the platform allows the organization to perform more experiments, select the best models and get them to production faster.
3: Focus on the reward – while mitigating risk
While many customers have grown accustomed to everyday AI applications like predictive text and Netflix recommendations, some may remain wary of the technology—and the organizations wielding it. To maintain integrity with the customer and other stakeholders, businesses must conduct themselves safely, fairly and transparently.
Mature retailers build model governance into each phase of the AI project lifecycle to ensure that every application is researched, developed and deployed in a safe, ethical way. An advanced AI strategy includes multi-checks at each step of the modeling lifecycle to help eliminate bias and ensure the validity of outputs. This may include fairness testing, which ensures underprivileged and protected groups are treated fairly, or sensitivity and boundary condition analysis, which studies how each input factor influences outcomes.
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