Start the Enterprise AI Journey with Churn Prediction
Churn prediction is a relatively quick win with machine learning, and its potential value to an organization is staggering.
Learn MoreIf you judged by the number of times the phrase “artificial intelligence” was used at NRF 2019 – Retail’s Big Show and Expo, you would think that advanced solutions are being rolled out across every retail enterprise. And while implementing AI solutions in physical retail is naturally more challenging than online retail, taking a step back, both are still surprisingly only in early stages. However, this is about to change.
With Enterprise AI, machine learning (ML) and data science solutions becoming a benchmark for business practices, retailers today have the unprecedented opportunity to shift the paradigm and leverage their data to elevate the customer experience in new, more meaningful ways.
Nevertheless, the tremendous benefits of AI to the retail economy are inevitably accompanied by serious challenges, such as:
This all may sound overwhelming, especially for midsize and smaller retailers, but it shouldn’t stop companies from embarking on their Enterprise AI journey. By setting up the right infrastructure for people, tools, and processes, as well as working on high-value use cases, retailers can deliver real business value from their AI initiatives.
Personalized recommendation engines: Personalization has become table stakes for retailers today, who are faced with fierce competition from e-commerce giants and an increasingly demanding customer base. AI-powered retails and brands use advanced ML algorithms to analyze browser history, page clicks, social interactions, past purchases, page viewing duration, location, etc. to gauge customer interests and preferences in a more complex and exhaustive way than previously possible.
Price optimization: AI and ML enable retailers to increase sales and boost their bottom lines through price optimization. This method involves, on one hand, tailoring prices to customers in a way that they view them as attractive, fair and non-arbitrary for the products they care most about, and on the other, predicting when it is or isn’t necessary to offer discounts.
Loyalty programs optimization: Moreover, AI offers a powerful set of applications for retailers to enhance and personalize their loyalty programs. With capabilities like natural language processing, image analysis, and semantic reasoning, marketers can benefit from having an adaptive and evolving understanding of the customer-to-brand engagement.
Behavioral and geospatial analysis: Geospatial analysis is helping retailers to better understand the interplay between their brick-and-mortar and online operations. Deep learning programs are capable of harnessing video surveillance of customers to analyze their behavior and preferences. Such analysis could be linked to the purchase information of the customer –– either their credit card or loyalty card –– and be used to target them with hyper-personalized, automated marketing.
Inventory management and stock optimization: AI will continue to optimize inventory for large and small retailers. Large companies are already pioneering technology that more precisely aligns inventory with customer demand. Inventory management is particularly salient in the grocery sector, where the ability to forecast demand reduces food waste and ensures that there is adequate supply of the items that customers want at a given time.
Dataiku is the platform democratizing access to data and enabling retailers to build their own path to AI. By making AI accessible to a wider population within the enterprise, facilitating and accelerating the design of machine learning models, and by providing a centralized, controlled, and governabale environment, Dataiku allows retailers to massively scale AI efforts, particularly through:
Making the transition into the age of AI isn’t easy for the retail industry, but it also isn’t insurmountable. Retailers and brands that take a step-by-step approach and set themselves up with the right infrastructure for people, processes, and tools can thrive.
Recommendation engines can be used across industries to provide value either to end customers or to employees of the organization itself.
Read moreChurn prediction is a relatively quick win with machine learning, and its potential value to an organization is staggering.
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