Transforming the Customer Experience in Retail With AI

If 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.

Challenges 

Nevertheless, the tremendous benefits of AI to the retail economy are inevitably accompanied by serious challenges, such as:

• Excessive cost of putting models into practice;
• Lack of resources and expertise in small and mid-size businesses ;
• Data collection;
• ROI estimation;
• Data governance;
• Data-culture change;
• and more…

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.

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High-Value Use Cases for AI in Retail

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.

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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 to harness 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.

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Dataiku for the Retail Industry

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:

• Dynamic pricing. Predictive price models built on Dataiku can include datasets from any data source, from complex SQL databases to simple Excel spreadsheets. This enables highly-targeted retail and online marketing forecast models that can show which price points are most likely to turn potential customers to proven buyers.
• Demand forecasting for inventory management. Dataiku can help retailers find the right balance by using demand forecasting to predict production and consumption quantities, by leveraging its user-friendly visualizations and web apps, to convey how your customers are likely to engage with your products based on the dimension of your choosing, and with datasets of your choosing.
• Customer Service and CRM Initiatives. At the end of the day, it’s all about how successfully a company engages with its customers, which is measured by how effective it is at collecting, tracking, and measuring customer interactions. Dataiku is highly effective at enabling users to collect, clean, and analyze these types of datasets for better understanding of what specific changes need to be made.

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.