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AI and data: the ideal combination for retail

Holographic image of atoms on a photo of a retail

AI and data: the ideal combination for retail

Thanks to the power of the cloud, technology and digital transformation, Artificial Intelligence has entered its maturity phase and is revolutionizing many business sectors. Between 2010 and 2020, the volume of data produced annually multiplied x25 and this trend is expected to continue and increase over the next 15 years, with the retail sector being one of its main players and beneficiaries.

As part of their processes, retail businesses generate huge amounts of data that they use from the very beginning, for example:

  • Data to optimize the supply chain
  • Data generated by customers throughout their shopping experience, whether online or physical. This data allows you to go further in optimization and embrace new processes, such as the personalization of product offerings
  • Data provided by third parties, such as: weather forecast, traffic or daily movement routines of people, which introduce additional dimensions reinforcing analysis, optimization and decision making

Today, the power of Artificial Intelligence makes it possible to leverage these three data sources combined to achieve unparalleled levels of optimization and reinvent a host of retail processes:

  • Customer experience
  • Marketing
  • Product offering and purchasing
  • Supply chain

Therefore, in an extremely competitive industry, where changes are constantly being amplified and accelerated, and where profitability is a matter of survival, Artificial Intelligence emerges as a strategically important source. That is why investments in Artificial Intelligence must be seen in light of its benefits in terms of customer experience and operational excellence.


¿Cómo la IA desafía al sector?

Since 2020, the retail sector is at a crossroads. Where the omnipresence of digital technology, environmental concerns and social changes have transformed consumer behavior, disrupting traditional models.

The sudden and unexpected Covid- 19 crisis has amplified these trends and made the need for brands to reinvent themselves even more urgent and necessary, having the power of Artificial Intelligence at their disposal.

1. Customer experience

Increasingly informed, autonomous and demanding consumers are no longer satisfied with being guided through the buying process; now, according to their desires and needs, they navigate between different channels expecting a better experience in terms of product offerings, services and usage. This is a challenge for brands that Artificial Intelligence can help overcome.

One of the main customer demands is the personalization of their experience as they move forward. Users currently expect:

  • Products and services adapted to their shopping habits and needs
  • Convenient access to products, both from the physical point of sale (space organization, signs, etc.) and digital channels (search engine, marketing clarity, etc.)
  • User experience adapted to their geographic location and lifestyle, considering their limitations
  • Pricing in line with their purchasing capacity and attractive promotional offers
  • Benefits linked to their loyalty program, allowing them to be recognized by the brand and employees

Artificial Intelligence has a key role to play in providing customers with the most pleasant, efficient and satisfying experience possible throughout their shopping journey.

Most used Artificial Intelligence applications:

  • Self-adapting homepage: by recognizing the visitor, based on their profile, purchase history and previous browsing experience, digital portals can personalize the customer experience by creating highly relevant screens at each stage of the interaction. The richer and more numerous these interactions are, the more accurate the picture of the user that AI can draw, and the better the personalization with each visit
  • Visual curation: thanks to machine learning algorithms, it is possible to transpose browsing behaviors into the real world. Automatic image processing makes it possible to base recommendations on aesthetic similarity and thus offer customers products that match their tastes or what they are looking for
  • Customised shop front: intelligent retail spaces recognize the customers in the store and adapt the presentation of products, prices and services to their profile, loyalty status or eligible promotions, creating a large-scale, personalized experience for all

2. Marketing

As an art of harnessing customer and market knowledge, marketing has been one of the first areas to benefit from digital and Artificial Intelligence, representing a real revolution, especially in retail, leading to significant advances in two key areas:

  • Customer insight: AI has made it possible to go beyond traditional Business Intelligence (BI) and Business Monitoring approaches. Thanks to Machine Learning (ML), it is possible to extract individual characteristics from large amounts of data that make it possible to understand customers, quantify and qualify their behavior, preferences, habits, etc., and thus anticipate their expectations. This type of actions are used in many areas for: Sales allocation, reducing abandonment, improving cross-selling, increasing customer satisfaction, among others
  • Campaign automation: AI facilitates the creation of tracking systems capable of evaluating campaign performance and identifying key trends. This allows optimizing spending, testing and campaign decisions thanks to recommendations generated by machine learning

AI most frequent areas of use in marketing:

  • Optimization of the pricing strategy: thanks to Machine Learning, prices can be optimized dynamically and in real time, as well as targeted advertising
  • Optimization of advertising messages: Machine Learning models, such as Random Forest, allow selecting the most relevant advertisement and targeting it to the right customer at the right time
  • Click & engagement: through the use of neural networks, Artificial Intelligence facilitates the tracking of clicks and the navigation path, optimizing navigation systems, improving the customer experience

3. Product offerings and purchases

For retailers, building a range of products and services consistent with brand strategy and aligned with customer expectations in each catchment area is a major challenge. They are constantly asking what products to offer and at what price point, and how deep and broad should the offering be. But these questions have taken a new turn thanks to digital technology, breaking down the concepts of choice, competition, availability and promotion.

Artificial Intelligence is becoming the essential tool for managing an increasingly complex, dynamic and multifaceted product offering.

For the retail sector, Machine Learning is undoubtedly one of the most promising tools for aligning product offerings and pricing with customer expectations. Thanks to its ability to explore historical data in a combined way, Artificial Intelligence also makes it possible to establish the link between physical and digital channels and ensure their consistency.

Finally, Machine Learning is widely used in the field of dynamic pricing, both at the physical point of sale and on the web. Many retailers use it to drive the best price and close the sale based on time, item and customer.

4. Supply Chain

The supply chain, covering all upstream and downstream flows, forms a dynamic system of extreme complexity. However, the profitability of the retail sector and the satisfaction of its customers depend to a large extent on its optimization.

In this data-rich environment, Artificial Intelligence appears as an essential tool for the supply chain, being one of the areas where it can be used the most. The optimization of models using Machine Learning (ML) and Natural Language Processing (NLP) introduces unprecedented operational efficiency.


Machine Learning applications specific to the supply chain allow predicting, among other things, demand levels, product routes, staffing requirements, promotions to be considered and the probability of their success, combined order packing possibilities, and the collection of checkout data for customized predictive analytical input.

One of the most common problems in the distribution sector is route optimization, however, Artificial Intelligence and its continuous learning yield results far superior to those obtained by traditional methods, achieving that each optimized route stores its data so that the learning algorithm can continuously improve.

AI-based image recognition is another promising area for the supply chain, for example: to reduce the major problem of spoilage and waste of fresh produce in food retailing, based on various parameters such as product shape and temperature, Walmart’s EDEN solution is able to assess the quality and freshness of the products. This information makes it possible to dynamically optimize logistics flows and prioritize the shipment to the nearest stores of products that need to be consumed sooner.

In retail, as in all business sectors, Artificial Intelligence represents both a huge opportunity and a huge change. The important issue, however, is where to start in order to put the company on the path to transformation and receive the benefits as soon as possible.

However, a change as profound and lasting as Artificial Intelligence cannot be improvised or rushed. That is why at Interfaz we recommend starting by establishing four foundational pillars to standardize the approach and quickly scale the most impactful projects.

  • Make Artificial Intelligence a strategic priority
  • Build a solid data foundation
  • Organize competencies
  • Link business processes to Artificial Intelligence data and methodologies.

Finally, this approach must be taken to the highest level of the company, with people who are committed and convinced that the future of the retail sector lies in Artificial Intelligence.

Source: Corral, V. (2021) | Navarro, J. (s.f.) | Darson, E. (2020)

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