AI and Low-Code: Transforming Customer Experience in Retail

Explore how AI and low-code platforms are reshaping retail by enhancing customer experiences, optimizing inventory, and streamlining operations.

Mar 25, 2025

Retailers are using AI and low-code platforms to improve customer experiences and streamline operations. Here's how:

Boosted Sales and Engagement: AI-powered tools like Sephora's chatbot increased app engagement by 25% and sales by 15%. Nike's AI-driven app boosted sales by 30%.
Faster App Development: Low-code platforms let businesses create apps quickly without deep coding knowledge. For example, Mendix allows drag-and-drop integration of AI models.
Smarter Inventory Management: AI systems reduced stockouts by 20% for Zara and cut supply chain costs by 15% for Unilever.
Automated Customer Service: AI chatbots now handle 85% of customer interactions, saving time and costs while improving satisfaction.
Data-Driven Decisions: AI-driven personalization contributed to $199 billion in retail sales during the 2023 holiday season.

Retailers can start by focusing on small, high-impact projects, ensuring clean data, and equipping teams with user-friendly tools. Combining AI and low-code is shaping the future of retail, making it more efficient and customer-focused.

AI Low-Code Solutions for Better Customer Experience

Custom Shopping Recommendations

In the 2023 holiday season, AI-driven personalization through low-code platforms contributed to $199 billion in retail sales. Retailers are leveraging AI to analyze customer data and deliver tailored shopping experiences. For example, Nike's Nike Fit app, which uses AI to scan feet and recommend the best shoe size, boosted sales by 30%. Similarly, Sephora's Virtual Artist app, which helps customers try on makeup virtually, saw a 20% increase in online sales.

Retailers are also turning to AI for marketing and content creation. A recent study shows that 58% use generative AI for marketing assets, 56% for personalized email campaigns, 54% for promotional offers, and 53% for auto-generating product descriptions. These tools are paving the way for even more automation in customer service and engagement.

AI Customer Service Automation

AI chatbots are transforming customer service by offering instant, tailored support. Research suggests that by 2025, these systems will manage 85% of customer interactions. A standout example is Photobucket's integration of the Zendesk AI agent:

"The Zendesk AI agent is perfect for our users [who] need help when our agents are offline. They can interact with the AI agent to get answers quickly. Instead of sending us an email and waiting until the next day to hear from us, they can get answers to their questions right away." – Trishia Mercado, Director of Member Engagement at Photobucket

Other businesses have seen similar success. Hello Sugar's AI solution automated 66% of customer interactions, saving $14,000 in monthly operational costs. Lush's AI system saves around 360 agent hours each month by handling routine inquiries and tagging tickets automatically.

Smart Inventory Management

AI-powered inventory management is tackling supply chain challenges while improving customer satisfaction. Last year, 78% of U.S. shoppers encountered out-of-stock items in stores. Unilever's AI system, which uses data from multiple sources to forecast demand and optimize logistics, cut supply chain costs by 15% and improved delivery times by 10%.

Dynamic AI models bring additional benefits, such as reducing total inventory by up to 3%, cutting inventory-related cancellations by as much as 40%, increasing conversion rates by 5–20%, and lowering cost-to-serve by 3–15%. These advancements not only improve operational efficiency but also ensure products are available when customers need them.

How to Implement AI Low-Code Solutions

Selecting Priority Projects

Begin by focusing on projects that have a big impact but are simple to execute. McKinsey estimates that generative AI could bring in $240 billion to $390 billion in economic value for the retail sector. To tap into this, prioritize areas that improve daily operations and enhance the customer experience.

Work closely with teams to pinpoint workflow challenges. For example, would simplifying inventory management or making order processes smoother make a difference? This collaboration not only highlights key issues but also helps gain support from the people who will use these systems most often. Once you've identified your projects, it's time to prepare your data for AI implementation.

Data Preparation Steps

Good data is the backbone of any AI system. Gartner reports that 33% of companies face data quality problems that block AI adoption. To avoid this, here’s a step-by-step guide to preparing your data:

Phase	Key Actions	Expected Results
Data Cleansing	Fix errors, remove duplicates, update outdated data	More accurate AI predictions
Data Standardization	Use consistent formats, normalize values, create a data dictionary	Improved machine learning performance
Security Measures	Set access controls, encrypt data, and back up files	Safeguarded sensitive information

"AI success is directly dependent on the data that the tool is fed, so if your data is siloed, disparate or outdated, your AI outputs will be flawed."
– Duane Barnes, President, RapidScale

Once your data is ready, focus on setting your team up for success.

Team Implementation Guide

A well-organized team approach is key to making AI low-code solutions work. Gartner notes that companies with strong change management practices are six times more likely to succeed with AI.

Equip your team with easy-to-use tools that fit seamlessly into their workflows. Rely on subject matter experts to guide the development process. Offer thorough training to ensure everyone knows how to use the new systems effectively.

Start small by rolling out projects that deliver quick results. This approach builds confidence and minimizes disruption. Regular feedback sessions are also essential to fine-tune the solutions and ensure they align with business needs.

Revolution or evolution: Generative AI's role in transforming ...

How Deployd Supports AI and Low-Code Projects

Deployd provides specialized services to help retailers implement AI-driven low-code solutions tailored to their specific needs. With over 5 years of experience, more than 30 completed projects, and an impressive 98% talent retention rate, Deployd delivers targeted support for retail businesses. Here's a closer look at how Deployd assists at every stage of AI-powered low-code projects.

Finding Low-Code Developers

Deployd connects retailers with skilled low-code professionals through a thorough hiring process. They carefully source and screen experts for key roles, including:

Mendix Functional Admin: Focuses on platform setup and maintenance.
Mendix Consultant: Provides strategic guidance and designs solution architectures.
Mendix Developer: Builds custom applications tailored to retail needs.
Mendix Tester: Ensures quality through detailed testing processes.

Platform Migration Services

Deployd offers specialized services to help retailers switch between low-code platforms with ease. Their migration strategies minimize disruptions, ensuring critical business logic stays intact while maintaining smooth daily operations.

Complete Project Management

Deployd handles every aspect of AI-powered low-code projects, from start to finish. This includes planning, gathering requirements, designing, iterative development, deployment, and ongoing support. Their approach ensures all project phases align with retail workflows, enabling faster progress and seamless integration without interrupting operations.

Conclusion: Next Steps for AI and Low-Code in Retail

Future AI and Low-Code Developments

The retail industry is undergoing rapid changes with the rise of AI and low-code technologies. Multimodal AI is making waves by allowing retailers to process different types of data - like product descriptions, images, customer reviews, and video feeds - all at once. This shift is transforming how businesses manage inventory and improve marketing strategies.

Conversational interfaces are also advancing, making product searches more seamless for customers, whether they're shopping online or in-store. AI-powered agents are enhancing personalized shopping experiences, while improved safety features are tackling security and fraud concerns.

"In 2025, the winners in retail will be those that can leverage AI to create value across their entire enterprise." - Paul Tepfenhart, Director of Global Retail Strategy and Solutions, Google Cloud

These advancements point toward clear steps retailers can take to stay ahead.

Action Steps for Retailers

To get the most out of AI-driven low-code solutions, retailers should prioritize the following:

Unify Data Infrastructure: A solid data foundation supports AI capabilities like inventory optimization, dynamic pricing, and fraud detection.
Start Small with Quick Wins: Automate repetitive tasks first to see immediate results and build momentum.
Equip Your Team: Provide tools like no-code workflow builders and easy-to-use AI applications. This makes it easier for employees to adopt new technology and deliver results faster.

The goal is to combine large-scale AI transformation with practical, actionable steps. By doing so, retailers can create better customer experiences and improve operations, all while ensuring scalability and measurable outcomes.