Using AI to analyze customer behavior and reduce markdowns

12 min read

How AI powered customer behavior analysis eliminates markdowns and maximizes retail profitability

For enterprise lifestyle retailers, the quest for sustained profitability often collides with the persistent challenge of markdowns. You know that feeling when inventory accumulates, and the only apparent solution is a sweeping discount, eroding margins and potentially devaluing your brand. This isn’t just a minor inconvenience, it’s a strategic drain. Globally, fashion retailers alone allocate over $1 trillion annually to markdown programs, many still relying on outdated analyses or gut instinct. What if you could anticipate these scenarios, understanding precisely why and when your customers would buy, allowing you to sidestep those broad, profit-killing discounts entirely? The answer lies in transforming how you analyze customer behavior, moving from reactive discounting to proactive, data-driven value delivery with agentic AI.

The hidden cost of markdowns why traditional approaches often miss the mark

Traditional markdown strategies, while necessary in the past, inherently carry significant inefficiencies. They often stem from a lack of precise demand forecasting and an inability to truly understand individual customer intent, leading to either overstocking or missed sales opportunities. This reactive approach can boost gross margins by 10 to 20 percent compared to traditional methods by simply adopting advanced analytics, illustrating the substantial hidden costs involved.

When you rely on aggregated data or seasonal trends alone, you risk:

Misjudging demand:
Forecasts based on limited variables often fail to capture the nuanced shifts in consumer preferences, economic factors, or even local events, resulting in excess inventory.
Broad brush discounting:
Applying a blanket markdown across an entire category or store assumes all customers value a product equally and respond to discounts in the same way, which is rarely true. This means you’re often leaving money on the table from customers who would have paid full price, or conversely, not attracting those who would buy with a slightly more targeted offer.
Eroding brand value:
Frequent, widespread markdowns can condition customers to wait for sales, diminishing the perceived value of your full-price merchandise and impacting long term brand equity.
Inventory distortion:
Incorrect pricing and markdown strategies contribute significantly to inventory distortion, impacting both cash flow and operational efficiency.

These challenges highlight a critical need for a more granular, intelligent approach to pricing and inventory management, one that directly addresses the complexities of modern retail and the vast data available.

The agentic AI advantage decoding customer behavior for precision pricing

Agentic AI isn’t merely about automating existing processes, it’s about fundamentally re-evaluating how you understand your customer and proactively optimize your operations. By leveraging advanced machine learning models, WAIR.ai helps you move beyond basic analytics to truly decode customer behavior, transforming how you forecast demand, optimize allocation, and manage inventory more intelligently. This deep understanding on how an agentic AI competitive advantage retail enables you to make decisions with surgical precision, dramatically improving profitability and reducing reliance on broad markdowns.

Understanding the why moving beyond basic analytics

To genuinely reduce markdowns, you must first understand the underlying psychological and behavioral triggers that drive purchasing decisions. Why does one customer buy immediately at full price, while another waits for a sale? Traditional analytics often provide descriptive data like “what happened,” but agentic AI delves deeper, exploring the “why.”

Here is how agentic AI provides a more profound understanding of customer behavior:

Qualitative and quantitative deep dive:
AI can synthesize vast amounts of qualitative data, such as customer reviews and social media sentiment, with quantitative data from purchase history, browsing patterns, and demographic information. This holistic view uncovers hidden correlations and true customer motivations that human analysis alone cannot easily discern.
Identifying psychological triggers:
By analyzing historical interactions and external factors, AI models can identify customers behavior patterns indicating a customer’s sensitivity to price, urgency, or specific product features. This allows for highly nuanced segmentation, recognizing that a customer’s purchasing psychology can vary by product category or even by individual item.

The power of predictive and prescriptive AI in action

The true power of agentic AI lies in its ability to not only predict future outcomes but also prescribe the optimal actions to achieve desired business goals. This shifts your strategy from reactive to proactive, allowing you to anticipate demand and price elasticity with unprecedented accuracy. Our unique agentic AI approach helps retailers achieve significant improvements, and you can understand how to implement and scale it by exploring implementing scaling agentic AI retail.

Consider these key capabilities:

Demand forecasting with AI:
Legacy forecasting models typically rely on a handful of variables, often 4 to 6. WAIR.ai’s advanced AI models process a significantly broader range of variables, including internal data such as sales history, stock levels, and product attributes, combined with external factors like local weather patterns, demographic trends, and macroeconomic indicators. For example, integrating weather data can improve accuracy by 8.6 percent and demographics by 5.2 percent, offering unmatched precision in predicting demand. This capability is central to our offerings like Wallie, the allocator, which optimizes initial distribution and replenishment.

Price elasticity modeling:
How much will demand change if you adjust the price? AI can model price elasticity at the SKU and segment level, identifying exactly where customers are price sensitive and where they are not. This goes beyond simple intuition, revealing the true willingness to pay for specific products within different customer groups.
Dynamic pricing in action:
Global players like Amazon demonstrate the power of dynamic pricing, changing prices up to every 10 minutes. While not every retailer needs this intensity, AI enables you to make real-time adjustments and micro-optimizations that respond to live demand signals, inventory levels, and competitor movements, ensuring you always capture maximum value.

From broad brush to surgical precision AI driven markdown optimization

The goal isn’t just to optimize markdowns, but to reduce or even eliminate the need for broad, profit-damaging discounts by employing surgical precision. Agentic AI allows for highly targeted strategies that speak directly to individual customer behavior and market conditions. This precision has a tangible impact, with AI driven markdown strategies resulting in an 18 percent increase in sell through and 9 percent higher margins in various case studies, and large enterprises seeing up to a €28.6 million annual reduction in markdown losses. Learn how WAIR.ai’s agentic AI can turn your retail merchandising into a profit engine by visiting agentic AI retail merchandising profit engine.

Segmenting for markdown sensitivity identifying true value

Not all customers respond to discounts equally, and treating them as such leads to wasted margin. AI excels at identifying subtle patterns to segment your audience with unparalleled accuracy.

How AI precisely segments customers:

Identifying price sensitive versus full price segments:
AI analyzes purchasing history, browsing behavior, engagement with past promotions, and even demographics to predict which customers are likely to purchase at full price versus those who require a discount to convert. This allows you to offer full price to those who are willing to pay it, preserving precious margin.
Targeted versus blanket discounts:
Instead of relying on broad, store-wide discounts, AI-driven retail systems in general can enable more localized or targeted approaches ensuring that markdowns are applied strategically and only when necessary. While WAIR.ai does not generate or manage personalized offers, Wallie’s 360-degree inventory analytics support these decisions by providing accurate visibility into product performance and regional demand patterns.

Predicting demand shifts leveraging browsing patterns and external signals

AI’s ability to process and interpret vast, disparate data sets means it can predict demand shifts far more accurately than human analysts. This predictive power allows you to act proactively, adjusting inventory and pricing before markdowns become a necessity.

This is how AI helps predict shifts:

Utilizing browsing patterns and digital footprint:
Beyond purchase history, AI analyzes how customers browse your website, what products they view, how long they stay on a page, and even what they abandon in their carts. These subtle signals, combined with data from loyalty programs and app interactions, provide invaluable insights into future demand.
Integrating external signals:
Factors such as local event calendars, weather conditions, and regional demographics can be incorporated into AI models to enhance forecasting accuracy. For example, if a major local event is scheduled, AI can predict an increase in demand for certain apparel items, allowing you to optimize stock levels and allocation proactively. WAIR.ai specializes in leveraging these external data points to improve demand forecasting and inventory planning.

Targeted offers not blanket discounts

With a precise understanding of customer behavior and predictive demand insights, you can move away from reactive, broad markdowns towards a strategy of proactive, targeted offers.

The benefits of targeted offers include:

Increased sell through and margins:
By offering the right discount to the right customer at the right time, you avoid unnecessary margin erosion on products that would have sold at a higher price, while still moving slow moving inventory efficiently. This has demonstrated an 18 percent increase in sell through and 9 percent higher margins.
Enhanced customer loyalty:
Personalized offers resonate more deeply with customers, making them feel understood and valued, leading to increased loyalty and repeat purchases. In fact, 78 percent of consumers become repeat customers if given personalized experiences and recommendations.
Reduced markdown losses:
Large enterprises have already seen up to €28.6 million in annual reduction of markdown losses by adopting AI driven strategies, proving the immense financial benefit. WAIR.ai provides a comprehensive guide to calculating retail AI ROI for your business.

Building your AI foundation technical deep dive and implementation roadmap

Implementing an agentic AI solution for markdown optimization requires a robust technical foundation and a clear implementation roadmap. This isn’t just about plugging in a tool, it’s about integrating intelligence into the core of your retail operations. WAIR.ai offers a comprehensive approach to integrating AI into your existing retail tech stack, ensuring seamless transition and maximum impact.

Data integration challenges and solutions overcoming silos

The biggest hurdle for many retailers is often fragmented data. Your POS system, CRM, e-commerce platform, and external data sources often operate in silos, making a unified view of customer behavior difficult.

This is how to overcome data silos:

Practical steps for integrating disparate data sources:
WAIR.ai helps establish robust data pipelines that bring together transactional data, inventory information, and other relevant operational datasets into a single, unified foundation for intelligent analysis. This comprehensive data foundation is crucial for powerful AI analysis. Learn more about the retail AI data foundation.
Frameworks for data quality management:
High quality data is paramount for accurate AI predictions. We work with you to implement data cleansing, validation, and enrichment processes, ensuring your AI models are trained on reliable information.

Anatomy of an AI markdown model demystifying the algorithms

While the underlying algorithms can be complex, understanding their basic function demystifies the process. WAIR.ai uses sophisticated deep learning models and agentic AI architectures designed specifically for retail environments.

Here is a simplified look at key algorithms:

Supervised learning for churn prediction:
Models are trained on historical data to predict outcomes based on labeled examples. For markdown optimization, this can predict customer segments likely to churn without a discount versus those who will buy at full price, or predict demand for specific SKUs.
Reinforcement learning for optimal pricing paths:
This type of AI learns by trial and error, identifying the best sequence of pricing adjustments (including markdowns) over time to maximize long term revenue, much like a virtual merchandiser continuously experimenting.

The role of a centralized AI pricing team collaboration for success

Successful AI implementation isn’t just about technology, it’s also about people and processes. A dedicated team or clear lines of responsibility are essential.

Consider these best practices for organizational structure:

Cross functional collaboration:
Foster collaboration between merchandising, pricing, marketing, and IT teams. This ensures that AI insights are integrated across all relevant business functions.
Continuous learning and iteration:
AI models are not static, they require continuous monitoring, retraining, and refinement. A dedicated team ensures that the AI system evolves with market changes and improves its accuracy over time. WAIR.ai supports this journey with expert guidance and ongoing support.

Ethical AI and trust navigating personalized pricing responsibly

As AI enables increasingly personalized pricing, ethical considerations and data privacy become paramount. Retailers must build trust by using AI responsibly and transparently, adhering to regulations like GDPR and CCPA.

It is critical to address these concerns:

Avoiding discriminatory pricing:
AI models must be designed to ensure fairness and prevent any inadvertently discriminatory pricing practices. Transparency in how prices are determined helps maintain customer trust.
Data privacy and security:
Robust data governance and security measures are non-negotiable. WAIR.ai prioritizes data protection and operates with full GDPR compliance, as we do not process or store any end-user or customer-specific data.
Building loyalty through transparent value delivery:
The goal of AI powered personalization is to deliver more relevant value to the customer, not to exploit their data. By focusing on enhanced customer experience and value, retailers can strengthen loyalty, not erode it.

Getting started your quick win AI markdown strategy for immediate impact

Embarking on your AI journey doesn’t have to be an all or nothing proposition. A phased approach allows you to demonstrate quick wins and build internal momentum.

Phase one data readiness assessment and pilot program

Start with a focused pilot to validate the impact of AI in a controlled environment.

This phase involves:

Data readiness assessment:
Evaluate your current data infrastructure, identifying key data sources and potential gaps. This helps you understand what data is readily available and what needs to be collected or cleaned.
Selecting a pilot category or cluster:
Choose a specific product category, brand, or a small group of stores that represent a manageable scope for your initial AI implementation. This allows you to demonstrate tangible ROI quickly.
Demonstrate clear ROI:
Focus on measurable outcomes, such as a reduction in markdowns for the pilot products, increased sell through, or a boost in gross margin. This early success provides compelling evidence to scale your AI initiatives further. Learn more about how to get started with retail AI implementation planning project management.

Phase two scalable deployment and continuous optimization

Once your pilot demonstrates success, you can confidently scale your AI solution across your broader operations.

This phase involves:

Gradual expansion:
Systematically extend AI driven markdown optimization to additional product categories, regions, or stores, building on the lessons learned from the pilot.
Continuous learning and refinement:
AI is not a set and forget solution. Establish processes for ongoing model monitoring, data integration, and performance review. This iterative approach ensures your AI models remain accurate and effective as market conditions and customer behaviors evolve.
Integrating agentic AI into daily workflows:
Seamlessly embed AI insights and recommendations into the daily decision-making processes of your merchandising and planning teams, while leveraging tools like Suzie to enhance product content creation and consistency across channels. Explore how WAIR.ai can empower your team with operational AI analytics retail efficiency.

The future of retail profitability is intelligent pricing

The era of blanket discounts and reactive markdowns is drawing to a close. The future of retail profitability belongs to those who embrace agentic AI to deeply understand customer behavior, predict demand with precision. By leveraging solutions like WAIR.ai’s Wallie and Suzie, retailers can not only mitigate markdown losses but unlock significant margin improvements, enhance customer loyalty, and ensure sustainable growth. It’s about more than just saving money, it’s about making smarter, more profitable decisions that empower your business to thrive in an increasingly complex market. Ready to transform your pricing strategy and maximize profitability? Schedule a meeting with a WAIR.ai expert today.

Frequently asked questions about AI customer behavior and markdowns

Q: What is agentic AI and how does it specifically reduce markdowns?
A: Agentic AI goes beyond traditional AI by not only predicting outcomes but also recommending and executing actions autonomously. For markdowns, it analyzes vast customer behavior data, predicts demand shifts, identifies price sensitive customer segments, and then prescribes precise, targeted offers instead of broad discounts, thereby reducing overall markdown necessity and maximizing profit.

Q: How accurate are AI driven demand forecasts?
A: Advanced AI models from WAIR.ai process significantly more variables (up to 42 compared to 4–6 in legacy systems), including external factors like weather and regional demand trends, leading to much higher accuracy. This precision directly reduces overstocking, a primary driver of markdowns.

Q: Can AI help identify price sensitive customers?
A: Yes, AI excels at segmenting customers based on their historical purchase patterns, browsing behavior, and responses to past promotions. This allows retailers to distinguish between customers who are likely to pay full price and those who require a discount to convert, enabling targeted offers that preserve margin.

Q: What kind of data does AI need to effectively optimize markdowns?
A: Effective AI markdown optimization requires integrating diverse data sources including POS data, CRM data, e-commerce browsing analytics, loyalty program data, and external factors such as weather, competitor pricing, and local event calendars. The more comprehensive the data, the more accurate and impactful the AI’s recommendations.

Q: Is personalized pricing with AI ethical and compliant with data privacy regulations?
A: When implemented responsibly, AI can greatly enhance operational efficiency and decision-making in retail. WAIR.ai designs its solutions with strict adherence to data privacy regulations like GDPR and CCPA, ensuring full compliance and transparency. Because WAIR.ai does not process or store any customer or end-user data, our systems are inherently privacy-safe and 100% GDPR-compliant.