AI or established analytics methods? Choosing the right approach can transform your retail strategy—but it’s not about replacing one with the other. While AI is gaining attention for its predictive power and automation, established methods continue to be essential for clarity, consistency, and decision-making grounded in reliable patterns. Here’s how the two approaches compare:
- AI Analytics: Enables automation, adapts to new patterns, processes unstructured data, and offers predictive modeling.
- Established Analytics Methods: Provide structured visibility, trustability from strong governance, and reliable reporting using verified business rules and known metrics.
Quick Comparison
| Aspect | AI Analytics | Traditional Analytics |
|---|---|---|
| Data Structure | Dynamic and flexible | Reliably static and predefined |
| Processing Type | Real-time and predictive | Real-time and historical |
| Query Flexibility | Adjusts to new questions | Driven by clear business rules & definitions |
| Ease of Use | Requires skilled language prompting | Visuals drive insight, and some technical skill may be needed |
| Applications | Personalization, predictions | Reporting, compliance, trend tracking |
Retailers like Starbucks and Lowes experimenting with AI for loyalty and inventory, and in some cases midsize brands are leading the way working with AI and data tools like those offered by our midsize-specific partners. But core analytics methods remain critical for tracking KPIs, informing operational decisions, and ensuring data integrity. The most effective strategies combine both—using AI for added discovery and established methods for reliable, explainable insight.
AI vs Standard Analytics: Core Differences
Let’s explore how each approach manages data, questions, and workflows—and where each offers strengths.
How Each System Processes Data
AI tools are designed to model patterns and surface suggestions across complex datasets. Meanwhile, established analytics platforms provide structured ways to query known data, often faster and with greater transparency.
| Aspect | Standard Analytics | AI Analytics |
|---|---|---|
| Data Structure | Structured and trustable | Dynamic and flexible |
| Processing Type | Real-time and historical | Real-time and predictive |
| Query Flexibility | Clearly answers defined questions | Adapts to new or changing prompts |
| Data Integration | Setup and mapping reduces conflict or confusion from data | Can auto-map new complex relationships |
AI platforms can help users generate queries or spot hidden correlations, while BI platforms typically allow you to monitor established KPIs, define thresholds, and create controlled dashboards.
Processing Time and Workflow
Many retailers still rely on spreadsheets or BI dashboards to explore data and develop hypotheses. AI tools can shorten this cycle—but this isn’t always necessary. In many cases, speed isn’t just about how fast results appear, but how clearly they’re understood and trusted.
"AI applies the appropriate machine learning algorithm to the appropriate problem, automating complete and exhaustive analysis of an entire data warehouse to answer business questions. This process takes seconds, instead of the days or weeks that humans could spend testing hypotheses one after the other."
This automation results in faster, more efficient workflows and significantly different outputs. However, workflow design should focus on fit-for-purpose tools—not just automation.
Types of Analysis and Results: A Picture Is Worth a Thousand Words
The type of insights each approach delivers also varies. Standard analytics is ideal for answering straightforward "what" questions using numerical data. AI, however, can tackle more complex "why" and "how" questions—but these models are only as good as the underlying data and assumptions. Established analytics also more frequently feature visualization, which good visualization is more complex and still emerging in the text-heavy AI world.
As Harvard Data Science Review points out:
"Graphics reveal data features that statistics and models may miss: unusual distributions of data, local patterns, clusterings, gaps, missing values, evidence of rounding or heaping, implicit boundaries, outliers, and so on. Graphics raise questions that stimulate research and suggest ideas."
But in other cases, language and complex pattern spotting can be more valuable:
"Instead of producing disparate visualizations, AI generates a data narrative in natural language. This narrative explains ‘why sales are up’ in words that business people can understand. By directly explaining ‘why,’ AI tackles the part of work that machines are really good at (computation, classification, regression, etc.)."
Required Skills and Training
Working with established BI tools often requires SQL, data modeling, or dashboard-building skills. AI platforms promise accessibility—but interpreting the results still requires critical thinking, especially when predictions lack context. Furthermore, ability to write a good prompt, to train and guide an AI and spot where it goes wrong, and the ability to put only good data into an AI may end up even more difficult.
- Established methods build literacy across organizations over time.
- AI platforms may reduce complexity but increase reliance on black-box systems.
System Flexibility and Growth
AI platforms promise adaptability as business questions evolve. But in practice, even AI systems require tuning and data training to stay relevant.
Established systems offer long-term stability, compliance, and reliability. They’re especially useful when:
- Metrics must remain consistent
- Reports need to meet audit or financial standards
- Data must be governed for quality
Retail Use Cases for Both Methods
AI Retail Applications
Retailers are using AI to handle operations that sit atop structured data from well-established systems. For example, AI-driven personalization has been shown to increase revenue by 40%.
Starbucks introduced its Deep Brew platform in 2019, which focuses on personalization. This platform has contributed to a 13% year-over-year increase in customer loyalty and added 4 million new loyalty members in Q4 2023 alone.
Lowes employs AI-powered shelf cameras in busy store areas to identify empty shelves, alert staff, and reduce stockouts by up to 65%.
| AI Application | Results | Example Retailer |
|---|---|---|
| Personalization Engine | 13% YoY loyalty growth | Starbucks |
| Supply Chain Optimization | 40% increase in forecast accuracy | Simons |
| Real-time Inventory | Up to 65% reduction in stockouts | Lowes |
| Customer Engagement | 4M new loyalty members in Q4 2023 | Starbucks |
Zara uses AI to fine-tune production schedules based on demand forecasts. By analyzing sales patterns, they avoid overproducing less popular items while ensuring enough stock of high-demand products.
"Predictive AI leverages data to deliver precise insights that forecast trends, optimize marketing strategies, and drive targeted customer engagement, ultimately leading to increased sales, strengthened customer loyalty, and maximized return on investment." – Cédric Chéreau, Managing Director, EagleAI
Retail Reimagined: Moving from Vision to Value with AI
AI excels at providing dynamic, real-time solutions, but traditional analytics still plays a key role in delivering consistent, essential reporting.
Established Analytics in Retail
While AI offers dynamic tools, more established analytics remains a cornerstone of retail strategies. It continues to play a critical role in various foundational operations.
Tesco’s Clubcard program is a great example of this evolution. Originally built on traditional analytics, it now incorporates AI to power its Clubcard Challenges, which create personalized goals for individual customers.
Carrefour has transitioned from basic customer segmentation to predictive analytics, combining the best of both toolsets and automating engagement through its loyalty program.
Key strengths of structured approaches:
- Reporting across financial or operational KPIs
- Standardized sales reports and tracking revenue
- Segmenting customers into clear groups based on known variables
- Tracking inventory turnover, returns, or cost trends
- Handling compliance and audit needs
- Analyzing historical trends
While these methods may not match the complexity of AI systems, they are indispensable for routine operations where straightforward analysis is enough.
These uses are foundational. Without them, AI can’t function effectively.
The best results come from combining the two – using traditional analytics for stable, consistent reporting and AI for advanced, predictive insights.
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Key Issues Retailers Face
Data Setup and Connection
Clean, usable, interconnected data is a non-negotiable starting point for AI, and a must have if your standard analytics are to go anywhere beyond confusing/conflicting answers to the most basic questions.
Retailers often face difficulties when trying to integrate data from various isolated systems. Here’s a breakdown of common challenges across key systems:
| System Type | Typical Data | Integration Challenge |
|---|---|---|
| Point of Sale (POS) | Transaction records | Problems with real-time syncing |
| Inventory Management | Stock levels | Inconsistent data formats |
| Customer Relationship Management (CRM) | Customer profiles | Ensuring privacy compliance |
| E-commerce Platform | Online behavior | Matching data across channels |
"Data-driven decision making is no longer a nice-to-have – it’s a must-have for modern retailers." – Forbes
To tackle these issues, retailers should focus on implementing strong data governance practices. Investing in tools that automate data validation and cleansing can also simplify integration processes. However, solving data issues is just one part of the puzzle – retailers must also ensure their budgets and teams are equipped to handle these challenges.
Budget and Staff Needs
AI systems often require a substantial upfront investment compared to established analytics tools. On top of that, there’s a shortage of skilled data scientists, which makes adopting AI even more difficult, while BI methods can be adopted by analysts, marketers, and ops teams. Retailers can address this by upskilling their current workforce and, when necessary, hiring specialists to fill skill gaps.
Understanding Results
As analytics tools become more advanced, interpreting their outputs has become increasingly complex. This creates challenges in turning insights into actionable strategies. Only 10% of companies using AI achieve maturity, largely because they struggle with understanding complex insights, explaining AI-driven recommendations, and trusting predictions.
While established analytics deliver simpler, more digestible reports, AI systems often require users with specialized knowledge to interpret their findings. To improve result interpretation, retailers should integrate AI into their existing systems and focus on training their teams. Companies with effective change management practices are reportedly six times more likely to succeed.
Selecting Your Analytics Method
Match Tools to Business Needs
Pick your analytics method based on your specific retail objectives. Traditional analytics is ideal for understanding past performance and meeting compliance requirements, while AI analytics shines when it comes to predicting trends and making real-time decisions.
| Business Goal | Recommended Approach | Key Benefit |
|---|---|---|
| Historical Performance Analysis | Standard Analytics | Insight into past trends |
| Real-time Inventory Management | Either (With Good Data) | AI Predicts and automates stock needs, Established analytics quickly surfaces real-time changes and follows your biz rules |
| Customer Behavior Analysis | AI Analytics | Identifies hidden patterns in large data |
| Compliance Reporting | Established Analytics | Simplifies data tracking |
After deciding on your goals, evaluate your data’s size, structure, and how often it updates to choose the most effective method.
Using Both Methods Together
By combining the strengths of both methods, you can get the best of both worlds. A hybrid approach blends the clarity of traditional analytics with the predictive power of AI.
- Use traditional analytics for compliance and straightforward reporting.
- Apply AI to predict customer behavior and optimize inventory.
- Maintain your current workflows while gradually integrating AI tools.
This balanced strategy ensures you’re meeting today’s needs while preparing for future growth.
Conclusion
Deciding between AI and traditional analytics is a crucial choice for any retail business. As Michael Dell aptly put it, "If you’re not applying AI across your organization and thinking deeply about reinvention, you’re already behind."
Blending both approaches can create a well-rounded analytics strategy. The key is to select tools and methods that provide insights tailored to your unique retail needs.
Success in retail analytics depends on three main factors:
- Data Quality: Ensuring clean, well-integrated data from all sources.
- Tool Selection: Choosing analytics solutions that align with your business goals.
- Implementation Strategy: Rolling out changes in phases, starting with essential metrics.
This combined approach strengthens the hybrid strategy mentioned earlier. With challenges like data overload and integration hurdles, having a solid analytics framework is essential to stay competitive. Balancing AI and traditional methods allows businesses to turn quality data into measurable outcomes.