Industry
Retail
Omnichannel CX and Intelligent Innovation – The Future of Retail
HIGHLIGHTS
- Changing consumer behaviors have meant intensified competition and demand fluctuation, in grocery retail.
- Grocery retailers have to be more perceptive of the ever-evolving consumer expectations by integrating real-time insights, AI-driven decision-making, and adaptive merchandising strategies.
- Agentic AI can help retailers take advantage of halo and cannibalization effects to better position products and ensure higher sales.
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Overview
The retail industry at large is evolving rapidly, driven by millennials and Gen Z consumers and their new shopping behaviors.
These shoppers prioritize value, convenience, and personalized experiences over brand loyalty. This behavior is often shaped by social media trends, influencer recommendations, and real-time product transparency. This shift has resulted in intensified competition and fluctuating demand in grocery retail through pricing, differentiation, and innovation.
To remain competitive, grocery retailers need to become more perceptive, an approach that integrates real-time insights, AI-driven decision-making, and adaptive merchandising strategies to understand customer needs and deliver accordingly. This will help them align with the shifting consumer preferences, optimize store and supply chain operations, enhance overall shopping experiences, and stay ahead of the competition.
By using the halo and cannibalization effects to their advantage, grocers can better position products and ensure higher sales. The halo effect refers to the rise in demand for a product due to the promotion, success, or visibility of another related product. With social media and influencer culture shaping purchasing behaviors, retailers need to anticipate pricing and sales shifts. For example, a promotion for oat milk can boost sales for granola and plant-based coffee creamers, requiring adjustments in pricing, inventory, and promotions to sustain profitability.
Conversely, the cannibalization effect occurs when a new product launch or promotion negatively impacts existing products in the same category. A discount on private-label almond butter, amplified by social media, could erode sales of a higher-margin branded almond butter. Retailers can perceive these impacts with a better and deeper understanding of customer data to adjust pricing, promotions, and inventory in real time.
The traditional approach
Retailers traditionally rely on sales data analysis, category management, and promotional tracking to estimate the halo and cannibalization effects.
However, these methods are reactive, which means the effects are identified only post transactions. That makes it difficult to adjust assortment, inventory, or pricing in real time. Here’s a quick look at some of the traditional methods deployed for analysis:
- Basket analysis and transactional data is the analysis of point-of-sale (POS) data to identify frequently purchased products. However, with this method, insights are only available post sales, limiting dynamic pricing, promotions, or shelf adjustments. This method also fails to capture demand shifts from social media trends.
- Promotional and demand modeling uses historical data to measure the impact of promotions, price changes, and campaigns on demand. However, since the method relies on past trends, it is unreliable for predicting social media shifts, competitor moves, or seasonal changes. Additionally, since the impact is assessed after the sales cycle, adjustments to pricing, inventory, or assortments are often late.
- Category management and cross-SKU strategies help retailers adjust assortments, pricing, and product placements to balance profitability. However, predefined category relationships are unhelpful in capturing emerging consumer preferences and market trends. Adjustments to shelf space, inventory, and pricing are often reactive rather than proactive.
AI and halo, cannibalization
The biggest drawback of traditional retail models is their inability to accurately identify halo and cannibalization effects in real time.
Generative artificial intelligence (GenAI) AI and agentic AI can help retailers perceive these trends using the vast troves of consumer data to detect product relationships, pricing interactions, and demand shifts proactively. GenAI can analyze sales data, social trends, and competitor movements, while agentic AI can automate effect detection, ensuring data-driven merchandising, inventory, and pricing strategies. Additionally, GenAI can structure relevant data sources to refine halo and cannibalization analysis. For example, if sales for oat milk rise, AI can determine if this boosts granola sales (halo) or shifts demand from almond milk (cannibalization), requiring adjustments in pricing, promotions, and inventory.
Key areas of impact include:
- Identification of product relationships by analyzing social media trends, historical sales data, and competitor pricing
- Removal of irrelevant signals (eg, store-level stock fluctuations, isolated regional trends), ensuring pricing decisions based on actionable insights
- Enhanced accuracy by selecting the best AI model for identifying halo and cannibalization effects
Social media and halo, cannibalization signals
Managing halo and cannibalization effects, ensuring product interactions, and creating cross-category synergies without disrupting margins are key challenges in grocery retail. Traditional merchandising and forecasting models struggle with reactive, post-sales analysis, limiting their ability to adapt to market fluctuations. To stay ahead, retailers must become more perceptive by deriving deeper insights from data and using static decision-making driven by agentic AI, enabling more dynamic assortment, merchandising, and supply chain optimization.
Social media trends and reviews directly influence product sales, pricing, and category performance. Combining data with the latest AI technologies can help retailers perceive these shifts and accordingly optimize pricing, promotions, and product strategies. The right analysis of halo and cannibalization effects helps guide assortment planning, demand forecasting, and competitive pricing decisions in real time.
Why agentic AI?
Agentic AI can help automate the detection of halo and cannibalization effects, continuously analyzing real-time sales data, basket composition, pricing shifts, and competitor trends.
It can help (see Table 1):
- Integrate with POS and sales systems to track demand shifts dynamically
- Detect cross-product sales dependencies, identifying real-time halo and cannibalization effects
- Monitor influence of competitor pricing to assess external impacts on sales trends
Table 1: Traditional approach versus agentic AI
| Aspect | Traditional approach | AI-Driven approach (GenAI + agentic AI) |
| Data source | Historical sales data, manual analysis | Real-time POS data, social media trends, competitor pricing |
| Speed of Insights | Reactive (post-sales analysis) | Proactive (real-time detection and adjustment) |
| Identification method | Basket analysis, category sales trends | AI-driven pattern recognition and automation |
| Accuracy | Limited (relies on past trends) | High (analyses real-time demand shifts and dependencies) |
| Scalability | Manual, time-consuming | Automated, scalable across thousands of SKUs |
| Social media influence | Not considered | Analyzed for emerging demand signals |
| Business impact | Prone to missed opportunities and margin loss | Optimized decisions for revenue growth and competitiveness |
Business benefits across domains
Securing deeper and more meaningful insights from social media trends and consumer interactions with agentic AI and GenAI specifically on halo and cannibalization effects can help retailers perceive and implement marketing and sales strategies that will ensure they stay ahead of the competition (see Figure 1).
Figure 1
Business functions benefit from agentic AI
Figure 1
Agentic AI and GenAI can also help retailers stay ahead of social media-driven demand shifts, optimize pricing, enhance marketing effectiveness, and improve operational efficiency.
Halo and cannibalization in grocery pricing: An Illustration
Factoring in halo and cannibalization effects in grocery retail pricing optimization is essential to maximize revenue and maintain category profitability. By addressing some of the critical implementation factors, retailers can maximize accuracy and effectiveness with AI-driven pricing solutions.
To efficiently implement this strategy, retailers need to follow a five-step approach (see Figure 2):
Figure 2
Implementing pricing strategies with agentic AI
Figure 2
An overwhelming influx of social media trends often has retailers struggle to determine whether a trend is relevant to their products or is driven by competitor-owned demand shifts. Reacting without proper filtering can lead to misguided pricing adjustments that may not reflect the actual consumer demand.
With GenAI-driven filtering and contextualized models, retailers can distinguish between relevant and irrelevant trends, while agentic AI can streamline real-time filtering, ensuring only meaningful insights shape pricing. This method ensures pricing decisions are grounded in actionable intelligence, allowing retailers to focus on contextualized market trends rather than reacting to all social media influences.
For example, a viral TikTok trend on a competitor’s oat milk brand may create the illusion of category-wide demand growth. Without AI-driven filtration, retailers might increase stock or adjust pricing for their own oat milk, only to find that the demand remains brand-specific, leading to misallocated inventory and ineffective promotions.
Not all large language models (LLMs) perform equally in price optimization and sentiment analysis. An AI platform can test multiple LLMs, allowing retailers to fine-tune models with custom data for better accuracy. However, AI models will have to be continuously validated using real sales data to ensure effectiveness, allowing retailers to automate pricing decisions.
Competitive pricing will need to be monitored in real time using AI-powered price tracking and benchmarking. AI can also map retailer SKUs to competitor equivalents, ensuring accurate price comparisons. Before changing prices, AI must compare new prices with competitor data. For example, if halo effect is detected for granola due to increased oat milk sales, AI will be able to recommend reducing its price. However, with competitor data monitoring added to the mix, AI will be able to check and make decisions adjusted to the competition’s pricing.
AI establishes cross-product dependencies, ensuring price adjustments are reviewed across linked products. This data-driven process integrates historical sales data, product pairing analysis, and consumer behavior trends to automate price recommendations while balancing halo and cannibalization effects. For example, lowering the price of Greek yogurt will trigger an AI-powered pricing review of granola, honey, and fresh fruit, optimizing cross-product sales through defined internal product linkages.
AI-powered simulations will help retailers assess potential revenue, margin impact, and competitive effects before executing price changes. AI can run multiple pricing scenarios, providing real-time recommendations on whether an adjustment enhances profitability or risks erosion. For example, before enacting price reduction for premium chocolate bars, AI would run a simulation and can alert the business that this could lead to a potential 15% sales drop in mid-range chocolate brands, allowing the retailer to alter strategy accordingly.
Looking ahead
Retailers have to ensure they are aware of shifts in consumer behavior not only as they happen but before they happen to maintain competitive advantage in the dynamic retail space.
Retail powered by agentic AI and GenAI can help ensure this. Additionally, understanding consumer shifts in real time can enable data-driven decisions that align with actual shopping behaviors. AI-driven insights can improve assortment planning, pricing, merchandising, supply chain, and customer engagement by adapting to real-time demand shifts. AI also filters genuine consumer demand from short-lived trends, ensuring halo and cannibalization effects are managed proactively.
By leveraging consumer-perceptive AI, retailers can:
- Enhance demand forecasting by analyzing real-time consumer sentiment
- Optimize product assortment and inventory based on evolving preferences
- Personalize pricing and promotions using dynamic, intent-driven models
- Improve supply chain agility by aligning inventory with demand signals
- Strengthen competitive positioning by adapting to consumer-driven trends
As consumers reshape retail, integrating AI-driven strategies will keep retailers agile, responsive, and aligned with shopper expectations.
