Shipping What Matters: New Analytics, Experimentation And Integrations At Alhena
Data driven guide for retail brands to prepare for Black Friday and Cyber Week, using AI to manage risk, protect margins and improve customer experience.
Every year, Black Friday and the surrounding Cyber Week period represent a disproportionately large share of annual retail activity and operational complexity. For example:
- In the United States, online sales on Black Friday in 2024 reached about US$10.8 billion, an increase of roughly 10.2 percent year on year according to Adobe Analytics.
- Globally, online holiday season sales influenced by AI tools reached US$229 billion in 2024, up from US$199 billion the year before. This reflects the growing influence of AI in how shoppers make decisions.
- In Spain, one study found that average spend during Black Friday was about €230 per person and that campaign sales accounted for nearly 25 percent of annual sales for many retailers.
These numbers show that while the opportunity is substantial, the margin for error is small. For retail brands, the work required to succeed is operational, technical and increasingly data driven.
The sections below outline key risks, the role of AI, revenue opportunities, and practical steps to prepare.
1. Inventory and Supply Chain Disruption
Why this matters
- Analysts frequently list product and storage management as a major issue during Black Friday. Catalog accuracy, available stock and fulfilment capacity are common weak points.
- Increased online shopping means higher order volumes, more varied fulfilment routes and higher operational complexity.
- Retailers are now using AI for demand forecasting and stock optimisation. Field studies show that generative and predictive tools can improve productivity and conversion under real world conditions.
What can go wrong
- Stock outs and mis priced inventory push shoppers to competitors.
- Bottlenecks in last mile delivery or store based fulfilment delay orders and reduce satisfaction.
- Over ordering ties up capital and can force margin damaging markdowns.
- Systems that update slowly or fail to integrate inventory with the ecommerce front end cause cancelled orders and negative experiences.
Operational and AI assisted considerations
- AI based anomaly detection can identify unusual logistics delays early.
- Forecasting models should incorporate holiday patterns, mobile channel growth and external factors like weather and lead time variability.
- Ship from store strategies require near real time inventory visibility. Any latency or sync issues will cause mis shipments.
- Strong integration between inventory systems, the PIM(Product Information Management) and the ecommerce engine reduces the risk of stock mismatches.
Preparatory actions
- Use historical Black Friday and Cyber Week data to forecast demand at the SKU and channel level.
- Work with logistics partners early to stress test capacity and returns handling.
- Build scenario models for both higher and lower than expected demand.
- Audit your PIM and inventory sync processes to minimise latency during peak hours.
2. Digital Platform Stability and Peak Traffic Resilience
Why this matters
- As online and mobile traffic grows, website and app performance become directly tied to revenue.
- Mobile accounted for more than half of online Black Friday sales in recent years, which makes mobile speed, checkout reliability and UI clarity critical.
- Backend systems and third party integrations such as payment processors and recommendation engines must scale predictably during high traffic periods.
What can go wrong
- Slow page loads or crashes lead to cart abandonment.
- Checkout failures on mobile devices significantly reduce conversion.
- Catalog and pricing updates that lag behind surge activity cause confusion and cancellations.
- Third party services with degraded performance create cascading failures across the purchase flow.
Operational and AI assisted considerations
- AI based monitoring can detect spikes in latency or rising error rates within minutes and route alerts to engineering teams.
- AI powered product recommendations must be fast and stable. Any delay added to page load harms conversion.
- Load testing should include mobile devices, various network conditions and worst case third party delays.
- Mobile first performance tuning, caching, CDNs and lightweight assets matter more during peak hours.
Preparatory actions
- Conduct comprehensive load and stress tests.
- Audit all dependencies including payment gateways, analytics scripts, chat systems and product feed integrations.
- Monitor latency, conversion rate, error rates and service health in real time on a war room style dashboard.
- Prepare fallback modes for recommendation engines, personalisation modules and chatbots.
- Prioritise mobile speed and reduce heavy assets in templates.
3. Consumer Trust and Promotional Transparency
Why this matters
- Surveys show that a meaningful share of shoppers do not fully trust Black Friday pricing and offers.
- Shoppers increasingly use chat based AI tools to evaluate deals, compare prices and confirm product fit.
- Mobile and social commerce play a major role in shaping trust and perception of fairness.
What can go wrong
- Inflated reference prices or unclear exclusions damage trust.
- Inconsistencies across web, app and social channels confuse customers.
- AI chatbots or recommendation engines that give inaccurate advice reduce confidence in the brand.
Operational and AI assisted considerations
- AI driven personalisation can improve trust by offering relevant products, but only when the underlying product, stock and pricing data is accurate.
- Chatbots that can answer questions about delivery timelines, return policies or stock availability reduce customer uncertainty.
- AI systems must be monitored for drift, bias and incorrect recommendations to avoid undermining trust.
Preparatory actions
- Audit all promotional pricing and ensure clarity across channels.
- Ensure AI based personalisation systems are fully integrated with real time inventory and pricing.
- Deploy AI powered customer support agents tested for high volumes with clear handoff to human teams.
4. Post Purchase Experience and Returns Handling
Why this matters
- Delivery, service and returns significantly influence repeat purchase behaviour.
- Holiday return rates can exceed 25 percent in some categories, which affects both margin and cash flow.
- Predictive analytics can reduce stock outs, improve profitability and support more accurate planning.
What can go wrong
- Delayed shipments reduce satisfaction and increase contact volume.
- High return rates erode margin, especially for categories with higher fit issues.
- Disconnected systems lead to slow refunds and inconsistent customer updates.
Operational and AI assisted considerations
- AI can forecast likely return rates by category to support inventory planning.
- Automated post purchase messaging keeps customers informed and reduces unnecessary support requests.
- Monitoring return volumes during the event helps identify problematic SKUs or mismatched promotions.
Preparatory actions
- Model return rates using historical data and build capacity accordingly.
- Communicate return terms clearly at checkout.
- Automate shipping updates, delivery notifications and refund workflows.
- Review promotions on SKUs with historically high return rates.
5. Maximising Revenue Through Personalisation, Upsell and AI
Why this matters
- Beyond mitigating risks, brands should aim to maximise revenue through relevant personalisation rather than blanket discounting.
- AI in ecommerce is projected to grow rapidly, partly because it increases conversion and average order value when implemented well.
What can go wrong
- Deep discounts without targeting reduce margin without raising long term value.
- Poorly placed upsell or cross sell attempts create friction.
- AI models that work slowly or use outdated data can reduce conversion at peak moments.
Operational and AI assisted considerations
- Recommendation engines can increase conversion when they surface relevant complementary products.
- Dynamic pricing models can respond to real time demand, margin targets and competitor moves.
- Mobile and social channels benefit from personalised experiences and AI driven chat support.
- Margin monitoring is essential because some tactics increase revenue but reduce profit.
Preparatory actions
- Audit your personalisation stack for data freshness and integration.
- Test bundles, cross sell nudges and dynamic pricing scenarios ahead of the event.
- Optimise mobile and social commerce flows that use AI based recommendations or chat.
Concluding Thoughts
The upcoming Black Friday season presents both opportunity and risk. The data points to rising online participation, growing mobile dominance and increasing use of AI at every stage of the shopper journey. For retail brands, preparation remains the most important lever.
Success is more likely when brands combine accurate forecasting, robust digital infrastructure, transparent promotion, responsible personalization and a well planned post purchase experience. The most reliable path is not dramatic discounting or high pressure tactics. It is careful operational design supported by data and augmented by AI tools that improve speed, clarity and decision making.
