The retail world faces a costlier problem right now.
The industry faces $500b worth of overproduction globally every year according to United Nations.
On the other hand, the Nuvama report highlights that a single year of poor performance exhibited by the stores can wipe out the entire accumulated profit over the years, especially in fashion & other fast-moving retail.
It’s clear that overstocking or understocking could pose significant issues to retailers apart from losing customers and sales alone.
This article explores how your retail business can avoid this costly misstep with the help of retail demand forecasting, powered by AI & ML, and how you can forecast demand effectively at product and store levels.
Role of demand forecasting in retail planning
Often, you may confuse retail demand planning with retail demand forecasting, but they do vary slightly.
Retail demand forecasting majorly involves predicting demand for a product, preferably at the product SKU and store level, whereas Retail planning involves managing inventory, pricing, promotions, and store operations.
So, retail demand forecasting plays a crucial role in the whole picture. It provides various data-backed insights that enable retailers to:
Retail demand forecasting plays a crucial role in this process by providing data-driven insights that enable retailers to:
- Optimise inventory management: Ensure that the right products are available at the right time and in the right quantities across all stores and online platforms.
- Improve supply chain efficiency: Reduce lead times and prevent stockouts or overstocking by aligning procurement with demand trends.
- Improve customer satisfaction: Minimise instances where customers encounter out-of-stock situations or delayed deliveries.
- Refine pricing and promotions: Adjust pricing dynamically based on demand predictions and maximise the impact of discounts and promotional campaigns.
- Improve financial planning: Forecasting demand helps in better financial management by reducing unnecessary costs and maximising revenue opportunities.
Here, leveraging advanced analytics and real-time data can develop a proactive approach to demand forecasting, which could lead to increased profitability and improved customer experiences.
Benefits of retail demand forecasting
Accurate demand forecasting provides multiple benefits, transforming retail operations and decision-making.
Some of the top benefits include:
1. Reduced inventory costs
Retailers can optimise stock levels, reducing the need for excessive safety stock and minimising storage costs.
As we mentioned earlier, the retail industry faces a major overproduction issue, and these costs can be saved and put toward retail expansion efforts.
A well-forecasted inventory prevents dead stock, which ties up capital and leads to discounts.
2. Increased sales and revenue
With the right products in stock, retailers can meet customer demand more effectively, leading to higher sales.
Demand forecasting helps identify fast-moving products and ensures their availability, reducing lost sales opportunities.
3. Better supplier and logistics management
Accurate demand forecasts allow you to plan procurement efficiently, reducing last-minute rush orders.
This, in turn, improves supplier relationships while ensuring smoother supply chain operations and preventing delays.
4. Improved customer experience
Consumers expect product availability and timely deliveries. By forecasting demand accurately, retailers can maintain optimal stock levels, preventing stockouts and ensuring a seamless shopping experience.
5. Minimised waste and markdowns
As we already know, excess inventory often leads to unsold products that require discounting, cutting into profit margins.
With accurate forecasting, retailers can align stock with actual demand, reducing the need for discounts and minimising waste.
6. Improved omnichannel fulfilment
With AI and ML, you can not only predict demand for offline channels but for online channels as well – at the street level.
With these granular and accurate demand insights, you can better allocate stock across different sales channels, ensuring both online and offline customers receive timely deliveries without overburdening any one channel.
How to approach retail demand forecasting?
For years, retailers relied on intuition, past sales trends, or any anecdotal data to justify their reasoning while stocking a product.
While this could work, it is not at all an ideal approach to retail demand forecasting.
Why? It’s simple. Every store’s demography varies significantly. So, while planning, you should take into account the local customer base’s interests, buying behaviour, spending patterns, affluence level, etc.
Trends, on the other side, change as we speak. So, you should also keep an eye out for that.
So you should not only analyse your past sales data, but you should also take these external factors into account while procuring stocks for your stores.
But tapping on these data on a store and product level is next to impossible without the help of AI and ML models.
For example, high-income customers may prefer premium brands, while budget-conscious shoppers typically prioritize affordability. By understanding these demographic trends, you can forecast demand with greater precision and adjust your product mix accordingly.
Let’s discuss how you could efficiently forecast retail demand with the help of these advanced tools.
Retail demand forecasting with AI & ML at the Product and Store level
Retail demand forecasting can actually be done at both the product level and the store level:
Product-level demand forecasting focuses on predicting demand for individual SKUs based on multiple influencing factors we’ve discussed before.
Store-level forecasting takes into account the unique characteristics of each store’s location and its surrounding market.
Key factors such as local demographics, competition, mobility patterns, and economic conditions all play a role in determining demand at the store level.
Analysing these factors and more ensures you stock the right products across different stores and locations.
Factors at play while forecasting demand
i) Demographics & customer profiles
Customer demographics are a fundamental factor in predicting product demand at a granular level.
Age, income, education, and occupation can influence purchasing decisions, and understanding these characteristics helps retailers tailor their product offerings.
ML allows for the integration of such demographic data with historical sales patterns, providing insights into which products will perform best in specific locations.
ii) Customer preferences & emerging trends
Customer preferences are constantly changing, driven in part by social media, influencer marketing, and online reviews.
AI and machine learning models can capture this shifting demand by monitoring trends and conversations online.
By analysing data from Google searches, social media platforms like Meta, and e-commerce reviews, these models can predict which products are gaining popularity, even before they become mainstream.
iii) Shopping habits & brand affinity
Some customers develop strong preferences for specific brands due to past experiences, loyalty programs, or perceived quality. This brand loyalty can drive repeat purchases, making certain products more predictable in demand.
ML models can analyse these shopping habits at a hyperlocal level, allowing you to forecast demand for brands with strong customer bases in particular regions.
iv) Seasonal & market trends
Seasonality plays a significant role in product demand, with certain items experiencing predictable surges at specific times of the year.
Forecasting models can integrate this seasonal data with other external factors such as climate patterns, local holidays, and regional shopping trends.
This dynamic approach ensures that your inventory is prepared for fluctuations in demand.
v) Mobility & foot traffic patterns
Real-time mobility data is an invaluable tool in forecasting demand at the store level.
By analysing foot traffic patterns, you can determine when their stores are most likely to experience high customer volume. For instance, stores near transport hubs or office areas may see a spike in peak hours.
This real-time data allows for proactive planning and ensures that stores are always prepared to meet demand when it’s at its highest.
Conclusion
A structured approach that includes both product-level and store-level demand forecasting ensures accurate predictions, enabling you to make tailored decisions across locations.
Book a 15-minute discovery call with us to use these tools to your advantage!