Master Demand Forecasting for Ecommerce and Retail to Stay Ahead of the Curve

Ecommerce and Retail Sales Continue to Grow Rapidly

According to Statista, retail e-commerce sales in 2023 is expected to exceed 6.5 trillion USD worldwide, once including brick & mortar sales, this brings expected annual sales in 2023 to over $30 trillion USD.

Serving customer demand world wide in the retailing and ecommerce industries is a complex landscape requiring agility and proactive decision-making. However, at its core, the essentials remain straightforward: have the right products at the right time at the right place to meet customer needs. Knowing how much is an essential strategy that fuels the ever-growing global retail & ecommerce industry.

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So, How Do You Gauge Consumer Desires?

Understanding consumer past and present needs is just half the battle; anticipating their future needs is the game-changer. This is where the art and science of demand forecasting for ecommerce and retailers come into play.

Continue reading to gain a comprehensive understanding of demand forecasting in the retail sector. We'll explore its core components, how it impacts aspects like inventory planning, and delve into its various types and methods. Everyone from emerging e-commerce brands in beauty to enterprise retailers in fashion & apparel operating thousands of brick-and-mortar locations can benefit.

Demand Forecasting in 2023

In 2023, demand forecasting has become more essential than ever, serving as the lifeblood for a wide array of businesses—from emerging e-commerce brands to enterprise retailers operating thousands of brick-and-mortar locations. This analytical practice is a strategic asset across various industry verticals such as clothing & apparel, beauty, and electronics. By employing a mix of historical sales data, market analytics, and emerging trends, businesses can finely tune their inventory to meet rapidly changing consumer needs and preferences.

In the digital age, those leveraging advanced forecasting technologies are experiencing a remarkable surge in sales. E-commerce platforms benefit from optimized stock allocation and reduced stockouts, while physical stores enjoy the perks of precise shelf-space management and lowered holding costs. The utility of demand forecasting extends beyond Consumer Packaged Goods (CPG) and manufacturing sectors, where it aids in efficient production planning and resource allocation. For example, in the beauty industry, demand forecasting helps to track ever-changing consumer preferences for skincare and makeup, offering invaluable market insights.

Different Forms of Demand Forecasting

We have a separate post going into the full taxonomy of demand forecasting, but let's discuss how demand forecasting can be broken down based on various factors like timeframe and scope:

By Level of Detail

Macro-level

What is macro forecasting? Trying to launch a new product line or expand into a new industry? Want to forecast several years into the future? then a macro forecast is the right tool. Generally In this approach, a more holistic view is taken, for example the market landscape or even the global economy where you operate is considered. Market research is vital here, making it suitable for businesses aiming to launch new products or enter new market segments.

For example, if a brand like Skull Candy wished to expand into apparel, macro-level demand forecasting would be crucial for sizing precise inventory planning and supply chain management.

Scope: Broader economic or industry level trends and variables

Data Sources: Aggregate data like GDP, unemployment or inflation rates, historical industry sales and growth rates

Time Horizon: Generally longer term macro trends, perhaps years into the future.

Objective: Helps businesses plan strategic goals and understand the market's long-term potential.

Application: Strategic decisions

Sensitivity: Less sensitive to short-term fluctuations in individual regions, categories, or products.

Micro-level

What is Micro-level forecasting? Contrary to macro demand forecasts, micro-level forecasts focus on internal operations. Key considerations include:

• Historical performance by product categories and SKUs
• Profit margins on various products
• Cash flow and production costs affecting inventory

Scope: Concerned with specific market segments, products, or even SKU levels.

Data Sources: Uses granular data like individual sales transactions, customer behavior, stock levels.

Time Horizon: Usually short-term forecasts, from days to months.

Objective: Helps in tactical decision-making such as inventory planning, production scheduling, and short-term financial planning.

Application: Useful for operational decisions and immediate adjustments.

Sensitivity: Highly sensitive to short-term changes and needs constant updating.

By Time Horizon

Long-Term Forecasting

Timeline: More than a year

Focus: Strategic decisions like capacity planning, entering new markets

Medium-Term Forecasting

Timeline: Months to a year

Focus: Tactical decisions like seasonal inventory management

Short-Term Forecasting

Timeline: Hours to weeks

Focus: Operational decisions, e.g., shift planning, restocking

The Benefits of Demand Forecasting for Retailers

Accurate forecasting affects various aspects of your business, from inventory management to financial planning. By precisely predicting customer demand patterns, businesses can optimize inventory levels, avoiding the pitfalls of overstocking or understocking, thus reducing warehousing costs and improving cash flow.

An on-point forecast allows for more strategic pricing strategies, enabling companies to adjust prices based on anticipated spikes or declines in demand, thereby maximizing revenue and profitability. Furthermore, understanding future demand trends aids in supply chain management, allowing businesses to align their purchasing, manufacturing, and logistics strategies for maximum efficiency.

Accurate demand forecasting also supports better marketing decision-making, enabling businesses to focus their promotional activities on products that are likely to be in high demand. In essence, accurate demand forecasting serves as the backbone of efficient operational planning, financial budgeting, and strategic decision-making, setting the stage for sustainable growth and competitiveness in the market. Some specific examples:

Meets Customer Expectations
With accurate demand forecasting, you ensure that products are available when customers want them, thereby enhancing customer satisfaction.

Optimizes Inventory Costs
Efficient demand forecasting leads to optimal stock levels, reducing warehousing expenses and improving efficiency.

Enables Dynamic Pricing
Understanding demand lets you create pricing strategies that reflect market desires, adding another layer to your competitive edge.

Mitigates Financial Risks
Data-driven demand forecasting provides solid evidence for budget planning, helping you make smarter financial decisions.

Considerations of Demand Forecasting for Ecommerce vs Brick-and-mortar

While out of scope of this article, there are typically different challenges associate with demand forecasting for ecommerce vs brick & mortar retailers. In general, ecommerce operations have larger volume of but more standardized data collection, a greater variety of data sources (e.g. clicks, views), and may be served by centralized warehouses. However brick & mortar's operating within a local community or mall setting, tend to be highly influenced by location but more predictable on a steady state aggregate level - so balancing the portfolio of product offerings, increasing match rates, and optimizing shelf storage become more important.

How do Enterprise Retailers Operate Demand Forecasts?

How does Apple forecast the demand of it's iPhones? For a large organization Apple, two broad categories demand forecasting exist: Qualitative and Quantitative

Qualitative Forecasting: Highly manual. Requires a human to inspect broad economic indicators, expert opinions, and market surveys. Often a reasonable guardrail.

Quantitative Forecasting: Can be highly manual. Relies heavily on past data, typically gleaned from sales and web analytics. Advancement in predictive AI & ML systems can make this more efficient. Check out our other posts on different forecasting techniques, and overview of quantitative methods.

What are the steps to demand forecasting?

Understanding the significance of demand forecasting is the first step to leveraging it effectively for your venture. So how do you go about sharpening your demand forecasting approach? Here's a simplified guide to get you on the right track.

1. Define Objectives

Like any meaningful business initiative, precise planning is foundational to successful demand forecasting. Begin by setting clear objectives and defining the timeframe for your forecasting efforts. Questions to guide your planning might include:

• Is it a new product launch like the iPhone 15? Expansion into a new territory? Forecasting next quarter's sales to plan your next inventory decision?
• How many units of specific products are we expected to sell?
• Will demand for certain product lines vary seasonally or cyclically?
• What external events or factors could impact demand?
• Could evolving customer preferences affect sales numbers?
• Is our existing infrastructure prepared to handle dramatic shifts in demand?

After outlining your objectives, secure alignment and agreement from all key stakeholders, such as your sales team, marketing staff, and decision-makers.

2. Gather and Organize Data

Accurate forecasting hinges on comprehensive data. Therefore, the more information you accumulate, the more precise your forecast will be. Various methods are available for collecting both quantitative and qualitative data.

Internal factors like past sales figures can be sourced from your CRM or order management systems. For external factors, you might consider conducting your own market research or tapping into existing industry reports and surveys.

3. Analyze Data Metrics

Once you have amassed sufficient data, the next step is to analyze this information to identify trends and patterns. While manual analysis might work for smaller operations, scaling businesses will benefit from using AI-driven analytics platforms. These platforms can rapidly sift through large datasets to extract actionable insights. However, it's crucial to complement these technological capabilities with your own business acumen to interpret the data effectively.

4. Implement Changes

Having analyzed the data and deciphered the trends, you are now equipped to take actions that will positively impact your business. For instance, if your data suggests a decline in consumer demand for a certain product, you can adjust your purchasing strategy to stock fewer units. If a product shows a seasonal demand curve, you could choose to only stock it during peak demand, thus avoiding unnecessary inventory costs.

Still need help?

Does this all sound complicated? We didn't even cover more specifics of demand forecasting, data management, model deployment, feature engineering etc. Bucephalus can help you. We're experts in the space of demand forecasting having worked with hundreds of customers around the world both small and large.

By leveraging our proprietary data sets and our AI attention-based transfer models enriched with human feedback, we can significantly enhance the accuracy of demand forecasting, particularly for ecommerce brands & retailers. Our advanced models ingest and process a vast array of data types to generate more nuanced and reliable forecasts.

By synthesizing these advanced techniques and varied data sources, the model offers a more holistic and adaptive approach to demand forecasting, resulting in higher accuracy and more effective inventory management strategies.

We're building the future of the world. Remember, in the fast-paced world of online retail, foresight is your most valuable asset.

Contact us today.