How to Create a Demand Plan from Scratch for Your DTC Business
Written by: Steve Clark
Demand planning is one of the most critical processes for any direct-to-consumer (DTC) brand. A well-structured demand plan helps optimize inventory, minimize stockouts and overages, and improve cash flow. Whether you're launching a new product or scaling an existing line, an accurate demand forecast can mean the difference between growth and inefficiency.
This step-by-step guide walks you through building a demand plan from scratch, based on proven methodologies used in high-growth DTC brands.
Step 1: Gather Historical Sales Data
The foundation of any demand plan starts with historical data. Download all available sales data by SKU, including:
Order volumes
Sales trends
Seasonal fluctuations
Customer purchasing behaviors
Pull at least 12-24 months of data if available to identify patterns and trends.
What if you don’t have enough data? If your brand is new or lacks sufficient historical sales data, you can use a top-down forecast instead. This involves forecasting your total monthly revenue and then allocating that revenue across products based on their expected contribution to sales. Once you've allocated revenue to each SKU, translate that into units sold based on your pricing. While not as precise as a statistical model, this method gives you a starting point to produce against until you build enough history for a more refined forecast.
Step 2: Build a Statistical Forecast (if you have the historical data)
Using the historical data, apply statistical forecasting methods to predict future demand. Some common approaches include:
Regression Analysis: Identify relationships between variables such as sales and marketing spend, promotions, or seasonality.
Profit Modeling: Incorporate unit economics to forecast demand while ensuring profitability.
This step provides a baseline demand forecast by SKU, helping to create a data-driven starting point.
Step 3: Account for New Product Launches
For new products, you won’t have historical data to rely on. Instead, benchmark them against similar SKUs by:
Comparing attributes like category, price point, and consumer base.
Applying expected growth curves based on previous product launches.
Considering any potential cannibalization effects—if your new product competes with an existing SKU, it could reduce demand for the original product.
Step 4: Incorporate Business Inputs
Data alone isn’t enough—your demand plan should also account for real-world inputs from different teams. Establish a monthly touchpoint with:
Marketing & Advertising: Capture upcoming promotions, influencer campaigns, and seasonal pushes.
Affiliate & Partnerships: Account for demand spikes from collaborations.
Product Development: Track planned innovations and SKU discontinuations.
By integrating cross-functional insights, your demand plan becomes more accurate and adaptable.
Step 5: Review & Adjust Based on Forecast Accuracy
The best demand plans aren’t static—they evolve. Set up a monthly forecast accuracy review, where you:
Compare actual vs. forecasted sales.
Identify SKUs with consistent over- or underperformance.
Adjust the model based on new trends, product changes, or marketing shifts.
Tracking forecast accuracy helps refine predictions over time, ensuring your demand plan stays reliable as your business grows.
Bringing It All Together
By following this process, you create a structured and iterative demand plan that keeps your inventory aligned with customer demand. This proactive approach minimizes costly mistakes, optimizes supply chain operations, and maximizes profitability.
If you're looking to streamline and automate your demand planning, Planster offers a robust solution designed for high-growth DTC brands. Our platform integrates historical data, statistical modeling, and real-time business inputs to ensure your forecasts stay accurate and actionable. Reach out to see how we can help take your demand planning to the next level!
