Modern brands operate in a complex environment with significant uncertainties. These uncertainties could come from many places - including supply chain, marketing efficiency, customer satisfaction, and of course broader macroeconomic environments.
While it’s impossible to accurately predict the future, planning and structured forecasting could significantly reduce some of the uncertainty, leading to better business outcomes.
Despite the many benefits of producing a thorough sales forecast that helps almost every aspect of operating a brand, many ecommerce executives and operators find the process hard.
Building an e-commerce financial forecasting model could feel like driving on a mountain road. While the destination is known – a monthly sales forecast ground in historical sales trends – the route to get there is never a straight line.
The first challenge is that most brands suffer from data silos - i.e. their critical data is distributed across many platforms. Unless they are using a unified analytics platform that would aggregate and unify data across all platforms, it becomes a chore to compile historical data for the purposes of building a forecasting model.
Second, blurring-of-the-lines occurs between demand forecasting and sales forecasting. While the two are related concepts, they serve distinctly different purposes in the realm of decision-making and business planning.
Before we go further, let us clearly define these two terms.
Sales Forecasting:
- Predicts total sales revenue for a future period.
- Focuses on financial aspects and planning revenue streams.
- Includes forecasting key financial metrics such as revenues, expenses, profits, cash flow, and financial ratios.
- Relies on historical sales data, considering trends, seasonality, and past marketing efforts.
- May also consider broader market trends and economic factors.
Demand Forecasting:
- Predicts demand for individual products or services within a portfolio.
- Essential for inventory management, avoiding overstocking or stockouts.
- Uses historical sales data at the product level.
- Consider factors like product life cycle stage, promotions, consumer preferences, and competitive landscape.
In summary, while sales forecasting focuses on overall financial performance, demand forecasting informs product-level decisions like inventory management. Both are crucial for eCommerce success, addressing different needs and strategic decisions.
Accurate Sales Forecasting Has Many Benefits
Now that we’ve established a brighter line between demand forecasting vs sales forecasting, let’s dive deeper into why sales forecasting is so important for DTC brands.
- Planning and budgeting: Predicting future sales, revenues and expenses, brands can create budgets, set financial targets, and accurately allocate resources to fund activities such as marketing, production or inventory management.
- Decision making: Sales forecasts are a brand’s holy grail when it comes to strategic business decisions. An accurate sales forecast helps brands navigate launching new products, entering new markets, and marketing campaigns.
- Risk management: Sales forecasting is essential for businesses. It ensures they have the right amount of products without excess or shortage. For companies facing seasonal demand changes, it helps anticipate sales peaks and lows, enabling efficient resource allocation.
- Investor relations: Financial forecasting is crucial for DTC brands seeking external funding or investment. A good sales forecast assures and attracts investors. Accurate projections of the brand's growth potential and financial health are key to maintaining investor relationships.
- Performance Evaluation: Financial forecasts are benchmarks for evaluating a brand’s performance against its goals and targets. By comparing actual financial results to forecasted figures, DTC brands can assess performance, identify areas for improvement, and adjust strategies as needed.
In addition, a big benefit of sales forecasting is that it forces business leaders to think deeper about other areas of interest in their businesses. This could be defining sales pipelines, refining customer retention strategies, or tailoring marketing more effectively to specified customer cohorts.
Sales Forecasting Is An Art, But Also A Science.
So far we’ve outlined the definition of sales forecasting, its importance to a DTC brand, and how it differs from demand forecasting. But how can a brand go about creating an accurate forecast?
While it may look tough at the onset, following a robust and well-defined process can make sales forecasting a powerful tool in your business’s arsenal. Follow these steps:
- Document Sales Process: Defining your sales funnel from start to finish is step one. I.e. all the steps involved in reaching your potential customers, converting them into actual customers, and bringing in repeat transactions. Also consider remarketing, upselling, cross-selling and all other parts of the sales process. Try to document every aspect of the process. The more detail available, the stronger your forecast will be.
- Gather Historical Data: Historical data serves as the foundation for your sales forecast. Information on historical demands such as average order values, average weekly/daily/monthly sales, seasonal effects, growth patterns, impacts of promotions and suchlike metrics can improve the reliability of your sales forecast. For Shopify merchants, these metrics might be sales finance reports, sales over time, sales attributed to marketing etc.
- Consider Alternative/External Scenarios: Factor in alternative or external scenarios that could impact sales, such as changes in market conditions, economic fluctuations, or unexpected events like natural disasters or public health crises. How does the state of the economy influence your customers' spending habits? What are the industry’s current trends or niches? For example, the growing popularity of the “TikTok Made Me Buy It” trend can have huge impacts for a DTC brand that sells similar items.
- Monitor Competitors: Keep a close eye on your competitors and their activities in the market. Analyze their pricing strategies, product offerings, marketing campaigns, and sales performance. Understanding competitor behavior can provide valuable insights into market dynamics and help adjust your sales forecast accordingly. Instead of viewing competition as a threat, consider it an opportunity to differentiate your brand. By focusing on ways to enhance the customer experience, such as offering a wider variety of product options or improving product quality, you can strengthen your brand's position in the market.
- Regularly Review and Adjust: Sales forecasting is an ongoing process. Continuously review and update your forecast using new data, market changes, and feedback from sales teams. Stay flexible and make adjustments to improve accuracy over time.
Choosing the Right Inputs for Sales Forecasting
To perform accurate sales forecasting, DTC brands should track and analyze a variety of key metrics. Here are some essential metrics to consider:
Historical Sales Data: Reviewing past sales performance provides valuable insights into sales trends, seasonality, and growth patterns over time. Analyzing historical data serves as the foundation for creating sales forecasts and identifying patterns that inform future projections.
Average Order Value (AOV): Calculating the average value of each customer transaction provides insights into purchasing behavior and revenue generation. Tracking AOV over time helps identify trends in customer spending and informs pricing strategies and cross-selling opportunities.
Customer Acquisition Cost (CAC): Understanding the cost of acquiring new customers relative to the revenue generated from those customers is essential for assessing the efficiency and profitability of marketing campaigns. Understanding historical CAC and making adjustments with inputs from the marketing team provide a good estimate of what CAC might be for the future periods.
Purchase Conversion Rate: Conversion rate is a measure of how many visitors are ending up making a purchase. While this could fluctuate, it is possible to ascertain average conversion rate from the historical data and make reasonable adjustments for a future period.
Historical data can only take you so far
While past performance is always a good measure to forecast future sales, relying only on historical data can result in a half-complete jigsaw puzzle.
- Patterns might change: Sales patterns often exhibit seasonality, with fluctuations based on factors like holidays, weather, or economic conditions. While historical data can reveal these patterns, it might not reflect changes in trends or shifts in consumer behavior that affect sales over time.
- New trends and strategies: Historical sales data provides insights into past performance, but it doesn't account for changes in market conditions, consumer behavior, or competitor strategies. It may not capture new trends, emerging technologies, or shifts in customer preferences that could significantly impact future sales.
- Incomplete Picture: Historical sales data typically reflects completed transactions, but it may not capture potential sales opportunities that were missed or lost due to factors like stockouts, pricing issues, or ineffective marketing strategies.
- External Factors: External factors such as changes in regulations, shifts in the economy, geopolitical events, or technological advancements can have a profound impact on sales. Historical data alone may not adequately account for these external influences, leading to inaccurate forecasts.
In summary, forecasts are not perfect and one should always recognize the uncertainty.
Is there a simple sales forecasting model?
There are myriad ways to forecast sales using the historical data, depending on the level of granularity and sophistication needed. Larger brands with dedicated data science teams use machine learning methodologies to arrive at a robust forecast. Common techniques here include various flavors of time series and regression models.
While there are sophisticated ways to approach sales forecasting, the fact remains that fewer than 25% of sales organizations have a sales forecasting accuracy of 75% or greater. Worse still, many organizations struggle to create sales forecasts at all.
We think that this is where a simple approach might benefit DTC brands. First breakdown the sales forecast into two components: Sales from new customers and then sales from returning customers. Sales from returning customers is estimated from historical sales analysis, by looking at contributions from each customer cohort.
Sales from new customers is forecast by estimating contribution from those acquired via. Paid channels and organic channels. Using the historical CAC and budgeted ad spend, estimating new customer revenue is a straightforward exercise.
By adding forecasted sales contribution from returning and existing customers, one can arrive at a simple, but powerful sales forecasting model that is rooted in historical data. Of course, certain adjustments may be necessary in order to apply evolving market trends, and any plans of introducing new products.
In Summary:
- DTC brands need accurate sales forecasting amidst data complexity.
- Sales forecasting predicts revenue, while demand forecasting manages product-level demand.
- Sales forecasting aids planning, decision-making, risk management, investor relations, and performance evaluation.
- Sales forecasting is an art, but also a science, and choosing the right forecasting methods and metrics to track will set you up for success.
- Simplifying forecasting with a focus on total revenue, returning customers, and new customer acquisition cost can enhance accuracy for DTC brands.
- Forecasting should adapt to changing patterns and trends, and always leave some room for uncertainty.
