When executed correctly, sales forecasting can be an integral part of decision-making, planning, and resource allocation for eCommerce businesses. 

That said, common mistakes can nullify or minimize their impact, leading to poor decisions and planning. 

In this article, we examine these forecasting mistakes that eCommerce businesses make and share strategies to avoid them. 

1. Using inaccurate and inconsistent data

Sales forecasting accuracy is mainly dependent on the quality of historical data. Only complete or accurate data leads to accurate sales forecasts and, ultimately, great decisions and plans. 

Inaccurate data can stem from human errors, software issues, data gaps, non-standardized data practices, and poor data integration and transmission errors. 

If you’re using spreadsheet data or computing your sales forecast in a spreadsheet tool, you must be conscious of duplicate data, poor null values (zero is different from null), poor or inaccurate formulas and calculations, inconsistent formatting, and missing values. The more complex the spreadsheet, the more likely the errors are. 

Strategies to avoid inaccurate and inconsistent data

The strategies you can utilize to avoid this include: 

Normalize data

You’ll likely need to utilize data from multiple sources for your forecast and analysis. Data from all sources must be similar in formatting and structure to minimize errors. 

So, for example, you don’t have one source storing date in the “month, day, year” format and another using the “day, month, year” format. This can happen with data sets involving suppliers from abroad, for example. 

Data normalization minimizes the risk of data errors and makes for a more seamless and accurate data analysis.

Clean the data before use

That means checking for outliers, duplicate data, missing values, accurate column names, rechecking your formulas and calculations, and more. 

Despite following the best data storage, integration, and pipeline practices, it is prudent to clean the data before inputting them into your sales forecast. 

Keep accurate inventory data

Use reliable inventory software and update and audit inventory data regularly to keep accurate inventory data. 

Accurate inventory data is essential in estimating demand and future sales. Long-term, accurate, and historical data can show sales patterns and trends you can factor into your projections. 

2. Factoring in only historical sales data

Historical sales data alone cannot capture the unpredictable and dynamic nature of the eCommerce industry. Sales forecasts based on historical data alone may provide valuable insights but are extremely limiting. 

Not considering other factors, like the impact of the macroeconomy on purchasing power, changing customer behavior, and the effect of marketing strategies limits the quality of decisions and plans attached to such forecasts. 

This is especially true when considering sales forecasts over a longer period. 

Strategies to avoid using only historical data

Here are some strategies to employ to avoid this mistake: 

Conduct market research

Conduct market research on current and budding trends that may impact your business. This can involve reading industry reports, listening to experts, learning about government policies, etc. 

The annual reports of big retailers like Amazon, Walmart, and Costco can provide valuable insights into risks, threats, and opportunities in the retail space. 

For example, specific government policies like interest rate hikes may suppress demand for your product. So, you’ll need to factor in these additional insights into your projections. 

Survey your customers

Surveys can help you unearth customer preferences and future purchase intentions. Questions like “Are you likely to make a purchase within the next 3 months?” can help you accurately estimate demand during the forecast period. 

Analyze your competitors

What your competitors do or do not do ultimately affects your market share. Hence, competitor analysis is a vital part of eCommerce forecasting. 

You want to assess their pricing model, the products they’re promoting on social media, the partnerships they’re building, the channels they’re focusing on, and more. Tools like Similarweb can provide insights into your competitor’s marketing channel distribution. 

Competitor analysis also helps you benchmark your performance against others. If your website traffic is 25% direct vs. 70% organic search compared to 40% vs. 55% for one of your competitors, then this suggests they have bigger brand recognition than you. 

3. Ignoring seasonality and cyclicality

When working on a sales forecast, one key question you must answer is: “We sell less or more during which periods?” 

Answering this question helps you account for the predictable fluctuations in future sales. These fluctuations can be due to repeated human behavior, holidays, weather, and major events. 

Accurate sales forecasting depends on accurately estimating demand for your product(s). 

For example, research shows that most wedding proposals occur between November and February, peaking on Valentine’s Day. So, if you sell wedding or romance-related products, you’ll probably sell more during these months and have a bit of a lull during the rest of the year.

When you pair that knowledge with new research predicting more proposals in 2024, you can anticipate more demand and plan accordingly. 

Strategies to incorporate seasonality into your sales forecast

Here are some strategies you can utilize: 

Use longer historical data

Avoid recency bias by using historical data that covers a longer time frame. If available, consider data from more than two years rather than only the last six months. 

Additionally, it’s more helpful to take an apples-to-apples stance when making sales forecasts. For instance, using the average sales in January to estimate the future sales in January and repeating the same for other months will help you better account for seasonality.

Enumerate reasons for the seasonality and cyclicality

Articulating why your eCommerce business experiences seasonality can help strengthen the formula used in projecting demand and help with marketing. 

While the reasons for seasonality, such as weather and holidays, may be straightforward, discovering others may require more effort. Consumer behavior and buying patterns are consistently evolving, so you need to keep a close eye on them. 

4. Not updating or revising your forecast

With new information, it’s essential to revise your sales forecast. Doing so will help you reassess the viability of your plans and estimated performance. 

Sticking to old forecasts despite new information can cause you to miss risks requiring urgent solutions or opportunities that can increase your profitability. 

For example, a recession in the economy will definitely impact sales, as will a new, effective alternative product entering the market. 

Strategies to encourage regular updates

You can incorporate the following strategies: 

Make sales forecasting an ongoing process

Static sales forecasts can quickly become obsolete and unhelpful. Treat sales forecasting as an ongoing process that requires fine-tuning and input as new data and information become available. 

By remaining nimble and responsive to changes, you can ensure that your sales forecasts remain reliable tools for planning and making decisions. 

Institutionalize periodic audits

Schedule regular reviews and audits of existing forecasts. Even without new external information, comparing your sales estimates to the actuals can reveal gaps in your forecasting process. 

Your demand forecast being overestimated or underestimated may not necessarily be an error on your part. It may just be that you got lucky, with a popular TikTok video highlighting one of your products, causing a significant rise in demand. 

5. Not segmenting data

Not segmenting data means avoiding dividing sales metrics into distinct categories or segments for analysis. By treating all products or customer groups similarly, businesses miss valuable insights that could significantly improve forecasting accuracy.

Segmenting data allows for a deeper understanding of sales patterns and the possible effect of external factors, enabling you to tailor forecasts to specific product lines, customer demographics, or geographic regions. 

For instance, a clothing eCommerce business may observe different buying behaviors for men’s and women’s apparel or variations in demand across different seasons or regions. Lumping the two segments together may cause the business to forecast demand based on factors or assumptions that notably apply to only one of the product categories. In such cases, you’ll likely inaccurately forecast overall sales or misjudge inventory needs, leading to missed revenue opportunities or excessive stockouts.

Strategies to prevent the mistake of not segmenting data

Consider utilizing the following strategies: 

Keep separate, accurate data for all segments

Keep separate data for all products and product categories. Even when using a singular database, use a unique identifier for each product to make data extraction and analysis seamless. 

This allows for the development of forecasting models tailored to each segment, considering its unique demand patterns, seasonality, and external factors that may influence sales within that segment.

Utilize advanced analytics tools

Advanced analytics tools like Klaviyo can help you automatically segment sales data. Moreover, such tools can efficiently analyze large datasets with little to no errors. 

6. Not adjusting forecast based on product lifecycle

Every product you sell is in one of the following stages: growth, maturity, and decline. Each stage presents unique challenges and opportunities that directly impact sales forecasting accuracy.

When you introduce a new product into the market and gain mass acceptance, the growth rate is usually rapid. Failing to anticipate this growth can result in stockouts, missed sales, and dissatisfied customers. 

In the latter stages of the product life cycle, the market becomes more competitive and saturated, or consumer preferences begin to shift, causing sales to decline or plateau no matter how much you pour into marketing and branding. 

Neglecting to adjust forecasts downward in these latter stages can lead to excess inventory, markdowns, and reduced profit margins. So, while historical data may help estimate expected demand, it does not account for the product life cycle.  

That’s one of the beauties of sales forecasts: They can show you when the numbers stop making sense, and it’s time to pivot your business model or add other products to your offerings to ensure you continue to meet your target sales performance.

Strategies to adopt

Monitor sales trends, market saturation, and customer feedback to anticipate demand fluctuations. The insight gained will help tailor forecasting models for each product’s current lifecycle stage.

7. Underestimating lead time

Lead time is one of the key performance indicators you’re familiar with but may not factor in when creating sales forecasts. 

For example, if you underestimate the time it takes for a supplier to deliver goods, you may run out of stock before replenishment arrives, resulting in lost sales, which affects your projected sales. 

There’s also the added negative impact of damage to your reputation, which harms sales. 

Research shows the pandemic still has a long-standing effect on supply chain lead time, with “88% of manufacturers still experiencing longer-than-usual lead times.” 

Strategies to prevent the underestimation of lead time

You can utilize these strategies: 

Conduct regular supplier assessments

Evaluate suppliers regularly for reliability, consistency, and delivery capabilities. Reliable partners with consistent lead times make for a more predictable sales cycle.  

Diversify your supplier base

Avoid relying solely on one supplier for your products. Diversifying your supplier base can mitigate the risk of disruptions and provide alternative options if one supply defaults and lead times unexpectedly lengthen.

Establish clear communication lines with suppliers

Communicate regularly and effectively with suppliers, manufacturers, and logistics partners to obtain accurate lead time estimates. Clearly articulate your expectations and deadlines to ensure alignment among all parties.

Takeaway: Avoid common pitfalls and increase sales forecast accuracy

When it comes to eCommerce forecasting, accuracy is paramount for your sales operations. 

Avoiding common mistakes such as relying solely on historical data, ignoring seasonality, and underestimating lead times is crucial for making informed decisions and plans. Instead, normalize your data, conduct market research, and segment your data for deeper insights. 

Remember, sales forecasting is an ongoing process that requires constant adjustment and refinement based on new information. By implementing these strategies, you can improve the accuracy of your forecasts and optimize your business’s performance.

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