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The way ecommerce used to work isn’t working anymore. Traditional supply chain planning technologies and processes fall short of changing gear in the face of black swan events — that now happen at a greater scale, causing intense swings in the global market. In 10 out of 10 cases, outdated forecasts backfire, setting businesses up for lost sales, excess inventory, and dissatisfied customers.

Today, growth-oriented online retailers have to learn the ropes of adaptive demand forecasting in ecommerce that uses real-time demand data to stabilize sales, optimize inventory management, and ensure effective resource allocation even during economic downturns. 

How accurate demand forecasting can save the day for your ecom operations

Ecommerce forecasting tools cannot predict future demand with surgical precision, but that’s not what they’re designed for. By combining historical sales data with market trends, product information, and other insights, ecommerce demand forecasting provides the intelligence needed for business owners to roll with the punches, regardless of the variables.

Inventory management 

Ideally, companies should have a balanced amount of raw materials or manufactured products on hand — enough to accelerate fulfillment, but not too much to increase inventory and warehousing costs. In reality, 70% to 80% of retailers’ cash is tied up in inventory.

Accurate ecommerce inventory forecasting allows merchants to prevent overstocking on slow-moving items, reduce stockouts, and maintain safety stock levels by seeing into demand fluctuations and current inventory levels. Whenever you’re dabbling into new products, forecasting tools can analyze historical sales of similar products and offer granular replenishment recommendations.

Getting a heads-up about peak periods allows companies to get their ducks in a row early and restock on high-demand products. Merchants can allocate fulfillment resources accordingly, ensuring faster turnaround times for customer orders.

Budgeting and financial planning

Building on historical data and consumer preferences, demand forecasting solutions provide a data-driven basis for calculating future sales and predicting expected revenue. Along with incoming cash flows, business owners can leverage demand data to size up upcoming expenses associated with fulfillment and procurement — and in some cases, even negotiate discounts with suppliers. 

Ecommerce sales forecasting tools also outline peak periods and times of cash flow blues, allowing ecommerce businesses to optimize expenses.

Advertising and marketing optimization

Merchants can use the combination of demand forecasting tools and market research to get a handle on the right target audience and focus marketing efforts on customers more likely to purchase their products. A detailed snapshot of seasonal trends and peak seasons also enables companies to optimize their marketing strategies and align campaign timing with commerce demand upticks.

Take your demand forecasting for ecommerce to the next level

Three signs your current demand forecasting tool is failing you 

In this time and edge, accurate demand forecasting serves as a vital compass, guiding around 43% of ecommerce companies through stormy seas. However, not all tools yield the same forecasting accuracy. Here are three telltale signs your current demand forecasting tool is playing false.

Underperforming forecast accuracy

Here’s the thing with old-school demand prediction tools: they’re heavily reliant on historical data. Although it’s a great starting point, historical data alone doesn’t cut it when it comes to long term demand forecasting — it’s myopic and unable to account for external factors. So unless your tool is supplemented with other forecasting techniques, it has a considerable margin of error.

Excessive chargebacks 

Today’s online shoppers are adamant about shipping times. Whenever there’s a delay in shipping due to inventory issues, a customer doesn’t think twice about opening a chargeback, consigning a merchant to additional costs and potential payment processor restrictions. Excessive chargebacks are a sure sign of a flawed sales planning tool that can’t predict demand right on the money.

Inventory imbalance

Healthy inventory levels are a clear indicator of a smooth-running forecasting system. Without proper demand forecasting, you’ll end up with an unbalanced inventory — too much, too little, or in the wrong place, paired with formidable inventory costs. Unexpected demand surges can quickly throw the inventory off balance, hindering your ability to meet customer demand. If you can relate to the case, chances are your forecasting system is going haywire.

Five challenges in predicting demand

Ecommerce data forecasting is fraught with challenges. Shifting consumer behavior, market volatility, supply chain disruptions, and data hiccups can all cloud the judgment of your forecasting efforts. Let’s look into the most significant obstacles that affect demand forecasting accuracy.

Data blind spots

Demand precision hinges on comprehensive data input, including historical sales data, market trends, consumer surveys, and any other indicators at your disposal. Limited data scope that ignores purchase history, demand patterns, broader economic triggers, and other drivers can lead to unreliable insights. 

Data management issues

Forecasting models collect data that is both high-quality and easily accessible. But when it’s scattered across different teams and systems, forecasters have a hard time producing reliable predictions. Inconsistent data management practices, mergers, and data latency can also throw a few wrenches into data management, causing discrepancies in forecasting.

Biases in data collection 

Biased data selection along with confirmation bias can tip the scales in favor of a particular outcome. For example, sales data tends to be overly optimistic at most times as salespeople, eager to hit quotas, often report idealistic predictions, skewing the forecasts towards a higher number. 

On the same note, customer input often lacks feedback from customers with average experience, while those who’ve had excellent or nightmarish encounters are more inclined to be vocal about it. 

Inadequate historical records

Ecommerce companies that have accumulated consistent historical data can use this data for accurate forecasting — unless it’s incompatible with the forecasting technology, which is often the case. Most businesses just throw this data to the back shelf, with no intention of analyzing it afterward or at least keeping it in check for later. As a result, poorly maintained data complicates reliable predictions, leaving companies with a modest basis for forecasting.

Rapid strategic changes 

Frequent strategy pivots make it challenging to forecast consumer responses as forecasting tools struggle to align customer needs with company actions. In simple words, every time a business calls an audible, they render their historical data ineffective, which can result in meaningless predictions.

Stop guessing, start predicting — with demand forecasting for ecommerce

Types of demand forecasting: is it about quality or quantity? 

All demand forecasting methods fall into two groups based on the nature of data: qualitative and quantitative. Each category has its strengths and applications — it all comes down to a particular context and available data.

Qualitative methods

Whenever there’s a lack of historical data to build on, companies turn to qualitative forecasting methods that leverage judgment from experts along with market research and other non-numerical data. 

Qualitative methods also fit the bill when a company is entering new markets or debuting products. Examples of qualitative forecasting include such approaches as a Delphi method, customer surveys, and scenario planning.

Quantitative methods

Quantitative methods rely on historical data and statistical techniques to generate long-term forecasts. Hailed as the most precise forecasting approach, these methods are contingent upon the availability of historical data. Time series analysis, regression analysis, and causal analysis all register as quantitative methods, helping companies project future values.

However, these methods are incapable of considering subjective or unpredictable factors, so companies usually employ a combination of quantitative and qualitative approaches to forecast demand with greater accuracy.

Demand forecasting techniques: from basic to advanced

Depending on their technological maturity and availability of external and internal data, ecommerce companies can lean into one of the three forecasting methods stated below — or combine them for better results.


Small businesses with limited data and simple forecasting needs usually use spreadsheets like Microsoft Excel and Google Sheets for basic statistical analysis and back-of-the-envelope demand forecasts. As the most fundamental tool for commerce demand forecasting, spreadsheets are intuitive and easy on the companies’ wallets but suffer from limited scalability and narrow analytical capability. 

Based on our tests, Excel can easily manage around 200-300 thousand rows without breaking a sweat, and a single sheet can handle just over a million rows. But, if you want to go beyond that, you’ll need to juggle data across multiple sheets, which is a real hassle and takes up a lot of time.

— Ivan Dubouski, Lead BI Analyst, *instinctools

Also, manual data entry is time-consuming and error-prone, which makes spreadsheet-based strategic planning an ordeal for business owners.

BI-enabled forecasting

Business intelligence tools take ecommerce demand forecasting a step further by providing advanced data visualization, historical data analysis, and reporting capabilities. Unlike spreadsheets, BI tools are fit for processing a spate of data and can pull it from various sources into a unified system. 

Combining internal intelligence like sales data, customer behavior, and inventory with third-party data like ecommerce benchmarks, market trends, and other sources, BI tools deliver a more nuanced look at the current state of demand well-being and its outlook. 

BI-powered forecasting takes over a lion’s share of data collection, processing, and reporting tasks, minimizing the need for human intervention and preventing errors. With BI tools like  Tableau, Power BI, or QlikView, business owners can gain better visibility into crucial business performance and transform complex datasets into straightforward charts, tailored to their specific KPIs.

Power BI is a powerhouse when dealing with large datasets. It smoothly processes up to 50-100 million rows – that’s a whopping 100-200 times more than what Excel can handle! So, if you’re working with massive amounts of data, Power BI is definitely the way to go for efficiency and convenience.

— Ivan Dubouski, Lead BI Analyst, *instinctools

However, business intelligence tools have a significant downside. Their decisions are rooted in historical data and cannot account for unpredictable events or trends. Also, BI tools are designed for structured data and ill-disposed to unstructured insights such as social media posts, images, and videos — which can lead to one-sided predictions.

Platform-based BI
open source BI

ML-enabled forecasting

ML-powered commerce demand forecasting steps in whenever ecommerce companies need to go down the rabbit hole of data. ML forecasting doesn’t just estimate demand based on historical and statistical data, it leverages sophisticated analytics models to combine historical and real-time data (both structured and unstructured) and predict more precisely where customer demand is headed.

Types of data for analysis

Thanks to its continuous learning capabilities and broader data scope, ML forecasting excels where other methods struggle. For example, unlike conventional methods, machine learning can predict demand for brand-new products by analyzing sales performance data on similar products. 

And as new data becomes available, the ML model can fine-tune its approach to align predictions with evolving insights. These tools can also streamline the data management workflow by automating the data preparation efforts of analytics teams.

ML-based Demand Forecasting on AWS

ML-powered forecasting requires commitment though, as companies need significant upfront investment to get started on the ML technology and establish in-house expertise. This type of forecasting is also associated with complexities in implementation and maintenance — but not if you join your efforts with a dedicated ecommerce development company.

Let’s take a look at a comparison chart that outlines the main differences between the three forecasting approaches.

Forecasting methodSpreadsheetsBusiness intelligence toolsML-enabled forecasting
AccuracyLow (limited to basic forecasting models)Medium
(improved accuracy due to larger historical dataset analysis)
High (sophisticated analysis algorithms)
Data sourcesMost often only internalStructured internal historical data and third-party dataStructured and unstructured internal and third-party data
Amount of data handledSmall datasetsLargeLarge
Maintenance complexityLowMediumHigh
Technology requirementsLowMediumHigh
Technical expertiseMinimal tech knowledge requiredRequires basic data analytics skillsRequires extensive  data science expertise 
Use casesBasic demand planning for small businesses, initial estimates, quick calculationsSales forecasting, inventory planning, budget planning, trend analysisComplex demand planning, new product forecasting, personalization, risk mitigation, prescriptive analytics

Gain full visibility into supply and demand

How to approach advanced demand forecasting? 5 crucial steps

If you need to solve the forecasting problem with something more robust than spreadsheets, here’s what your roadmap can look like if you want to set up a smart data-based forecasting process.

Step 1: Collect and aggregate data

An efficient forecasting method is contingent upon a wide selection of relevant and accurate data points. Here’s what data you need to lay the groundwork for a successful prediction model:

  • Transaction sales data, including SKUs, location, unit sold, and other information.
  • Metadata of an item, including its color, size, and more.
  • Price data with timestamps.
  • Promotional impact with detailed data on past promotions
  • Inventory availability, including in-stock or out-of-stock for each SKU at specific time intervals.
  • Location data.

Most forecasting models also call for website traffic data, search term insights, social media data, and weather information. You need to have it both for the past and for the future to supercharge the veracity of your forecasting solution.

Step 2: Get your data in order 

Ecommerce data forecasting models aren’t built from raw data as defects in the input can take a toll on the model accuracy. For example, prediction solutions, with their time-dependent features, are particularly sensitive to missing values as these gaps block the model’s ability to spot patterns and trends over time.

Common tasks involved in data preparation include: 

  • Identifying and filling missing entries with standardized values.
  • Ensuring data consistency in units, dates, and data types across all datasets.
  • Weeding out outliers
  • Creating new variables to enhance the model’s prediction capabilities
  • Merging datasets to promote more nuanced predictions

Once the data is clean and ready, data science teams divide it into training and testing groups. 

Step 3: Create a forecasting model

Based on the complexity of your problem and the availability of historical data, a forecasting model can be powered by different techniques, such as ARIMA, regression, random forest, and others. The development team then uses the training data to teach the forecasting model to predict the necessary outcome. 

At this stage, developers also experiment with hyperparameters to mold the learning behavior of the model and maximize its performance.

Step 4: Evaluate the model

When the model is up and running, the team evaluates its performance by making predictions on the testing data, comparing the output to the actual values, and calculating the relevant accuracy metrics. The quality of a demand forecast usually hinges on two metrics such as forecast accuracy and forecast bias. While the former determines how a given forecast stacks up against actual sales, the latter describes whether the forecast is over or under the actual sales.

Keep in mind that it’s easier to achieve a high prediction accuracy for products with consistent historical sales and little seasonal fluctuations, while the inherent volatility of trend-driven and weather-dependent products makes it harder to anticipate demand. So, in some cases, models can capture the general trends and potential shifts rather than outlining exact values.

After evaluating the results, developers spot areas for improvement and calibrate the model by adjusting hyperparameters, applying different techniques, or adding more data to the pipeline.

Step 5: Generate and use forecasts for decision making

When the model fits the predefined accuracy threshold, your team deploys it in the cloud and gears it up for forecast generation. Keep in mind that your forecasting model doesn’t operate on autopilot as it needs a constant stream of relevant data — both historical and current — to stay in shape. 

You can export forecasts to the analytic tool of your choice and leverage them for strategic planning.

Forecast with precision, navigate fluctuations with confidence

In today’s turbulent times, businesses have to anticipate demand in a whole new way — with no forecast silos, but with continuous adjustment. A combination of quality data, machine intelligence, and visualization tools are spearheading the way of new-age demand prediction, helping businesses translate forecasts into better business outcomes and stay afloat even in the direst of times. 

At *instinctools, we bridge expertise in data and ML algorithms with a systemic approach and domain knowledge — to help ecommerce businesses improve their forecasting approach and scale it to every business function. Let’s pool our efforts and move your demand planning from reactive to proactive.

Increase forecast accuracy up to 99% for improved decision-making

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Anna Vasilevskaya
Anna Vasilevskaya Account Executive

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