Going From Chaos to Clarity With Demand Forecasting In Ecommerce

The way ecommerce used to work isn’t working anymore. Traditional supply chain planning technologies and processes fail to adjust to global and local black swan events. In 10 out of 10 cases, outdated forecasts backfire, setting ecommerce businesses up for lost sales, excess inventory levels, and dissatisfied customers.
How growth-oriented online retailers can become reality-ready? Our guide has all the answers you need.

What is demand forecasting in ecommerce?

Demand forecasting in ecommerce is an adaptive process that uses historical and real-time demand data to identify patterns in consumer behavior and predict future trends. It helps businesses stabilize sales, optimize inventory management, allocate resources effectively, and ensure high customer satisfaction even under the influence of economic downturns and other external factors.

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 decrease unnecessary expenses, regardless of the variables.

Optimizing inventory supply levels

Ideally, ecommerce businesses should have an agile supply chain and a balanced amount of raw materials or manufactured products on hand — enough to satisfy demand without increasing inventory and warehousing costs. In reality, 70% to 80% of retailers’ cash is tied up in inventory.

Accurate ecommerce inventory forecasting provides full visibility. Merchants can see into their inventory data and demand fluctuations and act on them to cut costs without sacrificing customer satisfaction:

• Get a heads-up about peak periods
• Prevent overstocking on slow-moving items
• Reduce stockouts
• Maintain safety stock levels

Whenever you’re dabbling into new products, forecasting tools can analyze historical sales data on similar products and offer granular replenishment recommendations.

Budgeting and ecommerce financial forecasting

Demand forecasting solutions build on historical data and consumer preferences to predict future sales and expected revenue. Along with incoming cash flows, business owners can leverage demand data to size up upcoming expenses associated with fulfillment and procurement — and, say, decrease their overhead spending by negotiating discounts with suppliers. 

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

Advertising and marketing optimization

Merchants can combine demand planning with market research to identify the right target audience and focus marketing efforts on the most-likely-to-purchase customers. Demand forecasting tools for peak periods and slowdowns also enable companies to optimize their marketing strategies and budgets and align campaign timing with commerce demand upticks.

What ecommerce forecasting challenges can you face?

Shifting customer 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 influence demand forecasting accuracy.

Inaccurate or incomplete data

Accurately predicting customer demand is only possible when hinged on comprehensive data input, including previous sales data, market trends, consumer surveys, and any other indicators at your disposal. Limited data scope that ignores purchase history, demand patterns, broader economic indicators, and other drivers can lead to unreliable insights.

Data management issues

The more data is scattered across teams and systems, the harder it is to produce reliable predictions. Inconsistent data management practices, mergers, and data latency can also hinder the forecasting process, causing discrepancies.

Biases in data collection

Biased data selection along with confirmation bias can jeopardize the 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 is often skewed, as people with average experiences rarely give feedback, while extreme cases speak up more. 

Inadequate historical records

Accurate predictions depend on consistent historical data compatible with forecasting technology. Few ecommerce businesses can brag about it. 

Most companies just throw their historical data to the back shelf, with no intention of analyzing it afterward or keeping it in check for later. As a result, poorly maintained data complicates reliable predictions, leaving businesses with a modest basis for forecasting.

Rapid strategic changes

Frequent strategy pivots make it challenging to forecast and meet customer demand as forecasting tools struggle to align consumer 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.

Which demand forecasting method should you choose?

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: 

• Scenario planning
• Delphi method
• Customer surveys

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
• Causal analysis

Companies usually employ a combination of quantitative and qualitative approaches to factor in subjective or unpredictable aspects and forecast demand with greater accuracy.

What ecommerce demand forecasting techniques fit your needs?

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

Spreadsheets

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. 

However, they are time-intensive and error-prone due to manual data entry, provide limited scalability, and narrow down your analytical capabilities.

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

BI-enabled forecasting

Business intelligence tools take ecommerce demand forecasting a step further by providing automated data collection and processing, advanced data visualization, historical data analysis, and reporting capabilities, all while minimizing the need for human intervention. 

BI solutions, such as Tableau, Power BI, or QlikView, seamlessly combine internal insights into sales, customer behavior, and inventory with third-party data like ecommerce benchmarks, market trends, and other sources. The software grants better visibility into crucial business performance and transforms 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.

ML-enabled forecasting

An ML-enabled demand forecasting important distinction is its ability to go beyond traditional methods that rely solely on historical or statistical data. Instead, it leverages sophisticated analytics models to combine past and real-time data (both structured and unstructured) and predict demand more precisely.

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.

Whenever new data becomes available, the ML model fine-tunes 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-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 spreadsheets, BI tools, and ML-driven demand forecasting.

How to do advanced demand forecasting? 5 crucial steps

If you need to solve the forecasting problem with something more robust than spreadsheets, here’s your roadmap to setting up a smart data-based forecasting process.

Step 1: How to 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 to supercharge prediction veracity.

Step 2: How to get your data in order?

Defects in the input like missing values, duplicates, etc. take a toll on the accuracy of an ecommerce data forecasting model, blocking its 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: How to create a demand 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: How to 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: 

• Forecast accuracy determines how a given forecast stacks up against actual sales.
• Forecast bias 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. 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 demand 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: How to 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 ecommerce demand planning.

Instinctools’ expertise in setting up ML-driven ecommerce demand forecasting

A Canadian clothing manufacturer and online retailer was struggling with inventory management and stock levels planning. They wanted to switch from primitive Excel-based demand forecasting to advanced analytics. 

• Instinctools replaced spreadsheets with an ML-powered demand forecasting system.
• Our ML team trained the new system on the company’s data to ensure predicting future customer purchases with 90%+ accuracy. 
• We also integrated the new forecasting solution with Power BI to make accurate forecasts accessible across departments.
• The system elevated inventory forecasting: stockouts are reduced to 10% and overstocks stay below 5%.

Summary

Ever-shifting times prompt companies to move from reactive to proactive ecommerce demand planning. A combination of quality data, machine intelligence, and visualization tools are spearheading the way of new-age demand prediction, helping ecommerce businesses translate forecasts into higher revenue and stay afloat even in the direst of times. 

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