How To Forecast Customer Demand A Guide For E Commerce Retailers

How to Forecast Customer Demand: A Guide for E-Commerce Retailers

To understand the value of demand forecasting, especially in the context of e-commerce, all you need to do is recall back to the pillars of economics: supply and demand. It’s essentially the relationship between how much product a producer has versus how much the consumers want to buy. It’s what aids in determining the value or price point of a product and how much resource is needed for the quantity that answers the demand of the customers. And if you ask any eCommerce expert, they would say that this relationship is critical for pricing and producing products for an online business.

You might wonder how demand forecasting comes into play at this point. Consider that part of what makes online business and e-commerce so profitable is that you need a lot less capital to start it up. This is the type of business that many startup entrepreneurs or at-home businesses turn to, as it wouldn’t cost as much as establishing a brick-and-mortar physical store. But if you don’t consider demand forecasting and the laws of supply and demand properly, a business owner might find themselves with too much or too little product for their business. As a result, they end up losing money or profits anyway.

What is Demand Forecasting, and How Does It Work?

For any online business, especially those in the e-commerce market, demand forecasting should be a constant concern. Essentially, demand forecasting is defined as a process wherein an estimate is made about the consumer base’s demand over a defined period. This estimation is done by factoring in historical data, current market trends, and other related information. Data such as this is priceless to online businesses. It allows them to make educated guesses and carefully defined estimations of how much a product would be in demand in a specific time frame and the next trend.

Businesses need to be informed of the growth within their market continuously. The type of information that demand forecasting provides enables them to make informed decisions that affect the stock quantity, pricing, and even what their target markets ask for. After all, any business’s core and mission are to address an existing need—therefore, it caters to a demand. And demand forecasting helps business owners figure out what the need and the demand are for the foreseeable future, along with what comes next.

Without utilizing this process, businesses can put themselves at substantial risk of floundering in the ever-changing market. Without educated predictions to guide them, they might stagnate with the same products, same stock amount, and same preparations without adjusting to the demand’s highs and lows. Furthermore, they become woefully unprepared should the customer base start shifting towards something else. When the demand is high, a company would need extra staff and help in taking orders, answering questions, preparing and shipping, and more. If they don’t correctly predict the rise of demand, they won’t keep up with their customers, leading to delays, issues, an uptick of complaints, and bad customer experience.

How does it work?

Typically, the process itself is a form of data analytics that relies on algorithms. The algorithm considers numerous factors, turning them into parts of a mathematical equation predicting future customer demand. It’s critical to remember that the ‘demand’ could take innumerable forms. The type of demand could depend mainly on the kind of business you’re running—is it a clothing store? Utility services? A financial institution? This demand could take various forms. The algorithm will also consider numbers such as sales, transactions, and revenue.

Combined, retailers can determine the demand at present and in the near future and any estimated footfalls. This data can even predict the kind of queries or issues your help centers should expect from the customers.

There are various techniques to forecast demand, and the great thing about it is that it’s simple to create these forecasts right in Microsoft Excel through functionalities such as Exponential Smoothing and Linear Regression. However, it’s crucial to remember that these functionalities won’t consider special events, weather, seasonality, and other real-world-related factors that will affect the forecast.

To gain more accurate forecasts, you need to employ more advanced methods. Among the most highly recommended ones are forecasting algorithms that run on AI. This way, you wouldn’t have to adjust your demand forecasting computations for multiple driving factors manually. And the more demand drivers you add, the more accurate the predictions become. Better predictions lead to better operations and smoother sailing for your company.

Why You Need to Forecast Demand for Your E-Commerce Business

While all companies that cater to a need benefit from demand forecasting, the process is especially essential for e-commerce for various reasons.

1. Make products available to customers when they need them.

Being informed about the trends and predicting them means that you can address their needs when your customers need a specific product. For example: As summer days arrive, your customers in hotter areas will start buying your stock of fans, air conditioners, and evaporators. Now, while you may be rationally able to predict that sales increase during the summer, you may have no idea that there would be record-breaking heat waves wafting over your home state, to a degree never seen before in your usual climate.

If that happens, you’ll find yourself completely depleted of stock as customers search for more. Unable to find it with you, they’ll go elsewhere. It’s a potentially significant loss of profit. But with demand forecasting (and using data such as customer location history), you would have been able to prepare.

2. Increase brand awareness and conversions

When your company can adequately answer your customer’s demands, they remember you: excellent customer experience and quality service ring deep in consumers’ minds. When an online store has what they need, when they need it, and at the amount required, they are far more likely to come back and make a return purchase. It builds trust, brand loyalty and increases conversions. Furthermore, customers are likely to tell others looking for the same quality service or the same product. It spreads a precious word-of-mouth campaign about your brand.

3. Reduce financial risk

When you are adequately prepared through demand forecasting, there’s far less risk in your finances. Instead of simply pouring your resources into a product that you think will be a hit (only to have it end up selling at a loss to clear the inventory), you can more reliably predict what products your customers would be interested in and put your investment in that instead.

Furthermore, instead of being stuck with slow-moving stock, your awareness makes sure that your cash flow is going smoothly. There’s less of a risk of you wasting resources, and you’re also not taking reckless chances with your inventory.

4. Reduce inventory expenses

As much as possible, you want to maintain a balance between having enough stock against never having enough. Demand forecasting allows you to make informed inventory decisions. No more running out of your most popular items and no more unsellable stock. There won’t be too many warehouse rental expenses, and instead, you’re able to maximize your profits.

5. Develop a smart pricing strategy

Because you now know how much stock you have, how much the demand will be, and when customers are likely (or not likely) to buy it, you’ll be able to develop a more innovative pricing strategy. You can prepare special introductory prices to get them more interested, offer discounts to increase demand, bundle some promos to keep the stock moving, and other pricing methods that allow you to keep things swinging in your favor. With the proper predictions and correct forecasting, you can also advertise and market the product (and its changing prices) more efficiently.

Demand Forecasting Techniques

Now that you understand its value, it’s time to look into the details of demand forecasting and the techniques that can streamline your process. There are two effective techniques in forecasting that best apply for online stores and eCommerce.

1. Quantitative research methods

  • Barometric technique

Called the Index Number Method, the barometric technique takes after how a meteorologist might predict the weather through a barometer. Essentially, it looks between two economic periods and compares them, along with a few more index numbers, to determine which way the economy may swing.

  • Trend projection

Trend analysis is a type of demand forecasting that looks into the trends, the changes with the seasons, and even irregularities or random instances. It’s remarkably efficient when a company has a significant amount of data over a long time.

  • Econometric forecasting

This demand forecasting technique uses mathematical models and economic models and measures their relationship compared to economic events (such as a depression or a boom). This is a complex method often done through computers to simplify the process.

2. Qualitative research methods

  • Market research

By using one-on-one interviews and focus groups, companies can determine which trends are on the rise by asking a sample size of consumers themselves. The great thing about this method is that you can directly glean information from real people with plenty of opportunities to get essential details.

  • Delphi technique

Unlike the other demand forecasting methods, a panel of experts is assigned to create the forecast. Each of them has specific segments, and they will read out their forecasts in a round. In the next round, they will adjust their predictions based on the other experts’ information until they reach a near consensus.

  • SalesForce opinion

Similar to the technique above, a Sales Manager would gain input from each of the salespeople in their team. The salespeople will provide feedback and information based on their specific regions and categories. This allows the manager to compile all this feedback and create a forecast.

How to Get Your Forecasting Right

To ensure the most accurate and reliable demand forecast for your company, consider the following reminders:

1. Define your goals and timeframe.

It’s essential to have a clear goal and purpose for your forecast. More than just predicting what your customers will buy, you also need to think about the period, the category of the product, and whether or not you are creating a forecast for your customers or anyone.

2. Gather the required data.

Now it’s time to gather your data. Demand forecasting requires as much accurate and reliable data as possible to come up with an excellent prediction. Look into your sales channels, economic models, and customer purchase data to determine what kind of growth and trends could be coming up in the future.

3. Measure and analyze data.

Depending on the kind of data you have gathered, you can use predictive analytics and automation or do it manually. The critical part of the process is to compare your prediction to what the sales numbers say and then adjust your next forecast accordingly.

4. Estimate the demand and budget accordingly

Finally, now that you have completed your demand forecasting, it’s time to make an educated estimate of the demand in specific products and prepare your resources accordingly. Allocate areas of the budget as needed.

Real-World Demand Forecasting Examples

Consider some real-life scenarios wherein demand forecasting becomes critical.

  • A supermarket has enjoyed significant sales during the Thanksgiving season.

Both the holiday and Black Friday sales have created excellent demand customers, and they reaped significant profits. However, a competing supermarket opened up recently not far from their location.

At the same time, more people had migrated to their community. It’s time to do some demand forecasting while considering the number of people who may choose to go to the other supermarket and, along with what the newcomers to the community might need.

From the data they’ve gathered, they decided to launch some extra ads to get the population’s attention and fresh, inviting deals at competitive rates to the other supermarket. Projections expect a 5% increase in sales.

  • An electronics manufacturer is looking for an edge in the highly competitive electronics market.

They plan to stock and release one of the highly anticipated flagship smartphones. The electronics company hasn’t had much market research and planning experience, so they turn to demand to forecast. This change is so they won’t make the same mistake they had previously made when they acted without predictions.

They used market research and tapped into their customer base and their retailers to look into preferences, projections, and logistics. Now they have a much better idea of how to proceed with marketing and pricing the launch.

  • Finally, it’s a challenge to get a foothold in the very crowded cosmetics industry, so a growing cosmetics brand analyzes its numbers.

They typically see 10,000 orders every month. Their goal, however, is to bring these sales up to 30,000 per month by the following year. At present, they have 75,000 units of product, and they are studying the past sales, campaigns, and industry conditions. They also note how long it takes and how much it costs to replenish these stocks.

At the rate of their current sales to their stocks, they make a plan to improve the replenishment of their units, slowly increasing the numbers while the sales continue to grow upward steadily, ensuring they won’t be out of stock and match the pace of their sales.

Final words

Demand forecasting isn’t just another ‘tactic.’ It’s a robust method of gaining an edge in the market, maintaining company stability, and most of all, a path towards clearer, less risky growths for any online company.

Has your company used demand forecasting for your sales cycles? What specific factors affected your predictions the most? Let us know in the comments below.

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