To plan for your brand’s future, you’ll first have to look at your past. Demand forecasting utilizes historical inventory data to predict your future sales and help you easily meet customers’ expectations.
In other words, demand forecasting lays the foundation for your retail growth and ongoing success. Here’s how (plus how you can make the most of your forecasting efforts).
Ecommerce forecasting is the process of estimating future demand for your products. And the final forecasts are typically based on historical metrics like previous sales data and real-time inventory trends like current inventory levels.
Direct-to-consumer (DTC) brands can forecast demand for new products or items they’ve sold for years. Either way, forecasting aims to make accurate inventory predictions that can guide your marketing strategy and keep a consistent cash flow for your business.
All ecommerce brands need demand forecasting, whether they’ve been in business a few weeks or a few years.
Not only does forecasting reduce risks and optimize your inventory levels, but it increases customer satisfaction and decreases expenses at the same time.
Growing your retail business comes with its fair share of risks — from understocking on SKUs and encountering stockouts to overstocking your inventory and accumulating dead stock.
If your brand has perishable items, there’s also a chance these will expire before you can sell them. In this scenario, you risk losing revenue and creating a whole lot of waste.
Fortunately, demand forecasting cuts down on these risks in a big way by helping your brand make more informed inventory replenishment decisions (such as ordering the right amount of inventory at exactly the right time).
Demand forecasting is one of the best ways for your brand to achieve inventory optimization.
The forecasting process looks at product movement, previous sales, and the inventory you currently have on hand. And it provides you inventory visibility, so you know where your stock has been, where it is, and where it’s going.
This visibility into your sales channels and stock levels plays a pivotal role in ensuring you always have the right amount of inventory to satisfy demand.
In many ways, inventory forecasting is the backbone of customer satisfaction.
With forecasting, you can make replenishment predictions that accurately reflect product demand and customer buying patterns. This way, you can guarantee you always have products in stock and ready to ship when customers want them.
And when your products are well-stocked and awaiting shipment, you speed up the fulfillment process. For one, faster fulfillment means faster delivery. But it also gives way to a better customer experience (leading to happier customers) and improved conversion rates (thanks to greater brand loyalty).
Another important benefit of demand forecasting is its ability to decrease your overhead spending.
Because forecasting ensures you order exactly what you need to fulfill demand, you know you’re not overspending on products that might become excess inventory.
And when you avoid overstocking on goods with a low inventory velocity, you’re actually preventing your brand from paying extra carrying costs too.
Saving money via demand forecasting means you have more freed-up working capital to invest elsewhere in your business. For instance, you could use this extra cash to develop your marketing strategies or develop a new product.
The last reason ecommerce brands should get on board with demand forecasting is that it can help you refine your sales and pricing strategy.
For example, as you’re forecasting, you might notice that certain products are understocked.
This can happen when an item is in high demand, but there’s a low quantity of that SKU at your warehouse.
In this case, you might want to raise the price of these items to really capitalize on your low supply-high demand situation.
Alternatively, you might notice other SKUs aren’t selling as well as you’d anticipated. In this case, you may want to discount the product or bundle that product with some better sellers to help get it out the door.
Despite the obvious importance of sales forecasting, you might face a few challenges in forecasting accurately. These challenges typically include choosing the wrong methodology, using incomplete data, or not accounting for supply chain delays.
While there’s a wide range of forecasting methodologies available, they pretty much all fall into 2 categories: qualitative and quantitative.
Qualitative forecasting is primarily used when brands don’t have any historical data to leverage. They might lack this information because they’re a new business or haven’t kept accurate inventory records.
Either way, qualitative forecasting methods seek the opinions of customers, salespeople, and even industry experts to get around these information gaps when predicting future sales.
Quantitative forecasting, on the other hand, relies on historical data sets to predict demand and inform supply chain planning. The more data your brand has, the more accurate your quantitative predictions will be.
Depending on how much historical data you have at your disposal, one of these forecasting methodologies will likely work better for you than the other.
It’s best to choose your forecasting strategy carefully since this can have a big impact on the accuracy of your forecasting outcomes.
You already know that ecommerce forecasting uses historical inventory data to predict future sales. And yet, if this data is incomplete or simply inaccurate, it can throw a big wrench in your forecasting plans.
Bad data is a gateway to bad decision-making. After all, you can’t achieve quality forecasts if you don’t have accurate data to work from, and you can’t make high-impact plans without a quality forecast.
In this way, miscalculations or inaccuracies are bound to throw off your forecasts since you won’t be basing your predictions on correct inventory counts. So, be sure that all your historical data is properly validated before using it to guide your forecasting.
Like it or not, supply chain disruptions come with the territory of modern retail.
But over the last few years, DTC brands have also had to contend with a global pandemic that triggered a lack of raw materials, transportation delays, labor shortages, and more.
Because of these various issues, order lead times in 2022 are roughly 3x longer than usual. In certain sectors, lead times are hovering around the 6-month mark (with no signs of relaxing anytime soon).
While supply chain delays are certainly an inconvenience, they become even worse when they’re not factored into your demand forecasting.
Your brand will need to consider these potential bottlenecks as it’s reordering — and possibly build in some extra safety stock to offset a longer than anticipated delivery window.
As is true of most retail processes, a few external variables can affect your demand forecasting. Most common are seasonal trends, market size and location, product categories, and competitors.
Seasonal trends are when your products have periods of high and low customer demand, depending on the time of year. These fluctuations are more commonly referred to as seasonality, and they can significantly impact your forecasting plans.
While seasonality can come into play at any time of year, it’s most often seen around Black Friday and Cyber Monday. Just last year, for example, Shopify sales grew 23% during the holiday season (totaling $6.3B globally) — and it looks like 2022 could set even bigger and better records.
If your product catalog (or even a single SKU) sees a predictable surge at a specific time of year, you’ll need to factor it into your forecasting estimates.
The reality is that DTC brands come in all shapes and sizes. Some brands are focused more on a domestic customer base, while others serve customers worldwide.
Regardless of which camp your brand falls into (or if it falls somewhere in the middle), market size and location are important to look at as you’re working on your forecasts.
That is to say, customers from across the globe may have different needs or expectations regarding product preferences, shipping options, and so on.
For instance, a popular product line in your home country may not translate well elsewhere. So, your forecasting models will need to adjust accordingly to account for these differences.
Take Lay’s potato chips, for example. In the United States, Frito Lay sells milder, “more boring” flavors like onion and cheese. Meanwhile, elsewhere in the world, they sell curry-flavored and ramen-flavored chips – which can’t be easily found in the US. That’s because these aren’t as well-suited to the American palate.
In addition to your customers’ whereabouts, your product categories can also influence the amount of inventory you reorder.
If your brand sells high-priced goods (like electronics or luxury clothing), you’ll probably see customers make purchases less frequently. So, your forecasts will likely be more spread out or look at smaller replenishment sizes.
However, if you sell goods that are used every day and purchased more frequently (like health and beauty products), your brand might have more consistent forecasts more often.
Other retail brands in your space will inevitably impact your brand’s forecasting needs. After all, few brands ever sell in an isolated environment (there’s always another product vying for your customers’ attention).
This is especially true if a new competitor enters your specific field. Whenever this happens, these newcomers may siphon some of your customers. And if (or perhaps, when) they do, it’ll cause your product demand to dip.
Sometimes, the best way to approach competition is by concentrating on how your brand can differentiate itself – ideally, by serving customers better. Can you deliver more sizing or color options? Or introduce an improved formula for your product?
Making these tweaks (or rethinking your marketing efforts) can boost your brand perception and help you keep retention levels high.
When you’re ready to forecast for your own business, there are just a few steps to ensure a successful process.
The first step in ecommerce demand forecasting is to clearly define your goals. Before you can collect or analyze any of your data, you’ll need to clearly establish what you want to achieve.
Examples of forecasting goals might include reducing excess stock, avoiding stockouts, or selling a certain volume of your latest SKU.
Once you have your goals, you can set a timeline for reaching them. This gives you a more definite game plan and keeps you accountable for achieving these goals.
You’ll also want to get all your stakeholders on board with your goals. This will likely include key decision-makers on your ops team and your marketing team — really, anyone who may be affected by or contribute to your forecasting efforts.
With your goals firmly in place, you can start collecting internal and external data.
Internal data relates to your order history and product performance. In contrast, external data typically comes from industry briefings and news articles. These resources are a great way to shed light on consumer behavior and current market conditions.
As you’re gathering all this data, remember to look out for any anomalies — like seasonality or supply chain disruptions — that might cause your forecasts to go up or down.
After you’ve compiled all your relevant data, it’ll be time for you to analyze this information. You’ll want to comb through everything to find sales trends or demand patterns.
While it’s possible to analyze demand manually, it’ll be easier to do so using an inventory forecasting software like Cogsy. These systems can derive insights from your data in just seconds and help you make sense of inventory metrics like turnover ratio and backorder rate.
Analyzing your numerical or quantitative data is the only way to fully understand what’s going on with your inventory and anticipate what comes next.
For example, say your data doesn’t show any product sales within a specific timeframe. Is this because there was no demand at all? Or because you were sold out of that SKU and didn’t have the inventory to fulfill demand?
These are 2 very different scenarios that’ll radically change your forecasts — which is why understanding your data is just as important as recording it.
As mentioned above, there are primarily 2 categories for demand forecasting: qualitative and quantitative. But within each of these categories, there are several different demand forecasting methods you can choose from.
With your forecasting method in place, you’re ready to predict future demand for your products.
But don’t forget to weigh your forecasts against your current stock levels — especially when you start translating your demand forecast into an inventory plan. In other words, don’t forget to consider what you already have in stock so you don’t replenish low-selling SKUs.
In addition, you might also want to include a bit of safety stock in your forecasts. This way, you can create a buffer against supply chain disruptions or unforeseen surges in demand.
Even after you’ve generated your forecasts, you’ll still want to look over your estimates before officially placing your purchase order (PO). By checking your work, you can spot outliers in your data (like promotional campaigns) or correct any errors in your calculations.
With your predictions confirmed, you can then adjust your operations to align with these forecasts.
For instance, if you’re anticipating a decrease in demand for a particular SKU, you can adjust your PO to keep fewer units of that item in stock. Making small adjustments like this can help avoid having too much or too little inventory at your warehouse in a huge way.
Top DTC brands like Caraway trust Cogsy to help them take control of their inventory forecasting. That’s because Cogsy makes forecasting a breeze by storing all your relevant inventory data, supporting operational inventory planning, and even assisting with ongoing production planning.
Without insight into product movement or SKU performance, you’ll have a tough time creating accurate forecasts. Luckily, Cogsy’s innovative operations platform empowers you to monitor all your inventory data from a centralized source of truth.
Cogsy logs both your real-time and historical data in the same platform so you can reference it at any time. Plus, Cogsy’s automatically updates your info around the clock, so you can trust you’re always working with the most precise numbers possible.
This 24/7 visibility into your inventory is a game-changer for your forecasts. That’s because you’ll immediately know if demand changes and your projections need to update as a result.
Forecasting shouldn’t be followed by crossed fingers. If you’re basing forecasts on guesswork or gut feelings, there’s a strong chance your numbers won’t be accurate. And it will wind up causing needless headaches and costing you a lot of money.
Fortunately, there’s a much better way to go about things. Cogsy can help you feel more confident about your inventory planning. The Cogsy platform replaces your static spreadsheets with proactive sales predictions, then automatically forecasts future demand for your products.
These automations dramatically reduce the number of errors in your estimates. And fewer errors mean greater accuracy for your forecasts and your DTC brand as a whole. This way, you know exactly what you need to order and when to avoid a stockout without racking up unnecessary holding costs.
In addition to being a practical inventory planner for your Shopify store, Cogsy can also assist with your ongoing production planning.
A solid production plan is the surest way to meet your revenue and demand goals. With Cogsy’s planning feature, you can map out your growth plan to meet your inventory needs for the next year — no spreadsheets needed.
This way, you can communicate your 12-month plan with your suppliers and negotiate better terms based on the total minimum volume you plan to order within the year.
In fact, brands that do this with Cogsy have reduced their down payment on purchase orders by up to 50%. And it’s freed up tons of funds that they can use to grow not just bigger but better.
But don’t take our word for it – try it free for 14 days.
Ecommerce forecasting is done by estimating future demand for your products. These forecasts are typically based on historical metrics like previous sales data and current inventory trends like stock levels. An inventory forecasting software like Cogsy can help with forecasting by keeping track of all your real-time and historical sales data in one place and guiding you through inventory or production planning.
There are 2 primary categories of ecommerce forecasting: qualitative and quantitative. The most popular qualitative forecasting methods are market research, informed opinion, and the Delphi method. On the flip side, the most popular quantitative forecasting methods include trend projection, seasonal index, and straight-line method.
All ecommerce brands need demand forecasting, whether they’ve been in business a few weeks or a few years. Not only does forecasting reduce risks and optimize your inventory levels, but it increases customer satisfaction and decreases expenses at the very same time.