Authored by Shashank Singh, Customer Success Manager at PiWheel.

As the eCommerce industry is growing, the challenges of e-tailers are growing with equal intensity due to increasing competition, product clutter, inefficient supply chains, and finicky consumer demand. In such a scenario, demand forecasting forms a crucial part of the success of eCommerce. 

For brands selling on the online channel, it becomes critically important to understand and know what customer buys, when a customer makes the purchase, and how much is the purchase being done. An efficient demand forecasting method can help the brands in boosting sales and increase revenue to maximize profit and also helps them avoid overstocking & understocking. 

 

What is demand Forecasting? 

Demand forecasting is analyzing historical data by plugging in factors such as seasonality, Events, cannibalization, trends, and evolving demand to predict customers’ future willingness to purchase the products 

 

Why is eCommerce demand forecasting crucial?  

Given the Complexity, reach, and offerings of the eCommerce channel, demand forecasting becomes an integral part of the eCommerce operations. Some of the factors that are crucial include: 

  • The market size of eCommerce that is growing at a rapid pace  
  • New product categories being introduced in the channel 
  • Increasing competition 
  • Wide Variety of offerings  
  • Reach, coverage, and geographies being served are multiplying 
  • The new eCommerce channel might be complex compared to traditional channels, requiring high expertise 
  • The high opportunity cost of being out-of-stock 
  • In an inventory heavy model, it becomes indispensable to optimize the expenditure on demanded and non-demanded products 

 

What are the challenges in eCommerce demand forecasting? 

Forecasting demand is never easy. E-tailers are constantly dealing with the three Vs of big data: volume, variety, and velocity. This when coupled with fierce competition makes going tough for these E-tailers. Some other challenges include: 

  • Ability and quality of demand forecasting tools in the market  
  • Comparison shopping by customers 
  • Inventory decisions for an omnichannel player 
  • Price regulations in a multichannel setup  
  • Variations in buying trends & customer behavior across different geographies 
  • Easy customer switching  
  • Increase in returns 
  • Devising Product offerings to avoid cross-channel conflicts

 

How to Effectively Forecast Demand in Ecommerce? 
  1. Using Quality tools with great accuracy and deeper insights 

    Having correct and detailed information on the data points that are reliable, accurate, and deep can help in decision-making at multiple stages in the eCommerce value chain. 

    With the market flooded with numerous tools, it becomes critically important for brands to choose wisely from the available options that cater to their requirements. 

    Among the multiple capabilities that PiWheel offers, one such capability is offered through our tool Beacon which gives you an internal view of your Brand. This tool provides you with numerous data insights at the category, brand, and SKU levels. 

    Some useful insights from Beacon on SKU-level sales trends, Inventory as it provides total stock available and how many weeks on hand for that product before you replenish can help in efficient demand forecasting 

  2.  Following an Omnichannel strategy to avoid conflict among channels

    Some omnichannel retailers make the mistake of treating eCommerce as a separate business that necessitates separate tools and different approaches to succeed. 

    However, it is more similar rather than different. Here also you need to order inventory that customer buys, set prices to match the competition & maximize profits, replenish your delivery centers as if they were stores and warehouses, and so on. 

    Some effective tactics to excel –  

    • Coherency in communication across channels 
    • Centralized and unified inventory management for all channels 
    • Non-conflicting product assortment across different channels 
    • Dynamic price matching 

  3. Deploying different Sales forecasting methods

    Demand forecasting calculates the demand for each product using a much wider range of data and variables than that used in traditional sales prediction methods. Such as: 

    • Pricing 
    • Inventory 
    • Range of Assortment 
    • Complimentary products 
    • Seasonality 
    • Competitive dynamics 
    • Evolving customer needs 
    • Changing trends etc 

    As is customary, there are numerous methods for forecasting demand which are available and retailers can choose among them depending on the expertise and complexity involved. 

    To name a few –   

    • Statistical models using Excel – Regression, time series, etc. 
    • Generic statistical modeling/ analytics software 
    • Retail-specific software for analysis powered by AI 

  4. Tracking back the reasons for Returns & Abandoned cart 
    As a brand, it is very important to understand why a customer is switching to a different brand. Why is the customer not making a purchase after adding a product to the cart? Why is the customer returning your product?

    For instance – a customer adds the products to the cart and waits for the price drop during an event to make a purchase. Such information can be effectively used to plan for the stock in-warding during an event, like which products should have high inventory, what could be the estimated volume of sales that can be generated when we drop the price during an event, etc.  
Conclusion 

Ecommerce demand forecasting is all about predicting the future using historical data points. If you get it right, you can control your eCommerce operations to a huge extent and meet consumer demand. 

Our eCommerce tool can help you with a holistic view of your brand and alert you to mission-critical changes in price, stock, and competitor activity and reveal untapped opportunities.  

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