What Value can Shopify Merchants Gain from Machine Learning Sales Forecasting in 2021?

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TrueStock Team January 5, 2021
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Whilst 2020 has been anything but predictable, the uncertainty of 2021 is a real challenge that many businesses face. With the retail sector especially having to close bricks and mortar stores for weeks on end, there has been an unprecedented shift to eCommerce.

As more businesses will be looking to expand their online capabilities as well as streamlining operational processes to ensure successful order fulfilment, speed and accuracy of demand forecasting will be critical to business success in 2021.

With the fluctuations in demand experience by many retail merchants during 2020 there can be a mismatch between demand and supply which can be costly in an extremely competitive marketplace.

They can lead to missed sales opportunities, loss of profit, increasing expediting costs and poor customer service. To maximise the effectiveness of sales and marketing merchants must accurately predict future customer demand.

The requirement for accurate demand predictions is particularly important for online merchants as buyers can easily find alternatives at the touch of a button.

Demand patterns in certain industries have forced companies to pivot in different directions to accommodate new demand drivers. 69% of UK consumers state they are more likely to shop online in 2021 and 46.7% prefer to shop in a marketplace (Attest – Top UK Consumer Trends of 2021).

Businesses that do a better job of demand forecasting have significant better control over inventory and customer service.

Being able to enhance the ability to predict future sales and take the uncertainty out of demand forecasting is key to driving value across many parts of a retailer’s business.

Making better, faster, and more accurate demand prediction decisions provides a host of benefits across the supply chain, customer base, marketing, and financial functions of a company.

1. Faster, more accurate sales forecasts for 2021 demand planning

Whilst demand forecasting cannot be totally accurate it should always try and minimize the difference between the sales forecast and actual sales.

To create future forecasts, there are 3 key areas that need to be considered:

• Past performance

• Anticipated or happening changes

• Future growth plans

Sales forecasting using machine learning is a forecasting method proven to be more accurate, efficient, and adaptable to changes than traditional or legacy methods.

Machine learning combines historical sales data and other dynamic variables factors such as seasonal trends, the weather, events, pricing history, promotions and other marketing activities are considered, their importance scored and weighted to predict future sales demand.

Utilizing learning algorithms, large amounts of data and integrate external data sources that impact sales demand can be analyzed with speed.

Trends, patterns, and relationships within complex data sets can be identified quickly and the ability to learn and make predictions can take place without human intervention.

Constantly and automatically improving with time, machine learning enables companies to respond quickly to fast changing business situations.

According to McKinsey & Company reducing forecast errors up to 50% is achievable using machine learning-based techniques proving to be a valuable method in mimimizing the difference between the sales forecast and actual sales.

TrueStock’s machine learning sales forecasts come with forecast accuracy reports so Shopify merchants can see how close to reality their forecasts are.

Graph showing sales forecast horizon

Every forecast generated provides a lower, middle and upper estimate for sales demand each day. Your 120-day sales forecast can be viewed in as little as 15 minutes after implementation.

2. Predictability and greater accuracy leading to better decision making

Sometimes business decisions can be made with regards to demand based on emotions, gut instinct or with conventional forecasting methods.

With more accurate demand predictions through machine learning more informed decisions about pricing, sales growth strategies and market potential can be made.

3. Increased profitability, reduced costs and better productivity

Improved demand forecast accuracy can improve the bottom line through revenue growth, cost reduction and productivity improvement.

More accurate demand forecasts using machine learning methodology results can inform about precise future replenishment quantities and costs.

This can reduce missed sales opportunities through stockouts or mark downs on products that have been overstocked. Excess stock takes up extra space, ties up working capital and can result in increasing costs for the business.

The speed and accuracy of machine learning for demand forecasting can free up teams to direct their time for other business tasks.

4. Supply chain optimization and greater resilience

With the exponential growth of eCommerce especially during the pandemic in 2020, consumers are demanding product availability and faster delivery.

The retail landscape for 2021 will continue to be as competitive due to the number of businesses transitioning online, and customers are expecting products when and how they want them.

Giving suppliers a more reliable anticipation of demand for uninterrupted product supply is an important factor in ensuring that the supply chain is optimized.

Demand forecasting utilizing machine learning creates better predictions providing less uncertainty and more knowledge about demand variation.

5. Successful order fulfilment and enhanced customer customer loyalty

With increasing competition that is driven by customer demand product availability is a key factor in online business success. If a product is not available when and how they want it there is a high risk that consumers will source that product from a competitor.

Successful first-time orders can determine whether the customer will place future orders with the same Shopify merchant.

Increased accuracy with machine learning demand forecasts can help reduce those out-of-stock situations and boost customer satisfaction and their experience with the retailer’s brand.

6. Improved marketing focus and spend

As part of the sales demand forecasting process marketing activities are also considered by TrueStock.

Return on investment of marketing from different channels and locations are analyzed, and business insights provided by TrueStock allows marketing to pinpoint their spend and focus on promotional activities for 2021.

7. Successfully secure inventory loans

An inventory loan is a type of short-term business loan that retailers can use to buy stock.

As part of due diligence more accurate sales forecasts from TrueStock can be provided to financial institutions to show likely sales during a forthcoming financial period in 2021.

Sales forecasting has always been a complex issue and 2020 brought some additional challenges. However, the key challenge has always been forecast accuracy.

With machine learning sales forecasting for 2021 will be easier, faster, and more accurate as customer data from 2020 and anticipated or happening changes e.g., weather, events, promotions can be processed at speed with computer algorithms.

Never before have Shopify businesses had access to such intelligent sales forecasting for achieving the business growth they desire.

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