With eCommerce to double by 2023 to more than $6.5 billion (Shopify) it will be more important than ever for online retailers to predict demand for goods and services to achieve competitive advantage. Machine learning is a method of demand forecasting that provides retail owners and managers with the ability to predict future demand more accurately.
The term machine learning was coined in the 1950’s but it wasn’t until the past decade when eCommerce marketplaces were using the technology to analyze user’s historical data to search for patterns and deploy predictive models.
Helping businesses make smarter and faster decisions
Machine learning is a type of AI (Artificial Intelligence), that can collect, analyze, and learn from large sets of data from a range of sources. 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.
Machine learning combines historical sales data and other dynamic variables or 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.
Firstly, TrueStock uses historical sales data to configure an initial forecast model.
This is then followed by pricing history data as dropping prices can often correlate to an inflation in sales. Data from the performance of previous marketing campaigns are also analyzed followed seasonal data. Different seasons can affect product sales performance on a weekly, monthly, and annual basis.
External factors such as the weather and different events that can affect future sales and inventory requirements are considered before data is sanitized and formatted as a time series. The next stage of the process is the identification of days of unusual sales activity or anomalies.
The final stage of the TrueStock forecasting process is putting your through machine learning to produce the forecast ready for your analysis.
Improved precision of demand forecasting accuracy
Demand forecasts improve over time due to more data is inputted, processed, and analyzed through the TrueStock machine learning model. Forecast precision which measures how much spread there is between a forecast and the actual value and gives an idea of the value of errors
TrueStock uses six different metrics to measure how accurate a forecast is and is therefore able to provide more precise forecasts. No demand forecast is ever 100% accurate but with data science the gap is closed in terms of error reduction.
According to McKinsey study, AI-enhanced supply chain management may lead to improved accuracy by reducing forecasting errors by 20-50%. Lost sales due to products not being available can be reduced by up to 65% and inventory reductions of 20 to 50% are achievable.
"Supply-chain leaders are starting to realise the ability of machine learning-based methods to increase forecasting accuracy and optimize replenishment." - McKinsey
This is a forecasting method proven to be more accurate, efficient, and adaptable to changes than traditional or legacy methods such as moving averages. Advanced and predictive analytics via machine learning methods can eradicate the requirement for guesswork in sales forecasting.
Supply chains can be optimized by providing suppliers more reliable anticipation of demand for replenishment and an interrupted product supply. Retailers can ensure their customers receive products when and how they want them gaining their loyalty with satisfactory order fulfilment.
It is also a method that is much quicker to implement which is a significant consideration for new eCommerce retailers coming into the market.
Once you start using TrueStock you view your sales forecast in as little as 15 minutes.