Being able to predict demands for a service or products is a fundamental skill in sales that can make or break a business. It lets you manage your inventory, anticipate revenue, know and train when to onboard new staff, and plan for business-specific maintenance, especially during slower months. Predicting demands is also known as “forecasting”, and it’s a skill that is based on sales patterns and your ability to observe and take advantage of those patterns.
Traditionally, this skill is developed manually and is a key component for small businesses. But as businesses scale and as they gain a more diverse portfolio, forecasting demands at scale manually can become taxing and can create a large overhead by hiring a surplus of staff to stay on top of sales. This is where recent innovations in technology have risen up to meet the demands and skill sets of business owners.
Artificial intelligence (AI) has grown astronomically since its inception over seventy years again. Since then, the technology has become increasingly refined, and now we’re able to use its various subsets, such as machine-learning (ML), to aid us in a number of e-commerce tasks like demand forecasting.
As its name implies, ML allows for AI to learn patterns and insights based on the data that it gathers. Giving ML technology access to an order management system’s historical and real-time data has been proven to show an improvement in overall business operations. This also means that ML can develop the necessary skills to predict sales.
When it comes to demand forecasts, there are six specific types to know: short-term, long-term, external, internal, passive demand, and active demands.
Passive demands relies solely on past data without any limitations on time and observes numerous factors. This is the foremost important technique that every other one relies on in some way to predict future sales. Since there can be a lot of historical data to draw from, this is where ML can help do a lot of the heavy lifting.
Active demands rely less on technological intervention and rely more on specialized knowledge to help forecast demands. This is great for businesses that are seeing exponential growth or are just starting. It’s also used if there’s an internal restructuring or disparity between past and present growth such as in the case of a product going viral.
Short-term demand forecasting can only forecast in a set range of time, as short as a day and as long as twelve months, and allows for businesses to react quickly to fluctuations in customer demands.
Long-term demand forecasting extends its reach over a number of years. This observes the duration of trends over a longer period of time and allows businesses to gather the qualitative and quantitative data required to create or adjust their business strategies or advertising campaigns as necessary.
Internal, or micro, demand forecasting uses a number of factors, such as a company’s conversion rates, inventory, promotions, personnel, and past sales trends, to discover new areas of opportunities.
External, or macro, demand forecasting addresses a much broader scale of factors, such as the economy, industry trends, consumer trends, and supply chain stability. Think of the paper shortage a few years back that greatly impacted a number of industries. Shortages can often happen suddenly, but sometimes there are tells in the market to let you know when to expect a downturn.
As you can imagine, there are areas where ML can be a great benefit and others where human intervention is the most reasonable course of action. Thankfully, we no longer live in a time of “or” where we have to pick and choose. ML, and by extension AI, is becoming more accessible to everyone, and this allows businesses and individuals to more evenly match each other in the e-commerce space.
As more businesses continue to appear in this space, the technology will grow in direct proportion to vendor and consumer needs, leading to a more profitable and harmonic future for man and machine.
