Forecasting vs predictive analytics: What’s the difference?

Forecasting vs predictive analytics

Understanding market progression helps companies move forward and expand their business, and data analytics can help organisations understand the market.

For instance, tracking the evolution of consumer behaviour is an excellent way to plan to meet the changing demand—it is never a good idea to venture into the market without an effective strategy. 

While there are many data analytics models that help organisations get detailed insights into their operations, forecasting and predictive analytics, in particular, can give you insights into the workings of the market ecosystem and help understand your target audience.

Forecasting is a process that helps you identify future trends and the consumer behaviour patterns that may affect your business at a macro level and design strategies that they can count on when moving ahead in the industry. 

Predictive analytics, on the other hand, uses current statistics and gives you an explanation of the possibility of an outcome—you can define each business project or campaign, predict the possible outcomes, and design tailored-campaigns that can guarantee the best results. 

In this post, we discuss the differences and practical use cases for both models.

Forecasting vs predictive analytics: which is more accurate?

At first glance, forecasting may sound more accurate than predictive analytics as it uses data from the past and the present to estimate future trends. 

Predictive analytics, however, is not merely guessing. Instead, it uses advanced analytics algorithms that leverage current and historical data to predict possible outcomes in the future.

Predictive analytics leverages techniques like automated machine learning and artificial intelligence to create specific predictive models that help you identify patterns or possible outcomes of a model. 

Using predictive analytics and forecasting in business planning

When you estimate a trend in the market with the forecasting model, you look into past data and base your estimations on them. 

You could, for example, forecast your sales margin for seasonal products based on the data from the previous year. You can use this data to determine the quantity you need to supply to the market. 

Predictive analytics, on the other hand, helps you identify potential customers for your seasonal product. 

With such insights, you can understand your target audience by evaluating the relationship between demographics and customer preferences and base your marketing and supply strategies on them. 

Understanding the consumer behaviour and the market

The success of a business relies on understanding the behavioural patterns of its customers, allowing decision makers to tailor strategies according to customer behaviour.

Forecasting is one of the best ways to gain insights into your customer behaviour at a macro level—you can estimate challenges and opportunities in the market and customise your strategies to meet them accordingly. 

In other words, forecasting helps you strategise how to navigate the business world, ensure that you avoid potential pitfalls and risk factors, prepare for unavoidable challenges, and optimise your processes for better profits.

Predictive analytics let you understand consumer behaviour at a more micro level. 

It provides you with insights into the more human nature of consumer behaviour, helping you understand individual preferences, rank customers effectively, and plan how to deliver a better customer experience to maximise satisfaction. 

Leveraging forecasting and predictive analytics drives better decision making

Forecasting vs predictive analytics: which one is better for your business?

For better company growth, the trick is not figuring out which model is better for your company but identifying how to leverage both for different contexts of each business operation. 

Both models, when used intelligently, can provide business leaders with insights that they can leverage for better decision making. 

In the highly competitive market ecosystem, using all available techniques—systematically and appropriately—will guarantee better results. 

Cameron Lawson

Cameron Lawson

Highly experienced SAS and Development Operations consultant and strategist

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