How Predictive Analytics is Different from Demand Forecasting in Consumer-Driven Market?

In times of technological advancements, some companies consider predictive analytics a revolutionary mechanism whereas some consider it as an evolution of demand planning. The concept of behavior predictions has been here for a long time and certain ML algorithms like neural network and ARIMA have been here for more than a decade.

So how is predictive analytics revolutionary? How is it different from traditional forecasting and how it adds new values to the business?

Let's see what draws a line of difference between both the methods.

The concept of demand planning has renovated itself brought by huge data, technological advancements, and consumer-driven market and causing an evolution from traditional times series forecasting to a new approach which tends to understand consumers better.

Traditional Forecasting

•  Traditional forecasting depends on the numbers and using level and trend and seasonality observations to predict results.

•  Being a requirement-based approach, it has limited demand factors.

•  It forecasts sales and tells people what to order.

•  Demand Planning Forecasting is used when a relationship between variables is strong and provides outcomes to one specific question.

•  It primarily uses time-series and even causal modelling, for the most part, constitutes of simple ratios or a single dependent variable that is used to extrapolate out.

•  Traditional demand planning is somewhat limited in its approaches and inputs.

•  It can employ a single hierarchy and one best fit model to forecast the product.

Predictive Analytics

•  Predictive analytics is majorly about consumer behavior and it may use explanatory variables to predict results.

•  Being opportunity-oriented it has limitless factors.

•  It predicts the drivers and tells people why the consumer buys a certain product

•  Predictive Analytics can be used to find a relationship between unknown variables and provides multiple insights and solutions for the entire business.

•  It uses a combination of models and techniques (including ML algorithms) to evaluate big data sets.

•  Advanced predictive analytics can predict sales of items and use similar algorithms and approaches to predict pricing, market share, weather, conversion rates, advertising, in-store merchandising.

Current Scenario of Predictive Analytics and Traditional Forecasting

Currently, Predictive Analytics is being adopted by supply chains, marketing for promotional planning, and by sales for targeting customers who are likely to make a purchase. It is also being employed for the purposes of customer retention and product management for portfolio optimization. It is capable of exposing more insights on – why demand may occur and providing what-if capabilities and drivers that may be used to influence demands. The technology explores answers to ill-formed or even non-existent questions.

For a fact, certain companies are still stuck in the past using excel as technology and Base-Lift models for forecasting due to historical perception of facing challenges with predictive analytics. They tend to operate in the same way they have been for years while being in their comfort zone. Such companies are fearful and failing to proceed to the next level and realize the potential of predictive analytics.