I would like to know the difference between predictive analysis/ prediction and forecasting. Give cases when predictive analysis and forecasting is used.
Predictive analytics is business intelligence technology that produces a predictive score for each customer or other organizational element. Assigning these predictive scores is the job of a predictive model which has, in turn, been trained over your data, learning from the experience of your organization.
Predictive analytics optimizes marketing campaigns and website behavior to increase customer responses, conversions and clicks, and to decrease churn. Each customer's predictive score informs actions to be taken with that customer — business intelligence just doesn't get more actionable than that.
How is predictive analytics different from forecasting?
Predictive analytics is something else entirely, going beyond standard forecasting by producing a predictive score for each customer or other organizational element. In contrast, forecasting provides overall aggregate estimates, such as the total number of purchases next quarter. For example, forecasting might estimate the total number of ice cream cones to be purchased in a certain region, while predictive analytics tells you which individual customers are likely to buy an ice cream cone.
Predictive Analytics World events often include select sessions on forecasting since it is a closely related area, and, in some cases, predictive analytics is used as a component to build a forecast model.
Predictive analytics combines techniques from statistics, data mining and machine learning to find meaning from large amounts of data. Whether you’re in marketing, compliance, customer service, operations or any other business unit, your data can show where you are – and predict where you’re going.
How do organizations approach predictive analytics? The key stages for an analytical life cycle include:
Analytical data preparation – Source, clean and prepare the data for optimal results.
Visualization and exploration – Explore all data to identify relevant variables, trends and relationships.
Statistical analysis – Use everything from simple descriptive statistics to complex Bayesian analysis to quantify uncertainty, make inferences and drive decisions.
Predictive modeling – Build the predictive model using statistical, data mining or text mining algorithms, including the critical capability of transforming and selecting key variables.
Model deployment – Apply the new champion model, once validated and approved, to new data.
Model management and monitoring – Examine model performance to make sure it is up-to-date and delivering valid results.
Forecasting would be a subset of prediction. Any time you predict into the future it is a forecast. All forecasts are predictions, but not all predictions are forecasts, as when you would use regression to explain the relationship between two variables.
Prediction: An estimation of an event happening like spot/one-off estimates of a specific event in the future; usually at a specific point in time. e.g. Whether a credit card customer is going to default on his outstanding payment
Forecasting: Forecasts are a set of possible futures that include probability estimates of occuring. The entire set of scenarios should have a probability close to 1. Often, forecasts have more generalized time points (e.g., next five years, next decade, mid-range future, etc). It is possible that a forecast group can consist of a set of predictions with associated probabilities. It Is always associated with a time dimension i.e. estimation for some specific future date or over a period of time e.g. Total sales in July, 2017
Forecasts are a subset of prediction. Both involve two dimensions i.e individual assertion (projection judgement ) and precision. Forecast is a probabilistic judgement over a long period of time while prediction is a definitive and specific statement about an event occurring. It is challenging to forecast in the hyper domain due to the constant flux of events and its transient nature.
Forecasting is a subset of prediction. Prediction is to estimate the probability of an event happening. It can only forecast what might happen in the future, because all predictive analytics are probabilistic in nature. For example, whether a customer with outstanding credit card payment is going to default. Forecasting is always associated with a time dimension, that is that the purpose would be to estimate a value in future, so it can provide overall aggregate estimates. For example, estimate how many customers is going to apply for a credit card in the next quarter.
A prediction is a definitive and specific statement about when and where an earthquake will strike: a major earthquake will hit Kyoto, Japan, on June 28.
Whereas a forecast is a probabilistic statement, usually over a longer time scale: there is a 60 percent chance of an earthquake in Southern California over the next thirty years.
Like the words themselves, sometimes the difference between forecasting and predictive analytics approaches is revealed in the words used to discuss demand. For instance:
"What did this ITEM do last year?" exemplifies the notion that forecasting looks at item data, simulates the past, and attempts to apply it to the future. Truly, you don't need data to know what an item did. It didn't DO anything. An item is an inanimate widget, and the only thing it can DO, is wait.
"What does the SHOPPER do when X happens?" It is precisely the shopper/buyer's action that is revealed in predictive analytics. The shopper is the only player in this drama that can DO. The shopper is who the widget is waiting on.
An awakening that future action is what is being revealed makes it clear that intimate knowledge of the interaction with the buyer is the best indicator of demand. The customer transaction, the path to it, or the influencers on it, are what predictive analytics uncovers. The uncovered information reveals what will influence, motivate or drive the next transaction and what will deliver that shopper to act on the waiting widget.
Think of forecasting as a machine, cranking out data on what the item “did.” A technique that is duplicating the past and projecting this data on the future, or as I say, “post-casting.”
Predictive analytics is a deep intimacy with the people engaged in commerce, understanding what they’ll do next, showing what that is and why.
To learn more about Predictive Analytics and what it can do ,check out a recent Supply and Demand Chain Executive webcast this topic is discussed http://bit.ly/2a2kMZf
A prediction is a definitive and specific statement about when and where an earthquake will strike: The world will end in 2020 20 December
Whereas a forecast is a probabilistic statement, usually over a longer time scale: There is a 50% chance that the World will end within the next 100 years
Prediction is the generalize term & it's independent of time. Forecasting is the prediction with time as a one of the dependent variable. Eg-
Prediction- Predicting amount spend by user for certain case. It's happen over the period of time but not exact.
Forecasting- Best example is weather forecasting. Weather prediction is called forecasting since it's predicted with time as a dependent variable either for quarter or next month etc.
Forecasts are a subset of prediction. Both involve two dimension(s) i.e individual assertion (projection judgement ) and precision. Forecast is a probabilistic judgement over a long period of time while prediction is a definitive and specific statement about an event occurring. It is challenging to forecast in the hyper domain due to the constant flux of events and its transient nature.
The term forecast came from English’s Germanic roots, unlike predict, which is from Latin. Forecasting reflected the new Protestant worldliness rather than the other worldliness of the Holy Roman Empire. Making a forecast typically implied planning under conditions of uncertainty. It suggested having prudence, wisdom, and industriousness, more like the way we now use the word foresight – Source : Signal Versus Noise.A forecast is about probability while prediction is about certainty. It is our empirical observation that biological intuition signals certainty of an event using psycho-physiological inner knowing called RSIK © (Rock Solid Inner Knowing). These psycho-physiological markers are error prone, can lead us astray and require extensive training to be used in uncertain contexts.
Forecasting is German
Life experiences with the German/Nordic side of my family revealed a straightforward, logical and rules-driven approach to the world. Many conversations were process oriented, "This is THE way you do X!" (insert anything from, eat supper to blink your eyes).
Structure, rules and logic explain why it makes sense that the Germanic concept of forecasting accepts the age-old presumption that what occurred in the past is the best indicator of the future. This method assumes that the reasons for demand of a widget can't be known, and therefore a mechanical method, primarily a statistical forecast, is used to duplicate the past in order to provide visibility to the future. These statistical models are well-defined, precise, verified, and often, if only coincidentally, accurate. They are the foundation of an engineering-based solution for attempting to reveal the future. A classically Germanic approach.
Predicting is Latin
My Latin side of the family comes from Argentina, the land of the Tango, all day drinking (yerba maté tea, of course) and big, loud expressive families. These experiences centered on the person and understanding how to engage, motivate and manage them. The belief that there is so much to know about every person, and that you learn more with every interaction. forecasting provides overall aggregate estimates, such as the total number of purchases next quarter. For example, forecasting might estimate the total number of ice cream cones to be purchased in a certain region, while predictive analytics tells you which individual customers are likely to buy an ice cream cone.
Predictive Analytics takes a similar, more humanistic approach. Instead of relying on past historical activities, predictive analytics analyzes the influencers, interactions and activities of the actors in demand...the shopper/buyer. Revealing the future by getting into the head of the actors in commerce, rather than by analyzing the history of the object of commerce- the item. And, with the abundance of real-time consumer data available today, future demand for your organization’s products and services can be more precisely determined rather
Predictive analytics is a key method to truly leverage big data. At the center of the big data revolution is prediction. The whole point of data is to learn from it to predict. What is the value, the function, the purpose? Predictions drive and render more effective the millions of organizational operational decisions taken every day.
Predictive analytics is a form of data science. Moreover, it is the most actionable form. A predictive model generates a predictive score for each individual, which in turn directly informs decisions for that individual, e.g., whether to contact, extend a retention offer, approve for credit, investigate for fraud, or apply a certain medical treatment. Rather than solely providing insights, predictive analytics directly drives or informs millions of operational decisions.
Predictive analytics is a deep intimacy with the people engaged in commerce, understanding what they’ll do next, showing what that is and why. Forecasting as a machine, cranking out data on what the item “did.” A technique that is duplicating the past and projecting this data on the future, or as I say, “post-casting.”