What is Predictive Modeling?

What is Predictive Modeling Method?

Predictive Modeling refers to the use of algorithms to analyze data collected on previous events in order to predict the outcome of future events. In a business model context, this is most commonly expressed as the analysis of previous sales data to predict future sales outcomes, then using those predictions to dictate what marketing decisions should be made.

What is predictive analytics?

Predictive analytics is a form of technology that makes predictions about certain unknowns in the future. It uses several techniques to make these determinations, including artificial intelligence (AI), data mining, machine learning, modeling, and statistics.

Benefits of Predictive Modeling

The largest advantage you can gain from using this advanced technology is how easy it is to generate actionable insights; because the insights gained from business analytics predictive modeling are solely based on the truths of your organization’s actual business behavior, it is tailored specifically for your business’s needs and strategies. The other major advantage of this concept is the value it offers to optimize marketing spend; by using this technology to determine which customers in your customer base have the highest proclivity to buy, you can better plan out your marketing campaign to avoid wasting money on segments that are not likely to provide sufficient return.

Why Do I need Predictive Modeling Services?

To illustrate how key predictive modeling in strategic marketing serves in optimizing your organization’s marketing strategy, let’s consider an example. Suppose you were a business that publishes a catalog every quarter to advertise your new line of products to your customer base. If it costs a little over fifty cents (postage is $0.47 for the US, and printing en masse is likely around $0.03 per catalog at minimum) to print, and you have a total customer base of around 100 million customers, you are essentially looking at spending $50 million a quarter to send a catalog to every customer you currently have in your database. In order for this marketing campaign to break even, you would need to receive more than $50 million in revenue in return for this ad campaign.

Obviously, it would be absurd to justify doing this once, let alone every quarter. Your marketing strategy works best when you minimize costs and maximize return, so for this campaign to be effective, you need to focus on the customers most likely to buy. We need a mechanism to identify our best customer segments, and we can use their historical buying behavior and predictive modeling to determine this.

How Predictive Modeling is Done?

At their core, humans are creatures of habit; the reasons that caused them to buy from you in the past will be the same reasons they will buy from you in the future. Therefore, you can leverage what you learn from their past buying behavior to position yourself such that you meet their future desires.

Predictive modeling techniques break down into a few key steps:

  1. First, all previous data collected is analyzed to determine what patterns or parameters the customers you already have followed, and thus what patterns both they and your future customers will follow.
  2. Next, You can use this predictive model to see which marketing campaigns in the past have seen success with each different segment of your customer base.
  3. Finally, you can determine which products that were advertised during each campaign did or did not successfully see a rise in sales, which in turn translates to whether those products have a reasonable chance of success if advertised; after all, if a product gets advertised for your marketing but does not see valuable results, it would be better to emphasize other products.

The top four predictive analytics models are

The classification model: It is considered the simplest, as it organizes data for easy and direct query response.

The cluster model: In this model, data are clustered by common attributes. It consists of grouping things or people with shared characteristics or behaviors and planning strategies for each group at a larger scale.

Outliers model: Data points that are abnormal or outliers are used in this model. There is no one method to detect outliers because of the facts at the center of each dataset. One dataset is different from the other. A rule-of-the-thumb could be that you, the domain expert, can inspect the unfiltered, basic observations and decide whether a value is an outlier or not.

Time series: This model evaluates a series of data points based on time. Time series analysis can give valuable insight into what has happened over the course of days, weeks, months, or even years. Time series data analysis is the way to predict time series based on past behavior. Prediction is made by analyzing underlying patterns in the time-series data.

Best Approach to Predictive Modeling for Marketing

This technology offers a customized approach for marketing. A-One sizes fit all approach is usually not ideal for your marketing strategy, as the needs, your business, in particular, has might differ heavily from what works in a vacuum. A better approach in our opinion is Express Analytics’ ability to customize your models as per business requirements and do plenty of data wrangling and analysis before we implement these models.

Predictive Analytics Software

Generally, predictive models are just one type of advanced analytics and ML. Some other models like RFM, Identity resolution, Attribution models are not predictive in nature.

They fall into statistical models(RFM), Optimization models(Attribution), Clustering or segmentation models, text mining. Together we call them advanced analytics.

So I feel naming this just predictive analytics is not doing justice to our range of services or models. Here is the definition provided by gartner –

Advanced Analytics is the autonomous or semi-autonomous examination of data or content using sophisticated techniques and tools, typically beyond those of traditional business intelligence (BI), to discover deeper insights, make predictions, or generate recommendations.

Advanced analytic techniques include those such as data/text mining, machine learning, pattern matching, forecasting, visualization, semantic analysis, sentiment analysis, network and cluster analysis, multivariate statistics, graph analysis, simulation, complex event processing, neural networks.

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