What is Predictive Modeling?

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.

The largest advantage you can gain from using predictive modeling is how easy it is to generate actionable insights; because the insights gained from predictive modeling is 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 predictive modeling is the value it offers to optimize marketing spend; by using predictive modeling 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.

You can also look at Top 5 Predictive Analytics Use Case In Retail

To illustrate how key predictive modeling 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 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.

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 breaks 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.

Predictive modeling 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.

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 as 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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