Predictive Analytics: Step Up Marketing

Predictive analytics for marketing would have been adopted years ago – if only the the power to compute was omni present, data accessibility was faster, and the software were easier to use. Even though It has quickly become a buzzword most companies still find themselves using “Spreadsheets”. However companies which want to be future ready,want to know more than what happened. “Scoreboards” tell you what the score is not what the score “Will Be”. 

“Predictive analytics is the use of varied techniques ranging from data mining, statistical modeling, machine learning, and artificial intelligence to analyze current data to make predictions about future”

The goal of the article is not to provide deep down fundamentals of “Predictive Analytics”,it simply offers “Why and how it works”. Let's begin by listing some modern day application in real environment:

1 – Predictive Analytics for Customer Behavior

Companies like Amazon and Netflix have build their business based on understanding and Predicting customer behavior and preferences. However technology is being increasingly adopted across industries. 

Let’s discuss 3 primary classes of predictive models:

Cluster models (segments) – Mainly utilized for customer segmentation. Commonly used cluster models include behavioral clustering, product based clustering and brand-based clustering.

Propensity models (predictions) –Is a statistical scorecard that is used to predict the behaviour of your customer or prospect base. 

Collaborative filtering (recommendations) – Used in making automatic predictions about the interests of a user by collecting preferences or taste from many users. 

2 – Introducing Suitable Products/ Services To The Market

Data visualization is not just a tool which appeals to the eye, however provides information which can be used to, provide and guide actions based on customer and business insights. Data Visualization can be utilized to display

Customer base living in store’s neighborhood

Age-Range of people living in store’s neighbourhood.

This allows the marketing team to hone in on important guiding questions: Do they buy more hard Shirts or Tops? Is there a age-range that shows what should be stocked

3 – Quantify & Prioritize Leads

Forrester in its recent study identified 3 prominent categories for B2B marketing use cases that reflect and provide the foundation for greater use of Predictive Marketing Analytics

Predictive Scoring: It is used to Prioritize identified prospects, leads, and accounts based on their likelihood to take action

Identification Models: Recognizing and acquiring prospects with similar characteristics as existing customer. 

Automated Segmentation: Segmenting leads for personalized messaging.

4 – Targeting the Right Customers at Right Time with Right Content 

The simplest marketing application for Predictive Analytics is Targeting the Right customers at the Right Time with the most suitable offer and link it back to customer segmentation. Allowing Scheme optimization and Impact on ROI.

As per a study done by Gartner analysts, companies using predictive analytics are twice likely to identify high intent customer & optimize the market offers.

The idea is to create an enterprise brain that keep learning decisions on situation and in 5 years can function in order to produce accurate solutions and measures where situation resembles and previous event. Thats where world is moving so we may as well use it to our advantage.