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Predictive Analytics In Marketing

Harsha Potapragada
April 29, 2022
February 27, 2024
Table of Contents

1. What is predictive marketing?

2. Predictive analytics models: cluster, propensity, recommendations filtering

3. What predictive marketing can do for you

4. Other factors to keep in mind

A large part of B2B marketing success hinges on B2B marketing strategy. Teams put in hours of time and effort to come up with robust, encompassing plans to drive growth. However, it's impossible to determine how exactly your strategy will pan out...until now. Enter: Predictive Marketing

What is Predictive Marketing?

As is evident from the name, Predictive Marketing helps marketers predict their marketing outcomes in terms of expected traffic, expected leads, conversions and impact on ROI at various touch-points

In other words, predictive marketing is the process of forecasting the influence of marketing campaigns and tactics with the help of:

  • Historical data on audience behaviour
  • Consumer research
  • Purchasing history of target consumers
  • Holistic marketing analytics

This forecasting is done using predictive analytics. B2C/E-commerce firms like H&M and Amazon already use this to predict products that their consumers would be interested in buying based on their current search keywords and products that they are clicking and opening in the catalogue, their past purchases, what other products similar consumers have purchased after similar search queries, purchases, items, etc

Measurement Models for Predictive Analytics

  1. Cluster Models: These models are used to segment consumer based on behavioural data  (past purchases, brand engagement, etc) and demographic data. The most common predictive algorithms used for clustering are behavioural clustering, product-based clustering, and brand-based clustering.

  1. Propensity Models: As the name suggests, these models are used to evaluate consumers’ tendencies or inclinations to act/engage in specific way. These model evaluate the likelihood of a consumer to purchase, convert, etc. 

  1. Recommendation Filtering: H&M, Amazon and Netflix are some of the most common examples of firm's that use recommendation filtering. It refers to using past purchases or consumption history to find other sales/revenue opportunities. 

What can Predictive Analytics do
for B2B Marketers?

Predictive lead scoring: Predictive lead scoring helps you make efficient utilization of your total set of leads. In short, it involves the scoring of leads based on priority. The highest intent *or audience with the highest chances of converting) are scored higher and those who are not likely to purchase or remain in the funnel are scored lower. This  helps determine who to prioritize and divert marketing efforts towards.

Automated social suggestions: Predictive analytics can also analyze audience engagement trends across social channels to suggest the best times to post content, provide content suggestions, and conduct granular A/B testing of two or more variations of content to predict which one performs better.

Preventing customer churn: The most important step after acquiring a customer is not acquiring more customers, rather it is ensuring the engagement and retention of current customers. Predictive analytics also help you identify and re-engage customers who might churn with relevant marketing material.

Predictive SEO: In addition to improving traffic and  SERP rankings, predictive analytics like search trend insights can also prevent the loss of SEO momentum and ranking. Essentially, predictive SEO helps you determine if a webpage is about to lose its SERP rankings and predict topics for blog posts that your audience wants more of. 

In conclusion...

At the end of the day, predictions don’t always come true. So it is important to be aware of the fact that some of the predicted outcomes will not materialise as expected. There is always the human element to the actions of your human audience which even the best algorithms may fail to forecast. Predictive analytics, like any other form of data analytics require a lot of data to be able to make statistically significant predictions. Employing proper systems to collect, clean and crunch loads of consumer behaviour data, historical data and analytical data is key to ensure accurate predictions.

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