Understanding predictive analytics for marketing
According to a report by MarketsandMarkets, the predictive analytics market is expected to grow from $10.5 billion in 2021 to $28.1 billion by 2025. The increasing popularity of advanced technologies, such as cloud-based technologies, big data, and IoT, is propelling the growth of the predictive data analytics market.
This rapid growth is primarily driven by the increasing adoption of predictive analytics by businesses of all sizes. As organizations strive to stay ahead of the competition, they are turning to predictive analytics to gain insights into customer behavior and make better-informed decisions about future marketing campaigns.
What is Meant by Predictive Analytics?
Predictive analytics, also known as predictive modeling or predictive intelligence, is a branch of data science used to forecast future events. It is based on the idea that past events can be used to predict future events and relies heavily on statistical methods and machine learning algorithms.
How is Predictive Analytics Used in Marketing?
Marketing professionals use predictive analytics to identify patterns and trends that can be used to target customers with relevant products and services. Predictive analytics technology is used in various marketing applications, including customer segmentation, campaign management, and lead scoring, to improve campaign effectiveness and target customers with more precision.
For example, a company might use predictive analytics to target ads to customers likely to be interested in a new product. Predictive analytics can also be used to identify when and how customers are likely to switch products or discontinue service. By using predictive analytics, marketing professionals can make more informed decisions about where to allocate resources.
What are the Three Different Types of Predictive Analytics?
Predictive analytics is becoming increasingly accurate as more data is collected. The availability of powerful computing tools has made it possible to process large data sets quickly and efficiently.
There are three common techniques used in predictive analytics:
Decision trees are a popular predictive analytics model because they are easy to understand and implement. They divide the data into smaller sets until each set is as homogeneous as possible. These datasets are then used to create predictive models of consumer behavior.
Decision trees can handle both linear and nonlinear relationships between variables. However, they are also susceptible to overfitting, so care must be taken when using them on large datasets. Overfitting refers to a phenomenon in which the decision tree fails to allow for external circumstances outside the initial training data.
Neural networks can learn from data and improve their predictions over time. They are often used for image recognition and classification, pattern recognition, and forecasting.
Neural networks consist of interconnected processing nodes that can learn to identify data input patterns. The strength of the connection between each node represents the strength of the connection between the corresponding input and output values.
Neural networks are powerful predictive analytics models that can be used for various tasks. However, they require a large amount of training data to produce accurate predictions. They are also challenging to interpret, making it difficult to understand why the model makes certain predictions.
The regression technique can improve decision-making by identifying the relationships between a dependent variable and one or more independent variables. Regression analysis aims to find the best fit line or curve that describes the relationships between the dependent and independent variables.
It can be used to predict the dependent variable’s future values based on the independent variables’ values. Regression analysis can also be used to identify which independent variables have the most impact on the dependent variable.
What are the Four Steps in Predictive Analytics?
Predictive analytics can be a powerful tool for making decisions about everything from product development to marketing strategies. But before using predictive analytics, you must ensure that you have the proper infrastructure.
The following general steps can help you integrate predictive analytics into your business:
Identify the Problem or Question
The first step of predictive analytics is identifying the problem or question you want to solve. This step is vital because it determines the type of data you need to collect and the predictive analytics solutions you will use.
For example, if you want to improve your website’s conversion rate, you need to collect data on user behavior, such as page views and click-through rates.
Collect the Data
Once you have identified the problem, you can begin to collect data that will help you to find a solution. This data can come from various sources, including surveys, customer transactions, and social media.
Analyze the Data
Data analysis can be done using various methods, including statistical analysis and machine learning. This involves looking for patterns and relationships between different elements of the system. By understanding how these elements interact, you can predict future behavior.
Create and Deploy the Predictive Model
Before implementing your predictive model, you must train the system using algorithms specific to the problem you need to solve. This is essential in predictive analytics as it allows you to test your hypotheses and predict future events. Once the model is trained, you can then deploy it into production.
You must first evaluate your model against a validation set to deploy it. This helps you ensure that your model performs as expected and will not overfit the training data. Once you are satisfied with the performance of your model, you can then put it into production.
There are many ways to do this, but one popular method is to use a web service such as Amazon SageMaker. Doing this will allow you to easily host your predictive model so that others can access it.
What are Examples of Predictive Analytics?
You can find predictive analytics examples across industries all over the world. It is commonly used by brands in insurance, banking, marketing, financial services, telecommunications, retail, travel, healthcare, and pharmaceuticals to reduce risk and optimize the results of their marketing campaigns.
Netflix’s streaming service has over 33 million different versions, each customized for a different audience group. The company uses data and analytics to provide users with personalized recommendations, predict the popularity of original content, individualize marketing content, optimize production planning, and enhance general technical and business decision-making. As a result, the recommendation algorithm influences about 75% of viewer activity.
Predictive analytics has been an essential part of PayPal to help proactively plan for customer behaviors, and it continues to be as the company looks to the future. By understanding how customers will likely use the platform, PayPal can stay one step ahead of potential problems and ensure its users have the best possible experience.
Predictive analytics tools have helped PayPal identify and solve several issues, including fraud and improve customer service. They also use predictive analytics to proactively plan for server capacity and traffic patterns.
The H&M Group has committed to achieving a climate-positive value chain by 2040. It is using predictive analytics and AI to optimize its supply chain and achieve this goal.
H&M is also using AI-driven demand prediction to understand customer preferences better and optimize inventory levels accordingly. This approach has already yielded positive results for the company, with one H&M store in Stockholm seeing a significant increase in sales after adjusting its inventory based on insights from predictive analytics.
Enhance Your Marketing Efforts with Measured
As a business stakeholder, you are always looking for ways to enhance your marketing efforts and get ahead of the competition. Predictive analytics marketing can be a powerful tool to improve your bottom line.
At Measured, we believe that data should be used to empower marketing decisions. Our enterprise-grade, user-friendly predictive analytics software delivers a cross-channel source of truth reporting powered by incrementality experiments designed with precision for all marketing channels. This allows you to collate, organize and balance your marketing data, and to identify the true incremental contribution of every campaign, channel, and tactic.
By using the Measured predictive analytics platform, you can make decisions based on trusted cross-channel insights - meaning you can confidently invest in the channels that are most effective for your brand.
To learn more, book a free demo at Measured today.