What Is Predictive Marketing And How Will It Affect Marketing In the Future

What Is Predictive Marketing And How Will It Affect Marketing In the Future - MediaOne Business Group Pte Ltd

Marketing is no longer a game of chance. Now, more than ever, businesses rely on data and predictive analytics to inform their marketing strategies.

It sounds like marketing jargon — doesn’t it? Do you know what predictive analytics are and how they work?

Well, there’s only one way to find out.

What’s Predictive Marketing?

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Predictive marketing is a technique that uses data and predictive analytics to identify patterns and predict future outcomes.

By analyzing past trends, behaviours, and interactions, marketers can make more informed decisions about their strategies and target the right audiences with greater precision.

While this may sound complex and intimidating at first glance, it’s not as complicated as it seems.

In fact, many businesses are already using predictive marketing to boost their sales and engagement rates, increase conversion rates, and optimize their campaigns.

How Does Predictive Marketing Work?

Now that you know what predictive marketing is, you might be wondering how does it work? 

At its core, predictive marketing is a data-driven approach to decision-making. By collecting and analyzing large amounts of data — for example, through email lists, website clicks/traffic, social media interactions, etc. — marketers can track consumer behaviour patterns and draw correlations between various factors.

For example, suppose you run an online store and notice that your customers are most likely to convert after viewing a specific product. In that case, you might want to create an automated email campaign targeting this group with similar products or offerings. 

Another example might be using past marketing data to understand when your target audiences are most likely to engage with your content — for instance, analyzing website statistics to see if there’s a correlation between traffic during certain times of day and sales.

What Statisticians Say About Predictive Marketing

What Is Predictive Marketing And How Will It Affect Marketing In the Future - MediaOne Business Group Pte Ltd

According to a recent predictive intelligence benchmark report, predictive intelligence recommendations influenced 26.4% of orders. 

The study analyzed orders over 36 months. It found a notable increase in the number of sales influenced by predictive analytics — the total number of orders increased from 11.47% at the beginning to 34.7% at the end.

This data shows that predictive marketing is a powerful technique that can help businesses take their marketing strategies to the next level. 

91% of top marketers are either fully or partially using predictive analytics, and it’s easy to see why. 

The average list in conversion as a result of predictive analysis now rests at 22.66%. Yes, that’s a lot to digest, but the truth is that we’re only now getting a good idea of what predictive analytics can do for us.

With the right data analysis tools and a clear strategy, marketers can make more informed decisions about their campaigns, target the right audiences with greater precision, create optimized content for higher engagement rates, and ultimately increase sales and revenue.

Example of Predictive Marketing in Action

One company that has seen great success with predictive marketing is Amazon.  

With millions of customer interactions and vast amounts of data generated daily, Amazon has built a sophisticated system for analyzing consumer behaviour patterns and predicting future trends. 

Their landing page serves personalized recommendations to customers based on their past purchases, browsing history, and other factors.

It gets even more complex: Amazon uses machine learning algorithms to look at the products that the client clicked on in the past, the specific item they were interested in, and where they were in the buying process to make a recommendation that might just close the deal. 

And it doesn’t stop at that. 

They also consider the time and season of purchases and send notifications or emails offering coupons or discounts at just the right time to influence sales.

This powerful combination of data and machine learning helps Amazon stay ahead of the curve and deliver personalized experiences that keep customers engaged and coming back for more. 

The Pros and Cons of Predictive Marketing

While predictive marketing can be an extremely powerful tool for businesses, it also has its pros and cons.


  • Flexibility and adaptability: By using predictive analytics, businesses can quickly adjust their marketing strategies in response to shifting consumer trends and behaviours. 
  • Data-driven insights: Marketers have access to a wealth of data about their customers and prospects that they can use to make more informed decisions about optimizing campaigns and reaching target audiences. 
  • Efficient use of resources: Predictive marketing allows businesses to target and engage with customers more effectively, making the most of their marketing budget.


  • Data security and privacy concerns: As with any data-driven strategy, there is a risk of potential breaches or misuse of customer information.
  • Reliance on technology: While predictive analytics tools can help marketers analyze data and make more informed decisions, it helps to remember that these are just tools and cannot replace human expertise or creativity. 

How Predictive Analytics Data Affect Different Marketing Channels

Nowadays, a business cannot succeed without having a solid marketing strategy.

And with the help of predictive analytics data, marketers are better equipped to decide which channels to focus on and optimize for maximum ROI. 

Some of the most commonly used marketing channels that leverage predictive analytics include:

  • Web: Predictive marketing makes it possible to optimize website landing pages from static, one-size-fits-all pages to highly personalized experiences that appeal to different customer segments. 

Your site can collect data as soon as a user lands, clicks on something, and so on to better understand what visitors are looking for and how best to target them with ads and other content. 

Even better, the site can be programmed to make recommendations in real-time. Instead of making users jump through arbitrary hoops, the site can suggest other relevant products and services based on browsing patterns and history.

  • Email marketing: Email marketing platforms like MailChimp and MadMimi leverage predictions from customer data such as past purchases, browsing history, and other behavioural patterns to create dynamic email content tailored specifically for each contact or list. 

If you have a loyalty program, you can use predictive analytics to send targeted incentives based on customer purchasing behaviour.

  • Social media: Social media marketing is another channel that has undergone a big transformation thanks to predictive analytics. 

Now, marketers can use social listening tools like Hootsuite and SproutSocial to monitor mentions of their brand and key competitors across multiple platforms in real-time. 

With predictive analytics, marketers can use these signals to tweak their social media marketing strategies and better engage with their audience.

  • Email Campaigns: Email campaigns can also leverage predictive analytics to personalize and target the right messages to the right users at the right time.
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Email personalization boosts open and click-through rates and can help increase sales, leading to six times more transactions than non-personalized emails. 

  • SEO: The world of search engine optimization is also becoming increasingly data-driven. 

SEO techniques like measuring page rank, keyword research, and backlink analysis can all benefit from predictive tools that keep track of a brand’s search engine performance and help them identify new opportunities to improve visibility. 

Instead of playing catch-up to competitors, marketers can use predictive analysis to stay ahead of the curve and make changes before it’s too late. 

  • Advertising: As a subset of marketing, advertising is also becoming increasingly data-driven and powered by predictive analytics. 

With the help of predictive tools, marketers can gather data from past ad campaigns and use that information to create more targeted, personalized, and effective ad programs.

Predictive analytics can help advertisers constantly refine their strategies and improve ROI. 

  • Research: Marketing analytics isn’t a science but an art. The more data you collect and analyze, the more accurate your predictions will be.

 So while predictive analytics is a powerful marketing tool, it’s only one piece of the puzzle. The real magic comes from combining it with other marketing strategies and tools to create a comprehensive marketing strategy informed by one big data.

Features to Consider in Predictive Marketing Tools

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When choosing a predictive marketing tool, there are several key features you might want to consider. These may include:

  • Real-time data analysis and predictions. That is arguably the most powerful feature of any predictive marketing tool, as it allows you to quickly identify trends and make changes in response to changing conditions.  
  • Templates. Look for something that provides pre-defined templates for common marketing tasks, such as creating segmented email lists or setting up social media campaigns. Make sure the technology incorporates predictive analytics into these templates. You want a system that can easily integrate with your existing marketing tools, whether your CRM, email marketing software or other systems.
  • User-friendliness: Ideally, you should be able to quickly learn how to use the predictive marketing tool and get started without a lot of training. That will help you easily adopt the technology into your existing workflows and processes. 
  • Automation: The predictive marketing tool should handle much of the manual work for you, such as creating segmented email lists or managing social media campaigns. The technology must consider all data points and use predictive analytics to recommend the best course of action. 
  • Flexibility: While finding an exact match for your company’s unique needs and marketing strategies is almost impossible, you should look for a tool that provides some level of customization and flexibility. You also want to ensure the tool is compatible with your existing marketing systems and can integrate easily. 
  • Cost: Before committing to a particular tool, consider its upfront and ongoing expenses. These may include licensing fees, maintenance costs, or subscription charges for additional features. Do they offer a free trial or other demonstration? That should allow you to test out the tool and see whether it’s a good fit for your needs.

Predictive Intelligence in Email Marketing

Email technology has been around for more than 40 years. It’s the backbone of modern marketing and remains one of the most powerful tools for reaching new audiences and converting customers. 

Predictive intelligence is revolutionizing email marketing by making it more targeted, personalized, and effective. By leveraging predictive analytics to analyze past data and predict future outcomes, marketers can create hyper-targeted email campaigns tailored to each group of users. 

68% of marketers say email is core to their business, with 59% reporting that it has the best ROI of any marketing channel. And with predictive analytics, email marketing will only get better with time. 

Predictive intelligence can help you create a more personalized user journey using behavior-based triggers to personalize your send times and email content based on each individual’s preferences and browsing history. 

Here are the features to look for in an email marketing tool: 

Abandoned Cart Recovery: This feature allows you to create segmented email lists based on customers who’ve left items in their shopping carts, making it easier to re-engage these potential buyers and get them to complete their purchases. 

You can even use this opportunity to provide further product recommendations and cross-sell other items that you think may interest the customer. 

Segmented Email Lists: The ability to segment your email lists based on customer data and behaviors is one of the most powerful features of predictive marketing software. It allows you to create highly targeted campaigns that speak directly to specific groups of customers, thus increasing engagement and conversion rates. 

Automated A/B Testing: As a marketer, you know that testing is essential for optimizing your email campaigns. But manually creating, managing, and analyzing A/B tests can be time-consuming and resource-intensive. With automated A/B testing features, you can quickly run and monitor tests on your email campaigns without having to lift a finger. 

Social Media Integration: Predictive analytics can help you make the most of social media by analyzing customer data and predicting their engagement with social media posts. For example, you can use predictive analytics to identify which social media platforms your customers prefer and what types of content they engage with the most to create more targeted email campaigns that speak directly to these preferences.

Back In Stock Notifications: You can alert customers when their favorite items are available to purchase again. Use predictive analytics to identify which products are most popular and when they’re likely to be back in stock so you can send personalized email notifications to waiting customers. 

Marketing: Promotional emails, product launches, event invitations, and more — predictive marketing makes it easier to create high-impact campaigns that capture your audience’s attention and generate quality leads. 

New Arrivals: One of the most effective ways to boost sales is by informing customers about new products, promotions, and services. With predictive analytics, you can identify which items are likely to be a hit with your target audience and create hyper-targeted campaigns that drive conversions. 

The idea is to send your customers personalized emails showcasing new items in your online store. Predictive analytics uses customer history, browsing behavior, and purchase patterns to identify which new items are most likely to appeal to each group.

Post Purchases Emails: After a customer completes a purchase, you can use predictive analytics to recommend complementary products automatically, thank the customer for their purchase, and provide other relevant information about your services or products. 

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That will help you increase retention rates, drive repeat purchases, and establish strong relationships with your customers.

Transactional emails:  Predictive marketing software can also be used to create highly targeted transactional emails, such as order confirmations, shipping notifications, and returns reminders. These messages are crucial for driving customer engagement and can also help you build trust and strengthen relationships with your customers. 

Predictive Intelligence for the Web

What Is Predictive Marketing And How Will It Affect Marketing In the Future - MediaOne Business Group Pte Ltd

Marketers have long depended on web analytics software to gather important data and measure campaign effectiveness.

But with predictive intelligence, you can take this one step further by using machine learning algorithms to proactively identify customer trends, behaviours, and preferences before they happen.

That will give you an unparalleled advantage in the marketing world.

Here are some terms marketers use to describe data in web analytics software: 

  • Data mining: Using automated data analysis processes to uncover insights and trends in large amounts of raw data.
  • Influence: Measured when a customer clicks on a desired link or website after viewing another piece of content. When interacting with any predictive intelligence element, customers’ actions are tracked and analyzed for future marketing campaigns. 
  • Lift in Conversion Rate: The increase in conversion rates, customer clicks, or the number of purchases that result from implementing a specific predictive marketing campaign. 
  • Session: A period when a user is actively engaged with your website. 
  • User: A customer who visits your website and performs actions that can be tracked by predictive intelligence software. 
  • Bounce Rate: The percentage of users who visit one page on your website and then leave without taking any additional actions. 
  • Path analysis: How marketers analyse their website’s visitors and their behaviour, including where they came from, what path they took on your site, and what actions they performed. 
  • Behavioural data: All the information you can gather about customers’ behaviour when they visit your website, including their interests and browsing history. 
  • Audience segmentation: Breaking down your website audience into smaller groups based on shared demographics, behaviours, or interests. 

Types of Predictive Analytics Models 

There are three main types of predictive analytics models that marketers can use to drive their marketing campaigns:

Decision Tree: Ever wanted to understand what led to a customer’s decision? It could be as simple as whether they clicked on a specific link or viewed a particular piece of content. Decision trees are used to model these types of decisions and predict future actions based on user behaviour. 

This model places data into different sections based on variables such as market capitalization, price/earnings ratio, etc. 

As the name suggests, decision trees are useful for identifying which factors influence a customer’s choices and actions, allowing marketers to focus their efforts on the elements with the biggest impact. 

Branches indicate the choices or actions a customer could take, while leaf nodes represent the decision made by the customer. 

They’re the simplest predictive analytics models, making them a good choice for marketers who are new to predictive marketing and want to get up to speed quickly.

They’re also useful when a marketer has a short period and needs to quickly predict which actions are most likely to lead to the desired outcomes. 

Regression Models: Used to predict how different factors influence a particular outcome, such as how changes in market conditions affect sales. 

They’re statistical models trained to predict relationships between different variables and the event or outcome to measure. 

Marketers use it to identify patterns based on large data sets and when there’s a linear relationship between variables. 

The method works by figuring out a formula, or equation, to represent the relationship between the input data and the output variable. 

For example, you could use regression to determine how price, features, and other factors affect product sales or customer satisfaction.

Neural Networks: These are highly complex machine learning models that work by creating mathematical representations of inputs and outputs. 

Unlike regression models, which use a linear relationship between inputs and outputs, neural networks use non-linear relationships to make predictions. 

They’re also trained using large amounts of data over time, allowing them to identify patterns and discover the relationships between different variables. 

They try to mimic how our brains learn and make predictions by finding connections between multiple pieces of data. 

Neural networks are extremely versatile and can predict various outcomes, such as consumer purchasing behaviors, stock prices, or website traffic. 

They use artificial intelligence and pattern recognition to make predictions, and marketers can use them to gain deeper insights into customers and their behavior.

How Can Businesses Use Predictive Marketing? 

There are many different ways that businesses can use predictive marketing to drive their campaigns and improve their marketing efforts. Some of the most common ones include:

  • Predicting customer behavior: By analyzing past data, marketers can gain insights into what customers are likely to do in the future. That allows them to identify trends and patterns in customer behavior and understand why customers make certain decisions. 
  • Optimizing marketing campaigns: Marketers can use predictive analytics to improve the effectiveness of their marketing campaigns. By identifying which factors influence customer behavior, they can optimize different aspects of their campaign, such as product pricing, promotions, or even website design, to maximize engagement and conversions. 
  • Personalizing marketing efforts: Predictive marketing can also be used to personalize customer communications and improve customer experiences. By identifying which factors drive individual customers’ behaviors, marketers can create more personalized content that appeals to their specific interests and preferences. 

So, What’s the Future of Predictive Marketing

The future of marketing is predictive marketing, but what’s the future of predictive marketing? Many experts believe that predictive marketing will become even more sophisticated in the years ahead and will continue to evolve as businesses gain deeper insights into their customers’ behaviors.

First, expect to see AI and ML tools becoming more integrated into our day-to-day marketing flows. For example, marketers will start providing more customized content recommendations based on customer behaviors and preferences. 

Second, marketing campaigns will become more creative and relevant. Instead of relying on broad demographics and assumptions, marketers will have access to more detailed customer data that allow them to create personalized campaigns that truly resonate with their target audience. 

Finally, expect predictive marketing to become more integrated into other business areas. 

As AI and ML technologies advance, predictive marketing will become a core component of many different business functions, from product design and development to pricing strategies and customer support.

About the Author

Tom Koh

Tom is the CEO and Principal Consultant of MediaOne, a leading digital marketing agency. He has consulted for MNCs like Canon, Maybank, Capitaland, SingTel, ST Engineering, WWF, Cambridge University, as well as Government organisations like Enterprise Singapore, Ministry of Law, National Galleries, NTUC, e2i, SingHealth. His articles are published and referenced in CNA, Straits Times, MoneyFM, Financial Times, Yahoo! Finance, Hubspot, Zendesk, CIO Advisor.


Search Engine Optimisation (SEO)

Search Engine Marketing (SEM)

Social Media




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