Businesses today are on the lookout for advancements in technology and data modelling, to achieve a competitive edge in saturated markets.

One form of analytics that is gaining more attention is predictive analytics. And while predictive analytics has been around for years, it has become increasingly popular as more organisations adopt it.

The global predictive analytics market is projected to grow at a compounded annual rate of 21.9% through 2027, reaching US$35.45 billion. Now, while predictive analytics may be the next best thing for business growth, getting started with predictive analytics won’t happen overnight.

For predictive analytics to be a successful solution, you need to commit to this form of data modelling and invest in the necessary processes to get the ball rolling.

In this blog, we outline how predictive analytics is used, how it works, the benefits, as well as ways you can use it in future marketing strategies.

What is Predictive Analytics?

Predictive analytics is a subset of data science that builds, cleans, and organises raw data into data sets, using computer science, maths, statistics, and machine learning to extract valuable insights.

There are three types of analytics: descriptive, predictive, and prescriptive.

Descriptive analytics looks at historical data to describe what has occurred. Predictive analytics looks at past data and uses algorithms to predict what could happen.

Prescriptive analytics recommends what to do next based on what will most likely occur... and this data is the one digital marketers are currently chasing.

How Is Predictive Analytics Used?

There are numerous use cases for predictive analytics. Weather forecasting is one of the oldest and most recognised examples. Predictive models are also deployed to forecast election results and estimate the effects of climate change.

In a business context, predictive analytics is widely used in supply chain management, risk management, fraud detection, retail, healthcare, energy, financial markets, and even sports.

One of the most popular applications for predictive analytics nowadays is marketing. Predictive marketing analyses past and current data to identify patterns in customers, user behaviour, and marketing performance.

It uses statistics, predictive modeling, artificial intelligence, and machine learning, to uncover insights and forecast which marketing strategies and actions will most likely succeed.

And this is the main reason why predictive analytics has become so popular in the marketing world.

Data modelling has never been more important than it is today. The departure of third-party cookies, and the introduction of new data protection laws like GDPR means that we have less personal data at our disposal.

Using predictive analytics can help you gain the personal data you need to create those personalised user experiences your customers want and expect.

How Does Predictive Analytics Work?

When you decide to branch out to predictive analytics, there are several steps you need to follow:

Problem Definition

Start by identifying what problems you want to solve and what outcomes you want to achieve. This helps you set your objective, determine what data sets to use, and design the best predictive model.

Data Collection

Next, identify what data you need and your data sources. Predictive analytics works best if you have a large volume of data from multiple sources. You can find patterns and correlations this way.

You may need to use and configure a data mining or data aggregation tool if you're not yet collecting data in your organisation.

Data Cleaning

Raw data needs to be cleaned before you can analyse it. There could be duplicate records, inconsistent formatting, or incomplete fields. Data cleaning involves removing duplicates, fixing errors, and consolidating data from multiple sources into a single database.

Data Analysis

Next, you need to analyse your data to look for trends and patterns. You can use statistical regression methods or machine learning techniques to analyse your data.

We would recommend using GA4 at this part of the process. This is because GA4 is programmed to enhance your data using its own machine learning algorithms on your data set. This new data is then used to predict your users’ behaviour in the future.

Then, you can create your hypotheses and use statistics to build and test your conclusions. With this, you can validate or eliminate each hypothesis based on your statistical data.

Model Construction

Now, you have the data to create a predictive model to anticipate future results, events, or behaviours. And the primary classes of predictive models include: 

  • Cluster modeling for segmenting customers into groups
  • Propensity modeling for predicting customers’ likely actions or behaviours
  • Collaborative or recommended filtering for identifying sales opportunities and recommending relevant products or messages to customers


After you’ve finalised your predictive model, it's time to deploy it in real-time in the real world. You can now use your data to generate insights and predict outcomes that'll guide your sales and marketing strategies and campaigns.


It’s important to continue tracking and reviewing the accuracy of your predictive model to ensure its effectiveness. There could be new data you can use to improve it or external variables that could throw it off.

Eventually, you’ll have to adjust, improve, or even replace your model, so best keep on top of changes as they arise before there are too many variables to keep up with.

The Benefits of Predictive Analytics

Predicting customer behaviours, preferences, and actions offers numerous advantages for brands.

Here are some benefits of predictive analytics in digital marketing:

Better Customer Experience

    Predictive marketing can help segment audiences, which allows you to personalise the customer experience.

    Your website could be dynamic, giving recommendations to visitors in real-time. Your email campaigns could be segmented with personalised and more relevant messages.

    Your advertising could also be better targeted at the right customer segment, which can increase your conversions.

    Stronger Customer Loyalty

      You can use predictive analytics to identify problem areas and predict which customers might leave. You can use insights to reach out to these customers to prevent them from leaving and improve customer satisfaction, retention, and loyalty.

      Higher Revenue & Lower Costs

        You could improve your marketing performance with data-driven decisions, which will optimise the performance of your campaigns, channels, and budgets.

        You can also focus your marketing spend and lead follow-up efforts based on predicted outcomes. And you can take advantage of sales opportunities that your model unveils.

        4 Ways to Use Predictive Analytics in Marketing

        There are many ways you can use predictive analytics in marketing. The most common ones are:

        1. Predicting Customer Behaviour

        Your predictive model can help you understand and anticipate customer behaviour.

        It identifies patterns and similarities between variables, such as past purchase behaviour and the probability of future purchases or customer sentiment and possible rate of change.

        This way, you can make informed decisions and take the appropriate action.

        2. Predictive Lead Scoring

        Lead scoring works because it qualifies and prioritises leads for better conversion.

        It also allows your sales team to be more efficient and productive, as they can focus on prospects that are more likely to buy.

        So, using predictive analytics makes lead scoring more accurate, as it's based on data, not gut feel or assumptions.

        3. Targeting and Segmenting Audiences

        Predictive analytics makes it easier and faster to segment audiences based on behavioural and demographic data. This allows you to target customers and move them down the sales funnel with customised campaigns more accurately.

        You can use affinity analysis to segment customers based on similar attributes or response modeling to group customers based on how likely they respond to certain stimuli.

        4. Upselling and Cross-Selling Opportunities

        As predictive analytics can identify trends and patterns in behaviour, it becomes easier for you to market to customers more effectively. You can cross-sell or upsell to them based on their past behaviour, which can boost your profits.

        Final Thoughts

        Insights from predictive analytics are useless if you take no action.

        As digital marketers, we must always be on the lookout for new approaches to create marketing campaigns that are more customer-centric, as this will improve customer retention and ROI.

        To stay ahead of your competitors, now is the time to leverage the power of predictive analytics... before it’s too late.