How To Use Predictive Analytics In Marketing

In today’s world of digital transformation, data is an essential commodity that directly fuels growth and success. And with an abundance of data now available, businesses that harness the power of?data analytics?to make better decisions will have a competitive advantage over those that don’t. For example:

• Data-driven businesses are 58% more likely to beat revenue goals
• Data-driven businesses are 3x more likely to report a significant improvement in decision-making

There is a wide range of data analytics techniques businesses can deploy in order to improve efficiency, ensure growth and obtain a competitive advantage. Amongst them,?predictive analytics?is one of the most advanced and potentially advantageous.?

In this article, we’ll explore predictive analytics and how it works, focusing on one of its most powerful use cases – marketing.

What Is Predictive Analytics?

While it may sound complex, predictive analytics is relatively simple. It’s about using current and historical data to accurately predict future events, outcomes, trends, and behaviour.

These predictions are generated using a combination of statistics, predictive modelling, artificial intelligence (AI), and machine learning (ML), which analyse patterns and trends in data to predict possible future outcomes.

As a result, predictive analytics is often deployed to help organisations navigate during terms of uncertainty. This includes difficult economic times where consumer confidence drops, as predictive analytics can help forecast demand and identify where investments need to be made.

How Does Predictive Analytics Work?

Predictive analytics requires a good deal of planning, input, and expertise to yield results. Let’s take a look at some of the steps businesses can take to turn raw data into actionable insights about the future.

#1 Understand your goals

Before we even talk about data, it’s important to set the foundation for what you want to achieve. Understanding the business goals or issues you want to work towards is a critical first step upon which to build. In marketing terms, for example, underlying goals are likely to include:?

Understanding how customer behaviour may change in the future

• Boosting revenue through cross-selling, upselling, and optimised pricing
• Launching smarter, more effective marketing campaigns

#2 Develop a plan to collect the right data

Once you know what you want to achieve, it’s time to start looking at the data you’ll need to realise those goals. In many cases, customer data can be spread across a wide range of systems and platforms, so understanding what you have and where is key.?

Remember, predictive analytics often requires extensive work with large data sets. That’s why it’s crucial to ensure that you have the ability to collect and analyse sufficient marketing data to accurately predict outcomes.

Businesses should also consider broadening their insights with third-party data. For example, performance data from third-party retailer sites and additional industry data can help you obtain a better understanding of what benchmarks should be, how competitors are performing and the current state of the industry.?

#3 Analyse the data you have collected

Once you have collected the data you need, it’s time to analyse it. In order to make accurate and relevant predictions that ensure well-informed decision-making processes moving forward, predictive analytics needs data that is:

• Clean
• Complete
• In a suitable format

#4 Create a predictive model

Predictive modelling is a core function in the predictive analytics process. Data scientists build them using algorithms and statistical models and then ‘train’ them using subsets of data. Once they are proven to work effectively, they can be applied to full data sets to generate insights.?

Examples of predictive models include:

• Decision trees
• Neural networks
• Linear regression
• Time-series analysis
• Cluster models

Building a predictive model is a complex process requiring a great deal of expertise. A defective or poorly trained model will generate inaccurate predictions, which could lead to disastrous outcomes.?

#5 Use data for actionable insights

This is where the magic happens. Once you’ve outlined your goals, ensured that your data is relevant and clean, and built a predictive model that works, it’s time to put it all into action. Now you can use your predictive insights to guide your decision-making and give you a competitive edge.

Predictive Analytics In Marketing

So what is predictive analytics in marketing? Put simply, it’s the process of using customer data to make predictions about the future that help marketing teams become more effective and intentional in their decision-making.

In an ultra-competitive world, predictive analytics helps businesses to decode past buying habits, enabling them to project future buying habits. Armed with this information, marketers can make smarter, better-informed decisions, allowing them to:

• Preempt future demand, behaviour, and trends
• Create targeted, well-informed marketing campaigns?
• Maximise their resources?
• Engage existing customers and acquire new ones
• Outmanoeuvre the competition
• Identify when and where to target customers

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