What is predictive analysis in marketing?

6 min read

By performing a predictive analysis, your business can gain a better understanding of its customers. 

Using information you already know about each customer, a predictive analysis allows you to decide which of your offers and products are most likely to appeal to each individual.

Due to this, predictive analysis will help you serve the right offer to the right customer at the right time. In turn, this can help you allocate your marketing resources more efficiently and improve ROI.

But, what is predictive analysis in marketing and how can it help your business? Let’s take a look.

What is predictive analysis?

Predictive analysis is the process of using statistical techniques to analyse customer data. In doing so, a predictive analysis aims to analyse the likelihood of a customer taking a specific action in the future, such as responding to a marketing campaign or making a purchase.

The statistical techniques involved when conducting a predictive analysis include:

• Data mining
• Predictive modelling
• Machine learning

While a descriptive analysis will answer questions such as ‘what has just happened?’, a predictive analysis focuses more on hypothetical questions, such as ‘what is likely to happen if we do X?’

One of the most widely used forms of predictive analysis is credit scoring. With access to a person’s historical information (such as their loan applications and credit history), a bank can use predictive models to calculate a score that reflects the likelihood of that person making their payments on time in the future. Using this information, they can then decide whether this person’s credit application can be rejected or accepted.

How is it used within marketing?

Of course, the above example is great for showing how predictive analysis can be used in a wider business context. But, we still need to understand how predictive analysis can be used in the world of marketing. Well, to help, here are a few examples of how it can be used:

1. Audience segmentation

Predictive analysis can help you decide whether you should segment your audience based on their behaviour, demographics, interests, or any other variable. By experimenting with different cluster models, you can uncover hidden patterns and discover the audience segments that make the most sense for your business to use.

2. Customer acquisition

Predictive analysis also helps you use customer data to identify new prospects who resemble your existing customers in a meaningful way. This can help you identify which prospects are best to target.

3. Lead scoring

This practice involves using past customer data to rank identified prospects based on how likely they are to convert. You can then use this data to trigger relevant marketing messages and/or prioritise your sales team’s outreach efforts when a prospect reaches a certain threshold in your lead scoring model.

4. Providing content and ad recommendations

Successful e-commerce brands and streaming services have nailed the idea of providing customers with viewing recommendations and the next purchases for their customers. However, marketers are yet to fully embrace this trend.

By using collaborative filtering, you can analyse a customer’s past behaviour and then make recommendations for content consumption. You can also identify cross-selling and up-selling opportunities.

5. Personalising the customer experience

Predictive analysis can also help you personalise the customer experience. After all, once you’ve created meaningful audience segments, scored your leads, and provided triggered content recommendations to those leads and customers, you can increase the relevance of your marketing activities.

How does predictive analysis in marketing work?

So, it’s clear that predictive analysis can be incredibly helpful within the context of marketing. But, how is the process carried out? Well, to find out, let’s look at a simplified version of the process.

1. Define the question you’d like to answer

As we mentioned earlier, predictive analysis in marketing helps you discover what’s likely to happen based on what has happened before. So, before you get started, work out exactly what you’d like to discover. Good examples of questions you could answer include ‘which audience segment should I target in my next social media campaign?’ and ‘which piece of content should I serve to people whose free trials have expired in order to get them to convert?’

2. Collect relevant data

Once you’ve decided what question you’re looking to answer, you then need to gather relevant data that will help you answer the question. For example, if you’re looking at running a Facebook campaign and need to decide who to target, then you’ll need data relating to previous Facebook campaigns, including who saw these campaigns and how many people converted.

3. Analyse the data

Now you have the data, you should crunch the numbers and perform a descriptive analysis. To do this, list and answer simple questions. Using the above example of a Facebook campaign, these questions could include:

• How long does it take someone to convert after they’ve seen the ad?
• Do more males than females make a purchase after seeing the ad?
• Does the age of the customer influence how likely they are to convert?
• Does the ad creative used, make a difference to conversions?

The list of questions you can answer here is practically endless and you should try to answer as many questions as possible to build a hypothesis.

4. Test your hypothesis

Once you’ve analysed the data and built a theory, you need to test it using statistical techniques. As part of this, you should test every hypothesis you have and leave no stone unturned.

You should also never go with your gut. Data should drive every marketing decision you make, and testing each hypothesis to ensure accuracy will not only increase the effectiveness of your campaigns but will also boost your ROI.

5. Create a predictive model

Now you’ve tested all your hypotheses and eliminated the ones that were proven incorrect, you need to create a predictive model that helps you predict outcomes. At this stage, you’ll need the help of a piece of software that can help you with things like machine learning, Python, and R.

6. Deploy your new model

Once your predictive model is complete, you can run it and generate powerful insights you can put into action.

However, it’s important to add a caveat here. This is because a predictive model will not make decisions for you. Instead, it’s your responsibility to look at the data the model provides and then turn this data into actionable insights. Once you’ve done this, it’s time to put your insights into action.

How predictive analysis can be used

As we’ve alluded to, predictive analysis can be used for a number of different marketing purposes. However, popular ways of using predictive analysis in marketing include:

Predictive modelling can support decision making

Models that are created using statistical algorithms (like the ones we outlined above) can be used to support a marketer’s decision-making process in many different areas. For example, these models can be used to identify churners or determine the likelihood that a customer/prospect will purchase a new product.

To experience the maximum benefit of statistical modelling, you should build different models that can then be compared to each other.

Identifying customer characteristics to improve personalisation

With the help of a profiling tool, you can identify the significant characteristics your customers share. In a matter of seconds, you can apply a score across your whole database and then quickly pull out the customers that match the profile. By doing this, you can improve the targeting and personalisation of each of your marketing campaigns. Plus, by targeting customers who are most likely to respond positively to the campaign, you can boost your chances of success and boost your ROI.

Profiling users to generate cross- and up-selling strategies

Predictive analysis will help you understand the characters and needs of your customers. In doing so, it will also help improve your knowledge of your strengths and weaknesses in the market.

When profiling your customers, you can analyse your current customer base and discover new possibilities for expansion. Using this information, you can then create new cross-selling and up-selling strategies.

How Apteco can support your business with predictive analysis

If you want to know who your customers are, what they look like, and what they might purchase next, then we can help you. Our predictive analysis tools can provide you with data-driven insights that will improve campaign performance and help you communicate with your customers more effectively.

Data-driven marketing is as much about predicting future events as reporting on historical activities. Our predictive analytics tools will help increase loyalty and purchase frequency by analysing the likelihood of future purchases. They can also help you identify customers who are at risk of lapsing and identify areas of potential growth.