{"id":153165,"date":"2025-01-21T14:01:17","date_gmt":"2025-01-21T13:01:17","guid":{"rendered":"https:\/\/spotler.com\/guides\/how-does-predictive-ai-work"},"modified":"2025-08-29T14:19:04","modified_gmt":"2025-08-29T12:19:04","slug":"how-does-predictive-ai-work","status":"publish","type":"guides","link":"https:\/\/spotler.com\/en-gb\/guides\/how-does-predictive-ai-work","title":{"rendered":"How does predictive AI work?"},"content":{"rendered":"
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How does predictive AI work?<\/h1>\n<\/div>\n<\/div>\n

Predictive AI is a specific form of artificial intelligence, different from generative AI. With generative AI \u2013 such as ChatGPT \u2013 you can create content. With predictive AI, you create predictions based on a probability calculation.<\/strong><\/p>\n

Predictive AI uses historical data, algorithms and machine learning models to predict future outcomes or trends. It analyses patterns in existing datasets and extrapolates them to future scenarios. Let\u2019s explore how that works and what you can get from it.<\/p>\n

What can you do with predictive AI in marketing?<\/h2>\n

For marketers, predictive AI is a game changer. Every marketer has the core task of understanding their target group(s), increasing the demand for products or services and ultimately driving more sales. Predictive AI can help you in all of these areas and tasks:<\/p>\n

Predicting customer behaviour<\/h3>\n

With predictive AI you gain insight into customer loyalty and you can make great churn analyses. With the models that the AI uses, you can make predictions about purchasing habits such as order value and purchase frequency. This gives you the insight you need to split your audience into useful, effective segments.<\/p>\n

Campaign optimisation<\/h3>\n

Predictive AI can be used to improve your A\/B testing and to optimise the channels you use. For example, predictive AI can help you choose the moment in the campaign where a series of paid ads would be most effective.<\/p>\n

Lead scoring and segmentation<\/h3>\n

If you have insight into potential future conversions, predictive AI can use that information to classify leads, which can then provide information for meaningful segmentations.<\/p>\n

Dynamic Pricing<\/h3>\n

Many organisations want to base their pricing on the demand for products or services. If you can predict how that demand will develop, you can always adjust your prices accordingly at the right time. Predictive AI makes that possible.<\/p>\n

Personalisation of content<\/h3>\n

Predictive AI is particularly helpful in recommendation systems. Large organisations, in particular, use this. For example, Amazon works with recommendations for the right clothing size depending on customers’ styles and brand choices. This is simply because not every clothing brand uses the same size chart. If you choose a certain piece of clothing as a customer, you will hear from other customers via reviews and feedback whether it is smart to order the item of clothing a size larger or smaller. Customers love this.<\/p>\n

But an organisation like Netflix also fully uses these smart forms of personalisation. We would like to give an example of this. It clearly shows what predictive AI makes possible.<\/p>\n

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Example of predictive AI at Netflix<\/h3>\n

Netflix wants a lot of viewers on its platform. Fortunately, Netflix marketers are sitting on a goldmine of data. After all, customer preferences are widely available. Netflix uses all that data in a smart way. Netflix’s recommendation systems are personalised by using predictive AI. But what does that look like in practice?<\/p>\n

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Same recommendation, but still personalised<\/h4>\n

Netflix personalises movie posters based on previously viewed movies, a focus on certain actors and preferences for certain movie genres:<\/p>\n

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An example of different Netflix thumbnails by Den of Geek<\/a><\/figcaption><\/figure>\n

Netflix tries to attract more customers for this specific film or series with this smart recommendation technique. This is a great example of the power of predictive AI.<\/p>\n

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What data do you need for predictive AI?<\/h2>\n

Types and types of data you can use<\/h3>\n

It should go without saying that the benefits of predictive AI are only possible if your data is in order. After all, the predictions are made on the basis of historical data. If you do not store price developments and times of purchase in your systems, something like dynamic pricing becomes difficult. And if Netflix personalises movie posters, you have to store how often which customer prefers to watch which type of movie.<\/p>\n

The data you need to enable Predictive AI, therefore, depends on your business and mainly on what you want to do with predictive AI.<\/p>\n

Data that Spotler uses in its Predictive AI module<\/h3>\n

We want our customers to earn and save money with our software. For that reason, we focus on improving the Customer Lifetime Value (CLV). The data points required for this are: customer_id, transaction_timestamp, invoice_id, item_number and order_type. This data is loaded into a specific statistical model that is frequently used by data scientists to make predictions about order value and transaction frequency.<\/p>\n

In addition, we use the Random Forest model. We feed that model not only with the above data, but also with things like email behaviour and website interactions.<\/p>\n

Below is a brief explanation of both models. This will give you a good introduction to predictive AI and how it works.<\/p>\n

How to improve CLV with predictive AI?<\/h2>\n

If you want to gain insight into the future development of the CLV of customers, you start with the following historical data: the number of times a customer has bought something from you, the order value of each purchase and the time at which the last purchase took place. This is also known as an RFM model. RFM stands for Recency, Frequency and Monetary Value.<\/p>\n

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An RFM overview in Spotler Activate<\/figcaption><\/figure>\n<\/div>\n

Then the CLV is calculated by multiplying the customer value (based on RFM) by the average duration of a customer relationship. You can calculate the average duration from historical customer behaviour. This calculation does not give you insight into future developments and is deterministic in nature. However, once you know your CLVs, you can start to ask the questions you really want the answers to:<\/p>\n