{"id":207618,"date":"2024-11-25T15:31:46","date_gmt":"2024-11-25T14:31:46","guid":{"rendered":"https:\/\/spotler.com\/blog\/a-look-into-the-future-with-predictive-modelling"},"modified":"2026-01-23T13:56:44","modified_gmt":"2026-01-23T12:56:44","slug":"a-look-into-the-future-with-predictive-modelling","status":"publish","type":"blog","link":"https:\/\/spotler.com\/en-au\/blog\/a-look-into-the-future-with-predictive-modelling","title":{"rendered":"A look into the future with Predictive Modelling"},"content":{"rendered":"

The main headache for every marketing & communication professional is to be increasingly personal and relevant. Offers that are just that little bit more in line with the customer’s wishes, communication that really reaches the right person at the right time. Predictive modelling offers the solution to really understand the customer’s needs. In episode 22 of the Spotler podcast [Dutch],<\/a> experts Andreas and Albert share how they use models to make campaigns more effective and smarter.<\/strong><\/p>\n

What is predictive modelling?<\/h2>\n

Predictive modelling is a technique that analyses large data sets to discover patterns. Andreas describes it as a way to make predictions based on historical data. \u201cIt\u2019s about finding patterns that help make better decisions,\u201d says Andreas. Albert adds: \u201cThe best part is when you show that the predictions actually deliver value, for example through A\/B testing<\/a>.\u201d<\/p>\n

Why is this relevant for marketers?<\/h3>\n

Many marketers still see data as something abstract. Andreas notes that predictive modelling can help refine campaigns. He gives the example of how marketers often make broad selections for their campaigns. Models help to make these selections specific and target the customers who are most likely to convert.<\/p>\n

Albert emphasises that the timing of campaigns can also be improved. \u201cIt is not only important to know who to target, but also when and how.\u201d Example: <\/strong>a large shoe store that, thanks to predictive modelling, not only analysed online behaviour, but also linked it to offline purchases. This provided insights that would otherwise have gone unnoticed.<\/p>\n

\n

If only 2% of your total mailing list converts, you really only want to email those 2%. Predictive modelling helps you predict who those 2% are.<\/em>
Andreas Pohan Simandjuntak<\/strong><\/p>\n

\n
\n
<\/div>\n<\/div>\n
\n
\"Hoe<\/figure>\n<\/div>\n
\n
<\/div>\n<\/div>\n<\/div>\n<\/blockquote>\n

How does predictive modelling work in practice?<\/h2>\n

A practical example from the podcast is a case at a telecom company. Andreas explains how predictive modelling was used to predict which device and subscription would best suit a customer: \u201cWe used models to put together a perfect offer, including device, colour and subscription\u201d. This led to higher conversions, because customers felt addressed by the personalised offers.<\/p>\n

In another example, Andreas explains how predictions help identify customers who are about to cancel their subscription. By specifically targeting these customers with personalised offers, companies can increase customer loyalty.<\/p>\n

What are the challenges?<\/h3>\n

While the benefits are clear, adoption remains a challenge. Many companies do not yet have the necessary datasets or see it as too technical a process. Andreas also notes that marketers are often stuck in traditional methods, such as manual selections based on simple variables.<\/p>\n

Albert added that it’s important to help marketers understand how predictive modelling works and what it can deliver:<\/p>\n

\n

\u201cIt is crucial to present the results in an understandable way\u201d<\/em><\/p>\n<\/blockquote>\n

\n
\"Albert
During the podcast, Albert talks about the importance of a good presentation of predictive models.<\/em><\/figcaption><\/figure>\n<\/div>\n

How do you get started with predictive modelling?<\/h2>\n

Getting started with predictive modelling doesn\u2019t have to be complicated. Andreas advises starting small and experimenting. \u201cStart with one campaign and analyse the results,\u201d he suggests. A good starting point is, for example, preventing churn or offering next-best-offers.<\/p>\n

Albert also emphasises the importance of collaboration. \u201cWork with data experts and make sure the results feed back to the marketing teams.\u201d This creates a feedback loop that improves both the models and the campaigns.<\/p>\n

The future of predictive modelling<\/h2>\n

The future, according to both experts, lies in more automated and accessible solutions. Andreas predicts that software will become increasingly better at creating segments and giving actions based on the results. \u201cIt will become a kind of autopilot for marketers,\u201d he says.<\/p>\n

Albert expects that predictive modelling will be increasingly integrated<\/a> into marketing tools:<\/p>\n

\n

\u201cIt’s about not just giving marketers data, but also the tools to turn that data into concrete actions.\u201d<\/em><\/p>\n<\/blockquote>\n

Conclusion<\/h2>\n

Predictive modelling offers huge opportunities for companies looking to optimise their marketing strategies. It not only helps to make better decisions, but also to reach customers with the right message at the right time. By starting small and experimenting, marketers can discover the power of predictive modelling and take their campaigns to the next level.<\/p>\n

Have you thought about how your company can use predictive modelling? Get inspired by the examples and insights from the podcast and take the first step today.<\/p><\/p>\n","protected":false},"excerpt":{"rendered":"

Using predictive AI to work with data will give marketers a much better understanding of their audience. So how does it work?<\/p>\n","protected":false},"author":25,"featured_media":207619,"template":"","cat_industry":[2668,2663],"cat_topic":[2684],"class_list":["post-207618","blog","type-blog","status-publish","has-post-thumbnail","hentry","cat_industry-b2b-en-au","cat_industry-b2c-en-au","cat_topic-artificial-intelligence-en-au"],"acf":[],"_links":{"self":[{"href":"https:\/\/spotler.com\/en-au\/wp-json\/wp\/v2\/blog\/207618","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/spotler.com\/en-au\/wp-json\/wp\/v2\/blog"}],"about":[{"href":"https:\/\/spotler.com\/en-au\/wp-json\/wp\/v2\/types\/blog"}],"author":[{"embeddable":true,"href":"https:\/\/spotler.com\/en-au\/wp-json\/wp\/v2\/users\/25"}],"version-history":[{"count":1,"href":"https:\/\/spotler.com\/en-au\/wp-json\/wp\/v2\/blog\/207618\/revisions"}],"predecessor-version":[{"id":209726,"href":"https:\/\/spotler.com\/en-au\/wp-json\/wp\/v2\/blog\/207618\/revisions\/209726"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/spotler.com\/en-au\/wp-json\/wp\/v2\/media\/207619"}],"wp:attachment":[{"href":"https:\/\/spotler.com\/en-au\/wp-json\/wp\/v2\/media?parent=207618"}],"wp:term":[{"taxonomy":"cat_industry","embeddable":true,"href":"https:\/\/spotler.com\/en-au\/wp-json\/wp\/v2\/cat_industry?post=207618"},{"taxonomy":"cat_topic","embeddable":true,"href":"https:\/\/spotler.com\/en-au\/wp-json\/wp\/v2\/cat_topic?post=207618"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}