Marketing Budget Allocation for 2025: Data-Driven or a Roll of the Dice?

Gartner research has revealed that today, barely half of all marketing decisions are made based on data. These decisions are most often the result of a mixture of gut instinct and feelings. In a fragmented marketing landscape, with many possibilities in the mix, it’s the preferences of the most dominant specialists in the teams that are often to blame for this. While, in truth we need a holistic view to achieve the best results with the available budget.

Responsible budgeting for 2025

Marketing teams scrutinise every part of their marketing spend, looking for the best way to justify marketing activities and costs. That’s why marketing plans often fall back into using tried and tested concepts. Sometimes this can be campaigns that worked well over the last 5 to 10 years, but possibly lacked innovation and didn’t necessarily perform well. A successful campaign or high-performance channel does not always achieve a better result with more budget.

But how do you approach budgeting and annual planning in an ever-changing landscape of technology and legislation, coupled with increasing competition and expectations? How do you come up with a successful and easy-to-budget marketing strategy that starts from a new and innovative perspective?

At iO, we use data modelling, a technique that allows you to extract useful insights from intractable data. Thanks to the rise of AI, this approach is no longer just for large organisations with extensive data science departments.

Data modelling: playing with insider knowledge

Extending beyond traditional approaches to analytics, data modelling enables marketing teams to take a predictive and measurable approach when planning and budgeting for 2025 and beyond. And yes, that is exciting, and it does require a change in working methods, but more on that later.

Data modelling is a strategic approach that allows marketers to gather quantitative data that they can use to make informed financial decisions. The model allows you to analyse historical performance data and identify patterns and trends to predict future market conditions and consumer behaviours. This informed analysis allows you to predict future trends more accurately, so that you can align your budget allocations with potential market opportunities and challenges.

These predictive insights are precisely where data modelling makes the difference: you unleash algorithms and models on the data you’ve collected and map out future trends and patterns with crystal clarity. This allows you to make decisions based on data-driven insights rather than gut feelings, finally giving you an objective method that you can use to map out your marketing investments.

Data modelling: positioning yourself in relation to your competitors

In a world where multiple businesses are competing for the attention of the same target audience, it's essential to understand where your organisation is positioned in relation to the competition. But if you barely have time to scrutinise your own performance, you certainly don't have time to study that of your competitors.

Again, this is where data modelling takes the burden of work off your hands and helps you set up crucial benchmarking and competitive analyses. You develop a model once that you can use indefinitely. For example, you can compare marketing performance with, for example, industry averages and your competitors (benchmarking).

Data modelling as a marketing tool

Data modelling is nothing new. The logistics, retail and healthcare sectors have been using this for many years, and those models are still relevant and are still widely used today. However, the method is taking off, aided by AI. Proven models have been developed further and are easier to deploy. Other models, such as Robyn (Meta) and Lightweight MMM (Alphabet), were developed specifically for marketing applications. These marketing mix modelling tools use data modelling techniques to predict marketing impact and effectiveness. In addition, more and more SaaS solutions are coming onto the market that have integrated these models into their product.

Data Modelling for Marketing Teams: Examples

In the marketing context, data modelling methods often have the same starting point. Most methods analyse data from the past and use insights gleaned from it to predict the future. After all, models can analyse data and make connections faster than humans. Examples of applications for marketing include:

Customer behaviour: Understanding consumer behaviour to recognise purchase patterns, churn behaviour, or cross-sell opportunities.

Customer Lifetime Value: insights into customer types that are valuable or not.

Marketing Mix Modelling: insights into the effectiveness of marketing channels and how to get maximum returns from advertising budgets.

Sentiment analysis: insights into behaviour that contributes to positive or negative sentiments.

Segmentation/personalisation: insights into different customer segments and how they can best be served.

What do you need?

Although data modelling is becoming more and more accessible, it is not a magic box that turns Excel files into the most valuable insights. Organisations that are implementing data modelling have to fulfil a number of conditions, but you certainly don't have to be a multinational to work with it.

The right data: Organisations need access to several years’ worth of historical data, depending on the application. This is the power supply for data modelling; Without data, there are no insights. Fortunately, most organisations already measure and understand quite a lot, enough to get started – provided they scrub and merge multiple data sources.

The right people: It is best to call on experienced data scientists for data modelling. They can use structured data to apply the right models, and fine-tune them in such a way that the model can retrieve the insights that are relevant to the organisation.

The correct interpretation: If data is collected in an unstructured way, help from a data engineer and analyst will be required.

Data modelling is a revolution that takes time

But applying algorithms and models alone is not enough. If organisations gain insights – through data modelling – they must also learn how to use them.

To work with models, companies have to learn how to use computer-based advice. The world of data analysis is constantly evolving, and smart organisations seize the opportunities that this method offers.

Even though companies might not have all the answers immediately, it's essential that they incorporate data modelling and data-driven approaches into their plans for the future. In 2025, for example, compare your gut feeling with the results of data modelling, and assess which marketing decisions made over the course of the year have delivered the most benefit. This hybrid method will give you and your colleagues the time and information you need to introduce more data-driven decisions.

Don’t leave it to chance – stop rolling the dice.

Data modelling plays a crucial role in accounting for marketing activities and budgets, by providing predictive insights and competitive advantage. As we prepare for 2025, we should understand that data modelling has the power to help us make more informed decisions.

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Checklist: 16 tips to make your marketing and digitisation budget for 2025 smarter

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