Computational marketing is the use of advanced data-driven algorithms to drive better marketing decision making. It is a relatively new field in marketing, and it is being actively developed as businesses realise the potential of advanced analytics to improve their marketing output.
At its core, computational marketing uses analytical techniques such as machine learning, artificial intelligence, data mining, and natural language processing to compile, analyse, and interpret large amounts of data. By analysing data from customer behaviour and other sources, businesses can generate valuable insights that can be used to inform and improve marketing strategies and campaigns.
Computational marketing is divided into two main components: predictive analytics and prescriptive analytics.
Predictive analytics uses historical data to identify patterns in customer behaviour or market conditions that can be used to make predictions about future behaviour. This type of analytics is used to drive marketing decisions, such as what products or services to offer, when to run promotions, and what channels to use to reach customers.
Prescriptive analytics uses customer and market data to recommend potential actions for businesses to take. This type of analytics is used to recommend marketing strategies such as the use of personalisation, targeted advertising, or search engine optimisation.
The key benefit of computational marketing is its ability to provide more accurate, timely, and valuable insights than traditional marketing methods. Computational marketing allows businesses to identify opportunities and make decisions that would otherwise be difficult to make without the use of advanced analytics.
One of the most important aspects of computational marketing is its ability to provide near real-time insights. Rather than relying on reports or historical data, businesses can use predictive analytics to identify customer behaviour and develop marketing campaigns based on this in real-time. This is a significant advantage over traditional marketing techniques, which require manual analysis and cannot provide near real-time insights.
Computational marketing is also able to help businesses make better decisions by providing them with more accurate insights. By analysing large amounts of customer data and behaviour, businesses can identify trends and patterns, which can be used to recommend strategies and minimise risks.
In addition, computational marketing can help businesses manage their marketing budgets more effectively. By analysing customer behaviour and market conditions, businesses can identify opportunities and develop campaigns that maximise their budget and reach their target audience.
Computational marketing is quickly becoming the preferred approach to marketing, as businesses realise its potential to drive better decision making. By providing businesses with near real-time insights, more accurate predictions, and better budget management, computational marketing is helping businesses to better understand their customers and create more effective marketing campaigns.