The vital role of big data and AI in performing and forecasting marketing campaigns | 7wData
- Read original article here
Today, the wide and diverse world of marketing seems unrecognisable from its various incarnations before the era of interconnectivity and super-fast data.
Where focus groups had to be assembled and used as predictive machines for marketers in days of yore, we now have a considerably more efficient set of tools to play with.
The arrival of Artificial Intelligence (AI) and big data has had a profound effect on a variety of industries worldwide. The wealth of computing power and detailed analysis capable of being provided through this modern form of technology makes it possible to transform a range of professions for the better - and marketing has to be regarded as a key beneficiary.
AI software market revenue is forecast to increase to $120 billion by 2025.
Just a matter of years ago, it could’ve felt a little left-field to explore the range of applications both AI and big data could have in improving your various marketing strategies. But now, it feels more tricky to find an area of the industry that doesn’t seem ripe for this kind of enhancement.
It’s fair to say that modern technology can save a significant amount of time and money for marketers looking to optimise their campaigns and deliver persuasive messages to the right audiences at the right time. But how exactly is this done through the use of AI and big data?
Here’s a deeper look at AI and big data applications within the world of marketing, and how the technologies can help you to forecast your campaigns:
AI in marketing is continuing to grow exponentially. This form of technology is primarily used as a means of analysing the demographics of audiences alongside the analytics behind a business’ performance online. Predictive analysis has the power to highlight metrics like bounce rates, page visitors, the time spent on specific pages and click-through rates, while AI can help users to make smarter decisions based on such information.
Here, AI helps you to understand and concentrate on the specific areas in which your strategy can work best - or, alternatively, where it needs a little improving.
AI-driven predictive analysis can also interpret scores of data in order to build well-informed predictions for your future engagements too. This technology has the power to identify and investigate previous errors as a way of forming a prediction on how best to prevent the same problems arising in the future. This can help to direct more prospective users to your content and enhance their experience within your pages.
AI can also help you to anticipate how best to utilise your Call-To-Actions as a way of increasing your conversion rates. In fact, according to Ventana Research, as much as 68 per cent of entrepreneurs claimed to have developed a competitive edge with this technology. Furthermore, the consumer goods giant, Unilever, managed to reduce their forecasting errors by as much as 15 per cent while saving millions through the help of this form of analytics.
AI enables companies to assess how specific changes impact conversion rates. For instance, Walmart reported that a decrease in the load time of their website of just 1 second can lead to a 2 per cent increase in conversions - which is translated into millions of dollars.
Predictive analysis that offers concrete data can save companies a ton of energy and resources that would otherwise go into complex A/B testing.
Big data marketing revenue is expected to hit $103 billion by 2027.
The art of forecasting marketing campaigns is one that seemed unimaginable in a more analogue age. With so many metrics and such difficulty associated with the anticipation of customer behaviour, early tools designed to provide guidance for businesses tended to have a high margin for error - or be wholly misleading in some cases.
MarTech relies on the efficiency of predictive technology today - and you’ll rarely come across a sale online that hasn’t arrived as a direct - or indirect - result of the optimisation of big data.
