Predictive analytics promise to help us predict the future – a capability that has always fascinated mankind. In the world of marketing, data analysis that enables forecasts of the future, would be a powerful instrument for using budgets in a targeted way and optimizing the success of campaigns and measures.
From glass balls to oracles and cards, not only marketers would give a lot to be able to predict the success (or failure) of their campaigns and strategies. In the age of social media, mobility and the Internet of Things, which connects objects, processes and devices, an increasingly tremendous amount of data pertaining to device usage, human behaviour and other relevant subjects is promising to give us just this: a look into the future.
Data mining: gold prospecting in the digital age
Therefore, predictive analytics and big data are terms that are often uttered in the same breath. To be precise, big data is the reason why we need and can use predictive analytics. After all, connected customers leave tons of data traces in the internet every day. This includes socio-demographic data, images, videos, likes, shares and comments on social networks, forums and blogs as well as motion data from mobile devices, search histories, purchase histories and product usage data that is transmitted to the manufacturer. It is no longer possible to collect and analyse all of this data manually.
Predictive analytics uses automated simulation and analysis techniques that are based on game theory among other things. Algorithm-based text mining techniques are also used and organize non-structured text data from blogs or social media posts. In this way, predictive analytics creates scenario models and forecasts and defines probabilities for specific user behaviour.
On the customer’s track
Banks have already been using predictive analytics for some time now to identify credit risks and fraud. Facebook ads are displayed based on a similar principle. After all, Facebook continually analyses which websites its users visit and which topics they like, and shows them ads that reflect their specific interests.
The software-as-a-service start-up Moovly, which provides products for creating animated presentations and videos, used DataStories to develop a method for predicting the likelihood that a user will become a paying customer or keep a free account indefinitely. The company invests the majority of its marketing budget in those users, who will probably soon sign up for a paid account, and has thus increased its conversion rate from between 4 and 16 percent to between 79 and 84 percent.
Six steps into the future
Predictive analytics will make it possible to track customers’ behaviour and interests in real-time. Based on this knowledge, marketing measures can be optimized continuously using the following six steps:
- Create customer profiles using all available data
- Analyse behavioural patterns, personal information, sites and search terms
- Identify successful online campaigns
- Send personalized purchase recommendations
- Create individually tailored content and display it across digital channels
- Plan automated marketing measures.
Knowing what the “next best action” is
In combination with automation and AI technologies, predictive analytics will allow marketers to know today what customers will want tomorrow. This development will radically alter marketing and also have an immense impact on the marketing profession. Marketers will increasingly become IT, data and analysis specialists. Or they will require such experts, who can draw the right conclusions from the plethora of data. What’s more, the data revolution will require radical changes in the organizational structures of many companies. Data collected by various departments like sales, service and marketing will have to be merged on a common platform. And last but not least, the IT infrastructures have to be made fit for such technically challenging analyses.
For more information about Digital Marketing Trends 2017 also read our articles The customer era: ten marketing trends for 2017,
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