Marketers have been hearing about big data and the value it brings in terms of customer engagement for several years. If you work in the business-to-consumer (B2C) sector, or more specifically retail and e-commerce, you are likely already harnessing big data in some shape or form.
This year, the focus of digital marketers will shift from big data to better data and insights. The ability to truly understand customers by analysing their online behavior will play a significant role in enabling brands to rise above the noise, as well as enable marketers to create a more personalised experience by tapping into this bank of more meaningful data.
However, how can businesses use big data to create memorable, intimate experience when talking to millions of customers?
Part of this solution – in fact, one we think will dominate the marketing conversation this year – is leveraging big data to bridge the gap between the marketer and the customer.
Leveraging big data to better understand customers
Marketers are gathering detailed information about their customers on an ongoing basis. It’s essential to analyse, assess and execute marketing campaigns that reach and actually resonate with them to ensure a return customer. Big data and analytics are used to create customer profiles enabling marketers to:
- Better understand a customer’s buying behavior;
- Predict buying decisions;
- Offer recommendations; and
- Overall, create an improved online shopping experience.
Only with interaction can marketers engage with customers and the only way to engage with customers is by using big data. According to IDC’s 2015 Big Data Heat Map*, the Big Data Technology and Services market is expected to ‘grow at a five-year compound annual growth rate (CAGR) of more than 28 per cent from US $260.3 million in 2014 to US $711.2 million in 2018. The report also states that ‘Australian organisations expect Big Data and Advanced Analytics projects to deliver outcomes that will improve competitive advantage, enhance customer service and support, as well as aid with customer acquisition and retention’. Evidently, the opportunities for progressive marketers are massive!
Last year, marketers suffered from big data paralysis. Almost paralysed by the sheer volume and variety of data available to them, they were drowning in a data deluge. As we make our way through 2016, marketers are increasingly focusing on fueling personalisation with customer intelligence versus mindlessly executing it.
This is where the other part of the solution comes into play – machine learning.
Making the Prediction: Machine Learning will become a Star Player
Machine learning is simply pattern recognition and computational learning theory within artificial intelligence (AI). Typically, it is used in problem solving and finds the patterns we cannot see. It utilises algorithms to learn and make predictions from big data.
Recently, the Australian Computing Society (ACS) and the Commonwealth Scientific and Industrial Research Organisation (CSIRO), alongside other collaborators, released the Tomorrow’s Digitally Enabled Workforce report. The report states that there is huge demand for Big Data Analysts in Australia especially in the areas of machine learning, automation, cybersecurity, encryption and distributed cloud-based systems across industries.
In the past, marketers used to rely on human intelligence and small data sets to engage with customers. However, this year will be the year in which digital marketers will utilise larger data sets combined with machine learning to provide a more humanised and personalised approach to customers by better understanding their buying decisions and predicting future buying behavior. We foresee conversations around big data will remain focused on working with data from different sources and different channels – with the objective of finding out more about customers and lost opportunities. Combined with machine learning, it will lead to a more personal, more “human” approach to digital marketing.
About the author
Stuart Barker, Managing Director, APAC at Emarsys
