With the ongoing pandemic, it’s been a challenge for businesses to predict the future. Yet, like all journeys, access to information can smooth the road ahead. Future-ready companies have embraced data analytics to deepen their understanding of customers and drive bottom-line growth. To proactively navigate the changing marketing landscape with agility, agencies need to adopt an insight-driven approach and find their best route forward.
Today, data is not limited to numbers. It consists of almost everything from photos and videos to browsing activities and social media updates. Artificial intelligence is undoubtedly the major driving force behind some of the greatest technological evolutions and digital transformations we are witnessing today.
How does this all relate to marketing? This blog will help explain what augmented and predictive analytics is and how it’s becoming a key driver for businesses ready to modernise their analytics capabilities. Let’s dive in.
What is augmented analytics?
Coined in 2017 by Gartner, augmented analytics is the use of enabling technologies such as machine learning and AI to assist with data preparation, insight generation, and explanation to augment how people explore and analyse data in analytics and business intelligence platforms. The idea behind augmented analytics is to support human intelligence and speed up repetitive tasks by making smarter decisions.
Augmented analytics enables marketers to stay agile in the rapidly accelerating marketing landscape and empowers them to realise the full value of their data. Augmented intelligence can be used in many different fields. It can support financial professionals in making regulatory decisions or help healthcare professionals make medical decisions.
It allows marketers to identify and quickly adapt to emerging trends that might impact their businesses, such as market changes or unexpected user behaviours. For instance, augmented analytics may identify the channels a specific customer segment engages with the most and the types of ads that receive a maximum response. With this insight, marketers know precisely where and how to reach customers to elicit the desired action.
What is predictive analytics?
Predictive analytics is a category of data analytics aimed at making predictions about future outcomes based on historical data and analytical techniques such as statistical modelling and machine learning. The historical data is fed into a mathematical model that considers key trends and patterns in the data. For example, predictive analytics can be used to determine future population trends, allowing governments to gain a clear understanding of the challenges facing citizens and drive more informed decision-making.
In the marketing context, using behavioural data throughout the customer journey, you can predict engagement points on when you think a customer may convert. You can use the data to determine which customer segments and audiences will be the most effective to reach and create actionable insights. An example of predictive analytics is how banks use historical information about a person’s loan applications, past payments, and credit history to calculate a score that reflects the likelihood of that person making their payments on time in the future.
Businesses can better predict demand using predictive analytics and business intelligence. For example, consider a hotel chain that wants to predict how many customers will stay in a certain location this weekend so they can ensure they have enough staff and resources to handle the demand. With predictive analytics, marketing teams can assess how well their ads are performing, for example, and adjust them accordingly to maximize their impact. Putting all sources together allows marketers to gather valuable insights. This translates into more sophisticated segmentation, which in turn sharpens the marketing message.
Netflix is among brands that have successfully used predictive analytics to discover how their users search and what they want to see. They collect data from search keywords, ratings, dates watched, preferred genres. From all of this data, they then provide recommendations based on the user’s past behaviour patterns.
Are you winning the data race?
Identifying cutting-edge, actionable insights, backed by data can be a real game-changer. Augmented and predictive analytics are poised to transform the way businesses assess and implement data. Marketers can finally regain control of massive sets of data and deliver AI-led personalised customer experiences at scale.